Author Archives: author

Severe COVID-19 Pneumonia is Associated with Increased Plasma Immunoglobulin G Agonist Autoantibodies Targeting the 5-Hydroxytryptamine 2A Receptor

DOI: 10.31038/EDMJ.2021511

Abstract

Aims: To test whether plasma autoantibodies targeting the 5-hydroxytryptamine 2A receptor increase in COVID-19 infection; and to characterize the pharmacologic specificity, and signaling pathway activation occurring downstream of receptor binding in mouse neuroblastoma N2A cells and cell toxicity of the autoantibodies.

Methods: Plasma obtained from nineteen, older COVID-19 patients having mild or severe infection was subjected to protein-A affinity chromatography to obtain immunoglobulin G fraction. One-fortieth dilution of the protein-A eluate was tested for binding to a linear synthetic peptide QN.18 corresponding to the second extracellular loop of the human 5-hydroxytryptamine 2A receptor. Mouse neuroblastoma N2A cells were incubated with COVID-19 IgG autoantibodies in the presence or absence of selective inhibitors of G-protein coupled receptors, signaling pathway antagonists, or a novel decoy receptor peptide.

Results: 5-hydroxytryptamine 2A receptor autoantibody binding occurred in 17 of 19 (89%) patients with acute COVID-19 infection and increased level was significantly correlated with increased severity of COVID-19 infection. The agonist autoantibodies mediated acute neurite retraction in mouse neuroblastoma cells by a mechanism involving Gq11/PLC/IP3R/Ca2+ activation and RhoA/Rho kinase pathway signaling occurring downstream of receptor binding which had pharmacologic specificity consistent with binding to the 5-HT2A receptor. A novel synthetic peptide 5-HT2AR fragment, SN..8, dose-dependently blocked autoantibody-induced neurotoxicity. The COVID-19 autoantibodies displayed acute toxicity in bovine pulmonary artery endothelial cells (stress fiber formation, contraction) and modulated proliferation in a manner consistent with known ‘biased agonism’ on the 5-HT2A receptor.

Conclusion: These data suggest that 5-HT2AR targeting autoantibodies are highly prevalent may contribute to pathophysiology in acute, severe COVID-19 infection.

Keywords

COVID-19 infection, 5-Hydroxytryptamine 2A receptor, Inflammation, Neurotoxicity

Introduction

The SARS-Cov-2 virus mediates hyper-inflammation and dysregulated immunity leading to ‘cytokine storm’ [1]. Inflammation predisposes to hypercoagulability and human autopsy studies severe COVID-19 infection demonstrated widespread microvascular occlusion in the lung, liver, kidney, heart and brain [2]. Endothelial cells harbor angiotensin converting enzyme 2 (ACE2), the cellular receptor for SARS-Cov-2 virus entry [3] and the host response (to SARS-Cov-2 virus infection) in severely-affected persons is characterized by ‘endotheliitis’ [4]. Previously, we had reported increased circulating agonist IgG autoantibodies to the 5-hydroxytryptamine 2A receptor in subsets of diabetic microvascular disease or neurodegenerative disorders [5]. The autoantibodies promoted endothelial cell apoptosis and were neurotoxic in vitro [6,7]. Since the 5-hydroxytryptamine 2A receptor is expressed on platelets, innate and adaptive immune cells [8,9] and it was reported to mediate (in part) chronic inflammation in certain animal models of autoimmunity [10-12], here we tested whether agonist 5-hydroxytryptamine 2A receptor IgG autoantibodies increase in COVID-19 infection in association with severe disease.

Patients and Methods

Patients

Total nineteen patients were either admitted to an acute medical floor or intensive care unit at the Veterans Affairs New Jersey Healthcare System (VANJHCS; East Orange, NJ) between April-June 2020 because of symptomatic COVID-19 infection or became COVID-19 PCR positive while residing on a subacute VANJHCS rehabilitation or nursing home unit. Blood was drawn for testing and validation of a new Clinical Laboratory Service, COVID-19 antibody assay. Leftover, discard plasma was provided by the Clinical Laboratory Service (Dr. Cynthia Bowman) for the purposes of this research study. The study was reviewed by the local VANJHCS Investigational Review Board and determined to be exempt from informed consent requirement. Plasma samples were stored at-20 degrees C prior to isolation of IgG autoantibodies.

Patient 1

A 73-old-man who experienced pneumonia, respiratory failure, renal failure requiring dialysis and weeks-long period of hyperinflammation (i.e. markedly elevated WBC) who died 3.5 months after admission. During his months-long hospitalization, he was not treated with any medication having antagonist activity on the 5-HT2A receptor.

Patient 2

A 62-year-old man with major depressive disorder, hypertension, HIV, cirrhosis, who experienced COVID-19 pneumonia without respiratory failure. He was treated with the selective 5-HT2A receptor antagonist mirtazapine (45 mg nightly) during a several months hospitalization for intermittent abdominal pain of unknown etiology. He was discharged in stable condition to a long-term facility.

Patient 3

An 86-year old man with prior CVA, hypertension, dementia, atrial flutter with rapid ventricular response, and congestive heart failure who experienced pneumonia and respiratory failure. The tachycardia responded to digoxin therapy. He was treated with convalescent plasma and discharged in stable condition to a subacute rehabilitation facility.

Patient 4

A 72-year-old man with prior history of cerebrovascular accident, type 2 diabetes mellitus and hypertension who experience a mild COVID-19 infection

Patient 5

A 74-year-old man with refractory hypertension, prior history of TIA , type 2 diabetes melllitus who presented with intermittent left arm weakness for 1 day. He was treated with intravenous fluids and discharged home in stable condition.

Patient 6

A 73-year old man with diabetes and dementia who experienced an asymptomatic COVID-19 infection while residing on a long-term VA nursing home unit.

Methods

Protein-A Affinity Chromatography

Protein-A chromatography was carried out as previously reported [6].

Synthetic Peptides

All peptides were synthesized at Lifetein Inc. (Hillsborough, NJ) and had > 95% purity including QN..18 (QDDSLVFKEGSCLLADDN), SN.8 (SCLLADDN), QF.7 (QDDSLVF), and VC.7 (VFKEGSC). An additional control, a scrambled sequence of SN..8 having amino acid sequence LASNDCLD, (LD..8) consisted of the same amino acids as in SN..8, but arranged in a scrambled sequence.

Enzyme Linked Immunosorbent Assay (ELISA)

An enzyme linked immunosorbent assay employed 50 microgram per milliliter concentration of QN..18, which has an amino acid sequence corresponding to the second extracellular loop region of the human 5-HT2A receptor, as the solid-phase antigen. The ELISA was performed as previously reported [5].

Mouse Neuroblastoma N2 Cells

Mouse neuroblastoma N2A cells were cultured in DMEM with 10% fetal calf serum.

N2A Mouse Neuroblastoma Cell Neurite Retraction Assay

Quantitative determination of acute neurite retraction following the addition of COVID-19 plasma autoantibodies in the presence or absence of selective antagonists was carried out as previously reported [5].

N2A Mouse Neuroblastoma Cell Survival Assay

An MTT assay was used to assess mouse neuroblastoma cell survival following exposure to COVID-19 plasma autoantibodies; and was carried out as previously reported [5].

Bovine Pulmonary Artery Endothelial Cells

Bovine pulmonary artery endothelial cells (BPAE) were obtained from Sigma Chemical Co. and they were cultured in Medium 199 with 10% fetal calf serum.

Endothelial Cell Survival Assay

BPAE cells were plated in 96-well plates and incubated for 72 hours prior to the addition of a 1:50th dilution of the protein-A eluate fraction from COVID-19 or age-matched patients without COVID-19 infection. After 48 hours incubation at 37 degrees C in a CO2 incubator, endothelial cell survival (% basal endothelial cell number) was determined using a colorimetric detection system as previously reported [6].

Chemicals

Chemicals were obtained from Sigma Chemical Co., Inc. (St Louis, MO) except YM-254890 obtained from Tocris (Mpls., MN) and SB204741 obtained from Focus Biomolecules (Plymouth Meeting, PA).

Protein Determinations

Protein assays were carried out as previously reported [5].

Statistics

Comparisons were made using unpaired Student’s t-test; and Pearson’s correlation coefficient.

Results

Clinical Characteristics and Autoantibody Prevalence

The baseline clinical characteristics in the study patients are shown in Table 1. Mean age was 67.3 ± 8.9 years. Nearly all patients had one or more co-morbidities, essential hypertension and diabetes mellitus being the most common ones (Table 1). In an enzyme linked immunosorbent assay using the second extracellular loop of the human 5-hydroxytryptamine 2A receptor as the solid phase antigen, seventeen of nineteen (89.5%) COVID-19 patients tested positive for autoantibodies having significantly increased receptor peptide binding, i.e. > 0.06 AU or higher. The mean level of binding in a 1/40th dilution of the autoantibodies in each of nineteen COVID-19 patients tested was 0.123, i.e. three-fold above background (0.04) absorbance level (Table 1).

Table 1: Baseline clinical characteristics and autoantibody in the 19 Covid-19 patients

table 1

AAB-autoantibody; TIA- transient ischemic attack; AU-absorbance units

^ A 1/40th dilution of the protein-A eluate fraction of plasma was incubated with QN…18 linear synthetic peptide corresponding to second extracellular loop region of the human 5-HT2A receptor as reported [5].

Clinical Outcomes

Total ten of nineteen patients (53%) experienced pneumonia and overall 37% experienced respiratory failure. Seven patients died during the inpatient hospitalization: five patients having COVID-19 pneumonia and two patients due to a primary gastrointestinal disorder, either severe worsening of alcoholic hepatitis or from urosepsis complicated by a gastrointestinal bleed. Four of nineteen patients (21%) experienced end-stage renal disease as a manifestation of acute severe COVID-19 infection (Table 2).

Table 2: Clinical manifestation and outcome in 19 Covid-19 patients

table 2

GI-gastrointestinal; ESRD- end-stage-renal disease; Y-yes, N-no

Plasma Autoantibody Binding to 5-HT2A Receptor Peptide

Patients suffering with COVID-19 pneumonia, respiratory failure and the subset who progressed to death had highest autoantibody binding to the 5-HT2A receptor peptide (Figure 1). Respiratory failure leading to death (in two patients) was associated with mean autoantibody binding level (0.23 AU) more than 5.5-fold above background (0.04 AU) (Figure 1). COVID-19 with or without respiratory failure was associated with mean 3.25-fold increased autoantibody level compared to background (Figure 1). Persons with asymptomatic or minimally symptomatic COVID-19 infection (n=4) had much lower level of autoantibody binding, (mean 0.08 AU, Figure 1). Plasma autoantibodies in age-matched patients without COVID-19 infection (n=5) and not suffering from co-morbid vasculopathy or a neurodegenerative disorder(s) previously associated with elevated autoantibodies had no or nearly undetectable 5-HT2A receptor peptide binding (mean 0.04 AU, Figure 1).

fig 1

Figure 1: Plasma autoantibody binding to a linear synthetic 18-meric peptide QN…18 corresponding to the second extracellular loop of the human 5-hydroxytryptamine 2A receptor.

A 1/40th dilution of the protein-A eluate fraction of plasma was incubated with linear synthetic QN..18 peptide and binding was determined as previously reported [5]. ND-neurodegenerative disease; microvasc(ular) dz-disease; Asymp(tomatic).

Role of Hyperinflammation in the De novo Appearance of COVID-19, 5-HT2AR Autoantibody

A representative patient (Patient 1) who experienced multi-organ failure leading to death was a 73-year-old man who had a trajectory of white blood cell count level indicative of persistent hyperinflammation (Figure 2A). Plasma autoantibody binding to 5-HT2AR peptide was undetectable 5 days after the onset of symptoms, but (at day 35) had increased to 5.75 times higher than background level (Figure 2B). These are the first data to suggest de novo appearance of very high level of 5-HT2AR autoantibodies in association with hyperinflammation in severe COVID-19 infection.

fig 2

Figure 2: Clinical course (A) and de novo appearance of 5-HT2AR autoantibody (B) in plasma from a representative patient with severe Covid-19 pneumonia. Pt 1: A) 73-old-man who experienced pneumonia, respiratory failure, renal failure requiring dialysis and weeks-long period of hyperinflammation (i.e. markedly elevated WBC) who died 3.5 months after admission. B) Serotonin-2A receptor autoantibody binding was undetectable 6 days after hospital admission, but it had increased to 5.5-fold greater than background level approximately 1 month later (on day 36). Dashed line (A) indicates upper limit of normal WBC, or (B) lower limit of detection of 5-HT2AR peptide binding.

Pre-existing 5-HT2AR autoantibodies in patients having co-morbid neurodegenerative disease

An IgG immune response to the COVID-19 virus spike protein was reported to be present in essentially all patients tested more than 10 days after the onset of clinical symptoms, but not earlier [13]. Yet three patients who experienced only mild COVID-19 symptoms (Figure 3A) already had substantially increased level of 5-HT2AR autoantibodies (mean 3-fold above background ) in blood drawn less than 5 days after symptom onset (Figure 3B). All three patients had a co-morbid neurodegenerative disorder, (i.e. stroke, refractory hypertension or dementia) previously reported to be associated with high level of 5-HT2AR-binding autoantibodies [5]. These data are consistent with preexisting 5-HT2AR autoantibodies which may not have increased substantially after mild COVID-19 infection.

fig 3

Figure 3: White blood cell count (A) and plasma 5-HT2AR autoantibodies (B) in three representative patients with asymptomatic Covid-19 infection who had co-morbid neurodegenerative disease. A) White blood cell counts in three patients having minimally symptomatic Covid-19 infection B) Increased ‘preexisting’ 5-HT2AR autoantibody binding manifested less than 1 week after onset of Covid-19 symptoms occurred in three patients having a co-morbid neurodegenerative conditions previously associated with high autoantibody level [5].

Correlation between Baseline Risk Factors, or Inflammation and 5-HT2AR Autoantibodies

Consistent with a prior report [5] there was no significant correlation between age or body mass index and the level of 5-HT2AR autoantibody binding in plasma from nineteen COVID-19 patients tested (Figure 4A and 4B). After excluding four patients who had blood drawn for autoantibody determination < 5 days after symptom onset, white blood cell count (a marker of systemic inflammation) was significantly correlated (Pearson correlation coefficient R = 0.845; P < 0.01) with 5-HT2AR autoantibody binding (Figure 5).

fig 4

Figure 4: Lack of significant association between plasma 5-HT2AR binding autoantibodies and (A) age or body mass index (B) in 19 patients with Covid-19 infection. N=1 patient had missing data on body mass index (BMI).

fig 5

Figure 5: White blood cell count is significantly correlated with level of plasma autoantibodies to 5-HT2A receptor peptide in Covid-19 infection. Four patients were excluded from the analysis because blood drawing occurred less than 6 days after the initial onset of Covid-19 symptoms. Pearson correlation coefficient (R = 0.845; P < 0.01; N=15).

Association between Plasma 5-HT2AR Autoantibodies and COVID-19 Disease Severity

There was a gradient of increased plasma 5-HT2AR autoantibodies level for increasing severity of COVID-19 infection (Figure 6). Mean level of autoantibody binding in patients who experienced COVID-19 pneumonia, respiratory failure and death (n=5) was significantly higher (0.17 vs 0.08; P< 0.01) vs level in patients with mild or asymptomatic COVID-19 (n=4) (Figure 6). It was also significantly higher in patients who experienced COVID-19 pneumonia with or without respiratory failure (n=5) (0.13 vs 0.08; P =0.02) vs those having only mild or asymptomatic infection (n=4) (Figure 6). These data suggest a dose-response relationship may exist between level of 5-HT2AR agonist autoantibodies and severity of COVID-19 infection consistent with a possible pathophysiologic role for COVID-19 disease autoantibodies. We next examined COVID-19, plasma 5-HT2AR peptide-binding autoantibodies for toxicity in neuroblastoma or endothelial cells.

fig 6

Figure 6: Association between severity of Covid-19 infection and level of plasma autoantibody binding to 5-HT2AR peptide. **P< 0.01 respiratory failure and death vs. asymptomatic or mild Covid-19 infection. * P= 0.02 pneumonia with or without respiratory failure vs mild Covid-19 infection. Results are mean (SD) of binding in a 1/40th dilution of the protein-A eluate fraction of plasma.

Neurotoxicity Associated with COVID-19 Disease 5-HT2AR Autoantibodies: Pharmacologic Profile

In prior studies [6,7], plasma 5-HT2AR autoantibodies from patients having a neurodegenerative or microvascular disease caused acute neurite retraction and accelerated cell loss in mouse N2A neuroblastoma cells by a mechanism involving long-lasting activation of Gq/11/phospholipase C/IP3R/Ca+2 signaling, and RhoA/Rho kinase activation. Here, COVID-19 autoantibodies caused acute neurite retraction (in mouse neuroblastoma cells) which was nearly completely prevented (95%) by a 500 nanomolar concentration of the highly selective, potent 5-HT2AR antagonist M100907 (Table 3). COVID-19 autoantibody-induced neurite retraction was also significantly prevented by a similar or higher concentration of spiperone or ketanserin, antagonists that also have activity on the 5-HT2A receptor (Table 3). Specific antagonists of other classes of Gq/11-coupled G-protein coupled receptors, e.g. losartan, bosentan, prazosin, had much less (if any) protective effect on COVID-19 autoantibody-induced neurite retraction (Table 3). Taken together, the pharmacologic profile of neurotoxicity induced by COVID-19 autoantibodies is consistent with its binding to a linear synthetic peptide corresponding to the 5-HT2A receptor.

Table 3: Pharmacologic profile of Covid-19 infection autoantibody-induced neurite retraction.

Antagonist

[Conc]       GPRC % Inhibition of Covid-19 AAb Neurite Retraction
M100907 500 nM 5-HT2A/B/C

95%

Spiperone

500 nM 5-HT2A/B/C 72%
Ketanserin 5 µM 5-HT2A//B/C

70%

SB 204741

1 μM 5-HT2B  0%
Losartan 5 μM AT-1R

30%

Bosentan

5 μM ET1-R 10%
Prazosin 850 nM A1-AR

20%

Results are (mean +/- 15%) on inhibition of N2A neurite retraction in 130 nanomolar concentration of severe Covid-19 autoantibody (Pt 3) by indicated concentration of each GPCR antagonist. AT-1R- angiotensin II, type 1 receptor; ET1-R- endothelin 1 receptor; A1-AR- alpha 1 adrenergic receptor

AAb- autoantibody

Mechanism of Action of 5-HT2AR, COVID-19 Autoantibody Neurotoxicity

Co-incubation of COVID-19 autoantibodies together with a specific antagonist of Gq/11 (YM-254890), phospholipase C (U73122), inositol triphosphate receptor (2-APB) or RhoA/Rho kinase (Y27632) signaling each completely abolished acute neurite retraction by the autoantibodies (Table 4). This suggests that COVID-19 autoantibody signaling downstream of 5-HT2AR receptor binding occurs via Gq11-positively coupled to PLC/IP3R/Ca 2+ pathway activation and RhoA/Rho kinase signaling consistent with the previously reported signaling pathways involvement in 5-HT2AR peptide-binding autoantibodies from patients (without COVID-19), but having a neurodegenerative disorder or diabetic microvascular angiopathy [6,7].

Table 4: Effect of signaling pathway antagonists on Covid-19 autoantibody induced N2A neurite retraction

    Treatment

[Conc] % Covid-19 autoantibody-induced neurite retraction
YM-254890 (Gq/11 inhibitor) 1 µM

0% ± 0%

2-APB (IP3R inhibitor)

20 μM 0% ± 0%
U73122 (PLC inhibitor) 30 μM

0% ± 0%

Y27632 (ROCK inhibitor)

10 μM

0% ± 0%

Results are mean (SD) of two determinations on prevention of N2A neurite retraction in 130 nanomolar concentration of severe Covid-19 autoantibody (Patient 3) by the indicated concentration of pathway inhibitor.

Receptor Decoy Peptide Prevents Neurotoxicity from COVID-19 Autoantibodies

We next tested a receptor decoy peptide, SN..8, tentatively called ‘Sertuercept’ because it has amino acid sequence identical to an extracellular region of the serotonin 2 receptor (“Sertu”) involved in mediating long-lasting receptor activation [14] and it may function as a decoy receptor (“ercept”). Sertuercept previously demonstrated neuroprotection against toxic effects of plasma 5-HT2AR autoantibodies from patients lacking COVID-19, but having either diabetic vasculopathy or a neurodegenerative disease [5]. Here, co-incubation of COVID-19 autoantibodies together with increasing concentrations of Sertuercept dose-dependently prevented acute N2a cell neurite retraction; Sertuercept had IC50 of approximately 4 micromolar for half-maximal prevention of COVID-19 autoantibody-induced acute N2A neurite retraction (Figure 7). Ten micromolar concentration of Sertuercept afforded 87.5% protection against neurite retraction induced by a 130 nanomolar concentration of COVID-19 autoantibodies from Patient 2 (Figure 8). An identical (10 uM) concentration of scrambled peptide sequence LN..8 having the same amino acids as in Sertuercept or higher (20 uM) concentration of two peptides (e.g. QF..7 or VC..7) comprising adjacent subregions in the QN..18 sequence which comprises the second extracellular loop of the human 5-HT2A receptor did not significantly prevent COVID-19 autoantibody-induced neurite retraction (Figure 8). These data suggest that neuroprotection against COVID-19 autoantibody-induced toxicity is specific for the SN..8 peptide (Sertuercept) amino acid sequence.

fig 7

Figure 7: Dose-dependent inhibition of Covid-19 autoantibody induced N2A neuroblastoma cell neurite retraction by synthetic 5-HT2A receptor peptide fragment, SN..8. Mouse neuroblastoma N2A cells were incubated together with a 130 nanomolar concentration of autoantibody from a Covid-19 pneumonia patient (Patient 2) in the presence of the indicated concentration of 5-HT2A receptor peptide fragment SN..8. Acute neurite retraction was determined after 5 minutes as described in Methods.

fig 8

Figure 8: Specificity of 5-HT2A receptor peptide fragment SN. 8-mediated prevention of Covid-19 autoantibody induced N2A neurite retraction. *P< 0.01 compared to Covid-19 Pt 1 autoantibody (130 nM concentration) alone.

Mouse neuroblastoma N2A cells were incubated together with a 130 nanomolar concentration of autoantibody from a Covid-19 pneumonia patient in the presence of the indicated concentration of receptor peptide SN..8 or a scrambled sequence LD..8 or peptide (QN..7 or VC..7) corresponding to adjacent regions of QN..18 [5]. Acute neurite retraction was determined after 5 minutes as described in Methods.

Titer and Neurotoxicity of COVID-19, Plasma 5-HT2AR Autoantibodies

In eight patients having symptomatic COVID-19 disease, the mean titer of 5-HT2AR binding autoantibodies (determined in blood drawn on average 3.1 weeks after symptom onset) was ~ 67 nM IgG. Mean titer in two patients with either Alzheimer’s dementia or Parkinson’s disease (without COVID-19) was somewhat higher perhaps consistent with much longer duration of disease. Titer in symptomatic COVID-19 (n=8) or neurodegenerative disease (n=2) autoantibodies was significantly higher than in three uncomplicated diabetic patients without microvascular complications (Figure 9). Plasma autoantibodies from symptomatic COVID-19 disease (n=3) caused dose-dependent accelerated loss in mouse neuroblastoma cell N2a cells which significantly exceeded N2A cell loss induced by autoantibodies (tested at identical dilutions) from patients without COVID-19 infection (Figure 10).

fig 9

Figure 9: Increased titer of 5-HT2AR binding autoantibodies in plasma from eight Covid-19 patients: comparison to non-Covid patients having neurodegenerative disease or uncomplicated diabetes mellitus, ie. without vasculopathy or co-morbid neurodegeneration. *P< 0.01 vs binding in autoantibodies from uncomplicated DM diabetes mellitus, ie. without microvascular complications or neurodegenerative disorder.

Autoantibodies from Covid-19 patients, or non-Covid patients having either co-morbid neurodegenerative disorder or uncomplicated diabetes mellitus were tested for binding to QN..18 second extracellular loop of 5-HT2AR receptor peptide in ELISA. Covid-19 and non-Covid neurodegenerative disease autoantibodies displayed similarly high titer of autoantibody that exceeded binding in uncomplicated diabetes autoantibodies at each of two dilutions tested.

fig 10

Figure 10: Covid-19 autoantibodies cause dose-dependent accelerated loss in mouse neuroblastoma N2a cells compared to patients without Covid-19 infection. *P< 0.05 comparing neurotoxicity in Covid-19 protein-A eluates to nearly identical concentration of protein-A eluate from patients without Covid 19.

Pharmacologic Specificity of the COVID-19, 5-HT2A Receptor Targeting Autoantibodies

Three different antagonists having a relative order of their affinity constants (M100907< spiperone << ketanserin) on the 5-HT2AR each caused dose-dependent inhibition of COVID-19 autoantibody induced acute N2A neurite retraction (Figure 11). The IC50 for M100907 on 130 nM concentration of COVID-19 autoantibodies (Pt 2) was approximately 270 nM (Figure 11A). Spiperone and ketanserin was each tested against a lower (38 nanomolar) concentration of more highly potent, COVID-19 (Pt 3) neurodegenerative diseases autoantibody. Spiperone had an IC50 for inhibition of autoantibody-induced neurite retraction of ~ 300 nM (Figure 11B). Ketanserin had an IC50 of ~ 1.5 mM consistent with ketanserin having relatively weaker antagonism on the 5-HT2AR. Maximal concentrations of either spiperone or ketanserin afforded partial (72-77%) protection against the potent, Pt 3 COVID-19, neurodegenerative diseases autoantibodies (Figure 11B and 11C).

fig 11

Figure 11: Dose-dependent prevention of Covid-19 autoantibody-induced N2a acute neurite retraction by three different 5-HT2AR antagonists: A) M100907, B) spiperone, C) ketanserin.

A)130 nM concentration of Patient 2 plasma autoantibodies; B-C) 38 nM concentration of Patient 3 plasma autoantibodies.

Modulation of Endothelial Cell Survival by Autoantibodies in Severe COVID-19 Disease

Mean endothelial cell survival was significantly decreased (66 ± 18.5%, n=4 vs. 103.2 ± 1.8%. n=5; P =0.003) after 2 days incubation with a 1/50th dilution of the protein-A eluate from four COVID-19 plasmas compared to five age-matched patients without COVID-19 (Figure 12). Mean EC survival was significantly higher (114.5 ± 0.5%, n=2 vs. 103.2 ± 1.8%, n=5; P < 0.001) in the protein A eluates from two COVID-19 patients who had comorbid lymphoma or HIV disease compared to five patients without COVID-19 infection (Figure 10).

fig 12

Figure 12: Modulation of endothelial cell survival by plasma autoantibodies from symptomatic Covid-19 infection: comparison to age-matched patients without Covid-19 infection.

*P =0.003: Compared to EC survival in autoantibodies from patients with No Covid-19 infection.

^P < 0.001: Compared to EC survival in autoantibodies from patients with No Covid-19 infection.

Results are % endothelial cell survival in a 1/50th dilution of the protein-A eluate fraction of plasma as described in Materials and Methods. Dashed lines represent mean EC survival in each subgroup.

COVID-19 Autoantibodies Cause Endothelial Cell Stress Fiber Formation and Acute Contraction

Autoantibodies in patients having either diabetic microvascular complications [15] or a neurodegenerative disease [6,7] were previously reported to cause stress fiber formation and apoptosis in endothelial cells. In preliminary experiments, the Pt 3, COVID-19 autoantibodies (47 nanomolar concentration) caused stress fiber formation (within 5 minutes) and sustained contraction in bovine pulmonary artery endothelial cells (during 30 minutes continuous exposure). Pre-incubation with the receptor decoy peptide SN..8 (20 micromolar concentration) substantially prevented (~80-90%) endothelial cell contraction induced by (twenty-eight nanomolar concentration) of the Pt 3, COVID-19 and dementia autoantibodies (data not shown).

Discussion

Severe COVID-19 infection causes pulmonary inflammation and diffuse endothelial cell dysfunction predisposing to multi-organ failure. The present data are the first to suggest that systemic inflammation in severe COVID-19 infection can give rise to the de novo appearance of very high level of IgG autoantibodies that specifically target the 5-HT2A receptor expressed on vascular endothelial cells and on neurons. Even though acute respiratory failure may occur prior to the emergence of IgG autoantibodies (in patients who lacked preexisting autoantibodies) a significant association between antibody level and severity of COVID-19 disease suggests a possible role (for the 5-HT2AR-targeting autoantibodies) in contributing to endothelial cell damage and/or neurotoxicity underlying ongoing disease pathophysiology.

Inflammation may have driven (in part) the appearance of 5-HT2AR-targeting autoantibodies in severe COVID-19 infection consistent with a prior report of a significant association between increased peripheral inflammation and 5-HT2AR autoantibodies in patients lacking COVID-19 infection, but having either obese type 2 diabetes mellitus or traumatic brain injury [16]. Angiotensin converting-enzyme 2, the cellular receptor for SARS-Co-V2 virus is abundantly expressed on endothelial cells [3] perhaps making certain antigens expressed on endothelial cells preferential targets of humoral immunity in SARS-Co-V2 viral infection.
Endothelial cell inhibitory autoantibodies in patients having either diabetic vasculopathy or traumatic brain injury cross-reacted with heparan sulfate proteoglycan [16]. Anti-heparan sulfate proteoglycan autoantibodies occur in systemic lupus erythematosus and are thought to contribute to an increased risk of vascular thrombosis by interfering with the normal inhibitory effect of antithrombin III on thrombin [17]. Microvascular endothelial cell injury results in platelet adhesion and the 5-HT2A receptor which is expressed on platelets plays a role in platelet aggregation leading to 5-HT (serotonin) release.

Recent autopsy studies in COVID-19 patients revealed diffuse microvascular occlusions in key organs including lung, liver, heart, kidney and brain [2]. High level of 5-HT2AR-targeting autoantibodies was previously reported [5] in patients without COVID-19 infection harboring retinal artery or retinal vein microvascular occlusion. The etiology of small and large vessel thromboses occurring in severe COVID-19 infection is unknown and is likely to be multifactorial. Severe COVID-19 infection mimics aspects of systemic autoimmune disease including the presence of anti-phosphatidylserine autoantibodies implicated in causation of recurrent large vessel thrombosis e.g. anti-phospholipid syndrome [18] in systemic lupus erythematosus. For example, a recent study reported that approximately 25-50% of COVID-19 infected patients harbored either anti-phosphatidyl/prothrombin antibodies or anti-phospholipid antibodies in the circulation [19]. Viral infections, certain cancers e.g. Burkitt’s lymphoma [20] and systemic autoimmunity are all associated with an increased incidence of circulating immune complexes. The 5-HT2AR binding autoantibodies from two patients having COVID-19 infection and either co-morbid Burkitt’s lymphoma or HIV disease caused significant endothelial cell proliferation consistent with a prior report of increased cell proliferation evoked by the 5-HT2AR-targeting autoantibodies from a patient with discoid lupus erythematosus [21]. The 5-HT2A receptor is known to mediate ‘biased agonism’ such that structural differences in the agonist can direct downstream signaling toward activation of beta arrestin 2-mediated survival pathways [22].

The 5-HT2AR is not only widely expressed in vascular tissue [23], but also in the central nervous system [24]. Previously, we reported that the highly potent, endothelial cell inhibitory plasma autoantibodies in a subset of cancer fatigue patients caused excitation followed by prolonged ‘desensitization’ of synaptic input in cultured rat hippocampal pyramidal neurons [25]. Fatigue and neurologic symptoms are among the most common manifestations of ‘long haul’ COVID-19, a syndrome in which various nonspecific symptoms can persist for weeks following recovery from acute COVID-19 infection. Longer term follow up in a diverse patient population is needed to test whether 5-HT2AR-targeting IgG autoantibodies may persist for weeks or months following acute COVID-19 infection and whether persistently elevated autoantibody level or titer may correlate with a subset of persistent ‘long haul’ COVID-19 symptoms.

Recently, we reported that use (vs non-use) in hospitalized COVID-19 infection of existing FDA-approved, 5-HT2AR antagonists (to treat comorbid neuropsychiatric illness or for ICU delirium) was associated with a significant, 5-fold lower odds ratio for mortality [26]. Based on the present data, one possibility is that 5-HT2AR antagonist medications block harmful effects from agonist 5-HT2AR autoantibodies expressed at high level in most cases of severe COVID-19 infection.

In summary, nearly ninety percent of patients with COVID-19 infection, many having pneumonia and requiring hospitalization, harbored substantial titer of neurotoxic and endothelial cell toxic plasma IgG autoantibodies which bound to a linear synthetic peptide corresponding to the second extracellular domain of the 5-HT2A receptor. Binding was associated with acute neurotoxicity which could be prevented (in vitro) either with specific 5-HT2A receptor antagonists or by a serotonin 2A receptor peptide SN..8, Sertuercept, corresponding to a subregion important in mediating long-lasting 5-HT2A receptor activation [14]. Taken together, these data provide proof-of-principle that repurposing of existing FDA-approved 5-HT2AR antagonist medications or a novel decoy 5-HT2A receptor peptide (Sertuercept) might protect against harmful effects of 5-HT2A receptor agonist autoantibodies associated with COVID-19 infection.

Acknowledgement

Dr. Cynthia Bowman, Chief, Pathology and Laboratory Medicine Service, Veterans Affairs New Jersey Healthcare System (East Orange, New Jersey) for providing discard COVID-19 plasma samples used in the approved research study.

References

  1. Buszko M, Park JH, Verthelyi D, Sen R, Young HA, Rosenberg AS (2020) The dynamic changes in cytokine responses in COVID-19: a snapshot of the current state of knowledge. Nat Immunol 21(10): 1146-1151.
  2. Dolhnikoff M, Duarte-Neto AN, de Almeida Monteiro RA, da Silva LFF, de Oliveira EP, Saldiva PHN, Mauad T, Negri EM et al. (2020) Pathological evidence of pulmonary thrombotic phenomena in severe COVID-19. J Thromb Haemost 18(6): 1517-1519. [crossref]
  3. Nascimento Conde J, Schutt WR, Gorbunova EE, Mackow ER, et al. (2020) Recombinant ACE2 Expression Is Required for SARS-CoV-2 To Infect Primary Human Endothelial Cells and Induce Inflammatory and Procoagulative Responses. mBio 11(6): e03185-20. [crossref]
  4. Varga Z, Flammer AJ, Steiger P, Haberecker M, Andermatt R, Zinkernagel AS, Mehra MR, Schuepbach RA, Ruschitzka F, Moch H, et al. (2020) Endothelial cell infection and endotheliitis in COVID-19. Lancet 395: 1417-141. [crossref]
  5. Zimering MB (2019) Autoantibodies in Type-2 Diabetes having Neurovascular Complications Bind to the Second Extracellular Loop of the 5-Hydroxytryptamine 2A Receptor. Endocrinol Diabetes Metab J 3(4): 118. [crossref]
  6. Zimering MB (2017) Diabetes Autoantibodies Mediate Neural- and Endothelial Cell-Inhibitory Effects Via 5-Hydroxytryptamine-2 Receptor Coupled to Phospholipase C/Inositol Triphosphate/Ca2+ Pathway. J Endocrinol Diabetes 4(4): 10.15226/2374-6890/4/4/00184. doi: 10.15226/2374-6890/4/4/00184. [crossref]
  7. Zimering MB (2018) Circulating Neurotoxic 5-HT2A Receptor Agonist Autoantibodies in Adult Type 2 Diabetes with Parkinson’s Disease. J Endocrinol Diabetes 5(2): 10.15226/2374-6890/5/2/01102. doi: 10.15226/2374-6890/5/2/01102.
  8. Duerschmied D, Suidan GL, Demers M, Herr N, Carbo C, et al. (2013) Platelet serotonin promotes the recruitment of neutrophils to sites of acute inflammation in mice. Blood 121(6): 1008-1015. [crossref]
  9. Herr N, Bode C, Duerschmied D (2017) The Effects of Serotonin in Immune Cells. Front Cardiovasc Med 4: 48. [crossref]
  10. Wan M, Ding L, Wang D, Han J, Gao P (2020) Serotonin : A Potent Immune Cell Modulator in Autoimmune Diseases. Front Immunol 11: 186. [crossref]
  11. Almishri W, Shaheen AA, Sharkey KA, Swain MG (2019) The Antidepressant Mirtazapine Inhibits Hepatic Innate Immune Networks to Attenuate Immune-Mediated Liver Injury in Mice. Front Immunol 10: 803. [crossref]
  12. Xiao J, Shao L, Shen J, Jiang W, Feng Y, Zheng P, Liu F, et al. (2016) Effects of ketanserin on experimental colitis in mice and macrophage function. Int J Mol Med 37(3): 659-668. [crossref]
  13. Graham NR, Whitaker AN, Strother CA, et al. (2020) Kinetics and isotype assessment of antibodies targeting the spike protein receptor-binding domain of severe acute respiratory syndrome-coronavirus-2 in COVID-19 patients as a function of age, biological sex and disease severity. Clin Transl Immunology 9(10): e1189. [crossref]
  14. Wacker D, Wang S, McCorvy JD, Betz RM, Venkatakrishnan AJ, Levit A, Lansu K, Schools ZL, Che T, Nichols DE, Shoichet BK, Dror RO, Roth BL, et al. (2017) Crystal Structure of an LSD-Bound Human Serotonin Receptor. Cell 168(3): 377-389.e12. [crossref]
  15. Zimering MB and Pan Z (2009) Autoantibodies in type 2 diabetes induce stress fiber formation and apoptosis in endothelial cells. J Clin Endocrinol Metab 94(6): 2171-2177. [crossref]
  16. Zimering MB, Pulikeyil AT, Myers CE, Pang KC (2020) Serotonin 2A Receptor Autoantibodies Increase in Adult Traumatic Brain Injury In Association with Neurodegeneration. J Endocrinol Diabetes 7(1): 1. [crossref]
  17. Shibata S, Sasaki T, Harpel P, Fillit H (1994) Autoantibodies to vascular heparan sulfate proteoglycan in systemic lupus erythematosus react with endothelial cells and inhibit the formation of thrombin-antithrombin III complexes. Clin Immunol Immunopathol 70: 114-123.
  18. Peterson LK, Willis R, Harris EN, Branch WD, Tebo AE (2016) Antibodies to Phosphatidylserine/Prothrombin Complex in Antiphospholipid Syndrome: Analytical and Clinical Perspectives. Adv Clin Chem 73: 1-28. [crossref]
  19. Zuo Y, Estes SK, Ali RA, Gandhi AA, Yalavarthi S, et al. (2020) Prothrombotic autoantibodies in serum from patients hospitalized with COVID-19. Sci Transl Med 12(570): eabd3876.
  20. Heimer R, Klein G (1976) Circulating immune complexes in sera of patients with Burkett’s lymphoma and nasopharyngeal carcinoma. Int J Cancer 18(3): 310-316. [crossref]
  21. Zimering MB, Nadkarni SG, et al. (2019) Schizophrenia Plasma Autoantibodies Promote ‘Biased Agonism’ at the 5-Hydroxytryptamine 2A Receptor: Neurotoxicity is Positively Modulated by Metabotropic Glutamate 2/3 Receptor Agonism. Endocrinol Diabetes Metab J 3(4). [crossref]
  22. Schmid CL, Raehal KM, Bohn LM (2008) Agonist-directed signaling of the serotonin 2A receptor depends on beta-arrestin-2 interactions in vivo. Proc Natl Acad Sci U S A 105(3): 1079-1084. [crossref]
  23. Alsip NL, Harris PD, Durrani GE (1991) Multiple Serotonin Receptors on Large Arterioles in Striated Muscle. J Vasc Res 28: 537-541.
  24. Pandey SC (2000) Cellular localization of serotonin (2A) (5HT (2A)) receptors in the rat brain. Brain Res Bull 51: 499-505. [crossref]
  25. Zimering MB, Alder J, Pan Z, Donnelly RJ, et al. (2011) Anti-endothelial and anti-neuronal effects from auto-antibodies in subsets of adult diabetes having a cluster of microvascular complications. Diabetes Res Clin Pract 93(1): 95-105.
  26. Zimering MB, Razzaki T, Tsang T, Shin JJ, et al. (2020) Inverse Association between Serotonin 2A Receptor Antagonist Medication Use and Mortality in Severe COVID-19 Infection. Endocrinol Diabetes Metab J 4(4): 1-5. [crossref]

Characterisation of Microscopic Changes in Macroscopically Unaffected Peritoneum in Women with and without Endometriosis

DOI: 10.31038/AWHC.2021413

Abstract

Study question: Is there a difference in the occurrence of occult microscopic endometriotic lesions in normal peritoneum between women with and without endometriosis and if so are there other differences in the structure of the peritoneum between these groups?

Introduction: Occult Microscopically Endometriosis (OME) was firstly described by Murphy et al. in 1986. Since then there has been more research on the topic but without finding any conclusions about the clinical significance. Therefore, OME could be a physiological phenomenon that occurs in women with and without endometriosis (EM) or it could also be an early stage of real EM lesions.

Methods: For this study, we surgically removed the macroscopically unaffected peritoenum from the left and/or right paracolic gutter from 64 women with and 22 women without EM. The tissue was then immunohistochemically stained with antibodies of an Estrogen Receptor Alpha (ERa), a Progesterone Receptor (PR), Cytokeratin, CD10, and Anti-Smooth Muscle Cell Actin (ASMA).

Results: OME lesions were found in five of the 86 patients (5, 81%). One of these lesions was found in a woman without EM which is 4, 5% of the control group. In the group of women with EM, there were four patients with OME lesions which is 6, 3% of the cohort, so there was no statistically significant difference between these groups. Besides the OME lesions, there were immune cells found in the tissue of 12 women with EM (18, 8% of the EM cohort) but none in the control group. These findings did not correlate with the OME lesions.

Introduction

EM is one of the most common gynecological diseases and affects approximately 10% of women of a reproductive age [1,2]. It is defined as the presence of endometrial and/or stromal cells outside the uterine cavity and is most likely to be found disseminated on the peritoneum of the pelvic cavity like in the pouch of Douglas, on the sacrouterine ligaments, in the ovaries and the ovarian fossae [3-5]. Typical symptoms are dysmenorrhea, cyclical and acyclical pelvic pain, and infertility [6]. As the intensity of symptoms does not correlate with extending of infestation it often takes several years until the diagnosis is made [7-9]. Today’s gold standard to detect peritoneal EM is by laparoscopy [10-12]. But within this technique small EM lesions may be overlooked.

As the pathogenesis of EM has not been clarified and probably cannot be described by only one theory, we were wondering which part of the OME lesions take it in. We chose to concentrate on the impact of the peritoneal fluid, which is known to have several spaces in the peritoneal cavity where it is more present. One of these spaces is the right paracolic gutter, which is why we decided to examine the difference between the right and left paracolic gutters for the occurrence of OME [13,14]. OME was first described in 1986 by Murphy et al. [15]. It is defined by the presence of endometriosis in macroscopically normal-looking tissue. Even though there have been further studies, the meaning of OME is still unclear. Firstly, it could have an important status in the pathogenesis of endometriosis. Secondly, it could also be a physiological phenomenon with no disease value. To find out more about the clinical relevance of OME we histologically examined tissue specimens derived from visually normal peritoneum of the paracolic gutters of women with and without EM to detect the possible occurrence of OME. Due to the fact, that endometriotic lesions are associated with the local inflammatory response we also investigated the occurrence of IC and angiogenesis in this tissue.

Materials and Methods

Subjects

During the period between 2013 and 2016, peritoneal biopsy samples from 64 women with visible endometriosis and 22 women without visible endometriosis were collected during laparoscopy. The institute of pathology made the diagnostic assurance by histological examination. The most common reason for the operations in women with EM was EM resection. For women without EM, it was resection of fibroids. With the knowledge of the influence the peritoneal fluid has on the distribution of EM lesions, we chose to collect tissue from the right and left paracolic gutters. The goal was to see if the distribution of OME lesions is also influenced by it. All biopsy specimens were collected in accordance with the patients and were approved by the guidelines of the ethics committee. In Table 1 you can find the clinical profiles of the two groups.

Table 1: Subjects.

With EM n (%)

Without EM n (%)

Number 64

22

Age

Mean Range

29,9 years

18-47

36,4 years

18-50

Oral Contraceptives (OC) 24 (37,5)

4 (18,2)

Menstrual cycle

Menstruation Proliferation

Secretion

No Cycle (due to OC)

Unknown

7 (10,9)

6 (9,4)

14 (21,9)

24 (37,5)

13 (20,3)

0 (0)

3 (13,64)

3 (13,64)

4 (18,18)

12 (54,54)

Coexisting diseases

Adenomyosis (AM) Myoma (UM)

Sterility

Hypothyroidism

43 (67,2)

9 (14,1)

10 (15,6)

8 (12,5)

0 (0)

12 (54,5)

1 (4,5)

4 (18,2)

Antibodies

We performed immunohistochemical studies to investigate immunoreaction of target antigens in the serial sections of biopsies using the following antibodies: PR (Progesterone receptor), ERa (Estrogen receptor alpha), CD 10 (stromal cell marker), ASMA (Anti-Smooth Muscle Cell Actin), and Cytokeratin (glandular cell marker). Non-immune mouse immunoglobulin (IgG) antibody was used as a negative control. The detailed names, dilutions, and manufacturers are given in Table 2.

Table 2: Antibodies.

Name of antibody

Dilution

Manufacturer

Ms anti- Progesteron-R Dako PgR

1:50

Dako, Denmark

Ms anti-ER-alpha 1D5

1:60

Dako, Denmark

Ms ASMA abcam 1A4

1:50

Abcam, UK

Ms anti-CD10 ab951

1:50

Dako, Denmark

Anti-Cytokeratin MNF116 Dako

1:50

Dako, Denmark

Biotin-SP-conjugated AddiniPure Rabbit Anti-Mouse IgG

1:400

Dianova, USA

Immunohistochemistry

Firstly, we prepared 2 µm thick paraffin-embedded tissue slides which were then deparaffinized in xylene and ethanol. After that, they were either treated with Target-Retrieval-Solution (pH 9) or citrate buffer (pH 6) – depending on the antigen we were planning to use on it. Subsequently, the slides were incubated with the primary antibodies for 1 hour at room temperature and then for another hour with the biotin secondary antibody (Table 2), followed by incubation with avidin–peroxidase for 30 min and finally visualized with Fast Red Chromogen System (PR, ERa, CD10, Cytokeratin) or SIGMAFAST (ASMA). Finally, the tissue sections were counterstained with Mayer’s hematoxylene, cleared in aqua dest, and mounted.

Statistical Analysis

All data were analyzed by SSPS program, using exclusively metrical variables in independent samples. All groups to be compared in the evaluation were checked for normal distribution. Subsequently, the statistical test to be used was determined. If two samples were present, the Chi-square test or the Mann-Whitney test was carried out for normally distributed and non-normally distributed samples. The t-test was not used due to the small number of cases. A value of P < 0.05 was considered to be statistically significant.

Results

The Occurrence of OME Lesions

In total, we found 5 OME lesions, which is 5, 81% of all patients. Three of these lesions contained at least one glandular cell whereas the other two lesions contained stromal cells. There was only one lesion, which contained all three parts of a typical EM lesion (glandular cells, stromal cells, and smooth muscle cells (SMC)) (Figure 1). A summary of these results can be found in Table 3. Furthermore, the clinical profiles of patients with OME are given in Table 4.

fig 1

Figure 1: OME lesion 03, which contains all three parts of an EM lesion. A: Cytokeratin; B: ASMA.

Table 3: Summary of OME lesions.

OME lesion

01

02 03 04

05

Glandular cells

Yes

Yes Yes No

No

Stromal cells

No

No Yes Yes

Yes

SMCs

Yes

Yes Yes No

No

Size in µm

88 x 30

328 x 75 310 x 312 222 x 62

337 x 140

Table 4: Clinical profiles of patients with OME.

OME lesion

01 02 03 04

05

EM

No

Yes Yes Yes

Yes

Menstrual cycle

Proliferative

Menstruation Unknown Proliferative

No Cycle

OC

No

No No No

Yes

Age (years)

45

38 45

36

25

History

UM

AM, Sterility AM AM, UM

AM

Side

Right

Right Right Right

Left

Cell type in OME

Glandular cells

Glandular cells Glandular cells Stromal cells

Stromal cells

Four of these lesions were found in the right paracolic gutter with only one on the left side while four of those lesions were also found in patients with EM with only one found in a woman of the control group. For the group of patients with EM that is a proportion of 6, 3% and for the control group, it is a proportion of 4, 5%. A statistical evaluation was carried out using the chi-square test. This calculation resulted in a p-value of 0.768 and therefore shows no statistical relevance of the probability of occurrence of OME between the two groups of patients.

The Occurrence of Immune Cells in Peritoneal Tissue

Besides the OME lesions, we also detected some groups of immune cells. These cells were seen in the immunostaining pattern of CD10. In total there were 12 patients who had such groups (containing lymphocytes and granulocytes) in their peritoneal tissue. All of these patients were in the EM group and no inflammatory signs could be found in the control group. In the group of women with EM there were 18,8% demonstrably affected by inflammation of the peritoneum. The p-value of 0.029, determined using a chi-square test, shows the statistical relevance of this result.

The Occurrence of Blood Vessels in Peritoneal Tissue

To find out if the process of neoangiogenesis takes part in the development of OME we examined all tissue specimens for blood vessels. To take into account the difference in the size of the samples, the vessel density was determined using the hot-spot method.

In women with EM we found a slightly higher density than in women without EM (1, 74 vessels per mm2 in women with EM versus 1.66 vessels per mm2 in women without EM). However, this difference is with a p-value of 0.519 determined using a Mann-Whitney U test not statistically relevant.

Discussion

There has been more research done on this topic since Murphy et al. first described the occurrence of OME lesions in 1986. Synoptically this has all but confirmed the presence of OME. However, in the study of Redwine and Yokom, it was the other way around and they found OME to be more common in women without EM. It is important to point out that this study only used a small control group consisting of 10 women, which limits the meaningfulness of it [16-22]. Nevertheless, there has not been a statistical significance in the occurrence of OME between women with and without EM in any of the studies. Table 5 shows a summary of all the studies about OME.

Table 5: Summary of results of studies about OME [16-22].

Study

Year Operation Localization of removed tissue Frequency of OME in patients with EM

Frequency of OME in patients without EM

Murphy et al.

1986

Laparotomie Cul-de-sac 25%

Redwine

1988

Laparoscopy Posterior pelvic peritoneum 0%

0%

Redwine, Yocom

1990

Laparoscopy Cul-de-sac, Sacrouterine ligaments, Broad ligaments 4,4%

10%

Nisolle et al.

1990

Laparoscopy Sacrouterine ligaments 13%

6%

Nezhat et al.

1991

Laparoscopy Peritoneum, 3-5 cm next to EM lesions 15% (clin. diagnosis) vs. 3,9% (histolog. diagnosis)

0%

Balasch et al.

1996

Laparoscopy Sacrouterine ligaments 11%

6%

Kahn et al.

2014

Laparoscopy Pouch of Douglas, Uterovesicle space, Sacrouterine ligaments 15%

6,4%

Even though there is no significant difference between the occurrence rate of OME in this study compared to Nisolle, Balasch, and Kahn, et al. there are reasons why they found a higher rate. First of all the technical possibilities were significantly improved in the last few years. Furthermore and more interestingly, we examined tissue from the paracolic gutters, which is not known to be one of the most common sites for EM. In contrast, all the other authors decided to take tissues from sites of the peritoneum where EM is very likely to find in the pelvis [6,13,23].

The Meaning of OME

There are two potential meanings of OME. Firstly, it could be an early stage of a “real” EM lesion. In that case, it would be involved in the development and eventually even in the persistence and recurrence of EM after a successful treatment. Secondly, it could also be a physiological phenomenon in which endometrial cells settle in the peritoneum but later get broken down by the immune system. In that case, it would not have anything to do with the development of a “real” EM lesion.

The first case could explain why up to 50% of patients who underwent surgical EM resection, have a recurrence of complaints and “new” EM lesions within 5 years [24,25]. The opinion of Kahn et al. that OME lesions are biologically active and have growth potential would support this theory [22].

On the other hand, the fact that the prevalence of OME in women with and without EM is almost the same suggests that OME lesions have no influence on the development of EM or only in connection with other influencing factors that have not yet been finally clarified.

Distribution of OME Lesions

The peritoneal fluid has a typical distribution in the peritoneal cavity. Due to the force of gravity, it is usually located in deeper locations such as the Pouch of Douglas. However, negative intracranial pressures during inspiration and the influence of peristalsis regularly lead to a cranial flow of the peritoneal fluid. Therefore, the fluid runs over the paracolic gutters. The majority of the peritoneal fluid runs over the right paracolic gutter, as it is deeper than the left paracolic gutter. In this way, the fluid reaches the subdiaphragmatic space on the right side and from there is directed back into the deeper areas via the inframesocolic compartment. This circulation of the PF in the peritoneal cavity results in four places where it is particularly frequent/long [13,26]. As one of these places is the right paracolic gutter, we decided to examine both of the paracolic gutters to see if there is a difference in the occurrence of OME lesions. In this study the lesions were distributed in a 4: 1 ratio (right: left) in the paracolic gutters. This result suggests that the development of the lesions is justified or at least encouraged by the influence of the peritoneal fluid, their composition, and their flow directions [13,14,27]. Therefore, one could either support Sampson’s theory or say that retrograde menstruation causes endometrial cells to enter the PF and adhere to the peritoneum as they circulate, and assume that growth factors, angiogenesis factors, and inflammatory factors contained in the PF promote the development of OME lesions [27-29].

Immune Cells

Interestingly, when comparing the specimens in the paracolic gutters of women with and without EM, it became clear that immune cells were only found in tissue samples from patients with EM. The associations of immune cells could be an expression of the inflammatory response in the context of EM and OME lesions that have been eliminated by the immune system. However, since they tended to be found more often on the left side and OME lesions as well as normal EM lesions are mainly located in the right paracolic gutter, it can be assumed that there are inflammatory processes in the entire peritoneal tissue of women with EM. A study by Scheerer et al. from 2016 also found a significantly more frequent occurrence of immune cells in the peritoneal tissue of women with endometriosis compared to women without endometriosis [30].

The question of whether the peritoneum becomes flammable through the EM, or whether the peritoneum is more likely to develop EM lesions due to its inflammatory consideration is still open. However, five women with inflammatory tissue were under the influence of OC at the time of surgery. This medication can prevent the progression of EM lesions and improve the symptoms. However, this is not the case for all patients who take OC, and often after the pills have been discontinued the symptoms recur quickly [31,32]. This could be because the peritoneum is less penetrated by EM lesions, but it is still affected by inflammatory processes and may therefore promote the formation or regrowth of regressed lesions.

Amount of Blood Vessels

The pathogenesis of EM is known to be influenced by VEGF [33]. The growth factor leads to an increased blood flow to the tissue permeated by EM and thereby promotes the progression of the lesions [34]. In this study, there was no statistically relevant difference in the vascular density between women with and without EM. Furthermore, no relevantly increased vessel density could be found in the tissue pieces in which there were OME lesions. Therefore, they did not seem to be associated with neoangiogenesis. In contrast to the samples with OME lesions, however, an increased vascular density was found in samples with immune cells, which corresponds to a typical inflammatory reaction.

Conclusion

In this study, a few cases of OME were detected in both women with and without EM. There was no significant difference in the frequency of occurrence between the two cohorts. An important significant difference in the peritoneal tissue of women with EM compared to that of women without EM was the appearance of immune cells, which were only found in women with EM. Both lymphocytes and granulocytes were found, which, however, were in no case associated with an OME lesion in this study. These tissue samples also had an increased average number of vessels, which can be easily reconciled with an inflammatory reaction. Even though this result was not significant, it does show a certain trend.

As OME occurs in both tissue samples from women with and tissue samples from women without EM, it is likely that it is a physiological phenomenon in which endometrial cells settle in the peritoneum and are subsequently cleared by the immune system. The found hormone receptor status with a predominance of PR over ER of these lesions also supports this theory.

Concerning the causality of the inflammatory changes in the peritoneal tissue of women with EM, further research is required to be able to offer patients better and long-term successful therapeutic options.

References

  1. Laschke MW, Menger MD (2016) The gut microbiota: a puppet master in the pathogenesis of endometriosis? Am J Obstet Gynecol 215: e1-4. [crossref]
  2. O DF, Roskams T, Van den Eynde K, Vanhie A, Peterse DP, et al. (2016) The Presence of Endometrial Cells in Peritoneal Fluid of Women With and Without Endometriosis. Reprod Sci 24: 242-251. [crossref]
  3. Barcena de Arellano ML, Gericke J, Reichelt U, Okuducu AF, Ebert AD, et al. (2011) Immunohistochemical characterization of endometriosis-associated smooth muscle cells in human peritoneal endometriotic lesions. Hum Reprod 26: 2721-2730. [crossref]
  4. Fukunaga M (2000) Smooth muscle metaplasia in ovarian endometriosis. Histopathology 36: 348-352. [crossref]
  5. Prevalence and anatomical distribution of endometriosis in women with selected gynaecological conditions: results from a multicentric Italian study. Gruppo italiano per lo studio dell’endometriosi. Hum Reprod 9: 1158-1162. [crossref]
  6. Imesch P, Fink D (2016) [Endometriosis Update 2016]. Praxis (Bern 1994) 105: 253-257. [crossref]
  7. Rizner TL (2015) Diagnostic potential of peritoneal fluid biomarkers of endometriosis. Expert Rev Mol Diagn 15: 557-580. [crossref]
  8. Kavoussi SK, Lim CS, Skinner BD, Lebovic DI, As-Sanie S (2016) New paradigms in the diagnosis and management of endometriosis. Curr Opin Obstet Gynecol 28: 267-276. [crossref]
  9. Burney RO, Giudice LC (2012) Pathogenesis and pathophysiology of endometriosis. Fertil Steril 98: 511-519. [crossref]
  10. Wanyonyi SZ, Sequeira E, Mukono SG (2011) Correlation between laparoscopic and histopathologic diagnosis of endometriosis. Int J Gynaecol Obstet 115: 273-276. [crossref]
  11. Practice Committee of the American Society for Reproductive, M., Treatment of pelvic pain associated with endometriosis: a committee opinion. Fertil Steril 101: 927-935. [crossref]
  12. Giudice LC, Kao LC (2004) Endometriosis. Lancet 364: 1789-1799.
  13. Levy AD, Shaw JC, Sobin LH (2009) Secondary tumors and tumorlike lesions of the peritoneal cavity: imaging features with pathologic correlation. Radiographics 29: 347-373. [crossref]
  14. Meyers MA (1973) Distribution of intra-abdominal malignant seeding: dependency on dynamics of flow of ascitic fluid. Am J Roentgenol Radium Ther Nucl Med 119: 198-206. [crossref]
  15. Hopton EN, Redwine DB (2014) Eyes wide shut: the illusory tale of ‘occult’ microscopic endometriosis. Hum Reprod 29: 384-387. [crossref]
  16. Murphy AA, Green WR, Bobbie D, dela Cruz ZC, Rock JA (1986) Unsuspected endometriosis documented by scanning electron microscopy in visually normal peritoneum. Fertil Steril 46: 522-524. [crossref]
  17. Redwine DB (1988) Is “microscopic” peritoneal endometriosis invisible? Fertil Steril 50: 665-666. [crossref]
  18. Redwine DB, Yocom LB (1990) A serial section study of visually normal pelvic peritoneum in patients with endometriosis. Fertil Steril 54: 648-651. [crossref]
  19. Nisolle M, Paindaveine B, Bourdon A, Berlière M, Casanas-Roux F, et al. (1990) Histologic study of peritoneal endometriosis in infertile women. Fertil Steril 53: 984-988. [crossref]
  20. Nezhat F, Allan CJ, Nezhat C, Martin DC (1991) Nonvisualized endometriosis at laparoscopy. Int J Fertil 36: 340-343. [crossref]
  21. Balasch J, Creus M, Fábregues F, Carmona F, Ordi J, et al. (1996) Visible and non-visible endometriosis at laparoscopy in fertile and infertile women and in patients with chronic pelvic pain: a prospective study. Hum Reprod 11: 387-391. [crossref]
  22. Khan KN, et al. (2014) Occult microscopic endometriosis: undetectable by laparoscopy in normal peritoneum. Hum Reprod 29: 462-472.
  23. Chiantera V, Dessole M, Petrillo M, Lucidi A, Frangini S, et al. (2016) Laparoscopic En Bloc Right Diaphragmatic Peritonectomy for Diaphragmatic Endometriosis According to the Sugarbaker Technique. J Minim Invasive Gynecol 23: 198-205. [crossref]
  24. Vlek SL, et al. (2016) Laparoscopic Imaging Techniques in Endometriosis Therapy: A Systematic Review. J Minim Invasive Gynecol 23: 886-892.
  25. Zhu L, Lier MC, Ankersmit M, Ket JC, Dekker JJ, et al. (2019) Comparisons of the efficacy and recurrence of adenomyomectomy for severe uterine diffuse adenomyosis via laparotomy versus laparoscopy: a long-term result in a single institution. J Pain Res 12: 1917-1924. [crossref]
  26. Bricou A, Batt RE, Chapron C (2008) Peritoneal fluid flow influences anatomical distribution of endometriotic lesions: why Sampson seems to be right. Eur J Obstet Gynecol Reprod Biol 138: 127-134. [crossref]
  27. Koninckx PR, Kennedy SH, Barlow DH (1998) Endometriotic disease: the role of peritoneal fluid. Hum Reprod Update 4: 741-751. [crossref]
  28. Sasson IE, Taylor HS (2008) Stem cells and the pathogenesis of endometriosis. Ann N Y Acad Sci 1127: 106-115. [crossref]
  29. Gazvani R, Templeton A (2002) Peritoneal environment, cytokines and angiogenesis in the pathophysiology of endometriosis. Reproduction 123: 217-226. [crossref]
  30. Scheerer C, Bauer P, Chiantera V, Sehouli J, Kaufmann A, et al. (2016) Characterization of endometriosis- associated immune cell infiltrates (EMaICI). Arch Gynecol Obstet 294: 657-664. [crossref]
  31. Lindsay SF, Luciano DE, Luciano AA (2015) Emerging therapy for endometriosis. Expert Opin Emerg Drugs 20: 449-461. [crossref]
  32. Schweppe KW (2005) [Guidelines for the use of GnRH-analogues in the treatment of endometriosis]. Zentralbl Gynakol 127: 308-313. [crossref]
  33. Liu S, Xin X, Hua T, Shi R, Chi S, et al. (2016) Efficacy of Anti-VEGF/VEGFR Agents on Animal Models of Endometriosis: A Systematic Review and Meta-Analysis. PLoS One 11: e0166658. [crossref]
  34. Burney RO, Giudice LC (2012) Pathogenesis and pathophysiology of endometriosis. Fertil Steril 98: 511-519. [crossref]

Is there Evidence to Suggest that Maternal Obesity Impacts Breastfeeding Prevalence? – A Review

DOI: 10.31038/AWHC.2021412

Abstract

Globally, breastfeeding and obesity have become paramount importance for mothers and infants. This paper aimed at reviewing the literature to explore the evidence that maternal obesity can have a negative impact on breastfeeding rates. A review of the literature (academic journals) was conducted between 2005 and 2019 using the PRISMA 2009 and critical appraisal approach to critically evaluate the articles and reach an evidence statement.

Concerning the research question of the study, twelve research articles were considered for review. The review found maternal obesity/overweight as independent variables (defined as Prepregnancy or postpartum Body Mass Index) and breastfeeding rate as the dependent outcome variable.

The majority of the studies showed evidence of a negative impact of obesity on breastfeeding rates. Therefore, to understand breastfeeding behavior among obese women, researchers could consider conducting more empirical studies that use well-established theories, including the theory of reasoned action. This review may help clinicians recognize patients who are less likely to breastfeed and consider targeting early intervention.

Background

It is known globally that obesity has become a widespread public health problem. For example, in Nebraska, United States of America (US), the prevalence of adult obesity is reported to be 32.8% (up from 11.3% in 1990) and is ranked the 15th highest rate of obesity in the nation [1]; in addition to, increasing prevalence of obesity in the Arab World including Jordan, Kingdom of Saudi Arabia, United Arab Emirates and others [22,23]. The degree of obesity is measured by the Body Mass Index (BMI) that calculates weight in relation to squared height. BMI between 25 -29kg/m2 is classified as overweight and 30 kg/m2 or higher as obesity [2]. The burden of disease due to obesity extends beyond conventional health consequences to include social, psychological, emotional, economic, and societal costs [3-5].

Obesity is more common among women [6]. Research has indicated that the risk of emerging a variety of non-communicable diseases, including cardiovascular diseases, diabetes, arthritis, infertility, and breast cancer, increases among obese women [7,8]. Successful breastfeeding (BF) is a relatively complex process that begins even before the birth of the baby. Studies show that the first step is the woman’s intention to breastfeed, successfully initiating BF, and then successfully maintaining that process. For optimal benefits for both baby and mother, most authorities recommend exclusive BF to continue for up to six months [9].

BF is an integral part of developing the infant’s brain and body and impacts its health as it grows [10]. Medical conditions such as childhood obesity, gastroenteritis, and type 2 diabetes are increasingly seen among children who are breastfed at lower rates [11]. BF also affects mothers’ health, where there is a decreased risk of postpartum hemorrhage and type 2 diabetes as well as other conditions such as breast, uterine, and ovarian cancers [12].

Several maternal inputs determine the success of the BF process. Medical, socioeconomic, psychosocial, and lifestyle aspects have been repeatedly cited as factors associated with BF practice [13]. Maternal obesity has emerged as yet another element that might negatively impact BF [14-19]. This phenomenon is emerging as a public health concern globally. Referring to the earlier example, in Nebraska, fewer than fifty percent of infants are BF at six months of age, and only 20% are exclusively breastfed at that age. This paper aims to review the literature to explore the evidence that maternal obesity can negatively impact BF rates. This may help clinicians recognize patients who are less likely to breastfeed and consider targeting early intervention at women who are thought to be at a higher risk. The definition of BF rates can be referred to as: “ever breastfed refers to those infants who have been put to the breast, even if only once; and exclusive breastfeeding concerns infants who have only received breast milk during a specified period of time. The cut-off points regarding the duration of exclusive-breastfeeding – 3, 4 and 6 months – are in line with past and current WHO guidelines [13,57]”.

Literature Review

Context

Globally, obesity prevalence is three times since 1975. In 2016, more than 650 million adults were classified as obese, which accounts for 13% of the world’s population; 40% of them were women, according to a 2018 report World Health Organization [20]. The cause of obesity is multiple factors, including physical activity levels, dietary patterns, medication use, food, and education [21]. The Center for Disease Control and Prevention (CDC) reported on obesity as a serious concern due to its association with reduced quality of life, poorer mental health outcomes, and the leading causes of death across the US and extending globally, including diabetes, stroke, heart disease, and cancer [21].

Within Eastern Mediterranean Region (Middle East), seven countries population (adults) were ranked among the ’20 most overweight nations’, including Kuwait (73.4%), Qatar (71.7%), Kingdom of Saudi Arabia (69.7%), Jordan (69.6%), Lebanon (67.9%), United Arab Emirates (67.8%) and Libya (66.8%) [22,23]. Reports from the International Diabetes Federation stated that there were 374,100 new cases of diabetes report in Jordan as an example and are mostly related to obesity [22]. Women suffer more from obesity, especially when they get pregnant. The World Health Organization reports that obesity is a major problem among Jordanian women, with 40% [20,27]. A Jordanian national survey in 2007 showed that obesity is most prevalent among women of reproductive age. From the research report, some of the critical factors for obesity amongst women included marriage at an early age, wealth status, parity, lack of appropriate place for women to exercise, and smoking [20,27].

It is undoubtedly known, there is a global recognition of the advantages of BF for both mothers and infants [1-3]. BF has been lately described as “personalized medicine.” For newborns, the new series published in 2016 by Lancet noted critical evidence demonstrating the notion of BF as a vital cornerstone of children’s survival, growth, health, and development, and thereby associated positively with life expectancy and prosperous future [24]. Additionally, the Lancet series highlights the economic benefits of BF. For instance, it was found that babies who were not breastfed across countries faced financial losses of over $300 billion annually due to the lowered cognitive ability levels, resulting in reduced earning capacity for these persons [24]. The World Health Organization noted the importance and benefits of exclusive breastfeeding (EBF) having more significance and positive social impact in settings of poor nutrition, poverty, and poor personal hygiene, where the baseline disease rates are higher [1,5,58]. Annually, the lives of over 800,000 children less than five years of age can be saved provided that optimal BF is administered [24,25,60].

About twenty percent of neonatal deaths may be prevented with BF initiation within the first hour after birth [7,8,58] in low-income and middle-income countries. Furthermore, the continuation and optimal BF practices have the potential of preventing at least twelve percent of all under-5 deaths [9,26,58]. Research studies have indicated that children who are exclusively breastfed are less vulnerable to developing associated childhood illnesses and fourteen times more likely to combat ill-health than those who are not breastfed [10,26,58,59]. The EBF rates prevalence is lower, and childhood mortality is higher among low-income and middle-income countries [27,58]. In Jordan and Ghana, for instance, the documented rate of infant mortality is 17 per 1000 live births and 53 per 1000 live births; while, the mortality rate of children younger than five years is 21 per 1000 live births and 31 per 1000 live births, and these death rates are moderately related to lowered prevalence of EBF practices, respectively [27,58].

Prevalence

For the first six months, postpartum, early initiation of BF and EBF are strongly endorsed [12]. Globally, the rate of BF initiation is sub-optimal [12,60]. Despite the significant developments in some World Health Organization (WHO) regions, the prevalence of EBF remains of great concern to low-and medium-income countries, as illustrated in a study using data from 66 countries [13,60]. The study reviewed the prevalence of EBF among infants younger than six months for fifteen years, from 1995 to 2010. This study revealed that most EBF among infants increased from 33% in 1995 to 39% in 2010 across developing countries [13,60]. The WHO 2009 report showed that the prevalence of EBF rate (39%) is globally low; within in low-income and medium countries, there is a 36% EBF rate [2,13,60].

In 1997 Jordan, the Demographic and Health Survey (DHS) indicated that the rate of EBF was twelve percent among babies less than six months old [27]. The prevalence of EBF fluctuated for many years in Jordan; in 2002, EBF was 26.7%, then dropped to 22% in 2007, and then had a slight increase to 23% and 26% in 2012 and 2017, respectively [27]. Several Jordanian cross-sectional studies reported suboptimal BF initiation rates ranging from 13% to 19% [27]. Eastern Mediterranean Regional (Middle East Region) data about BF and EBF are not well reported from the Arab World, and cross-sectional studies collect most data. In Saudi Arabia, the rate of initiation of BF among Saudi mothers was at 92%, as compared with the prevalence of initiation of BF as 98% in the United Arab Emirates and 57% in Qatar [28,60].

Barriers

Many factors were identified as obstacles to infant feeding traditions, proper dietary nutrition [13,58]. Some literature relates the lack of BF or even reluctance to do so to poor maternal knowledge or attitude of the mother and maternal and infant medical conditions. Several studies have identified other important factors related to health providers’ attitudes and practices and supportive policies and enabling health facility infrastructure. Several published Jordanian studies reported several adverse challenges and obstacles that influence the initiation and EBF. Of these, Dasoki et al., and Khassawneh et al., showed that Cesarean births and no endorsement of BF initiation policies were obstacles to BF [28-30]. Abuidhail [31] reported that mothers expressed that infants remained hungry after BF. Abu Shosha [32] showed that short intervals between pregnancies and physical breast problems during BF were addressed. Additionally, Khassawneh et al. [28,29] reported the place of work as another obstacle that contributed to mothers’ inability or desirability to practice BF.

Moreover, studies showed that women commonly said they did not intend to practice BF on their newborns, particularly EBF in the first six months [27-32]. Such an intention may be due to the limitations of social support and systems and the challenges posed in the workplace. In these studies, respondents shared concerns about the side effects when mothers BF, in terms of perceived pain and changes in body figure and weight [27-32]. Based on the above-given reasons for not practicing BF, women may be influenced by the knowledge, attitudes, and practices across countries. Adequate knowledge about EBF is the fundamental tool that can direct EBF practice among mothers [27-29]. Therefore, this review paper’s main objective is to review the literature to explore the evidence that maternal obesity can negatively impact BF rates.

Methods

This literature review’s search strategy involved visiting the EBSCO HOST web and Academic Search Premier, PubMed, Web of Science, CINAHL plus full text, Science Direct, EMBASE, Bio Med Central, Wiley online Library, All Health Watch and MLA International Bibliography databases. The inclusion criteria specified peer-reviewed scholarly research articles published in academic journals between 2005 and 2019, full text with references available and English. Items were excluded from the review if they did not answer the research question; if they tackled obesity in children, men, or women who had not given birth; if they were reviews of literature of any kind or if they fell outside the specified search period. A variety of keywords were used, including BF, BF behavior, BF practices, lactation, BMI, maternal, obesity, overweight, observational and cohort studies, and randomized controlled trials. A total of 2,830 articles were located. However, an assessment of these articles revealed that only eleven of them fit the criteria. Further research was carried out through exploring the South Wales University “Find it” global article search, which contributed one more article. A total of twelve research studies were finally considered for review to answer the research question. The PRISMA 2009, Hill and Spittle house, 2004 critical appraisal approach, and the Critical Appraisal Skills Program (CASP, 2013) were primarily used to critically evaluate the articles and reach an evidence statement (Figure 1) [33-35,56,61].

fig 1

Figure 1: PRISMA 2009 Flow Diagram

Results

The review considered maternal obesity/overweight as independent variables (defined as Prepregnancy or postpartum BMI) and BF rate as the dependent outcome variable. The analysis included assessing whether the studies addressed potential confounding variables that interfere in the relationship between obesity/overweight and BF rates. Besides, the PRISMA 2009 was utilized with the summary as follows: In 2006, Oddy et al. investigated the association of maternal Prepregnancy overweight and obesity with BF duration [36]. This prospective study, conducted in Western Australia, covered 1803 live-born infants and their mothers. Results indicated that, after adjusting for socioeconomic, demographic, biological, and medical factors of mothers and infants, Prepregnancy obesity and overweight had no relationship to the initiation [36]. However, they showed a significant effect on reducing BF at any period before six months; obese mothers were more likely to stop BF at two months Odds ratio (OR 1.89 [95%] CI: 1.45, 2.47) compared to normal-weight mothers (OR 1.76 [95%] CI: 1.35, 2.28) and for less than six months [36].

Mok et al., 2008 investigated the relationship between Prepregnancy BMI of obese mothers and BF practices concerning the initiation and continuation at three months postpartum. The study covered 1432 mothers at the Centre Hospitalier Universitaire de Poitiers, France, in 2005. Obesity was significantly associated with lower BF initiation and continuation rates at one month (p ≤ 0.0001) and three months (p ≤ 0.001). An interesting finding of this study points to the psychological factors that may affect BF. Women reported feeling uncomfortable to breastfeed in public at 3 months [37].

In a cohort study, Liu et al., 2010, investigated race as a contributing factor to the negative impact of maternal obesity on the prevalence of BF. The analysis examined the relationship between maternal obesity and BF initiation and duration among women in South Carolina. This is one of the few studies which explored the effects of race in detail. A random sample of 2,840 black and 3,517 white women was drawn from a population-based Pregnancy Risk Assessment Monitoring System (PRAMS) dataset, which included women who gave birth from 2000 to 2005 [38]. The study revealed that Prepregnancy weight of white women negatively affects BF, especially in morbidly obese mothers (OR 0.63, [95%] CI: 0.42, 0.94). The study also showed that, while black obese women did not initiate BF, obesity did not affect the duration of BF when BMI was continuously measured (adjusted hazard ratio 1.03, (95%) CI: 1.01, 1.04) [38].

The association between BF initiation and maternal Prepregnancy BMI was investigated in 2013 by Thompson et al. in Florida, US. This study used a large population-based sample amounting to 1,161,949 singleton mothers who gave birth between 2004-2009. Women reported Prepregnancy weight, height measurements, and initiation of BF in the instantaneous postpartum period. The results of the study indicated that, after adjustment for the confounding variables, including race (Hispanic and other races), obese mothers were less likely to initiate BF compared to normal-weight mothers (OR: 0.84 (95%) CI: 0.83, 0.85). However, this finding did not apply to overweight mothers [39].

A population-based cohort study investigated a large sample of 22131 women delivering in four hospitals in Ontario, Canada. The findings pointed to a negative impact of obesity on BF intention and initiation. This study recruited women who had full-term live births between 2008- 2010. Study results showed that obese mothers, constituting 21% of the study sample, were less likely to plan to BF. In contrast, overweight mothers (27.7% of the sample) were likely to practice BF as normal-weight mothers. Both obese and overweight mothers were less inclined to initiate BF in hospitals and upon discharge than mothers with normal weight [OR: 0.67(0.60-0.75), 0.68 (0.62-0.76)], respectively [40].

Several elements may contribute to the negative impact of maternal BMI on BF. Some of these are related to the mother’s body shape, which hinders the infant’s physical positioning or leads to mechanical failure during suckling at the nipple [36,41]. Psychosocial factors contribute to embarrassment related to body size or shape, thus interfering with BF, mainly when practiced in public. Other factors are associated with obtaining proper health education and counseling from health professionals [36,41-43].

In the US, a national cohort study conducted by Hauff et al., 2014 found that maternal BMI did not affect BF intention and initiation. However, the duration of “ever” BF and “any” BF, as defined by the authors of overweight and obese mothers, was negatively affected by psychosocial factors. Obese women have a 29% increased risk to stop “any” BF than normal-weight mothers (adjusted hazard ratio for the cessation of BF among obese mothers 1.29 (95%) CI: 1.09-1.53). However, this did not apply to overweight mothers [44]. This longitudinal study suggested that overweight and obese mothers, in contrast to normal-weight mothers, were less confident in their ability to practice BF amongst their infants then they had initially intended. Additionally, their BF continuation was adversely influenced by social networks, friends, and relatives who had a poor BF history [44].

Keely et al., 2015 conducted in depth semi-structured interviews with a group of 28 obese women living in Scotland. The participants were recruited between 2011-2013 and represented different ethnic groups and social classes in the study area. This qualitative study aimed to identify obstacles to BF and learn more about women’s views concerning BF practices and the support provided by family, community, and health services [45]. The findings indicated that obese mothers had intentions to BF for at least 16 weeks. However, several of them failed to continue beyond a few days of initiation [45]. The rest could not continue beyond 6-10 weeks. Challenges identified as contributing to this behavior included physical, social support, and psychological factors [45]. This study’s contributions to the literature could be outlined in three themes: a lack of privacy, the impact of birth complications, and low uptake of specialist BF support [45].

A longitudinal cohort study conducted by Verret-Chalifour et al., 2015 in Quebec-Canada on a sample of 6,592 pregnant women confirmed the negative impact of high Prepregnancy BMI on BF initiation [46]. Obese mothers had a 26% increased risk of non-initiation of BF as compared to normal-weight women (relative risk 1.26 [95%] CI: 1.08- 1.46) [46].

A study from 2018 found that the incidence of self-reported BF problems was comparable across weight status groups: normal-weight and overweight. “Not enough milk” was the principal reason for providing infant milk formula [47]. Overweight women were more likely than normal-weight women to agree that infant formula was as good as breast milk [47].

Several qualitative and quantitative studies from 2019 also confirm that overweight and obese women are less likely to practice BF, have more difficulty with BF, and are strongly influenced by psychosocial factors such as poor self-efficacy and fear of negative evaluation of others based on their weight. [48-50].

Conclusion

The research question under review showed a high prevalence of maternal obesity ranging between 10-25%. The formal studies were mostly cohort, prospective, and population-based, used relatively large samples, and adjusted for most of the potential confounders. Many of the studies were carried out in developed countries, limiting the generalizability of the evidence for public health practice in different settings [51]. The majority of the studies showed evidence of a negative impact of obesity on BF rates. These data strongly suggest that although obese women may experience some additional challenges with BF initiation mechanics, perhaps a more important consideration is their perception of the opinion of the critical others in their social environment. Therefore, to understand BF behavior among obese women, we should consider conducting more empirical studies that use well-established theories, including the theory of reasoned action (TRA) [52]. TRA is a theory that is well manifested in the literature of understanding and predicting human behavior. TRA proposes that ‘intention’ is the main predictor of the behavior and response as a function of two variables: attitudes held towards behaviors, practices, ethics, and subjective norms [52]. TRA concedes humans need to be part of society. Consequently, TRA proposes that those who created an individual’s society and perceived as necessary to the individual, such as family members and friends, significantly influence an individual’s intention to perform a behavior [48]. About the reviewed literature results, it is expected that TRA will provide an excellent theoretical background to study further the effect of subjective norms on obese women’s intention to BF and to continue BF.

The reviewed literature also suggested a difference in intention and duration of obese women based on race. This difference raises the point to the need to explore the effect of culture – individuals’ collective perception of social norms, roles, and values in their environment which controls what behavior is desirable or should be circumvented to shape the intention of an obese woman towards BF [53,54]. For example, Hofstede’s studies on cultural dimensions have described two main groups for cultural differences; individualism and collectivism. Hofstede’s cultural index described individualism as independence from paying more consideration to one’s rights over one’s duties and social interaction [53,54]. On the other hand, collectivism was described as a higher degree of harmony between individuals and groups [55]. Thus, it is expected that the type of culture that obese BF women belong to will determine the degree to which they perceive others’ opinions to be essential and how it will shape their intention to breastfeed and continue for a set duration.

Another concern is a deficit of information among mothers about the importance of BF in both infants’ and mothers’ health. Given the high rates of maternal obesity and low prevalence of BF across the world, physicians and other health care providers are in an ideal position to educate patients- particularly those overweight and obese mothers or mothers-to-be on the benefits of BF and exploring with them their perceptions of factors that may be interfering with their intentions or willingness to breastfeed their infants. Attention to this issue can significantly improve the health of the people in our state for years to come.

Acknowledgments

The authors would like to acknowledge Mohammed Bin Rashid School of Government, Dubai, UAE, and the Alliance for Health Policy and Systems Research at the World Health Organization for financial support as part of the Knowledge to Policy (K2P) Center Mentorship Program [BIRD Project].

References

  1. State Briefs [Internet]. The State of Obesity. [Cited 2019 Jun 24].
  2. WHO | Mean Body Mass Index (BMI) [Internet]. WHO. [Cited 2019 Jul 2].
  3. Kelly T, Yang W, Chen CS, Reynolds K, He J (2008) Global burden of obesity in 2005 and projections to 2030. Int J Obes 2005 32: 1431-1437. [crossref]
  4. Puhl RM, Heuer CA (2009) The stigma of obesity: a review and update. Obes Silver Spring Md 17: 941-964. [crossref]
  5. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants – The Lancet [Internet]. [Cited 2019 Jul 2].
  6. WHO | obesity among women [Internet]. WHO. [Cited 2019 Jul 2].
  7. Cohen DA (2008) Obesity and the Built Environment: Changes in Environmental Cues Cause Energy Imbalances. Int J Obes 32: S137-142. [crossref]
  8. Hu FB, Li TY, Colditz GA, Willett WC, Manson JE (2003) Television Watching and Other Sedentary Behaviors in Relation to Risk of Obesity and Type 2 Diabetes Mellitus in Women. JAMA 289: 1785-1791. [crossref]
  9. Collective GB, UNICEF. Nurturing the health and wealth of nations: the investment case for breastfeeding. World Health Organization. 2017 Jul. [Internet]. [cited 2019 Jul 2].
  10. Moore ML (2001) Current Research Continues to Support Breastfeeding Benefits. J Perinat Educ 10: 38-41. [crossref]
  11. Allen J, Hector D (2005) Benefits of breastfeeding. New South Wales Public Health Bull 16: 42-46. [crossref]
  12. Heinig MJ (2001) Host Defense Benefits of Breastfeeding for the Infant: Effect of Breastfeeding Duration and Exclusivity. Pediatr Clin North Am 48: 105-123. [crossref]
  13. Yngve A, Sjöström M (2001) Breastfeeding determinants and a suggested framework for action in Europe. Public Health Nutr 4: 729-739. [crossref]
  14. Amir LH, Donath S (2007) A systematic review of maternal obesity and breastfeeding intention, initiation and duration. BMC Pregnancy Childbirth 7: 9. [crossref]
  15. Krause KM, Lovelady CA, Østbye T (2011) Predictors of breastfeeding in overweight and obese women: data from Active Mothers Postpartum (AMP). Matern Child Health J 15: 367-375. [crossref]
  16. Leonard SA, Labiner-Wolfe J, Geraghty SR, Rasmussen KM (2011) Associations between high prepregnancy body mass index, breastmilk expression, and breastmilk production and feeding. Am J Clin Nutr 93: 556-563. [crossref]
  17. Li R, Zhao Z, Mokdad A, Barker L, Grummer-Strawn L (2003) Prevalence of breastfeeding in the United States: the 2001 National Immunization Survey. Pediatrics 111: 1198-1201. [crossref]
  18. Turcksin R, Bel S, Galjaard S, Devlieger R (2014) Maternal obesity and breastfeeding intention, initiation, intensity and duration: a systematic review. Matern Child Nutr 10: 166-183. [crossref]
  19. Wojcicki JM (2011) Maternal prepregnancy body mass index and initiation and duration of breastfeeding: a review of the literature. J Womens Health 2002 20: 341-347. [crossref]
  20. World Health Organization. Obesity and Overweight: Key Facts. 2018 Feb.
  21. Center for Disease Control and Prevention. Obesity and Overweight. 2017 Aug. Division of Nutrition, Physical Activity, and Obesity, National Center for Chronic Disease Prevention and Health Promotion.
  22. Nahhas R (2017) Obesity a major health problem in Jordan. The Arab Weekly.
  23. Buckingham C, Sauter MB (2018) The World’s Most Overweight Countries. 2018 Jul; 24/7 Wall St Journal.
  24. Lancet Journal (2016) Breastfeeding Series, Lancet 387: 404-504.
  25. Debes AK, Kohli A, Walker N, Edmond K, Mullany LC (2013) Time to initiation of breastfeeding and neonatal mortality and morbidity: a systematic review. BMC Public Health 13: S19. [crossref]
  26. Issaka AI, Agho KE, Renzaho AM (2017) Prevalence of key breastfeeding indicators in 29 sub-Saharan African countries: a meta-analysis of demographic and health surveys (2010-2015). BMJ Open 7: e014145.
  27. Department of Statistics (DOS) and ICF. Jordan Population and Family and Health Survey 2017-18. 2019 March. Amman, Jordan, and Rockville, Maryland, USA: DOS and ICF.
  28. Khasawneh W, Khasawneh AA (2017) Predictors and barriers to breastfeeding in north of Jordan: could we do better?. Int Breastfeed J 12: 49.
  29. Khassawneh M, Khader Y, Amarin Z, Alkafajei A (2006) Knowledge, attitude and practice of breastfeeding in the north of Jordan: a cross-sectional study. Int Breastfeed J 1: 1-17. [crossref]
  30. Dasoqi KA, Safadi R, Badran E, Basha AS, Jordan S, et al. (2018) Initiation and continuation of breastfeeding among Jordanian first-time mothers: a prospective cohort study. Int J Womens Health 10: 571-577. [crossref]
  31. Abuidhail J (2014) Colostrum and complementary feeding practices among Jordanian women. MCN Am J Matern Child Nurs 39: 246-252. [crossref]
  32. Abu Shosha G (2015) The influence of infants’ characteristics on breastfeeding attitudes among Jordanian mothers. Open J Nurs 5: 295-302.
  33. Loi UR, Gemzell-Danielsson K, Faxelid E, Klingberg-Allvin M (2015) Health care providers’ perceptions of and attitudes towards induced abortions in sub-Saharan Africa and Southeast Asia: a systematic literature review of qualitative and quantitative data. BMC public health 15: 139. [crossref]
  34. Burls A (2014) What is critical appraisal? Hayward Medical Communications.
  35. Young JM, Solomon MJ (2009) How to critically appraise an article. Nat Clin Pract Gastroenterol Hepatol 6: 82-91.
  36. Oddy WH, Li J, Landsborough L, Kendall GE, Henderson S, et al. (2006) The association of maternal overweight and obesity with breastfeeding duration. J Pediatr 149: 185-191.
  37. Mok E, Multon C, Piguel L, Barroso E, Goua V, et al. (2008) Decreased full breastfeeding, altered practices, perceptions, and infant weight change of prepregnant obese women: a need for extra support. Pediatrics 121: e1319-1324. [crossref]
  38. Liu J, Smith MG, Dobre MA, Ferguson JE (2010) Maternal obesity and breastfeeding practices among white and black women. Obes Silver Spring Md 18: 175-182. [crossref]
  39. Thompson LA, Zhang S, Black E, Das R, Ryngaert M, et al. (2013) The association of maternal prepregnancy body mass index with breastfeeding initiation. Matern Child Health J 17: 1842-1851.
  40. Visram H, Finkelstein SA, Feig D, Walker M, Yasseen A, et al. (2013) Breastfeeding intention and early postpartum practices among overweight and obese women in Ontario: a selective population-based cohort study. J Matern-Fetal Neonatal Med Off J Eur Assoc Perinat Med Fed Asia Ocean Perinat Soc Int Soc Perinat Obstet 26: 611-615. [crossref]
  41. Rasmussen KM, Dieterich CM, Zelek ST, Altabet JD, Kjolhede CL (2011) Interventions to increase the duration of breastfeeding in obese mothers: the Bassett Improving Breastfeeding Study. Breastfeed Med Off J Acad Breastfeed Med 6: 69-75. [crossref]
  42. Ahluwalia IB, Li R, Morrow B (2012) Breastfeeding practices: does method of delivery matter? Matern Child Health J 16: 231-237.
  43. Jarlenski M, McManus J, Diener-West M, Schwarz EB, Yeung E, et al. (2014) Association between support from a health professional and breastfeeding knowledge and practices among obese women: evidence from the Infant Practices Study II. Womens Health Issues Off Publ Jacobs Inst Womens Health 24: 641-648.
  44. Hauff LE, Leonard SA, Rasmussen KM (2014) Associations of maternal obesity and psychosocial factors with breastfeeding intention, initiation, and duration. Am J Clin Nutr 99: 524-534. [crossref]
  45. Keely A, Lawton J, Swanson V, Denison FC (2015) Barriers to breastfeeding in obese women: A qualitative exploration. Midwifery 31: 532-539.
  46. Verret-Chalifour J, Giguère Y, Forest JC, Croteau J, Zhang P, et al. (2015) Breastfeeding initiation: impact of obesity in a large Canadian perinatal cohort study. Plos One 10: e0117512. [crossref]
  47. Mallan KM, Daniels LA, Byrne R, de Jersey SJ (2018) Comparing barriers to breastfeeding success in the first month for non-overweight and overweight women. BMC Pregnancy Childbirth 18: 461.
  48. Chang YS, Glaria AA, Davie P, Beake S, Bick D (2019) Breastfeeding experiences and support for women who are overweight or obese: A mixed methods systematic review. Matern Child Nutr
  49. Huang Y, Ouyang YQ, Redding SR (2019) Maternal Prepregnancy Body Mass Index, Gestational Weight Gain, and Cessation of Breastfeeding: A Systematic Review and Meta-Analysis. Breastfeed Med Off J Acad Breastfeed Med 14: 366-374. [crossref]
  50. Claesson I-M, Myrgård M, Wallberg M, Blomberg M (2019) Pregnant women’s intention to breastfeed; their estimated extent and duration of the forthcoming breastfeeding in relation to the actual breastfeeding in the first year postpartum-A Swedish cohort study. Midwifery 76: 102-109. [crossref]
  51. Yost J, Dobbins M, Traynor R, DeCorby K, Workentine S, et al. (2014) Tools to support evidence-informed public health decision making. BMC Public Health 14: 728.
  52. Fishbein M, Ajzen I (1976) Misconceptions about the Fishbein model: Reflections on a study by Songer-Nocks. Journal of Experimental Social Psychology 12: 579-584.
  53. Hofstede G (1984) Cultural dimensions in management and planning. Asia Pacific journal of management 1: 81-99.
  54. Qasem W, Fenton T, Friel J (2015) Age of introduction of first complementary feeding for infants: a systematic review. BMC pediatrics 15: 107. [crossref]
  55. Rahman MH, Moonesar IA, Hossain MM, Islam MZ (2018) Influence of organizational culture on knowledge transfer: Evidence from the Government of Dubai. Journal of Public Affairs 18: e1696.
  56. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6: e1000097. [crossref]
  57. OECD (2009) Breastfeeding rates. OECD Family database. OECD – Social Policy Division – Directorate of Employment, Labour and Social Affairs.
  58. Nukpezah RN, Nuvor SV, Ninnoni J (2018) Knowledge and practice of exclusive breastfeeding among mothers in the tamale metropolis of Ghana. Reprod Health 15: 140. [crossref]
  59. Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, et al. (2008) Maternal and child undernutrition: global and regional exposures and health consequences. Lancet 371: 243-260. [crossref]
  60. Al Ketbi MI, Al Noman S, Al Ali A, Darwish E, Al Fahim M, et al. (2018) Knowledge, attitudes, and practices of breastfeeding among women visiting primary healthcare clinics on the island of Abu Dhabi, United Arab Emirates. Int Breastfeed J 13: 26. [crossref]
  61. Fadlallah R, El-Harakeh A, Bou-Karroum L, Lotfi T, El-Jardali F, et al. (2019) A common framework of steps and criteria for prioritizing topics for evidence syntheses: a systematic review. Journal of Clinical Epidemiology 120: 67-85. [crossref]

Homelessness and a Free Clinics Response to Emerging Infectious Disease Outbreaks: Lessons from COVID-19 Patients

DOI: 10.31038/IJNM.2020112

Abstract

Background: The first reported death among homeless persons in Miami Dade County was a 26-year-old male who presented with a fever at one of the free clinics in Homestead, Florida, and was immediately transported to the nearest public hospital in the area where he later died from COVID-19. Since that first death, other homeless persons have died from COVID-19. The purpose of this paper is to report the impact of congregant living in two homeless shelters and a free clinic’s response to COVID-19 in south Florida.

Problem: Homeless and underserved populations in South Florida are faced with medically complex needs that are partially met by onsite clinics. Unfortunately, the COVID-19 pandemic has further limited access to onsite clinic and hospital outpatient services. Therefore, follow up care and recovery support are minimal and adversely impact quality of life, resulting in a cost burden to the healthcare system.

Methods: Nasopharyngeal swabs were collected daily, Monday through Friday, from homeless persons living in and around the two shelters for 3-months, along with persons identified through contact tracing. All samples were tested for COVID-19 by reverse transcription polymerase chain reaction with results reported from the local Department of Health (DOH) laboratory.

Interventions: To decrease the spread of COVID-19, any shelter homeless person reporting symptoms or suspected of being positive for COVID-19 was assessed, tested, and separated from other residents until confirmation results were delivered. If positive, the resident was quarantined for 14-days in a single-room hotel designated for homeless persons testing positive for SARS-CoV-2, the virus that causes COVID-19.

Results: The clinic staff, assisted by the local DOH, conducted 545 coronavirus tests to 408 sheltered and unsheltered homeless persons living in and around the two shelters. Of the 545 tests performed, 56 (10%) were positive, 458 (84%) were negative, 44persons recovered from COVID-19, 4 (1%) persons died, 2 (<1%) persons were re-infected with COVID-19, 23 patients were hospitalized during the period of this study, and 108 persons were placed in quarantine, which included persons exposed during contact tracing.

Conclusion: Due to a lack of follow up, many homeless persons become super spreaders of COVID-19. Unless timely interventions using face coverings, quarantine, social distancing, and frequent hand washings are initiated, the spread of COVID-19 will continue among homeless persons resulting in greater morbidity and mortality among this population.

Keywords

Homeless, COVID-19 outbreak, Homeless shelters, Homeless clinics

Introduction

COVID-19 has taken the lives of over 1,305,189 people world-wide and greater than 244,250 in the U.S. In addition, confirmed global COVID-19 cases have reached 53,517,017 in 191 countries/regions, and in America, over 10.8 million confirmed cases [1]. As of November 14, 2020, Florida has the 3rd highest number of positive cases behind Texas and California, and reported 877,933 positive COVID-19 cases and 17,517 deaths [2]. Miami-Dade (MDC) and Broward (BC) counties lead the state in the number of positive cases and deaths, with MDC reporting 200,876 cases and 3,711 deaths, and BC reporting 95,371 cases and 1,592 deaths [1,2]. Included in the reported cases and death tolls are homeless persons adversely affected by COVID-19. The Miami Rescue Mission (MRM) provide shelter services to over 2000 homeless persons annually.

Background

The first reported death among homeless persons in MDC was a 26-year-old male who presented with a fever at one of the free clinics in Homestead, Florida, and was immediately transported to the nearest public hospital in the area where he later died from COVID-19 [3]. Homeless individuals and families are at increased risk for contracting and transmitting COVID-19, as well as other communicable diseases. Due to poor living conditions and limited access to healthcare resources, homeless people of all ages are vulnerable to acquiring COVID-19. This article will address measures taken to protect homeless men and women residing in local homeless shelters from the spread and increased morbidity and mortality associated with COVID-19 in south Florida.

History

The Miami Rescue Mission Clinic (MRMC) is a free clinic that provides primary medical services to over 11,000 homeless, destitute, uninsured, underinsured and underserved populations in south Florida annually. The MRMC has had a consistent presence in the South Florida community since 2009, addressing basic healthcare needs of homeless persons by assisting them in navigating through complex healthcare systems when additional care is needed, obtaining the necessary resources with area specialists and local hospitals, and facilitating lasting medical improvement toward empowering the patients to manage and take control of their own healthcare needs. All services including labs, medications, referred specialists and more sophisticated care arranged are provided at no cost. The MRMC is associated with the MRM, a homeless shelter, that is geographically located across the street from each other in one of the most underserved areas in South Florida, where one would find at any time of the day, homeless men and women sleeping on the sidewalks adjacent to both the MRM and the MRMC. A similar picture is seen in South Broward County (BC) at our MRM Broward Outreach Center (BOC). The MRM, Inc. is a not-for profit, 501c3 corporation that has provided meals, shelter, life-changing programs, and hope to men, women and children in need since 1922. The MRM onsite services include low demand shelter beds (24-hour – 7-days-a-week residential stay), overnight beds, three daily meals, transitional housing, case management, workforce development, life skills, health care, and stabilization services. In 2019, the MRM provided over 900,000 meals, an increase of 300,000 meals from 2018 and over 600,000 nights of safe shelter to people in need, living in MDC and BC.

COVID-19

COVID-19 is a human coronavirus frequently associated with upper respiratory tract infections (URTIs), but can also cause lower respiratory tract infections (LRTIs), such as pneumonia or bronchitis due to inflammation of the lung parenchyma [4,5]. The coronaviruses are positive-stranded ribonucleic acid (RNA) viruses named for their appearance as seen under an electron microscope, which shows elliptic virion projections of corona (crown-like spikes) from the Latin word for crown [4,6-8]. Prior to 2002, coronaviruses contributed 10% to 30% of the common colds and did not cause severe harm to humans [9,10]. However, since the outbreak of SARS-CoV in 2002 and MERS-CoV in 2012, genetic mutations of these coronaviruses resulted in severe respiratory illnesses when attached to human proteins in human respiratory tracts as well as increased mortality rates stemming from associated pulmonary and coronary emboli [9,11,12]. The World Health Organization (WHO a) issued the interim name “2019-n-CoV” on February 11, 2020, because it originated in the year 2019, the “n” indicating novel, and the “CoV” referring to coronavirus, categorizing the virus under SARS-CoV-2, later to COVID-19 [6,13]. COVID-19 is known to spread from person-to-person, between people who are in close contact with one another (less than 6 feet apart), through respiratory droplets, and touching contaminated items or inanimate surfaces [14]. It is also known that measures to prevent the spread of COVID-19, require proper hand-washing, use of personal protective equipment, social distancing of 6-feet apart, covering mouth and nose when coughing or sneezing, proper disposal of tissues, and properly cleaning frequently used surfaces with Food and Drug Administration (FDA) approved cleaning and disinfecting solutions [14,15]. Initially, due to limited personal protective equipment (PPEs) and close living conditions, the MRMC and the MRM shelters worked closely together to quickly address the Centers for Disease and Prevention (CDC) recommendations on preventing the spread of COVID-19 in our facilities [14].

The Problem

To protect MRM shelter residents from the spread of COVID-19 during the incubation period when residents were free of viral infection symptoms and viral antigen testing had not begun, MRMC staff initiated educational seminars emphasizing social distancing, frequent hand washing and importance of identifying and reporting symptoms of fever, cough, and difficulty breathing immediately to their case worker and clinic staff. The MRM also implemented strategic interventions by not allowing any new homeless admissions to either site location, spacing the beds in each dorm to at least six feet apart, providing hand sanitizers and disposable wipes to all residents, and cleaning frequently touched surfaces with FDA approved disinfectants [15]. The local health department was contacted to assist in providing antigen testing for all MRM shelter residents. Also, during the early outbreak of COVID-19 in South Florida, clinic and shelter staff closely monitored levels of transmission in MDC and BC knowing that the homeless population served has a higher risk of increased exposure and continuing disease transmission because of the large numbers of people living together.

MRM Clinic Rapid Response to COVID-19

To maintain disease surveillance and control, the MRMC and MRM staff worked twenty-four hours on-call to respond to any reported COVID-19 symptoms, with the understanding that presenting symptoms of fever, cough, shortness of breath or general malaise may be the only indication of the onset of COVID-19 [8,16]. It was equally important to initiate quarantine efforts if indicated, as we differentiated COVID-19 signs and symptoms from that of the flu virus and allergy symptoms. Understanding the incubation period for a virus helped to determine the quarantine period necessary to prevent and control viral spread [12,16]. According to the WHO, the incubation period for COVID-19 is between 2 to 10 days [7]. The main symptoms of COVID-19 are fever, tiredness, cough, and shortness of breath [8,15,16]. However, allergy symptoms are more chronic and present with sneezing, itching (eyes or skin), wheezing, post nasal drip, and coughing [16-18]. The flu virus may present with symptoms similar to COVID-19, but usually do not involve shortness of breath, except if the lower respiratory system has become involved and the condition has worsened. Common signs and symptoms of the flu virus include fever and chills, runny nose or nasal congestion, cough, occasional sore throat, myalgia, fatigue, headaches and body aches [19]. Residents reported to MRMC showing any of the symptoms listed were tested for COVID-19, and if positive, immediately quarantined in a local hotel, single person occupancy, for fourteen (14) days. At the end of the quarantine period, residents were retested for the antigen and if negative, returned to their dormitory at the MRM shelter. If a resident retested positive, the 14-day quarantine was repeated. Those residents who tested negative, but presented with symptoms were advised to stay in their dorm rooms until symptoms subsided and appropriate treatment plans were initiated. For those with more serious symptoms such as breathing difficulties, elevated temperature, and a productive cough (which can indicate pneumonia and warrant immediate medical attention) were seen by the health care provider (physician, physician assistant, or advanced practice registered nurse) via telehealth and transported by local fire and rescue services or the MRM transport van (depending upon the critical physical state of the patient) to the nearest local emergency care center for further evaluation and management. In an ongoing effort to maintain the health and well-being of the MRMC patients, the MRMC dispensed over 500 medications to homeless patients living at one of the MRM-BOC shelters over a two-month period.

Methods

First, adjustments made by the MRMC to the COVID-19 pandemic involved a rapid transition to telehealth for residential clients. Second, clinic staff provided urgent primary care to clients after local hospitals, clinics and community health centers cancelled the majority of specialty care visits, such as mental health and other critical services. Third, clinic staff provided patient medication refills delivered to the shelters to decrease emergency room utilization and a greater financial impact of existing stressed health care services due to COVID-19. Fourth, COVID testing and re-testing was initiated by the MRMC staff assisted by the local Department of Health (DOH). COVID-19 testing was conducted using nasopharyngeal swabs daily, Monday through Friday, from homeless persons living in and around the two shelters for 3-months, along with persons identified through contact tracing. COVID test results of homeless persons tested was provided by the DOH laboratory. Nasopharyngeal swabs were used because the research has shown that larger amounts of positive COVID-19 virus and viral RNA can be detected early in the disease using nasopharyngeal samples rather than throat swabs, and is independent of symptom presentation or severity [20,21]. Fifth, after clinic hours ended, clinic and shelter providers coordinated their efforts to verify priority patient needs, creating social distancing in dormitories and a single-use area in the clinic, reviewing client documentation (identifications, medical records, and symptoms) to determine the need for quarantine. Sixth, care coordination for contact tracing with local health department officials was ongoing. Seventh and ongoing was the reentry of quarantined patients back to the facility, avoiding stigmatization of previously affected individuals, and addressing the COVID deaths of clients. These action steps were taken in a rapid-fire format to reduce the spread of COVID-19 among the homeless population served. Studies have shown that viral shedding in respiratory secretions are common and can occur up to 3-days before the first clinical symptoms appear [22,23].

Interventions

The MRMC closed its clinic doors at the peak of the COVID-19 pandemic in response to mandatory shutdown orders by Florida’s Governor Rick DeSantis of non-essential businesses and orders for social distancing and face covering requirements of essential businesses for about 2-days to allow for increased purchasing of personal protective equipment (PPEs) for staff and patients and to create a social distancing design in the clinic. Following several coordinated MRMC health care and primary care providers (HCPs/PCPs) and MRM staff meetings and telehealth trainings, telehealth visits were initiated by having the on-call or on-site provider to log-in to the MRMC electronic medical record system (EMRS) and a web-video conferencing platform that is accessed by patients at each of the shelters in a designated area for private consultation. The web-video conferencing telehealth sessions allow the HCPs or PCPs and patients to connect using technology to deliver required health care services.

Telehealth

The Telehealth format allowed for synchronous (real-time telephone or live audio-video interactions with the patient using a smartphone, tablet, or computer). The caseworker/on-site medical technician was equipped with marginal medical equipment, such as temporal thermometers, digital blood pressure machines, weight scales, and oxygen saturation finger monitors. Biometric and anthropometric readings were obtained and reported by the onsite shelter medical technician while the consulting PCP conducted the remote evaluation and documented findings and planned treatments in the EMR. The MRMC PCPs conducted 397 telehealth visits over a 3-month period (June to September, 2020). Although, asynchronous (technology where messages, images, or data are collected at one point in time and interpreted or responded to later) and remote patient monitoring (direct transmission of a patient’s clinical measurements from a distance in real time or post-dated times to the PCP) are available, these two modalities were not used [24]. However, MRMC staff provided daily telephone welfare checks (362) over the same 3-month period to patients placed in quarantine at hotels or to those persons with symptoms of upper respiratory tract conditions, but tested negative or was diagnosed with other co-morbid conditions that warranted close follow-up. Because all clinic services are free, MRMC did not receive any telehealth reimbursement using the International Classification of Diseases (ICD) code – 10 99211 for office or other outpatient visits or Current Procedural Terminology (CPT) code – 99371 for telephone call by a physician to patient or for consultation or medical management or for coordinating medical management with other [25,26]. By quickly implementing Telehealth in the MRMC and making it available to homeless shelter residents, transmission of COVID-19 and other preventable diseases were mitigated, providing a safer option for HCPs, PCPs, and the patients served.

Results

The MRMC HCPs assisted by the local DOH staff, provided 545 coronavirus testing to 408 sheltered and unsheltered homeless persons living in and around the MRM and BOC persons and conducted 362 wellness telephone encounters (Table 1). Of the 545 tests performed, 56 (10%) were positive, 458 (84%) were negative, 44 persons recovered from COVID-19 (which includes individuals that tested negative and were added to the contact tracing), 4 (1%) persons died, 2 (<1%) persons were re-infected with COVID-19, 23 patients were hospitalized during the period of this study, and 108 persons were placed in quarantine, which included persons exposed during contact tracing. Ninety patients were tested at least two times during this study and one patient tested positive three times (15 Days after the first positive test and 7 days after the second positive test), The negative test for this patient came after 42 days after the first positive test.

Table 1: COVID-19 Testing Information.

Tests

N

%

N. of Tests performed Outcome

545

100.0%

Positive

56

10.3%

Negative

458

84.0%

Lab/STD

31

5.7%

Most of the individuals tested were male (83.6%, 341 Individuals), see Figure 1 and the average age of the individuals tested were 47.7 years. Regarding race and ethnicity, 74% of the patients were black and non-Hispanic (327 patients, 80%) (Table 2, Figures 2 and 3), 96% of the patients who tested positive were male and their average age was 49.3 years; 60% were black and 64% reported no Hispanic origin (Table 3), 23 patients were hospitalized and 4 died due to COVID-19. All 27 patients were male with an average age of 55.1 years (Table 4). The average age of patients who were hospitalized were 53.1 years, while the average age of the deceased patients and were on average 55.2 years old. The ages of the 4 victims who passed away ranged between 56 to 74 years of age.

fig 1

Figure 1: Demographics of Homeless Persons Tested for COVID-19 by Gender.

Table 2: Patient Demographics who were Tested for COVID-19.

Demographic Characteristics

N %
Number of Patients 408

100.0%

Age

Mean Age ± SD

47.7 ±14.6

Gender

Male 341

83.6%

Female

67 16.4%
Race and Ethnicity

Black

303 74.3%
Hispanic

3

Non-Hispanic

300
White 104

25.5%

Hispanic

78
Non-Hispanic

26

Asian

1 0.2%
Hispanic Origin

Hispanic

81 19.9%
Non-Hispanic 327

80.1%

fig 2

Figure 2: Demographics of Homeless Persons Tested for COVID-19 by Race.

fig 3

Figure 3: Demographics of Homeless Persons Tested for COVID-19 by Ethnicity.

Table 3: Patient Demographics who Tested Positive for COVID-19 at least in one test.

Demographic Characteristics

N %
Number of Patients 53

100%

Age

Mean Age ± SD

49.34 ± 12.9

Gender

Male 51

96.2%

Female

2 3.8%
Race and Ethnicity

Black

32 60.4%
Hispanic

0

Non-Hispanic

32
White 21

39.6%

Hispanic

19
Non-Hispanic

2

Hispanic Origin

Hispanic 19

35.8%

Non-Hispanic

34

64.2%

Table 4: Patient Demographics and Presenting Symptoms who were Hospitalized or Died due to COVID-19.

Demographic Characteristics

N %
Number of Patients

27

Age

Mean Age ± SD

55.15 + 10.6

Gender

Male 27

100.0%

Female

Race and Ethnicity

Black

23 85.2%
Hispanic

Among the four deaths from the homeless shelter, three were confirmed COVID-19 positive and one unconfirmed. The three confirmed COVID-19 deaths occurred within 6-days of each other. Each victim had preexisting conditions and were being treated at the MRMC prior to hospitalization for a history of diabetes mellitus, obesity and hypertension. The fourth homeless death occurred one-month following the first three deaths and was unconfirmed for COVID-19. All victims were males, three were non-Hispanic Blacks and one Hispanic. Each of the three deaths presented to the emergency room with shortness of breath, fever and cough; admitted to the intensive care unit where their conditions deteriorated rapidly; and decompensated requiring increased oxygen and later intubation.

Contact Tracing, Quarantine and Reentry

Contact tracing plays a significant role in identifying positive cases, interrupting viral transmission and helps to prevent further spread of the virus. Contact tracing involves four-steps: (1) case investigation of close contacts, (2) contact tracing of exposed individuals, (3) contact support through education, information and exposure reduction, and (4) self-quarantining by staying at home and maintaining social distancing of at least 6-feet for th14-days [27]. MRMC HCPs conducted contact tracing on 108 patients. Of the 108 patients, 65 tested positive for COVID-19 and were placed in quarantine at the designated hotel. All hotel rooms used for housing positive COVID-19 patients were properly decontaminated using FDA approved disinfecting agents. Patients quarantined were required to wear face coverings when exiting the room for individual meals, when in contact with family members during the quarantine period, and when outside or in close contact with other people. Asking everyone to wear masks has helped to reduce the spread of COVID-19 by persons who may be unaware that they have the virus [16,27]. The N95 and KN95 masks are both rated to capture 95% of particles. The KN95 masks are made in China and require wearers to pass a fit test [28]. The N95 masks produced by the 3M company have stronger breathability standards. However, both the KN95 and the N95 mass filtration efficiency captures salt particles and a tested flow rate of 85L/minute [28]. Surgical masks provide approximately 63% filtration and cotton hander kerchiefs provide about 28% filtration [28]. It has been reported that “several 3M masks were able to capture over 99% of tiny 0.01-micron particles (10 times small than the coronavirus), even while on people’s face” [28].

Management and Treatment Options

Patients, staff, volunteers and visitors to the MRM or BOC experiencing any coronavirus disease were required to practice general prevention measures to include adequate rest and sleep, eating a well-balanced diet, washing hands frequently with a hand sanitizer (60% alcohol minimum) or soap and water for 20-seconds or longer, drying hands thoroughly with a clean towel or air dry, avoiding touching eyes, nose, or mouth with unwashed hands or after touching surfaces, covering mouth with a tissue or sleeve when sneezing or coughing, using a protective face covering, and calling the PCP before visiting the clinic. The HCPs were required to notify health authorities to assist with contact tracing as needed [27]. The foregoing requirements are essential for vulnerable populations and people of color who are disproportionately affected by COVID-19 because the virus is increasing at alarming rates among this group due to underlying health and economic disparities [29]. Data from the COVID-19 tracking project traces racial and ethnic data from reporting states across America and show that people of color account for 24% of COVID-19 deaths but represents only 13% of the U.S. population [30,31]. In a recent article by Washington & Cirilo [32] on vaccinating homeless persons, 76% of the participating population were members of an ethnic minority group and consisted of 117 non-Hispanic Blacks, 50 non-Hispanic Whites, 35 Hispanics, and 7 Haitians; with males (177) outnumbering females (32) in the active group. Currently the racial/ethnic make-up of MRMC patients are seen in Table 4.

To address early identification of COVID-19 in homeless shelter residents, the MRMC has partnered with a COVID-19 research and development company that is piloting a non-invasive pre-screening device, COVID PlusTM Monitor, that provides real-time subclinical markers for COVID-19 and can be worn by both children and adults [33]. The instrument is able to detect sub-clinical abnormalities associated with inflammatory markers that have shown strong correlation between COVID-19 and hyper-inflammatory states like hypercoagulation [33-35]. The COVID PlusTM is able to “allow healthcare providers to identify potentially infected patients, directing them to seek further testing and medical intervention, and avoiding the spread of infections among the general public” especially among homeless persons [33]. The device provides data within 3 to 5 minutes on abnormalities found in blood flow and other COVID-19 related complications and can track disease severity, progression, and recovery [33]. The COVID PlusTM device has been tested on over 1,000 COVID PCR positive subjects, using hundreds of biometric markers that identify patterns commonly associated with COVID-19 [33]. The goal established by the MRMC is early identification of COVID-19 among sheltered homeless persons. Once identified, actions can be taken to quickly quarantine those individuals to reduce the spread of COVID-19 among persons in congregant living facilities, such as a homeless shelter. The early identification also includes the essential workers who provide for their food, safety and shelter.

Vaccine Therapy

Nonetheless, homeless populations and racial/ethnic vulnerable groups are at-risk for contracting COVID-19 and would greatly benefit from increased accuracy in SARS-CoV-2 testing and a safe vaccine therapy. Now that Pfizer’s vaccine BNT162b2 has received emergency use authorization (EUA) from the FDA [36,37], it is critical that frontline healthcare workers, volunteering or employed by the Free Clinics, receive the COVID-19 vaccine in the first distribution. The CDC and U.S. Surgeon General encourage the continued wearing of face coverings, physical distancing, proper isolation, quarantine of infected individuals, and contact tracing to help us mitigate SARS-CoV-2 spread. Nonetheless, a safe and effective preventive vaccine is needed for healthcare workers and the general public to help create herd immunity against COVID-19 and to ultimately control this pandemic.

The MRMC currently has a vaccination program for the homeless and have vaccinated hundreds of homeless men and women with both pneumonia vaccines, PPSV23 and PCV13, quadrivalent Influenza, tetanus, diphtheria and acellular pertussis (Tdap), and Hepatitis C vaccines over the past five-years, reducing the incidence of vaccine preventable illnesses among the homeless population in MDC and BC [32]. A proven safe and effective COVID-19 vaccine could greatly reduce morbidity and mortality rates among disparate homeless populations. Homeless persons living in and around homeless shelters are among the most vulnerable, are considered high risk due to their multiple co-morbid conditions and transient characteristics, and should also be considered in the first or second round of vaccine therapy once made available to the general public.

Conclusion

Coronaviruses are respiratory diseases that infects older children and adults, including homeless men and women, more commonly than younger children [27,36]. The chances of dying from the virus is age dependent and influenced by the social determinants of health (where we live, eat and work), persons living in crowded facilities such as homeless shelters, and persons with higher comorbid conditions having worse prognoses [4,29-30]. Homeless persons and people living in poor communities with decreased access to health care and healthy foods, employment struggles, high toxic stress (allostatic loads), and factors surrounding coronaviruses, increase the risk of getting the disease and dying from the disease [29]. There were many challenges faced by homeless populations, shelters, and free clinics when the pandemic hit South Florida. The seven-step method implemented by the MRM and MRMC at the onset of Florida’s State-wide shut down may have saved more lives than the four persons that died from SARS-CoV-2. However, interventions like contact tracing and disease management were constrained due to the transient nature of the homeless population. The socio-demographics were constantly changing as individuals left the shelter and were not allowed to return during the shut-down, especially when we had minimal PPEs and test kits to protect the frontline workers and to determine positivity rates. Currently, we have an EUA approval for Pfizer’s BNT162b2 vaccine. Although frontline workers mainline employed by hospitals and long-term care facilities are receiving the vaccine first, healthcare workers assigned to provide healthcare services to the homeless must be considered as frontline workers and receive the COVID-19 vaccine. The challenges still remain to reduce hesitancy to receiving the vaccine for both healthcare workers, the general public, and homeless persons living in and around homeless shelters. More information is still needed on the safety and efficacy of the vaccine, especially when used in vulnerable populations who present with multiple co-morbid health conditions. In conclusion, homeless persons rely on health care services provided by free clinics, hospitals, and emergency rooms when they become ill. Due to the COVID-19 pandemic, the obstacles to receiving health care increased and many homeless persons with mild or undetectable symptoms are not seen by health care providers or discharged from health care facilities with minimal or no treatment. Due to a lack of follow up, many homeless persons become super spreaders of COVID-19. Unless timely interventions using face coverings, quarantine, social distancing, and frequent hand washings are initiated, the spread of COVID-19 will continue among homeless persons resulting in greater morbidity and mortality.

Acknowledgement

The authors have no conflict of interest to disclose.

References

  1. COVID-19 map. https://coronavirus.jhu.edu/map.html. Published November 14 2020. Accessed November 14 2020.
  2. Florida Department of health COVID-19 response. https://floridahealthcovid19.gov/ Published November 14 2020. Accessed November 14 2020.
  3. Gutierrez P, Washington-Brown L, Bretones-Graham G, Pereira-Amorim E (2020) Cardiac Related Deaths among the Homeless Persons with Covid-19: Case Presentations. International Journal of Cardiovascular Diseases and Diagnosis 5: 033-038. www.scireslit.com
  4. Pan A, Liu L, Wang C, Guo H, Hao, et al. (2020) Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA 323:1915-1923. [crossref]
  5. Woo PC, Lau SK, Chu C, Chan K, Tsoi H, Huang, et al. (2005) Characterization and complete genome sequence of a novel coronavirus, coronavirus HKU1, from patients with pneumonia. Journal of Virology 79: 884-895. [crossref]
  6. World Health Organization. WHO | International Classification of Diseases, 11th Revision (ICD-11). 2020a. Retrieved from https://www.who.int/classifications/icd/en/ World Health Organization. Coronavirus disease (COVID-19) outbreak. Retrieved February 14, 2020, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
  7. World Health Organization. 2020b, January 10. Coronavirus; Current novel coronavirus (2019-nCoV) outbreak. Retrieved February 15, 2020, from https://www.who.int/health-topics/coronavirus
  8. Vassilara F, Spyridaki A, Pothitos G, Deliveliotou A, Papadopoulos, A (2018) A Rare Case of Human Coronavirus 229E Associated with Acute Respiratory Distress Syndrome in a Healthy Adult. Case Reports in Infectious Diseases 1-4.
  9. Casey, G. Coronavirus – a developing outbreak (2020) Kai Tiaki Nursing New Zealand, , 26(1), pg: 26. http://db03.linccweb.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141750278&site=ehost-live&scope=site
  10. Hand J, Rose E, Salinas A, Lu X, Sakthivel SK, et al. (2018) Severe respiratory illness outbreak associated with human Coronavirus NL63 in a long-term care facility. Emerging Infectious Diseases 24: 1964-1966. [crossref]
  11. Chen T, Wu D, Chen H, Yan W, Yang D, et al. (2020) Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ.
  12. Washington-Brown LJ, Cirilo R (2020) Coronavirus Disease – 2019-nCoV (COVID-19). Journal National Black Nurses Association 31: 19-25.
  13. Sauer, L. M. What is coronavirus? Health. https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus. Accessed February 11 2020.
  14. Centers for Disease Control and Prevention. Coronavirus Disease 2019 (COVID-19): How to protect yourself and others. Retrieved February 13, 2020, from https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/prevention.html.
  15. S. Food and Drug Administration (FDA). Important Information on the Use of Serological (Antibody) Tests for COVID-19 – Letter to Health Care Providers | FDA. (2020, June 19). Retrieved from https://www.fda.gov/medical-devices/letters-health-care-providers/important-information-use-serological-antibody-tests-covid-19-letter-health-care-providers.
  16. Backer JA, Klinkenberg D, Wallinga J (2020) Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travelers from Wuhan, China, 20–28 January. Eurosurveillance: European Communicable Disease Bulletin [crossref]
  17. Centers for Disease Control and Prevention. Symptoms of Coronavirus|CDC. 2020. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
  18. Asthma and Allergy Foundation of America [AAFA] n.d. Allergy symptoms. https://www.aafa.org/allergy-symptoms/.
  19. Centers for Disease Control and Prevention. Flu symptoms & diagnosis. 2020, September 2. https://www.cdc.gov/flu/symptoms/
  20. Fu Y, Han P, Zhu R, Bai T, Yi J, et al. (2020). Risk factors for viral RNA shedding in COVID-19 patients. Eur Respir J [crossref].
  21. Tan F, Wang K, Liu J, Liu D, Luo J, et al. (2020) Viral Transmission and Clinical Features in Asymptomatic Carriers of SARS-CoV-2 in Wuhan, China. Front Med (Lausanne) [crossref]
  22. He X, Lau EHY, Wu P, (2020) Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med , 26: 672-675.
  23. Lee S, Kim T, Lee E, Lee, C, Kim H, et al. (2020) Clinical Course and Molecular Viral Shedding Among Asymptomatic and Symptomatic Patients With SARS-CoV-2 Infection in a Community Treatment Center in the Republic of Korea. JAMA Intern Med. 180:1447-1452.
  24. Centers for Medicare and Medicaid Services [CMS] Telehealth. 2020 April 24. https://www.cms.gov/Medicare/Medicare-General-Information/Telehealth
  25. American medical Association [AMA]. CPT® overview and code approval. 2019, 25. https://www.ama-assn.org/practice-management/cpt/cpt-overview-and-code-approval
  26. Centers for Disease Control and Prevention. ICD – ICD-10-CM – International classification of diseases, icd-10-CM/PCS transition. Centers for Disease Control and Prevention. 2019, March 1. Accessed September 2020, https://www.cdc.gov/nchs/icd/icd10cm_pcs_background.htm
  27. Centers for Disease Control and Prevention. Coronavirus disease 2019 (COVID-19); Interim infection prevention and control recommendations for patients with confirmed 2019 Novel Coronavirus (2019-nCoV) or persons under investigation for 2019-nCoV in healthcare settings. 2020c, February 12. https://www.cdc.gov/coronavirus/2019-nCoV/hcp/infection-control.html
  28. Alizargar J (2020) Wearing masks and the fight against the novel coronavirus (COVID-19). Pulmonology. [crossref]
  29. Johnson A, Buford T (2020) Early Data Shows African Americans Have Contracted, Died of Coronavirus at an Alarming Rate. www.medscape.com.
  30. COVID Tracking Project | The COVID Tracking Project. 2020, Retrieved from https://covidtracking.com/
  31. National Geographic. African Americans struggle with disproportionate COVID death toll. Retrieved from https://www.nationalgeographic.com/history/2020/04/coronavirus-disproportionately-impacts-african-americans.
  32. Washington-Brown LJ, Cirilo R (2020) Advancing the health of homeless populations through vaccinations. Journal of the American Association of Nurse Practitioners. [crossref]
  33. COVID PlusTM Monitor. 2020, https://www.tigertech.solutions/
  34. Chen T, Chen H, Yan W, Yang D, Chen G, et al. (2020). Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ, m1295. 2020.
  35. Wu C, Chen X, Cai Y, Xia J, Zhou X, (2020) Risk Factors Associated with Acute Respiratory Distress Syndrome and Death in Patients with Coronavirus Disease 2019 Pneusmonia in Wuhan, China. 2020, JAMA Internal Medicine 180: 934-943. [crossref]
  36. Lai C, Shih T, Ko W, Tang H, Hsueh, P (2020) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. International Journal of Antimicrobial Agents [crossref]
  37. U.S. Food & Drug Administrations (FDA). Pfizer-BioNTechCovid-19 Vaccine EUA letter of Authorization (11 December 2020). https://www.fda.gov/media/144412/download

Co-occurring HIV Risk Behaviors among Males Entering Jail

DOI: 10.31038/PEP.2021211

Abstract

People going through the United States (US) criminal justice system often exhibit multiple behaviors that increase their risk of HIV infection and transmission. This paper examined the pattern of co-occurring HIV risk behaviors among male jail detainees in the US. We conducted multivariate analyses of baseline data from an HIV intervention study of ours, and found that: [1] cocaine use, heroin use and multiple sexual partners; and [2] heavy drinking and marijuana were often co-occurring among this population. From pairwise analyses, we also found that [1] heroin and IDU [2] unprotected sexes with main, with non-main, and in last sexual encounter were mostly co-occurring behaviors. Further analyses of risk behaviors and demographic characteristics of the population showed that IDU were more prevalent among middle ages (30-40) and multiple prior incarcerations, and having multiple sex partners was more prevalent among young males younger than 30 years, African American race, and those with low education. Our findings suggest that efficient interventions to reduce HIV infection in this high-risk population may have to target on these behaviors simultaneously and be demographically adapted.

Keywords

HIV risk, Co-occurring behaviors, Correctional facilities, Male jail detainees

Introduction

Over seven million people passed through the criminal justice system in the United State (US) in year 2012 [1]. Among this population, it was estimated that about 2% was infected with HIV including those unaware of their infection [2-4] — as a contrast, the prevalence among the US adult population is around 0.3% according to the US Centers for Disease Control and Prevention (CDC). The prevalence of HIV infection within jails and prisons was estimated to be about 3 to 6 times higher compared with that among non-incarcerated populations [4-8].

The reasons for this increased burden of HIV among populations in correctional settings are multi-factorial and include increased rates of substance abuse, mental illness, poverty and health disparities [9]. Persons who interact with the criminal justice system may be disenfranchised from health services in the community, such as screening programs. That makes the time of incarceration an important public health opportunity to provide HIV prevention and testing services and linkage to care [10-12].

The time period preceding incarceration has been shown to be characterized by increased substance use and risky sexual behaviors that increased exposure to HIV, viral hepatitis, and other transmitted diseases [13-18]. Release from correctional facilities might also be a time of high-risk of acquiring or spreading infections as persons re-entered their communities and resumed risk behaviors [19-21]. Thus, correctional-based HIV counseling and testing programs and prevention interventions may help to decrease their risk behaviors following release from the correctional environment and therefore reduce new HIV infections in this as well as the general population.

Although studies have documented prevalent (direct and indirect) HIV risk behaviors before entering jail (including heavy drinking, substance abuse, sexual promiscuity, and unprotected sex) [19-26] there is limited understanding of the interrelationships among these risk factors. To effectively target prevention interventions to persons at the greatest risk of HIV infection among this population, it is critically important to understand their risk profiles and quantify which risk behaviors are more likely to co-occur. In this paper, co-occurring behaviors are defined as behaviors that occur within certain time period (e.g. a 3 months window) and not necessarily always in the same episode (i.e. concurrently). This definition is consistent with the need of broader interventions on behaviors that are predictive of (not necessarily determinative of) each other and jointly place an individual at a higher risk of HIV infection.

In this paper, we conducted a secondary analysis of data from a study on HIV counseling and testing in jail [27]. Specifically, we used the baseline data of the study to investigate: 1) whether certain risk behaviors were co-occurring and to what extent, and 2) whether risk behaviors were prevalent among people with certain demographic characteristics.

Methods

Prior Study and Data

We previously conducted a two-arm randomized study [27] to assess HIV risk behaviors among males entering the Rhode Island Department of Corrections (RIDOC) jail and compared the efficacy of two methods of HIV counseling and testing (conventional versus rapid HIV testing) with respect to reducing post-release HIV risk behaviors. A total of 264 HIV-negative males met the study enrollment criteria, provided the written informed consent, were recruited within 48 hours of incarceration, and completed the study. The study was approved by the Miriam Hospital institutional review board, the Rhode Island Department of Corrections (RIDOC) Medical Research Advisory Group, and the Office for Human Research Protections of the Department of Health and Human Services. More details of the study are available elsewhere [27].

In this paper, we focused on data that were collected at the baseline of the study, including demographic information and self-reported HIV risk behaviors during 3 months prior to incarceration. The self-reported risk behaviors were collected using a written quantitative behavioral assessment survey on participant’s recent drinking, substance use behaviors (cocaine use, heroin use, marijuana use, injection of any drug) and sexual behaviors (multiple sexual partners, unprotected sex at last sexual encounter, unprotected sex with main partner, and unprotected sex with non-main partner). Because only data at the baseline prior to intervention randomization were used in this paper, we did not distinguish study participants by their study arms.

Statistical Analyses

We conducted three sets of statistical analyses, outlined as follows:

Analysis I

The co-occurrence of two risk behaviors (pair-wise analysis) was assessed using logistic regressions where one behavior (Behavior 1) was used as the dependent variable, and the other behavior (Behavior 2) as an predictor variable. The results are shown in Table 1. All regressions were adjusted for the following demographic covariates: age (categorized as <25; 25 ∼ 30; 30 ∼ 40; and > 40 years), race (Caucasian; Black; Hispanic; others), number of prior incarcerations (dichotomized at median: < 7; ≥ 7), length of incarceration as severity index of crime leading to incarceration (<2 wks; 2 wks ∼ 1/2 yr; > 1/2 yr), and education (did not finish high school; otherwise).

Table 1: Pair-wise association among risk behaviors.

table 1

(a) The table is not symmetric because the analyses are adjusted for the following covariates as predictors of Behavior 1: age, race, prior incarcerations, length of incarceration, and education.
(b) The numbers in parentheses are sample sizes.
(c) Bold indicates a p-value < 0.05 and italic < 0.10.

Pair-wise co-occurring risk behaviors were quantified using odds ratios (ORs), where an OR > 1 (OR < 1) suggests that the existence of one behavior was predictive of the existence (or absence) of the other behavior.

We used the available complete data for assessing risk behaviors, so the analysis sample size varied (range: 73-256 as in Tables 1 and 2). The overall missing data on risk behaviors were moderate (<5%), if we did not count systematic missingness as missing values (e.g. Missing sexual behaviors for those without sexual partner). Throughout, we made the missing at random (MAR) assumption [28]; that is, we assumed that with the same demographic profile, those who provided complete answers to the baseline questionnaire had engaged in similar risk behaviors as those who did not [29].

Table 2: Multivariate analyses of co-occurring risk behaviors.

table 2

(a) The table is not symmetric because the analyses are adjusted for the following demographic covariates: age, race, prior incarcerations, length of incarceration, and education.
(b) The numbers in parentheses are sample sizes.
(c) Bold indicates a p-value < 0.05 and italic < 0.10.
(d) *** “Unprotected sex at last sexual encounter” is not included as a predictor variable in the model.

Analysis II

Multiple co-occurring risk behaviors were assessed using multivariate logistic regressions where one risk behavior (Behavior 1) was used as the dependent variable and other behaviors (Behaviors 2) as predictor variables. The results are shown in Table 2. Similar to Analysis I, the co-occurring of Behavior 1 with other behaviors was characterized by ORs, which have similar interpretations except that the ORs in Table 2 are conditional ORs after accounting for all other behaviors of (Behaviors 2). Again, all analyses were adjusted for the same set of demographic characteristics as in Analysis I. When heavy drinking, cocaine, heroin, marijuana and multiple sexual partners were used as dependent variables, we excluded the risky behaviors ‘unprotected sex with main partner’ and ‘unprotected sex with non-main partner’, because they only applied to subsets of study participants with sexual partners and including them would reduce the sample size by half and reduce the analysis power. When ‘unprotected sex with main partner’ and ‘unprotected sex with non-main partner’ were used as the dependent variables, ‘unprotected sex at last sexual encounter’ was excluded from predictor variables because the later behavior strongly correlated with the former behaviors and therefore overwhelmed the associations of the former two behaviors with other risk factors. IDU was excluded from the analysis because the prevalence of injection drug use was low (overall 8%) leading to sparse data for multivariate analysis and unreliable estimates due to collinearity of heroin use and IDU [30].

Analysis III

Further, we examined the associations between HIV risk behaviors and various demographic characteristics using logistic regressions, where each risk behavior was used a dependent variable and predictor variables included: age, race, the number of prior incarcerations, length of incarceration as severity index of crime leading to incarceration, and education. The predictor variables were categorized in the same way as in Analyses I and II. The associations of each risk behaviors and certain demographic profiles were characterized by ORs.

Data were extracted and prepared using Access 2003 [31]. All analyses were conducted using the statistical program R [32]. Analysis lack of fit was assessed using Hosmer-Lemeshowtests. Statistical significance was set at a p-value < 0.05.

Results

Among the 264 male HIV-negative participants, the median age was 30 years (range 18-65); the majority was Caucasian (52% Caucasian, 22% Black, 14% Hispanic, 12% others); 51% did not finish high school; and the median number of lifetime incarcerations was 6 (range 1-200). Within the prior 3 months before incarceration, 103 (39%, data not available (NA) = 1) were heavy drinkers; 27 (10%, NA = 1) used heroin; 100 (38%, NA = 1) used cocaine; 161 (61%, NA = 1) used marijuana; and 22 (8%, NA = 1) had injected any type of drug. For the same time period, 203 (77%) had a main sexual partner and of those 170 (84%, NA = 1) never used a condom; 111 (42%) had a non-main sexual partner and of those 37 (36%, NA = 3) never used a condom; 81 (31%) had both main and non-main sexual partners; 61 (26%, NA = 4) had multiple (≥ 3) recent sexual partners; and 233 (90%, NA = 4) did not use a condom at last sexual encounter.

In Analysis I, cocaine use was found to be highly predictive of heroin use (OR = 5.21 with a 95% confidence interval (CI) of 1.8-15), IDU (OR = 6.65, CI = 1.9-23), and multiple sexual partners (OR = 2.45, CI = 1.1-5.3); see Table 1. Heroin use and IDU were mostly co-occurring, suggesting that injection might be the preferred route of heroin use. Heavy drinking and marijuana use were predictive of each other (OR = 2.88, CI = 1.6-5.3). Participants who had unprotected sex with their main and non-main sexual partner(s) were more likely to have unprotected sex at last sexual encounter (OR = 25.7, CI = 9.1-73.0 and OR = 88.6, CI = 15-200, respectively). Notably, unprotected sex with main partner and with non-main partner(s) was likely to co-occur (OR = 6.43, CI = 1.56-78.8). In terms of protective behaviors, participants who reported IDU and those with multiple sexual partners were found to be more likely to use condoms at “the last sexual encounter”, though this finding was marginally statistically insignificant (p-values = 0.08 and 0.07, respectively).

In Analysis II, we found that (1) cocaine use, heroin use, and multiple sexual partners, and (2) heavy drinking and marijuana use were mostly co-occurring (Table 2). Heavy drinking and marijuana use were highly predictive of each other (OR = 3.40, CI = 1.7-7.1). Cocaine use was predictive of heroin use (OR = 9.20, CI = 2.7-38.7) and multiple (≥ 3) sexual partnerships (OR = 2.56; CI = 1.1-6.0).

The analyses that examined the relationships between risk behaviors and demographic characteristics (Analysis III) showed that male jail detainees with age between 30-40 were more likely to abuse cocaine (OR = 8.6, CI = 3.5-23.2), heroin (OR = 4.7, CI = 1.2-23.7), and IDU (OR = 2.8, CI = 1.2-6.9). Younger males with age <30 were more likely to abuse marijuana (OR = 3.8, CI = 2.2-6.9) and had multiple sexual partners (OR = 2.1, CI = 1.2-3.8). African American were more likely to have multiple sexual partners (OR = 3.7, CI = 1.8-7.9), but less likely to engage in unprotected sex in last sexual encounter (OR = 0.3, CI = 0.1-0.6), with main partner (OR = 0.3, CI = 0.1-0.9) and non-main partner(s) (OR = 0.1, CI = 0.03-0.4). Having more than 7 prior incarcerations was predictive of heavy drinking (OR = 1.8, CI = 1.1-3.2), cocaine use (OR = 2.5, CI = 1.4-4.6), and IDU (OR = 2.9, CI = 1.1-7.7). Finishing high school was predictive of having less sexual partners (OR = 0.5, CI = 0.3-0.9) but more likely engaging in unprotected sex in last sexual encounter (OR = 3.3, CI = 1.6-7.2) and with main sexual partner (OR = 3.2, CI = 1.3-8.0).

Discussion

Our results indicate that males entering jail exhibit high rates of substance use and sexual risk behaviors that increase their risk of HIV and other infectious diseases. Our study adds to the existing literature by demonstrating high risk behaviors among incarcerated populations and by highlighting whether certain risk behaviors are more likely to be co-occurring thus compounding risk for HIV infection.

Particularly from our pairwise and multivariate analyses, we find that cocaine is co-occurring with several other risk behaviors including heroin use, injection drug use, and multiple sexual partners. Cocaine use has been reported to not only increase the probability of HIV transmission, but also the potential of poor health outcomes in those living with HIV infection [22,33-35]. Given that there is currently no pharmacotherapy based intervention for cocaine addiction as there is for opiate addiction, our study supports the need of developing behavior-based interventions for cocaine abuse that is appropriate for incarcerated populations in addition to addressing opiate use and risky sexual behaviors. Since jail incarcerations may be as short as several days, behavioral interventions such as contingency management (CM) [36-39] may provide immediate reinforcement for abstinence from cocaine use, and cognitive behavioral interventions that are paired with CM upon release may offer a bridge for continued abstinence following community re-entry [40]. However, these interventions have not been implemented among incarcerated populations [41].

The finding that unprotected sex with main partner is co-occurring with unprotected sex with non-main partner(s) is another important finding, as this suggests that some participants could be involved with concurrent sexual relationships. Concurrent sexual partnerships in incarcerated populations have been reported in several studies [42-46]. Further accounting for concurrent sexual partnerships (and social/sexual networks) in our analyses would strengthen our conclusions, but unfortunately as one limitation of this paper, collecting concurrent behaviors data is not a focus of our original study.

Heavy alcohol use and marijuana use are common substances used by this population and found to be mostly co-occurring. Previous findings with younger incarcerated men [47] suggested that prior to incarceration, the use of marijuana alone and alcohol alone increased the likelihood of multiple sexual partners (i.e. 3 or more) and when in used in combination, sexual HIV-risk behaviors and inconsistent condom use behaviors with female partners increased. Similar finding also can be found in [15,48]. Comparable to other drugs of abuse, alcohol and marijuana use can impair judgment thereby preventing safer sex behaviors, and hence remain an important domain for intervention.

This paper has several limitations. The risk behavior data were self-reported which might have introduced bias and possibly an underreporting of risk behaviors given the environment in which participants completed the questionnaire. Our findings are not generalizable to incarcerated women or the entire population, because it is known that incarcerated women have a different rate of HIV infection and other transmissible diseases compared to men. The study sample size is limited and study participants are restricted only to those at the RIDOC, which limits our analysis power to identify all co-occurring risk behaviors.

As the U.S. incarceration population continues to grow and disproportionate rates of HIV infection continue to rise among incarcerated individuals, the implications for intervention are important and imperative. Jails provide a unique opportunity for structural interventions for this high-risk population. The results of this study offer more insight into the risk behaviors of males entering the RIDOC jail, and elucidate the educational, counseling, and intervention needs of men at risk for HIV infection within the criminal justice system.

Sources of Funding Support

The research is supported by the Providence/Boston Center for AIDS Research (grant P30AI42853). Dr. Pinkston’s work is partially supported by a National Institute of Mental Health grant (5R01MH084757).

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. Glaze L, Parks Correctional Populations in the United States, 2011. Retrieved Dec 12, 2012, from http: //bjs.ojp.usdoj.gov/content/pub/pdf/cpus11.pdf; 2012.
  2. Monitoring Selected National HIV Prevention and Care Objectives by Using HIV Surveillance Data—United States and 6 US Dependent Areas—2010. HIV Surveillance Supplemental Report 2012;17(3): part A.
  3. Spaulding AC, Stephenson B, Macalino G, Ruby W, Clarke J, Flanigan T (2002) Human immunodeficiency virus in correctional facilities: A review. Clinical Infectious Diseases 35: 305–312.
  4. Spaulding AC, Seals RM, Page MJ, Brzozowski AK, Rhodes W, Hammett TM (2009) HIV/AIDS among inmates of and releasees from US correctional facilities, 2006: declining share of epidemic but persistent public health PLoS One 4(11): e7558. [corssref]
  5. National Commission on Correctional Health Care. The health status of soon-to-be- released inmates: A report to Congress, Volumes 1 and 2. National Commission on Correctional Health Care; 2002, Chicago.
  6. Dean-Gaitor HD, Fleming PL (1999) Epidemiology of AIDS in incarcerated persons in the United States, 1994-1996. AIDS 13: 2429–2435. [corssref]
  7. Hammett TM (1998) Public health/corrections collaborations: Prevention and treatment of HIV/AIDS, STDs, and TB. US Department of Justice, Office of Justice Programs, National Institute of Justice.
  8. Maruschak L. HIV in prisons, 2004. Retrieved March 6, 2009, from http: //www.ojp.usdoj.gov/bjs/pub/pdf/hivp04.pdf;
  9. Springer R (2004) Patient safety initiatives–correct site verification. Plast Surg Nurs 24(1): 6–7.
  10. Harrison P, Beck Prisoners in 2005. Washington, DC: Bureau of Justice Statistics, US Dept of Justice; 2006. NCJ publication 2006; 215092.
  11. Braithwaite R, Hammett T, Mayberry R. Prisons and AIDS: A public health challenge. New York: Jossey-Bass;
  12. Braithwaite R, Arriola K (2008) Male prisoners and HIV prevention: a call for action ignored. Am J Public Health 98(9 Suppl): S145–9. [corssref]
  13. Wohl AR, Johnson D, Jordan W, et al. (2000) High-risk behaviors during incarceration in African-American men treated for HIV at three Los Angeles public medical centers. J Acquir Immune Defic Syndr 24(4): 386–92.
  14. Conklin TJ, Lincoln T, Tuthill RW (2000) Self-reported health and prior health behaviors of newly admitted correctional inmates. Am J Public Health 90(12): 1939–41. [corssref]
  15. Altice FL, Mostashari F, Selwyn PA, et al. (1998) Predictors of HIV infection among newly sen- tenced male prisoners. J Acquir Immune Defic Syndr Hum Retrovirol 18(5): 444–53.
  16. Stephenson BL, Wohl DA, McKaig R, et al. (2006) Sexual behaviours of HIV-seropositive men and women following release from prison. Int J STD AIDS 17(2): 103–8.
  17. Weinbaum CM, Sabin KM, Santibanez SS. Hepatitis B, hepatitis C, and HIV in cor- rectional populations: a review of epidemiology and AIDS 2005;19 Suppl 3: S41–6.
  18. Mertz KJ, Schwebke JR, Gaydos CA, Beidinger HA, Tulloch SD, Levine Screening women in jails for chlamydial and gonococcal infection using urine tests: feasibility, acceptability, prevalence, and treatment rates. Sex Transm Dis 2002;29(5): 271–6.
  19. Chandler RK, Fletcher BW, Volkow ND (2009) Treating drug abuse and addiction in the criminal justice system: improving public health and safety. JAMA 301(2): 183– [corssref]
  20. Morrow KM. HIV, STD, and hepatitis risk behaviors of young men before and after incarceration. AIDS Care 2009;21(2): 235–43. [corssref]
  21. Milloy MJ, Buxton J, Wood E, Li K, Montaner JS, Kerr T (2009) Elevated HIV risk behavior among recently incarcerated injection drug users in a Canadian setting: a longitudinal analysis. BMC Public Health 9: 156.
  22. McCoy CB, Lai S, Metsch LR, Messiah SE, Zhao W (2004) Injection drug use and crack cocaine smoking: independent and dual risk behaviors for HIV Ann Epidemiol 14(8): 535–42. [corssref]
  23. Shannon K, Rusch M, Morgan R, Oleson M, Kerr T, Tyndall MW. HIV and HCV prevalence and gender-specific risk profiles of crack cocaine smokers and dual users of injection drugs. Subst Use Misuse 2008;43(3-4): 521–34.
  24. Effectiveness of interventions to address HIV in prisons. Geneva, Switzerland: World Health Organization 2007.
  25. Fazel S, Bains P, Doll H (2006) Substance abuse and dependence in prisoners: a systematic review. Addiction 101(2): 181–191. [corssref]
  26. Koulierakis G, Gnardellis C, Agrafiotis D, Power KG (2002) HIV risk behaviour correlates among injecting drug users in Greek prisons. Addiction 95(8): 1207–1216.
  27. HIV risk behavior before and after HIV counseling and testing in jail: a pilot study. J Acquir Immune Defic Syndr 2009;53(4): 485–90.
  28. Rubin DB. Inference and missing data (with discussion). Biometrika 1976;63: 581–592.
  29. Little RJA, Rubin DB. Statistical Analysis with Missing Data 2nd ed. Hoboken, NJ: John Wiley & Sons; 2002.
  30. Armitage P, Berry G, Matthews JNS. Statistical Methods in Medical Research. Malden, MA: Blackwell; 2002.
  31. Microsoft Access (2003). Microsoft Co (www.microsoft.com); 2003.
  32. R Development Core R: A Language and Environment for Statistical Computing. Vienna, Austria; 2010. ISBN 3-900051-07-0.
  33. Strathdee SA, Sherman SG. The role of sexual transmission of HIV infection among injection and non-injection drug users. J Urban Health 2003;80(4 Suppl 3): iii7–14. [corssref]
  34. Moore J, Hamburger ME, Vlahov D, Schoenbaum EE, Schuman P, Mayer Longitudinal study of condom use patterns among women with or at risk for HIV. AIDS and Behavior 2001;5: 263–273.
  35. Lucas GM, Griswold M, Gebo KA, Keruly J, Chaisson RE, Moore RD (2006) Illicit drug use and HIV-1 disease progression: a longitudinal study in the era of highly active antiretroviral therapy. Am J Epidemiol 163(5): 412–20. [corssref]
  36. Petry NM, Alessi SM, Hanson T (2007) Contingency management improves abstinence and quality of life in cocaine abusers. J Consult Clin Psychol 75(2): 307–15. [corssref]
  37. Rash CJ, Alessi SM, Petry NM (2008) Contingency management is efficacious for cocaine abusers with prior treatment attempts. Exp Clin Psychopharmacol 16(6): 547–54. [corssref]
  38. Ledgerwood DM, Alessi SM, Hanson T, Godley MD, Petry NM (2008) Contingency management for attendance to group substance abuse treatment administered by clinicians in community clinics. J Appl Behav Anal 41(4): 517–26. [corssref]
  39. Petry NM, Barry D, Alessi SM, Rounsaville BJ, Carroll KM (2012) A randomized trial adapting contingency management targets based on initial abstinence status of cocaine- dependent patients. J Consult Clin Psychol 80(2): 276–85. [corssref]
  40. Epstein DH, Hawkins WE, Covi L, Umbricht A, Preston KL (2003) Cognitive-behavioral therapy plus contingency management for cocaine use: findings during treatment and across 12-month follow-up. Psychol Addict Behav 17(1): 73–82. [corssref]
  41. Polonsky S, Kerr S, Harris B, Gaiter J, Fichtner RR, Kennedy MG (1994) HIV prevention in prisons and jails: obstacles and opportunities. Public Health Rep 109(5): 615–25.
  42. Adimora AA, Schoenbach VJ, Martinson F, et al. (2004) Concurrent sexual partnerships among African Americans in the rural south. Annals of Epidemiology 14(3): 155–160. [corssref]
  43. Mumola C, Karberg Drug use and dependence, state and federal prisoners, 2004. Re- trieved March 6, 2009, from http: //www.ojp.usdoj.gov/bjs/pub/pdf/shsplj.pdf; 2006.
  44. Adimora AA, Schoenbach VJ, Martinson FEA, Donaldson KH, Stancil TR, Fullilove RE (2003) Concurrent partnerships among rural African Americans with recently reported heterosexually transmitted HIV infection. JAIDS 34(4): 423–429.
  45. Khan MR, Doherty IA, Schoenbach VJ, Taylor EM, Epperson MW, Adimora AA (2009) In- carceration and high-risk sex partnerships among men in the United States. J Urban Health 86(4): 584–601. [corssref]
  46. Manhart LE, Aral SO, Holmes KK, Foxman B (2002) Sex partner concurrency: measurement, prevalence, and correlates among urban 18-39-year-olds. Sexually Transmitted Diseases 29(3): 133–143.
  47. Valera P, Epperson M, Daniels J, Ramaswamy M, Freudenberg N (2009) Substance use and HIV-risk behaviors among young men involved in the criminal justice system. The American Journal of Drug and Alcohol Abuse 35(1): 43–47.
  48. Edlin BR, Irwin KL, Faruque S, et al. (1994) Intersecting epidemics–crack cocaine use and HIV infection among inner-city young adults. New England Journal of Medicine 331(21): 1422–1427.

Driving to Comply: Mind Genomics, Arizona, and the COVID-19 Vaccine

DOI: 10.31038/JIPC.2021111

Abstract

The paper presents a statewide study of responses to COVID-19, done in Arizona, USA, as preparation for the upcoming vaccine, promised for 2021. The objective is to determine the key messages which would engage Arizonans, and interest them in as preparation for a state-wide vaccination campaign. The process followed the Mind Genomics protocol, a protocol used to uncover how people think about the ordinary topics of their lives, done by exposing them to systematic combinations of messages, and determining which individual messages drove their ratings. The data confirmed previous North American findings, that there are two major mind-sets when it comes to COVID-19, the Pandemic Onlookers who are not involved and are engaged by one set of messages, and the Pandemic Citizens, who are involved, want to be guided by the government, and are engaged by another set of messages. These two mind-sets distribute throughout the population but can be quickly identified through a six-question, 30-second intervention, the PVI, Personal Viewpoint Identifier.

Introduction

During the past 50 years, researchers have adopted more and more structured approaches to gaining information about people, whether these people be consumers of products, clients for services, and now citizens who need government guidance in the case of emergencies. Clients of services may include individuals who are already sick and need medical help, whether from doctors, or from hospitals, as well as from pharmacists, and so forth. Indeed, it is well accepted that the customer, whether patient of a physician or patient in a hospital is due good service, at a fair price, and in a reasonable time [1-3].

The issue becomes ‘sticky’ when the client or the customer is the citizen, and the need is for guidance which has medical aspects involved, aspects which may need to be personal to be effective. For example, COVID-19 continues to suggest that bland messaging from the government about the dangers of COVID-19 appears to be effective for some individuals, but not for others. Some citizens believed the information and took precautions suggested by government spokespeople, whereas others flaunted the recommendations, frequently and with abandon.

The recent COVID-19 Pandemic has affected many states in what can only be considered a true crisis. The origin of the research reported in this paper was the effort to begin a program of understanding the mind of the Arizonan, a state, a defined entity in the United States. The objective was to find out how the Arizonan felt about the different aspects of the COVID-19 virus, to classify the citizen, not according to who the citizen is, but how the citizen thinks. The slighter longer-term goal was to use this information to drive next-steps in communication, specifically to tailor communications about protection from COVID-19 using the specific way the citizen thinks.

The study reported here represents the first effort to apply the emerging science of Mind Genomics to the citizens of an entire state, with the goal of improving communication about the pandemic, doing so during the crisis, rather than as an academic exercise AFTER the virus.

During the past decade, the increasing sophistication of marketers has moved from selling ideas to selling better lives through public messages, hopefully effective ones. The basic notion is quite simple; the more one knows about the customer with respect to the specific topic to be ‘messaged,’ the more effective the message will be. Despite the simplicity of the idea, the actual implementation is fraught with problems from beginning to end.

Marketers attempt to ‘know’ their customers, but for most topics the effort to know customers is expensive relative to the opportunity. For example, for most small items, such as shoes or dresses, or even houses, it costs much more to discover the proper messaging than the marketer is willing to pay. There emerges a culture of fast, qualitative research, if any research at all. The marketer hires a competent focus group or individual moderator, moves on with the test, and determines next steps, such as the proper words.

This paper presents the first part of an attempt to understand the mind of the Arizona citizen with respect to COVID-19, in preparation for the upcoming vaccine, promised in 2021. The objective is to understand the motivating messages which ‘reach citizens,’ not only in terms of actual messages, but themes which could be used later on to drive vaccination. The anti-vaxxer movement has gained strength over the years for various reasons, ranging from religious to conspiracy theory, as well as disbelief, and indifference [4-7].

Knowing the nature of how people respond to messages about COVID-19, and how people respond to messages about vaccination provides a way of convincing people to do what is medically appropriate.

Method

The approach presented in this paper is called mind genomics. Mindy genomics is an emerging psychological science based in experimental psychology, anthropology, sociology, consumer research, statistics, and political polling, respectively. It does not, of course, take into account the full gamut of these sciences but finds the topics and methods of the science to be relevant, and to form a good foundation for the science.

The fundament of Mind Genomics is the focus on the world of the everyday, about the decisions that we make as we confront problems and situations in our daily life. What are the criteria which convince us about the ordinary? We are not talking about the attempts to elucidate basic principles of behavior by putting people into artificial test situations, unusual experiments, watching their response and then concluding about a certain type of thinking which must be going on to result in that behavior. Rather, we are talking about responses to stated everyday situations, the pattern of the way a person thinks deduced from the way a person reacts [8].

It is important to emphasize the worldview of Mind Genomics, the world of experiment, and the history with deep roots in experimental psychology. The word ‘experiment’ is key; data which emerges from the science should be based upon experiments. The experiments, in turn, are different ways of obtaining opinions, ways emerging from the recognition that the respondent often wants to please the interviewer and be seen in a way that is today called ‘politically correct.’ This bias makes itself known in surveys when the respondent changes the criterion of the rating, based upon the specific topic of the survey question. The goal of the respondent defeats the purpose of the survey.

Mind Genomics presents these respondent-generated biases. Rather than having a person answer a survey questionnaire, item by item, the experiment puts different messages together in combinations, presents this combination or the set of combinations to a respondent, obtains a rating of the combination, and then through regression analysis at estimates the contribution of each individual element or message. The approach is simple because the messages present simple situations and issues that the respondent encounters every day. The respondent simply responds to the designed combination, from which the judgment criteria emerge by linking the individual elements or messages to the responses.

The Arizona study and the Mind Genomics protocol now follow. The protocol is illustrated by the specifics of the study.

Step 1 – Topic, Question, Answers (Messages, Elements)

The researcher must select the topic select four questions which illuminate the topic, and create four answers, in phrase form, which address each question. Table 1 shows an example of the exercise. Note that the Mind Genomics worldview is that these experiments are cartographies, mapping out the different topics of the mind. Anyone can become a Mind Genomics researcher simply by following the steps, the most important step being Step 1. It is also important to note that Mind Genomics is quick, iterative, inexpensive, building knowledge quickly, often in a matter of hours. The feature of iteration means that the questions and answers or elements shown in Table 1 need not be the final materials. One might go through four or five iterations, improving, throwing out what doesn’t ‘work’, or doesn’t convince respondents, replacing the discarded with new material, and then move on to the next iteration. In this fashion, Mind Genomics is as much a learning system as it is a scientific testing and research technology.

Table 1: The four questions and the four answers (aka messages, elements) to each question

Question A: What is the perceived risk of COVID-19?
A1 COVID-19 is spreading quickly in Arizona
A2 New strains of the virus – causing concern
A3 Government should be doing more
A4 Everyone should take care of themselves
Question B: What are my practices of masking?
B1 Stay home so I don’t have to worry about masks
B2 Masks protect me
B3 I mask up to protect older people that I love
B4 Avoid places where people aren’t wearing masks
Question C: Who do I trust for information about the virus?
C1 I trust my doctor’s advice
C2 My employer gives the best information about the virus
C3 My religious leader tells me how to stay safe
C4 I listen to my family and children about staying safe
Question D: Where do I get my news?
D1 Local Arizona media keeps me up to date
D2 Social media gives me the fastest news
D3 News from my employer is accurate
D4 My friends and family pass along the news

The reader should note that we report the results of the first experiment regarding how to understand and how to motivate Arizonans to consider the COVID-19 vaccine. The materials selected in Table 1 for questions and answers have appeared in part in other studies [9], albeit with some of the language changed, based upon previous results in other countries. It is also worth noting that the study was done overnight in Arizona, approximately four hours after the study was launched on the internet.

Step 2: Prepare the Introduction to the Respondent, and the Rating Question

The ideal format for a Mind Genomics questionnaire differs for consumer/citizen studies vs. medical/legal studies. For consumers and citizens, the objective is to understand how they react to specific messages, in terms of the degree to which the messages motivate them to do something, in this case to obtain a vaccine. In such cases, the less said the better in the introduction. The introduction just introduces the topic. The specific messages, their content, their tonality, and the mind of the respondent will drive the respondent’s rating. The rating scale is a simple 5-point Likert Scale [9].

The introduction and the rating question appear below:

This is a study to understand the effectiveness of COVID-19 messages in Arizona. You will be presented with a series of statements. Rate each set of statements using a five-point scale

How likely are you to get a COVID-19 vaccine? 1=No way 5=Yes, I absolutely agree

Step 3: Build the Test Vignettes

The respondent evaluates combinations of elements, not single elements alone. It is the set of 24 combinations, created according to an underlying experimental design, which is the mechanism by which the respondent’s underlying attitude towards a topic can be obtained and the tendency to be politically correct defeated or at least strongly stymied. The vignette, appearing as an example in Figure 1, presents a combination of elements in a manner which seems haphazard, almost created by random.

FIG 1

Figure 1: Example of a vignette.

The reality underlying the construction of the vignette is as far away from randomness as one can get with a systematic design. It is true that the combination is not written to tell a story. The objective of the vignette specifically, and Mind Genomics generally, is, figuratively, to ‘throw combinations of messages at the respondent, and see the rating.’ There is no underlying store to which the respondent can anchor, and be consistent within that anchor, and common principle. Rather, Mind Genomics is simply the response to seemingly random combinations. The respondent sits at the computer for about two-minutes, responding to 24 of these combinations, feeling that they are random, not realizing that the combinations have been systematically created. The respondent attempts to cope with the overload, but quickly relaxes into an almost automatic response, the type called System 1 by Nobel Laureate, Daniel Kahneman [11]. The respondent eventually ends up assigning the rating in an almost automatic, passive way, frustrated in the attempt to ‘game the system’ by the rapidly appearing and disappearing combinations.

There are two powerful aspects of the experimental designs used by Mind Genomics, of which the 4×4 (four questions, four answers to each question) is only an example. The first aspect is that the elements are statistically independent, viz. in a statistical sense all 16 elements are independent so that they can be used without concern in an OLS (ordinary least-squares) regression to uncover the relation between the elements and either the response or the linkage of the element to response time, the time needed to process the information and respond. The second aspect is that all the 24 vignettes used by a respondent are different from the 24 vignettes evaluated by a second response. The benefit there is that the Mind Genomics procedure covers a lot of the design space [12].

Across the set of 24 vignettes each person will encounter the same number of each of 11 different structures, albeit with different specific elements. The structure is defined as the questions which generate the elements, but not the specific elements themselves. The 11 structures comprise the six different structures for two-element vignettes, (AB AC AD BC BD CD), the four different structures for three-elements vignettes (ABC ABD ACD BCD), and the one structure of four elements (ABCD). We will see that some of these structures are, on average, stronger performers than other structures, when the data from the respondents is analyzed by structure.

Step 4: Run the Experiment and Create a Simple Topline Report (Surface Analysis)

Mind Genomics studies are run entirely on the internet, in a structure which is presented as a survey, not as an experiment. The appellation ‘experiment’ often irritates and confounds prospective respondents. The 500 respondents were members of a set of panels, used by the online study vendor, Luc.id of Louisiana. Luc.id provides populations of respondents from different geographical areas, of specific demography and activities. The panelists had to be residents of Arizona over the age of 18.

Table 2 shows the average ratings on the 5-point scale, and the average response time for each of the 11 structures. Each vignette in the study was assigned one of the 11 structures, depending upon the elements appearing, those elements dictated by the underlying experimental design. The respondent rated each vignette with the rating and the response time recorded. The response is operationally defined as the number of seconds, to the nearest tenth of second, elapsing between the appearance of the vignette and the rating.

Table 2: How average rating and average response time covary with structure of the vignette

Structure Questions

Rating

Response Time

ALL Total

3.4

3.8

AD Risk News

3.4

4.0

AB Risk Masking

3.4

4.0

ABC Risk Masking Trust

3.4

3.9

CD Trust News

3.5

3.8

BCD Masking Trust News

3.4

3.8

ACD Risk Trust News

3.4

3.8

ABCD Risk Masking Trust News

3.4

3.8

ABD Risk Masking News

3.4

3.8

AC Risk Trust

3.1

3.8

BC Masking Trust

3.4

3.7

BD Masking News

3.5

3.6

Table 2 shows a modest range in the average ratings, from a high of 3.5 to a low of 3.1). This suggests that the either the elements are seen to be equal, or there are deep differences among people in the types of elements with which they agree, but these deep differences cannot easily be seen. The differences are not emerging out the structure of the vignette, suggesting that respondents ‘graze’ for the information they need, rather than proceeding linearly through the vignette. If respondents were to proceed linearly through the text of a vignette, the vignettes with more elements would show higher response times, due to the longer times needed to read three and four elements. In contrast, the vignettes with fewer elements would show lower responses times but they do not. The data suggest that it is the nature of the information which drives the response times. The topic of ‘risk’ is the most engaging, the topic of ‘masking’ the least engaging.

One of the recurring themes in social research is that the differences in the responses may well be due to who the respondent IS. That is, there is an ongoing belief that people vote based upon who they are. Thus, much of the news reported focuses on differences between groups of people who can be easily identified, such as gender, or age-cohorts (e.g., Baby Boomers vs. Millennials vs. Generation X, etc.).

The data from this study allows us to look at the average rating and the response time from different, identifiable groups, as shown in Table 3. Table 3 shows the average age, the average rating, and the average response time, for each defined group. Table 3 also shows averages from transformed data (see Step 5 below). We see little difference in the average ratings, but we do see substantial differences in the average values of the response times, differences which make sense. Young respondents (age 18 – 29) read and rate much faster than average (2.8 seconds per vignette vs. 3.8 seconds on average), whereas old respondents (age 65+) read and rate more slowly (5.5 seconds on average).

Table 3: Average age, 5-point rating, response time (RT), and binary transformed ratings) for Total, Gender and Age, respectively

table 3

It is important to keep in mind that the differences in response time may be due both to age and to topic. We know that when the topic moves from social issues such as vaccine and COVID-19, to issues that are more ‘fun’ such as products, the response time usually diminishes, perhaps because the respondent does not have to think about the topic quite as seriously.

Step 5 – Prepare the Data for Regression Linking Elements to Responses

The underlying experimental design allows us to link the presence or absence of each element to the rating and to the response time. Yet, there is a problem with the data, one which must be solved before the analysis can proceed in a smooth manner. The problem or issue is the way one should interpret the results of a Likert Scale. From author HRM’s experience, managers commissioning the study or working with the data often ask about the meaning of the rating, such as ‘what does a 4 mean on the scale, from a practical point of view?” What the manager needs is a more black-and-white metric, one which reduces the task of interpreting the data.

Consumer researchers and public opinion pollsters are well-aware of the problems with managers interpreting the data for simple scales. Indeed, in the words of S.S. Stevens, Doyen of modern-day psychophysics, ‘one of the hardest problems in science is to go from a scale to a yes/no’ [13].

Researchers world-wide have suggested simple ways of dividing Likert Scales. For the five-point scale used today, researchers had suggested using the ratings of 5 & 4 as the key variable. Vignettes rated 5 or 4 are assigned the value of 100, vignettes rated 1, 2 or 3 are assigned the rating of 0. This is called the ‘Top2 Box,’ abbreviated here ‘Top2’. The reason is simple; The top 2 scale points (or ‘boxes’) are the ones selected.

In this spirit, we have created four new variables to use in our exploration:

Agree with the need for/goal of vaccination

Top1: Rating of 5 transformed to 100, ratings of 1, 2, 3 and 4 transformed to 0

Top 2: Rating of 5 and 4 transformed to 100, ratings of 1, 2, and 3 transformed to 0

Bot1: Rating of 1 transformed to 100, ratings of 2, 3, 4 and 5 transformed to 0

Bot 2: Rating of 1 and 2 transformed to 100, ratings of 3, 4 and 5 transformed to 0

A small random number less than 10-5 is added to each of these numbers to create some variability around the ratings. When a respondent assigns all ratings 1 & 2, or 4 & 5, respectively, regression analysis will ‘crash’ because the regression needs a bit of variation in the dependent variable, the transformed number. The transformation prevents the crash of the regression modeling but is far too small to affect the data in a meaningful way.

Step 6: Relate Elements to Ratings by OLS Regression

OLS (ordinary least-squares) regression relates the presence or absence of the 16 elements to the dependent variable. We begin with two dependent variables, the 5-point rating scale, and the response time. We add four more dependent variables, emerging from our transformation to the binary scales; Top1, Top2, Bot1, Bot2. These were defined in Step 5.

The basic equation is simple:

Dependent Variable = k0 + k1 (A1) + k2(A2) … k16(D4)

Simply stated, the dependent variable is the sum of a single base number (additive constant), and the contributions of the elements in the vignettes, these contributions being estimated by the OLS regression, and shown as k1-k16.

The value k0 is not estimated for the response time, RT, simply because it has no meaning. The value k0 is also not estimated for the 5-point scale, to give a sense of the number of rating points contributed by each element. For the other five dependent variables, k0 is the estimated value of the dependent variable in the case where all the elements in the vignette are 0, viz., absent. Such a situation, a vignette without elements, is impossible according to the underlying experimental design.

Table 4 presents the data from the Total Panel, showing only the positive coefficients. The data are incomplete, but to show all coefficients, negative values as well as 0, overwhelms the reader. The positive coefficients are those which drive the response towards the top of the scale, whether the scale be Top1 (highest possible agreement with getting a vaccine), Top2 (strong agreement with getting a vaccine), or towards the bottom of the scale, Bot1 (highest possible disagreement with getting a vaccine), or Bot2 (strong disagreement with getting a vaccine).

Table 4: How the 16 elements drive the ratings, both transformed binary ratings, original 5-point rating, and response time.

 

 

TOP1 TOP2 BOT1 BOT2 RATING

RT

Additive constant

28

53 15 28 NA

 NA

A1 COVID-19 is spreading quickly in Arizona 1.0

1.1

A2 New strains of the virus – causing concern 0.9

1.1

A3 Government should be doing more 0.9

1.0

A4 Everyone should take care of themselves

1

1.0

1.1

B1 Stay home so I don’t have to worry about masks 1 1.1

1.2

B2 Masks protect me 1.0

1.1

B3 I mask up to protect older people that I love 1 1.0

1.2

B4 Avoid places where people aren’t wearing masks 1.0

1.2

C1 I trust my doctor’s advice 1.0

1.1

C2 My employer gives the best information about the virus 1.0

1.1

C3 My religious leader tells me how to stay safe 1.0

1.1

C4 I listen to my family and children about staying safe

1

1 1.1

1.1

D1 Local Arizona media keeps me up to date 1.0

1.0

D2 Social media gives me the fastest news 1 1.0

1.0

D3 News from my employer is accurate 1 0.9

1.0

D4 My friends and family pass along the news 1 1.0 1.0

The actual interpretation of the data is left to the reader, but the Total Panel shows little in the way of patterns. The additive constant for Top1 tells us that about a quarter of the responses would be ‘5’ in the absence of the elements. Note that the additive is a theoretical, computed value, since all vignettes comprised 2-4 elements. The additive constant is a good parameter to give a sense of the ‘baseline’ level of feeling. For Top1 (strongest interest), we see an additive constant of 28, low, and in need of a ‘push’ from the elements. When we look at positive responses, 4 and 5, combined into the variable Top2, see a little over half, 53% of the responses are expected to be positive. Similarly, when we look at the negative part of the scale, about 15% of the responses are expected to be extremely negative, and a little less than twice that number (viz., 28%) are expected to be strongly or moderately negative.

Our next task is to use judgment to identify, where possible, elements with high positive coefficients for either Top1 (ideal) or Top2 (strong or moderate interest in the vaccine). Table 4 shows us no strong elements at all, a disappointing finding. From our first effort, and looking at the total panel, we find that no elements drive interest in being vaccinated. The answer may be either that we have not found that ‘magic bullet,’ or that we may have a powerful element, but it is lost in ‘noise’. We soon will see that the latter is probably the case, that there is noise in the data emerging from different groups of people, with varying, occasionally conflicting opinions.

A second look is at the response times. Do opinions of these messages engage the respondent? Engagement might be either good or bad, good when the message is a driver for vaccination, bad when the message is irrelevant, and a time waster. The model for the response time is lacking a constant. No elements engage by having the respondent focus on the element for more than 1.2 seconds.

Our first conclusion is that there is no pattern, that all the messages are irrelevant, and that the experiment was unable to uncover any element which is promising. That is, when we treat all of the respondents in the same way. We are either dealing with irrelevant elements, certainly a strong possibility in the absence of any other reasons to think otherwise, OR we are dealing with elements which push in opposite directions, cancelling each other out.

Step 7: Granular Understanding by Clustering to Uncover Mind-sets

We saw above that there are few differences among the elements in terms of those driving positive interest to get vaccinated. Some of this ‘flatness’ may emerge from the fact that people think in different ways, effectively canceling each other when they are blended together in a database which does not recognize these individual patterns.

Mind Genomics studies have uncovered the existence of different groups of ideas which go together, different mind-sets of these related ideas. It is not that people differ, but rather that the ideas they hold are of different types, even when the topic is the same. By clustering the patterns of coefficients across the individual respondents, viz., putting together people with similar patterns, Mind Genomics can identify these basically different groups of ideas. These different groups are the so-called ‘mind-sets’ [14,15].

The process of clustering is a standard statistical method. The method of k-means clustering looks at the 16 coefficients of each respondent, based upon the relation between Top2 (dependent variable) and the presence/absence of the elements. The additive constant is computed, but not used here. The clustering, based upon similarity of patterns, divides the 500 patterns into one, two, and the three groups. Each respondent is a member of only one of the groups, with two groups, or a member of one group when three groups are extracted [15].

The original analysis by clustering uses the coefficients obtained for the Top2 analysis, meaning that ratings of 4 and 5 are converted to 100, and ratings of 1-3 are converted to 0. We will remain with that clustering. For the prescription of what to feature in the messages, we will the make analysis more stringent, however. We will look at the models or equations relating the presence/absence of the 16 elements to rating 5:, How likely are you to get a COVID-19 vaccine? 1=No way 5=Yes, I absolutely agree. This is the Top1 equation, showing which elements are the strongest. Thus, we keep the clustering method the same (based on Top2), but the reportage as more stringent (use Top1 data for modeling).

Table 5 shows the positive coefficients for the Top1 model. It is clear that there are few elements which are strongly effective for each mind-set. These are the elements to select for the final messaging. The selection is far easier when the criterion is low, but the downside of the process is that the coefficients are low, albeit the most powerful. The only exception to the pattern of low coefficients emerges from mind-set MS3, the Pandemic Activist, comprising about 1/3 of the respondents.

Table 5: Strongest performing elements for vaccination, viz., highest coefficients for TOP1 (Definitely will vax)

table 5

The important consideration here is that the message be strong. Choosing a message which contributes to rating 5 (definitely will vax) is better than a message which contributes to both rating 4 and 5 (definitely/probably will vax.) The choice towards the messages which are most effective, recognizing that there can probably be at most three messages.

The final thing to keep is mind is the radically different elements which score well. These elements are clearly touching different aspects of the COVID-19 experience, suggesting quite different mind-sets among the respondents.

To get a sense of the power of a tough criterion, such as Top1, consider the same Table, but the more typical case, wherein the elements are the strong performers, but for Top2 (Definitely/Probably be vaccinated). Many of the elements are the same, but the first impression from Table 6 is a greater richness of information. That richness is certainly satisfying, but when it comes time to put the information into practice one will inevitable be confronted with the question about which of the strong performing elements is actually the ‘strongest’. That is, having a wealth of information is rewarding for the stage when one seeks understanding, but problematic when the task is to choose the one, two, or three elements from the set, and allowed only those choices.

Table 6: Strong performing elements for vaccination, viz., highest coefficients for TOP2 (Definitely will vax, probably will vax, ratings 5 and 4)

table 6

Step 8: Understand the Engagement Power of the Elements Using RT (Response Time)

Figure 2 shows the distribution of measured response times for the vignettes, independent of the structure of the vignette and the specific elements. A great many vignettes are rated faster than two seconds, most vignettes rated in fewer than five seconds. As we see below, there is very little difference in the response times linked to the different messages.

fig 2

Figure 2: Distribution of measured response times for the vignettes.

The final element-level analysis links the elements to estimated response times for the elements. The equation for response time comprises the 16 independent variables, the elements, but does not make provision for an additive constant. The rationale for leaving out the additive constant is that in the absence of any elements (again a hypothetical case) there is no expectation of any response at all.

Table 7 shows the estimated response time attributed to each element. The important thing to note is that strong performing elements in Table 5 are not necessarily those with long response times, viz., those which are engaging. Indeed, most of the response times are around 1.0 – 1.2 seconds per element, with a few shorter and a few longer. The results suggest that the respondents do not ‘whiz through’ the elements when making their ratings. They do ‘whiz through’ for other studies, especially the less serious studies having to do with brands and products. Thus, one can feel good that the respondents are actually paying attention to the information, at least in terms of taking the time to read the vignettes.

Table 7: Estimated response time for each element, by each mind-set.

table 7

Step 9: Artistic Judgment for Next Steps – Identify the Elements Which have the Greatest Staying Power

One of the ongoing issues in any messaging campaign is the probability that at some time the messages will simply ‘wear out.’ The wear out is habituation, a well-known phenomenon in psychology, wherein the stimulus fails to evoke attention as it continues to be repeated. Experimental psychology demonstrates this phenomenon in rigorous studies, such as the measuring attention reactions of cats presented with the same tone in a steady, expected, repeated, monotonous fashion. Habituation occurs in our everyday life; simply witness people who live near train tracks, and who quickly become accustomed to the noise.

How can we identify messages which have staying power, especially messages which are good to being with? One way to do this uses the actual data from the study. This time, however, the data matrix is divided into equal fourths (viz., vignettes 1-6, 7-12, 13-18, and 19-24). One takes the set of elements to be used in the proposed messaging, viz. one winning element for each mind-set. The selection of the winning element is a matter of judgment, and may involve ‘gut feelings,’ viz., intuition, which move beyond the actual data. The approach here considered only the elements doing well among the three vignettes in the Top1 metric. These were D1, A2, B4:

Local Arizona media keeps me up to date

New strains of the virus causing concern

I mask up to protect older people that I love

These three elements became the only predictors of Top1, Bot1, and RT (response time). The vignettes (fourth = 2, fourth = 3), and for the final vignettes (fourth = 4). By looking at the coefficients for each element across the four sets of evaluations, we get a sense as to whether or not the elements are ‘wearing out’.

Figure 3 suggests that repeating the messages will enhance the impact of each element in terms of driving the respond to agree to a vaccine (Top1), and for the most part will reduce the resistance (Bot1). The only exception to this general trend is element B3, which shows no loss in negativity with repetition, and perhaps even a slight increase, perhaps resentment at being reminded. The same analysis can be done for any set of messages, to determine whether the messages will change with repeated exposure. Figure 4 show the same analysis, this time for strong performing elements using their coefficients for Top1, but a combination ‘artistically’ sensed as inferior:

fig 3

Figure 3: Likely wear-out of messages for the vignette which seems ‘more artistic’. The graphs show the expected change of the coefficient for each promising element, when evaluated in sets of six vignettes each. The combination comprises D1, A2 and B3, winning elements from the three mind-sets, selected by artistic sensibility as ‘working together’.

fig 4

Figure 4: Likely wear-out of messages for the vignette which seems ‘less artistic’. The graphs show the expected change of the coefficient for each promising element, when evaluated in sets of six vignettes each. The combination comprises D1, A2 and B3, winning elements from the three mind-sets, selected by artistic sensibility as ‘working together’.

News from my employer is accurate

I listen to my family and children about staying safe

Avoid places where people aren’t wearing masks

The approach does not replicate the actual events in the world, but rather may be analogous to the process of ‘accelerated aging’ in the world of food science, with the attempt to determine the ‘shelf life’ of a product, so that the product can be pulled from the market shelves before it changes in quality and becomes significantly less palatable [17].

Step 10: Find the Mind-sets in the Population for Targeted Messaging

Ongoing patterns of results from Mind Genomics cartographies, of the type done here, albeit in many other areas, suggest that there exist clearly different mind-sets, but that these mind-sets are distributed in the population in an almost random way, at least to the outside researcher who only has data from who the respondent IS (geo-demographics), how the respondent THINKS (personas based upon large-scale segmentation), or how the person BEHAVES (either in everyday life, or in tracked shopping behavior.)

In none of the standard analysis of WHO, THINKS, or BEHAVES can we find easy covariation with the mind-sets. That is, it is quite unlikely to know how a person will think about a topic just be knowing the typical information available to the researcher. There may on occasion be some happenstance covariation that can be used, but as far as a robust system to link together mind-sets and people, there does not seem to be a recognized tool.

Table 8 shows the distribution of the three mind-sets by gender, by age, and by ethnicity. It is clear from Table 8 that simply finding the mind-set will be difficult in the population. The next best thing is to use set of messages woven together to incorporate the essence of one message for each mind-set, as Figure 3 suggests.

Table 8: The distribution of respondents by mind-set, gender, age, and ethnicity. The numbers in the body of the table are the actual number of respondents who classified themselves at the start of the Mind Genomics experiment, in the self-profiling questionnaire

 

Total

MS1 MS2

MS3

Total

494

181 169

144

Male

192

65 62

65

Female

302

116 107

79

Age 18-29

160

62 51

47

Age 30-49

181

63 60

58

Age 50-64

79

30 28

21

Age 65+

74

26 30

18

Caucasian

322

120 118

84

Latinx

85

30 27

28

Other

81

30 21

30

The fact that mind-sets can so easily emerge from data, and be found at any level of granularity desired, and virtually for any topic, in as a fast as one hour, suggests that a new way of thinking is needed to use the mind-set segments. It is no longer sufficient to spend days, weeks, or months cogitating over the application of mind-set segmentation when the actual results had been obtained in a matter of hours.

During the past four years authors Gere and Moskowitz have worked on algorithms to classify the respondent as a member of a mind-set, recognizing that the algorithm should be quick to develop, easy to implement, and inexpensive. The algorithm also must minimize the ability of a respondent to ‘game the system,’ by guessing what the interviewer wants to hear.

The approach developed emerges out of the actual experiment and data set used to create the mind-sets in the first place. This first step ensures that the elements used to assign a new person to a mind-set are relevant to the topic, moving away from the potential error-propagating step of searching for other language that can be used for assigning the respondent to the mind-set. This first is close in, and immediate. As soon as the mind-sets are determined so is the performance of each element for each mind-set.

The second step uses a Monte Carlo system to introduce noise, and then assign respondents to the mind-set in the present of the noise.

The third step aggregates the data and generates the decision rule which is most resistive to the introduced ‘noise’ and correctly types of the mind-sets in the presence of the noise.

The resulting approach is called the PVI, the personal viewpoint identifier. The set-up is done according to a Microsoft Excel template (Table 9). The template requires the researcher to provide specific information about the mind-sets (viz., name, feedback), as well as an optional video or landing page corresponding to the mind-set, right after the respondent is assigned to one of the mind-sets. At the bottom of Table 9 is the summary data from the mind-sets, used by the PVI to create the actual calculation table.

Table 9: Template for the creation of the PVI (personal viewpoint identifier).

table 9

Once the input in Table 9 has been processed to create the PVI, the result comes back in a link. The respondent who clicks on the link is led to the PVI on the web. Figure 5 shows the introductory page, which introduces the respondent to the reason for the short study, obtains permission, and obtains background data. Figure 6 shows the set of questions, comprising background questions (not part of the classification algorithm), and six questions answered by one of two answers. These six questions are the PVI. Each respondent sees the six questions in a different order. The data are stored in a database for further work, and the results sent back to the respondent either in a detailed form, or just an email with mind-set membership, and something about the mind-set to which the respondent belongs (Figure 7).

fig 6

Figure 5: The orientation page for the PVI. The link (as of January, 2021) is: https://www.pvi360.com/TypingToolPage.aspx?projectid=1270&userid=2

fig 6, 6

Figure 6: The questions about one’s concerns, and the six questions for the PVI.

fig 7

Figure 7: Feedback page for insertion into the database. The respondent receives a simple email showing the three mind-sets, viz., their names and the feedback, as well as the mind-set to which the respondent belongs. This example is from a person in Mind-Set 1, the Pandemic Observer.

Discussion and Conclusions

The study reported here typifies what, in the emerging science of Mind Genomics, is called cartography, for want of a better word. The cartography is not designed to test hypotheses, in the traditional view of some scientists [18]. There are no working hypotheses to falsify. The cartography, as the word connotes, explores the topic, and maps its detailed features. Here the features are the words. As we begin to create cartographies, there are usually several sequential cartographies or iterations. At the start we need not know whether the questions are the correct ones, and certainly whether the answers are correct or event relevant. Yet, we do the experiment, we put a ‘stake in the ground,’ discover what works and embellish it, discard what does not work, and then add new material for the next iteration [19].

Although this might not seem to be the most elegant way of creating a database, it certainly is the quickest, and in fact allows the database to create to be created by all sorts of people, whether these are professionals in the healthcare world, patients, doctors, or hospital administrators, or even relatives of those who are patients. The notion is not to get it right, because there is no ‘right’ – at least not at the start. Rather, the notion is that through responses to descriptions, the vignettes, the underlying patterns will emerge, in the way the underlying structure emerges from the many pictures taken by the MRI and reassemble the structure after the fact through a computer program.

A key benefit of Mind Genomics is its availability to anyone, expert or amateur alike, and the possibility that the discoveries may be made by virtually anyone. A dedicated analyst working with dozens of transcripts of interviews lasting an hour or two about the topic might emerge with similar findings, but not as crisp, nor as data rich. In contrast, the novice but avid researcher, can do an iteration overnight, following the templated approach of Mind Genomics. The templated approach forces the research to focus on the messages, do the experiment, obtain the data, and face the bare facts, specifically how the messages drive the response. The data are archival, the learning is incremental and expansive, and the result resides in a searchable data warehouse, ready for reanalysis to provide new insights. The information can be searched for words, for meanings, and for new correlations, done, at virtually any time after the study, and by virtually anyone. These data from the first study on COVID-19 in Arizona give a sense of the potential.

Practical Conclusions – Driving Vaccination in Arizona

The focus of this paper is both on method and on results. Both are important during this period of the COVID-19 pandemic. The rationale of showing what can be done in one day is not so much to provide a perfect answer or write a perfect paper, as it is to show a revolutionary change in what could be learned in a short time at a low cost. Cost, time, and the power to iterate to a better answer are important for the obvious reasons; costs of medical treatment and of medicines are increasing, making prevention increasing attractive. The more that we can learn about people ‘in the moment’ with respect to issues which emerge, the more likely it will be that we can communicate more effectively with people. This communication includes providing the necessary information and the suggestions, both tailored to the mind-set of the person, and perhaps both more convincing, more motivating. It is no simple thing to motivate people. The faster and easier it becomes to learn the necessary facts and words, ideally in ‘real time,’ the more likely it we be that people will be guided gently, through words, to live healthier lives, and to take better care of themselves. The cost of the medical interventions might be lower.

The data here suggest that it is vital to consider the different mind-sets of respondents. In light of the speed, ease of analysis, and low cost, as well as a tool to determine the mind-set of the respondent, the prudent action would be to do one to three or four Mind Genomics cartographies, as done here, eliminating the poor performing elements, and building upon the elements which look like they work. Table 6 shows the dramatic increase in performance of elements, and the clearly different mind-sets. Several more cartographies, each last no more than a day, should build a new set of ‘Table 6’s’ with increasingly strong performing elements. It is unlikely that there is a single ‘magic bullet,’ for all mind-sets, but there are clearly a number of strong elements for each mind-set.

Acknowledgments

Attila Gere thanks the support of Premium Postdoctoral Research Program of the Hungarian Academy of Sciences.

References

  1. Benyamini Y (2011) Why does self-rated health predict mortality? An update on current knowledge and a research agenda for psychologists. Psychology & Health 26: 1407-1413. [crossref]
  2. Benyamini Y, Blumstein T, Lusky A, Modan B (2003) Gender differences in the self-rated health–mortality association: Is it poor self-rated health that predicts mortality or excellent self-rated health that predicts survival? The Gerontologist 43: 396-405.
  3. Dulmen S, Sluijs E, Dijk L, Ridder D, Heerdink R, Bensing J (2007) Patient adherence to medical treatment: a review of reviews. BMC Health Services Research 7: 55. [crossref]
  4. Boodoosingh R, Olayemi LO and Sam FAL (2020) COVID-19 vaccines: Getting Anti-vaxxers involved in the discussion. World Development, 136: 105177. [crossref]
  5. Burki, T (2020) The online anti-vaccine movement in the age of COVID-19. The Lancet Digital Health 2: 504-505. [crossref]
  6. Kennedy AM, Brown CJ, Gust DA (2005) Public Health Reports 120: 252-258. [crossref]
  7. Zimet GD, Rosberger Z, Fisher WA, Perez S, Stupiansky NW (2013) Beliefs, behaviors and HPV vaccine: correcting the myths and the misinformation. Preventive Medicine 57: 414-418. [crossref]
  8. Gere A, Zemel R, Papajorgji P, Moskowitz H (2019) “Candy Is dandy”: The mind of sexuality as suggested by a Mind Genomics experiment. In Sex, Smoke, and Spirits: The Role of Chemistry, 17-31.
  9. Sullivan, GM, Artino Jr AR (2013) Analyzing and interpreting data from Likert-type scales. Journal of Graduate Medical Education 5: 541-542. [crossref]
  10. Bellissimo, N, Gabay, G, Gere, A, Kucab, M and Moskowitz, H (2020) Containing COVID-19 by Matching Messages on Social Distancing to Emergent Mindsets: the Case of North America. Int J Environ Res Public Health 17(21): 8096 [crossref].
  11. Kahneman D (2011) Thinking, Fast and Slow. Macmillan.
  12. Gofman A, Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127-145.
  13. Stevens, personal communication to Howard Moskowitz, 1968.
  14. Moskowitz HR (2012) ‘Mind Genomics’: The experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiology & Behavior 107, 606-613. [crossref]
  15. Moskowitz HR, Gofman A, Beckley J, Ashman H (2006). Founding a new science: Mind Genomics. Journal of Sensory Studies 21: 266-307.
  16. Jain AK, Dubes RC (1988) Algorithms for Clustering Data. Prentice-Hall, Inc.
  17. Schwarz M, Rodríguez MC, Sánchez M, Guillén DA, Barroso CG (2014) Development of an accelerated aging method for Brandy. LWT-Food Science and Technology 59: 108-114.
  18. Popper KR (1963) Science as falsification. Conjectures and Refutations 1: 33-39.
  19. Benyamini Y, Idler EL, Leventhal H, Leventhal EA (2000). Positive affect and function as influences on self-assessments of health expanding our view beyond illness and disability. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55(2), P107-P116. [crossref]

Development and Testing: Cervical Cancer Prevention Questionnaire Based on Theory of Planned Behavior in Chile

DOI: 10.31038/AWHC.2021411

Abstract

The purpose of this study was to developmentally and psychometrically validate the cervical cancer prevention questionnaire (CPCC-16) based on Theory of Planned Behavior in Chilean women. The patient sample was 967 women. Confirmatory factor analysis was used to evaluate factor structure, Cronbach’s alpha for internal consistency and t-test for criteria validity. The development and validation of the questionnaire resulted in six factors with 16 items, demonstrating a bi-factorial structure. Cronbach’s alpha was higher than 80 in the questionnaire and its factors. To generate a valid and reliable questionnaire that measures, under a theory of behavior, more than one preventive behavior in cervical cancer is an important advancement that fills a gap in nursing research.

Keywords

Cervical cancer, Prevention, Instrument development

Precis: The CPCC-16 questionnaire is a validated and reliable instrument, with 16 items distributed in a bi-factorial structure, useful for clinical and research area.

Call Outs: Behaviors are the principal causes of deaths from cancer, and thus, a reliable and validated questionnaire is necessary to measure these behaviors (before method).

The validated and reliable questionnaire will be useful to measure cervical cancer preventive behaviors as a whole, but it will also be useful to identify theory constructs (after method).

The new questionnaire will be useful to measure more than one preventive behavior in cervical cancer; therefore, it fills a gap in clinical and research area (after discussion).

Introduction

Theory of Planned Behavior (TPB) has been a framework to explain and predict behaviors [1], and its ability as a framework intervention has been supported by previous studies [2-4]. TPB postulates that the motivations of people to change are based on their perceptions of norms, attitudes, and control over behaviors, and each of these factors can either increase or decrease the intention to change their behavior. The intention to change behavior is directly related to behavioral change [5,6]. Cervical Cancer (CC) prevention has been one topic that has been studied under this theory [7-11].

There are two methods to stop CC: to prevent its pre-cancer and to identify and treat the cancer before it becomes a true cancer [12,13]. The first method includes behaviors, including the use of condoms during sex, limiting the number of sexual partners, not smoking and obtaining the Human Papilloma Virus (HPV) vaccine; the second method includes having regular screenings [13,14]. The CC prevention questionnaire (CPCC-16) was developed based on TPB to measure CC preventative behaviors.

Background

Even where screening is widely available and methods to prevent CC are known, there is an important barrier in adopting these methods by women. Thus, understanding the factors that affect preventive behavior remains an important issue.

TPB has been previously used by several studies to understand how cervical cancer preventive behaviors are carried out, and the intent to perform the behavior is explained [8,15,16]. The main behaviors studied are those related to the detection of CC, such as adherence to the HPV [8,11,17] tests [7,10,15,18,19]. Some studies have described the use of TBP and HPV vaccination intentions [16,20]. The use of condoms has been studied; however, these studies are not always related to CC prevention and are mainly examined in an adolescent population [21,22]. To the best of our knowledge, there have been no previous studies using questionnaires to measure more than one preventive behavior using TPB as a framework.

Regarding the psychometric properties of the questionnaires used in CC prevention, the reliability and/or validity of the instruments has not always been reported [10,11,16,20,21], or they have been incompletely reported [7-9,17,19]. Research on TPB with other behaviors indicates the same problem [2,3,23]. The author and creator of TPB [1] supports these findings, describing the measures of the theory constructs as fallible with respect to reliability and construct validity, and thus, it is difficult to test the theory.

With regard to TBP as a framework, the literature indicates that the most important theory construct studied has been intention [24] and that some research studies have only partially studied the TBP components [15].

Behaviors are the principal causes of deaths from cancer, and infections such as HPV are responsible for up to 25% of cancer cases in low and middle-income countries [25]. If the solid foundation of TBP and the relevance to prevent CC worldwide are considered, then a questionnaire that is reliable and valid, which permits the ability to simultaneously measure more than one CC preventive behavior and to test the four-principal construct of TPB, may be useful in different countries and contexts. Thus, it is useful to have a CC prevention questionnaire based on TBP.

The purpose of this study was to develop and psychometrically validate a new questionnaire based on Theory Planned Behavior (TPB) with relation to Cervical Cancer (CC) prevention (known as the CPCC-16 questionnaire).

Method

This study is a part of a larger cross-sectional study about Social Determinants related to the adherence to CC screening (FONDECYT #11130626); this article focuses on the development and testing of one of the questionnaires used in the project, which was performed in two phases: scale development and psychometric evaluation.

Sample/Participants

This research study was performed on a total of 967 Chilean females, between 25 to 64 years old, under Chilean national public health care coverage (known as FONASA); these participants attended four primary health care centers in the Servicio de Salud Metropolitano Sur-Oriente (Southeast Metropolitan Public Health Service) in Santiago, Chile. The sample size was calculated according to the larger study aims considering an effect size of 0.1, power analysis of 80%, 15 latent variables and 40 observed, and a significance level of 95%. The sample was obtained according to the recommendations related to the questionnaire validation [26-28]. The exclusion criteria included having had a hysterectomy and CC disease. Females who had agreed to participate were randomly selected and recruited by telephone between March 2014 and October 2015.

Scale Development

The questionnaire was developed based on TPB and according to Icek Ajzen’s recommendations [29]. The first step was to define the behavior; therefore, four behaviors were included: annual gynecological check, updated Papanicolaou test (Pap), condom use on sexual relations and having a single partner (at the same time). Behaviors related to the HPV vaccine or HPV screenings were not considered because they are not available in the public health care system where the study was performed. Figure 1 shows the construct and preventive behaviors considered in the questionnaire. The second step was defining the population; females between 25 to 64 years old were selected because they are the target group for cervical cancer screening and prevention interventions in Chile. The third step was formulating items; they were developed to assess the major constructs of TPB for each preventive behavior selected in the first step: attitude, subjective norm, perceived behavioral control, and intention. The items were given feedback from content experts and then pilot tested on ten females from the target population. The questionnaire was developed in the Spanish language and back-translated for this article.

fig 1

Figure 1: Theory of Planned Behavior Constructs and Cervical Cancer Preventive Behaviors considered in the Original Questionnaire.

Psychometric Evaluation

Construct validity was performed by Confirmatory Factor Analysis (CFA), and reliability was assessed using ordinal Cronbach’s alpha. Three models were adjusted: one model with the four TBP constructs, the second with a bifactorial model considering the four TBP constructs and the four CC prevention behaviors, and the last model considered the four constructs from TBP; four behaviors but three of them were grouped in one factor. Diagonally Weighted Least Squares (DWLS) were used to estimate the models because the variables were measured using the four-point ordinal scale. The fit model was evaluated using normed chi-squared (chi-squared/degree of freedom) with two comparative fit indices: Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI); Root Mean Square Error of Approximation (RMSEA) was used as parsimonious fit indices. We considered CFI and TLI values >0.95, with RMSEA <0.05 as good; CFI and TLI values between 0.90-0.95 and RMSEA between 0.05-0.08 were acceptable; and CFI and TLI values <0.90 or RMSEA >0.08 were unacceptable. The statistical significance of each item as it is related to the factor, as well as whether an item shared a common conceptual meaning with the factor, was also considered. The PAP test and gynecological check status were used as external criteria to validate the questionnaire. The scores of each factors of the questionnaire were calculated by regression method, using standardized variables and the factor scores between women with adherence to CC screening and without adherence or with annual gynecological check and without it were compared using t-Student test for independent samples. Data were analyzed using R Statistical Program and the lavaan package.

Instrument

The proposed instrument (Appendix 1) consisted of 16 items related to CC prevention behaviors, which were divided into 4 dimensions according to the TPB constructs: attitudes (4 items), subjective norms (4 items), perceived behaviors control (4 items), and intentions (4 items). Each item was evaluated using a Likert scale of four alternatives (strongly agree/very good=1 to strongly disagree/very bad=4). Such a scale is used to force directionality of a response (de Vellis, 200 [30]) in a population where culture (Hispanic) tends to avoid conflict, resulting in a frequent selection of neutral alternatives (Antshel, 2002 [31]).

Ethical Considerations

This study was approved by the University of the Principal Investigator and by the health care service to which the women belonged. Written informed consent was obtained by all of the participants. All of the questions that the women had about CC were answered after the interview.

Results

The mean age of the participants was 43.37 ± 10.77 years, with the mean educational level being 10.97 ± 3.4 years. The CC preventive behaviors of the women are shown in Table 1.

Table 1: Characteristics of the women (n=967).

Characteristic

Value

Annual Gynecological Check, n (%)

537 (55.5)

Pap test in the last three years, n (%)

740 (76.5)

Have a partner, n (%)

766 (79.2)

Number of partners, mean ± SD (range)

2.69 ± 2.73 (1 to 40)

Use of Condom, n (%)

Always

65 (6.7)
Almost always

85 (8.8)

Never always

102 (10.5)
Never

715 (73.2)

Three models were calculated to achieve the best fit with the data (Table 2). The first model considered the proposed questionnaire with four factors, but the goodness of fit was not good (TLI <0.9 and RMSEA=0.231); the modified indices suggested the inclusions of correlations between the items with similar wording; and the correlations between intention and subjective norms (r=0.863) and intention and perceived behaviors control (r=0.939) were too high.

Table 2: Fit Statistics for the three models calculated (n=967).

Factor Model

x2/df

CFI TLI

RMSEA (CI 95%)

Four Factor

52.75

0.909 0.888

0.231 (0.226-0.237)

Eight Factor (bifactorial)

1.77

0.999 0.998

0.028 (0.020-0.036)

Six Factor (bifactorial)

1.94

0.999 0.998

0.031 (0.024-0.038)

These results suggested the consideration of second-order models, which explain the high correlations between factors, but this approach did not resolve the problem related to the correlations between preventive behavioral items. Thus, a bifactorial model was tested as a plausible alternative; one side with four factors related to TPB and the other side with four factors related to CC preventive behaviors were considered; the four factors within each side were correlated but not between the sides. The second model showed a good fit but with two high correlations: one of the correlations between an annual gynecological check and single partner (r=0.839) and the other between an annual gynecological check and updated PAP test (r=0.98). Thus, the decision was to place the correlations together into one factor. This model good fits the data, thus indicating a bi-factor structure with six factors, four of which were the TPB components and the other two were CC preventive behaviors. The results of external criteria validly of the questionnaire are in Table 3.

Table 3: External criterion validity through comparison between groups for Papanicolaou test and Gynecological check.

Factor

Papanicolaou Test in the last three years

Anual Gynecological Check

Yes

No   Yes No  
Mean (SD) Mean (SD) P value (a) Mean (SD) Mean (SD)

P value (a)

1. Cervical cancer preventive behaviors

-0.06 (0.39)

-0.16 (0.41) <0.001 -0.05 (0.38) -0.12 (0.41)

0.004

2. Condom use as cervical cancer prevention

-0.03 (0.63)

-0.05 (0.63) 0.631 -0.03 (0.64) -0.04 (0.63)

0.876

3. Attitude to cervical cancer prevention

-0.00 (-35)

-0.05 (0.38) .045 -0.02 (0.35) -0.01 (0.36)

.738

4. Perceived norm

-0.01 (0.40)

-0.11.46 .002 -0.02 (0.42 -0.06 (0.42)

.152

5. Perceived behaviors control

-0.00 (0.43)

-0.29 (0.65) <0.001 .03 (0.39) -0.19 (0.60)

<0.001

6. Intention

-0.01 (0.26)

-0.11 (0.30) <0.001 -0.00 (0.25) -0.07 (0.30)

<0.001

(a) T-Student was used to compare group values.

The new questionnaire (Appendix 1) called CPCC-16 (Conductas Preventivas en Cáncer Cérvicouterino-16 items/ Preventive Behaviors on Cervical Cancer -16 items) consisted of 16 items, which were distributed into six factors. The complete standardized parameters of the bifactorial model are shown in Figure 2. According to the bifactorial structure, each item corresponds to two factors. A summary of the CPCC-16 bifactorial model with factors, number of items and Cronbach’s alpha are shown in Table 4.

fig 2

Figure 2: Complete Standardized Parameters for the Bifactor Model of CPCC-16 (n=967).

Table 4: Name of factor, items and Cronbach’s alpha (n=967).

Factor

No of items

Cronbach’s alpha

1. Cervical cancer preventive behaviors

12 items

0.95

2. Condom use as cervical cancer prevention

4 items

0.93

3. Attitude to cervical cancer prevention

4 items

0.81

4. Perceived norm

4 items

0.87

5. Perceived behaviors control

4 items

0.81

6. Intention

4 items

0.86

CPCC-16 Questionnaire

0.94

Discussion

The first considerations to note are how the structure of the original questionnaire, without varying the number of items, was shown throughout the analysis. The initial questionnaire was created considering the underpinning of TPB constructs, where four factors were proposed. However, a questionnaire where only the theory constructs are considered was unacceptable, and thus, it was necessary to include the behavioral dimensions. However, although the second tested model with eight factors has good fit, it should not be considered a final model because it has two dimensions highly correlated (Pap test and Gynecological check). Thus, the result was a model with six dimensions in which all of the factors loading were significant, although some of the factors exhibited values lower than 0.4.

The final questionnaire was very consistent with another underpinning construct, that was not considered from the beginning (CC preventive behaviors). This result allows us to extend its usefulness, not only to test the TPB but also to analyze and explain CC preventive behaviors using this theoretical model.

The criteria validity shows that the final questionnaire is useful to associate the TPB constructs with the behaviors. The women with different CC screening or gynecological check behaviors did not show differences in condom factor scores; explanations could be because the condom use is not associated with cervical cancer prevention [32,33], or because the use of condom could be overestimated in the sample since it is an expected social behavior.

There are many contexts where the new questionnaire could be useful and where the TBP has demonstrated its utility: to assess the acceptability of preventive behaviors as a whole among the target group of women currently engaged in preventive programs [8,11], to evaluate the evolution of the intention across time [8,17], to evaluate the strategies of cervical cancer prevention programs [8,11,20] and to determine the factors that influence the behaviors [7,9,10,15,18,20]. CC remains a relevant problem, particularly in underdeveloped countries and ethnic minorities in developed countries [10]. Thus, instruments related to the prevention of CC could be very useful in many contexts. Theoretically based models of behaviors are also useful and necessary for the development of effective interventions [2,9,16,34].

A second consideration, related to the results, is why three preventive behaviors proposed in the initial questionnaire were collapsed into 1 factor, CC preventive behaviors (annual gynecological check, updated PAP test and to have a single partner). The second model tested demonstrated that each of the four behaviors proposed as a factor, but the results suggested other structures. This finding could be explained by it being necessary to consider who participated in the behavior and who decided it Therefore, CC preventive behavior factor focuses on behaviors in which the decision is primarily or only related with females, and the condom use factor indicates a behavior in which consensus is required between a woman and her partner.

Other explanations to account for the second consideration and indirectly related to the previous consideration could be that the CC preventive factor included behaviors that are not clearly associated with sexual life, and thus, it is not necessary to have an active sexual life. This is in contrast to the second factor where having sexual intercourse is the principal focus. The association between HPV and CC is one of the strongest described [13], but the relationship between sexual behaviors and CC risk has not been previously recognized by the women [35]. The use of a condom as CC preventive behavior was described in only 5.6% of the women [32], and in a Chilean study [33], only 27.6% of the women described sexual intercourse as a risk factor of CC. Thus, these reasons could explain the way that the preventive behaviors were grouped.

Related to factor loading of the items, there are only two items (items 3D and 4D), which have values less than 0.3, that could indicate that both items may be explained by the condom use factor rather than the intention and perceived behavioral control factors. To the best of our knowledge, there are no studies in which the results fit into a bifactorial model for preventive behaviors and TBP. This may be due to the focus of the research study on only one behavior and not as a group. There are many diseases that can be prevented by practicing some behavior, and the TPB is a solid theory that can help with its understanding. Thus, the CPCC-16 could be considered an example of how more than one behavior could be studied under a bifactorial structure under this theory.

One limitation of this research is that the new questionnaire did not include all of the CC preventive behaviors recognized in the literature, so it could be useful to develop a new version by adding these behaviors. However, it is important to consider that to include other behaviors; the questionnaire could require that the age range of the population be broader where the questionnaire will be used, because preventive behaviors, such as HPV vaccination, are targeted at a younger population where the decision does not often depend on them alone.

Conclusion

The new questionnaire is a contribution to the measurement of preventive behaviors in cervical cancer, enabling its use in research and a clinical setting. The use of TPB as a framework of this questionnaire and the structure shown by the questionnaire are important contributions to advance the cervical cancer arena because the new questionnaire will not only be useful to measure cervical cancer preventive behaviors as a whole but also identify the theory constructs. To have a valid and reliable questionnaire that measures, under a theory of behavior, more than one preventive behavior in cervical cancer is an important advance because it fills a gap in clinical and research area.

References

  1. Ajzen I (2015) The theory of planned behaviour is alive and well, and not ready to retire: a commentary on Sniehotta, Presseau, and Araujo-Soares. Health Psychology Review 9: 131-137. [crossref]
  2. Armitage CJ, Talibudeen L (2010) Test of a brief theory of planned behaviour-based intervention to promote adolescent safe sex intentions. British Journal of Psychology 101: 155-172. [crossref]
  3. Booth AR, Norman P, Goyder E, Harris PR, Campbell MJ (2014) Pilot study of a brief intervention based on the theory of planned behaviour and self-identity to increase chlamydia testing among young people living in deprived areas. British Journal of Health Psychology 19: 636-651. [crossref]
  4. Steinmetz H, Knappstein M, Ajzen I, Schmidt P, Kabst R (2016) How Effective are Behavior Change Interventions Based on the Theory of Planned Behavior? Zeitschrift für Psychologie 224: 216-233.
  5. Edberg M (2015) Individual health behavior theories. In M. Edberg (Ed.), Essentials of Health Behavior.Social and Behavioral Theory in Public Health. Burlington: Jones & Bartlett Learning.
  6. Grim M, Hortz B (2016) Theory in health promotion programs. In C. Fertman & D. Allensworth (Eds.), Health promotions programs. From Theory to Practice (second ed., pp. 55-56). San Francisco: Jossey-Bass.
  7. Jalilian F, Emdadi S (2012) Factors Related to Regular Undergoing Pap-smear Test: Application of Theory of Planned Behavior. Journal of Research in Health Sciences 11: 103-108. [crossref]
  8. Ogilvie GS, Smith LW, van Niekerk D, Khurshed F, Pedersen HN, et al. (2016) Correlates of women’s intentions to be screened for human papillomavirus for cervical cancer screening with an extended interval. BMC Public Health 16: 213.
  9. Roncancio AM, Ward KK, Fernandez ME (2013) Understanding cervical cancer screening intentions among Latinas using an expanded theory of planned behavior model. Behavioral Medicine 39: 66-72. [crossref]
  10. Roncancio AM, Ward KK, Sanchez IA, Cano MA, Byrd TL, et al. (2015) Using the Theory of Planned Behavior to Understand Cervical Cancer Screening Among Latinas. Health Education and Behavior 42: 621-626. [crossref]
  11. Smith L, Khurshed F, van Niekerk D, Krajden M, Greene S, et al. (2014) Women’s intentions to self-collect samples for human papillomavirus testing in an organized cervical cancer screening program. BMC Public Health 14: 2-9. [crossref]
  12. American Cancer Society. (2016, December 5, 2016). Can Cervical Cancer Be Prevented? Retrieved from
  13. Shepherd JP, Frampton GK, Harris P (2011) Interventions for encouraging sexual behaviours intended to prevent cervical cancer. Cochrane Database of Systematic Reviews 4: CD001035. [crossref]
  14. Centers for disease control and prevention. (2017, February 9, 2017). What Can I Do to Reduce My Risk of Cervical Cancer? Retrieved from
  15. Chen S-L, Tsai S-F, Hsieh M-M, Lee L-L, Tzeng Y-L (2016) Factors Predicting Nurse Intent and Status Regarding Pap Smear Examination in Taiwan: a Cross-sectional Survey. Asian Pacific Journal of Cancer Prevention 17: 165-170. [crossref]
  16. Gerend MA, Shepherd JE (2012) Predicting human papillomavirus vaccine uptake in young adult women: comparing the health belief model and theory of planned behavior. Annals of Behavioral Medicine 44: 171-180. [crossref]
  17. Ogilvie GS, Smith LW, van Niekerk DJ, Khurshed F, Krajden M, et al. (2013) Women’s intentions to receive cervical cancer screening with primary human papillomavirus testing. International Journal of Cancer 133: 2934-2943. [crossref]
  18. Kim H (2014) Awareness of Pap testing and factors associated with intent to undergo Pap testing by level of sexual experience in unmarried university students in Korea: results from an online survey. BMC Women’s Health 14: 2-13. [crossref]
  19. Sandberg T, Conner M (2009) A mere measurement effect for anticipated regret: impacts on cervical screening attendance. The British Journal of Social Psychology 48: 221-236. [crossref]
  20. Bennett K, Buchanan J, Adams A (2012) Social-Cognitive Predictors of Intention to Vaccinate Against the Human Papillomavirus in College-Age Women. The Journal of Social Psychology 152: 480-492.
  21. Espada JP, Morales A, Guillen-Riquelme A, Ballester R, Orgiles M (2016) Predicting condom use in adolescents: a test of three socio-cognitive models using a structural equation modeling approach. BMC Public Health 16: 35.
  22. Jemmott JB, Hennessy M (2012) The Reasoned Action Approach in HIV Risk-Reduction Strategies for Adolescents. The ANNALS of the American Academy of Political and Social Science 640: 150-172.
  23. Noonan D, Kulbok P, Yan G (2011) Intention to smoke tobacco using a waterpipe among students in a southeastern U.S. College. Public Health Nursing 28: 494-502. [crossref]
  24. Exum ML, Turner MG, & Hartman JL (2011) Self-Reported Intentions to Offend: All Talk and No Action? American Journal of Criminal Justice 37: 523-543.
  25. World Health Organization. (2017, February 17). Cancer. Retrieved from
  26. Nunnaly J, Bernstein I (1994a) The assessment of reliability. In Nunnaly J & Bernstein I (Eds.), Psychometric theory: McGraw Hill.
  27. Nunnaly J, Bernstein I (1994b) Exploratory factor analysis II: Rotation and other topics. In Nunnaly J & Bernstein I (Eds.), Psychomethic theory: McGraw Hill.
  28. Pett M, Lackey N, Sullivan J (2003) Assesing the characteristics of matrices. In Pett M, Lackey N, & Sullivan J (Eds.), Making sense of factor analysis. The use of factor analysis for instrument development in health care research: housand Oaks: Sage publications.
  29. Ajzen I (2006) Constructing Theory of Planned Behavior Questionnaire Retrieved from
  30. de Vellis R (2003) Guidelines in scale development. In de Vellis R (Ed.), Scale development: Theory and applications (pp. 60-101): Thousand Oaks: Sage Publications
  31. Antshel K (2002) Integrating culture as a means of improving treatment adherence in the Latino population. Psychology, Health & Medicine 7: 435-449.
  32. Raychaudhuri S, Mandal S (2012) Socio-Demographic and Behavioural Risk Factors for Cervical Cancer and Knowledge, Attitude and Practice in Rural and Urban Areas of North Bengal, India. Asian Pacific Journal of Cancer Prevention 13: 1093-1096. [crossref]
  33. Urrutia M (2012) Creencias sobre Papanicolaou y cáncer cérvicouterino en un grupo de mujeres chilenas. Revista Chilena de Obstetricia y Ginecología 77: 3-10.
  34. Luszczynska A, Durawa A, Scholz U, Konll N (2012) Empowerment Beliefs and Intention to Uptake Cervical Cancer Screening: Three Psychosocial Mediating Mechanisms. Women and Health 52: 162-181. [crossref]
  35. Opoku CA, Browne ENL, Spangenberg K, Moyer C, Kolbilla D, et al. (2016) Perception and risk factors for cervical cancer among women in northern Ghana. Ghana Medical Journal 50: 84. [crossref]

Appendix 1

Instructions: The following phrases are some ideas about behaviors. Mark your level of agreement with a cross for each phrase. There are no right or wrong answers, so if you are not sure about some questions or do not know an answer, feel free to answer with what you think.

1. How do you evaluate each of the following behaviors:

Very good

Good Bad

Very Bad

1.A Have a gynecological check (with a nurse midwife or gynecologist) annually
1.B Take the PAP test when appropriate.
1.C Have a single sexual partner (at the same time)
1.D Use condoms in (all) sexual relationships.
2. Most people who are important to me would agree to:

Strongly Agree

Agree Disagree

Strongly Disagree

2.A Have a gynecological check (with a nurse midwife or gynecologist) every year
2.B Take the PAP test when appropriate.
2.C Have a single sexual partner (at the same time)
2.D Use condoms in (all) sexual relationships.
3. I am confident that I can:

Strongly Agree

Agree Disagree

Strongly Disagree

3.A Have a gynecological check (with a nurse midwife or gynecologist) every year
3.B Take PAP when appropriate.
3.C Have a single sexual partner (at the same time)
3.D Use condoms in (all) sexual relationships.
4. In the future I want to:

Strongly Agree

Agree Disagree

Strongly Disagree

4.A Have a gynecological check (with a nurse midwife or gynecologist) every year
4.B Take PAP when appropriate.
4.C Have a single sexual partner (at the same time)
4.D Use condoms in (all) sexual relationships.

Inhibition of the Dimerization of SARS-COV-2 Encoded Nucleocapsid Protein by Chlorophyll A, Halothane and Tetraethylene Glycol Monooctyl Ether

DOI: 10.31038/JPPR.2020334

Abstract

SARS-COV-2 is the etiologic agent of COVID-19. There is currently no effective remedy for SARS-COV-2 infections or COVID-19. Dimerization of SARS-COV-2 encoded Nucleocapsid protein (NCp) is a prerequisite step for it to act as an essential co-factor for the replication, transcription and packaging of SARS-COV-2 genome. Molecules that prevent the dimerization of NCp are potential prophylactics and therapeutics for the control of SARS-COV-2 infections and virulence. Here, through interrogation of chemical ligand data banks and thermodynamic calculations, we show that Chlorophyll A, Halothane and Tetraethylene glycol monooctyl ether (TGME) are inhibitors of the dimerization of NCp. Chlorophyll A is the most potent inhibitor of NCp dimerization with dissociation constant (KD) of ~28 pM. Chlorophyll A binding caused the dissociation constant (KD) for NCp-NCp interaction to increase from ~7.2 pM to ~1000000 pM. Chlorophyll A also bound to NCp mutated at phosphorylation sites S186, S197 and S202 (S186F, S197L and S202N) and phosphorylation recognition sites RNpSTP, (S197L) and RGTpSP (RG203/204KR and RG203/204KT) with dissociation constants of ~12 pM, ~6.1 pM, ~27.8. pM, ~27.8 pM and ~2.2 pM respectively. These results show that Chlorophyll A, a chemical ligand that is present in high abundance with good absorption properties and near-zero toxicity is a potential very potent prophylactic and therapeutic that acts via disruption of NCp dimerization.

Introduction

SARS-COV-2 is the etiologic agent of COVID-19 [1-4], a highly debilitating disease of the respiratory system that has so far killed in excess of 1 million individuals (still counting) worldwide [5,6]. There is currently no known cure for COVID-19. Although, there are press releases of candidate vaccines that are more than 90% effective in preventing symptoms of COVID-19, there are no vaccines for the control of SARS-COV-2 infections [6-11]. In order to prevent SARS-COV-infections and virulence of 2, the replication cycle of SARS-COV-2 must be inhibited. Among the SARS-COV-2 encoded proteins [1-4], the Nucleocapsid protein (NCp) has an essential role in the initiation and control of the replication, transcription and packaging of the SARS-COV-2 genome [12-23]. Dimerization and oligomerization of SARS-COV-2 Nucleocapsid protein (NCp) is a pre-requisite for NCp to act as a co-factor for the initiation and control of replication, transcription and packaging of of the SARS-COV-2 genome [16-23]. Targeting NCp dimerization is an attractive avenue for a drug discovery program that aims to block the replication, transcription and packaging of the SARS-COV-2 genome. Molecules that act to prevent dimerization of NCp are prime therapeutic candidates Because of its critical function in the replication, transcription and packaging of the SARS-COV-2 genome, it is not surprising that NCp has been considered a prime target for drug discovery programs [24-26]. NCp has also been proposed as a vaccine target [27-32]. NCp is phosphorylated at multisite sites which control its function [20,33-38]. However, NCp has been shown to undergo mutations at several of its phosphorylations sites [33]. While phosphorylation of NCp at key sites within a phosphorylation rich domain has been proposed to act as a cellular response mechanism for phosphorylation dependent sequestration of NCp by Protein 14-3-3, mutations in the phosphorylation sites appear to be viral mechanism for avoiding sequestration of NCp by Protein 14-3-3 [30,38]. Mutations of phospho-sites including serine 186 (S186F), serine 197 (S197L), serine 2020 (S202N) and phosphorylation recognition sites RG 203/204 (RG203/204KR and RG203/204KR) have been shown to occur in strains/sub-strains of SARS-COV-2 that were isolated in various populations around the world [30,38]. Any molecules that bind to NCp to disrupt its dimerization must not only bind to the wild-type NCp but also to the mutated NCps. In this work, it is shown that a chemical ligand, Chlorophyll fulfills this requirement and that Chlorophyll A bound NCp and mutated NCps at picomolar concentration and causing major increases in the dissociation constants (KDs) for the wild-type and mutated NCp-NCp dimer interactions.

Methods

The structure of dephospho-SARS-COV-2 Nucleocapsid protein (NCp) was rendered de novo using the Quark Program pursuant to Zu and Zhang [39,40]. The identification of a chemical ligand that binds to NCp was performed by interrogating Chemical Ligand Data Banks using the Coach Program pursuant to Yang et al. [41,42]. Each chemical ligand that was identified was then analyzed for its ability to bind NCp. Renaming of chains in NCp-chemical ligand and NCp-chemical ligand-NCp complexes were performed by using the Rename Chain PDB File Program pursuant to Rath, E. [43]. Mutations of the phosphorylation sites and phosphorylation recognition sites of NCp was performed using the Build Model Program of FoldX pursuant to Guerois et al. and Schymkowitz et al. [44,45]. The bindings of identified Chemical ligands-NCps complexes to wild-type NCp and mutated NCps were analyzed by Docking Experiments using Z Dock Program pursuant to Pierce et al. [46]. Monomeric and dimeric wild-type and mutated NCps rendered in this work were analyzed and visualized by the CCP4 Molecular Graphics Program Version 2.10.11 as described by Mc Nicolas et al [47] and the ZMM Molecular Modeling Program as described by Garden and Zhorov [48]. The dissociation constants (KDs) for the binding of chemical ligands to NCp monomer and dimers was determined by first obtaining the Gibbs Free Energy (ΔGGFG) using the Prodigy-Ligand Program pursuant to Vangone et al. and Kurkcuoglu et al. [49,50] and then calculating KDs from the equation KD = e(-ΔG/RT). The dissociation constant (KD) for the NCp-NCp interactions in NCp dimers were determined by using the Prodigy-Protein-Protein Program pursuant to Vangone and Bonvin, and Xue et al. [51,52].

Results

The structure of dephospho NCp (amino acids 123-310) which encompasses the phosphorylation rich domain of NCp (amino acids 185-209) was rendered de novo using the Quark Program pursuant to Xu and Zhang [39,40]. Figure 1 shows the structure of the rendered monomeric structure of NCp. The structure of the dimeric NCp was determined by Docking Experiments and is summarized in Figure 2. Monomeric NCp was used to interrogate Chemical Ligand Data Banks using the COACH program pursuant to Yang et al. [41,42]. A number of Chemical Ligands that bound to NCp, including Halothane, Tetraethylene glycol monooctyl ether (TGME) and Chlorophyll A were identified (Figure 3). The 3 chemical ligands bound monomeric NCp with dissociation constants (KDs) of ~66 µM, ~78 nM and ~28 pM respectively and dimeric NCps with dissociation constants (KDs) of ~66 µM, ~24 µM and ~12 pM respectively (Figure 4). The binding of Halothane to dimeric NCps was accompanied by an increase of the dissociation constant (KD) for the NCp-NCp interaction from 7.2 pM to 150 pM. The binding of TGME caused the dissociation constant (KD) for the NCp-NCp interaction to increase from 7.2 pM to 4000 pM while the binding of Chlorophyll A resulted in an increase of the dissociation constant (KD) for the NCp-NCp interaction from 7.2 pM to 1000000 pM. The binding of Chlorophyll A to NCp was further characterized because of its very high affinity binding to NCp (~28 pm for monomeric NCp and ~12 pM for dimeric NCp) and extremely profound effect on the structure, conformation (Figures 3-6) and dissociation constant (KD) for the NCp-NCp interaction (from 7.2 pM to 1000000 pM). Chlorophyll A formed contact points with amino acid residues, Tyrosine 50, Glutamic acid 52, Glycine 53, Arginine 55, Glycine 56, Glutamine 59, Serine 61, Serine 62, Arginine 63, Serine 66, Leucine 100, Leucine 101, Aspartic acid 103, Arginine 104, Lysine 135, Threonine 141, Alanine 142, Tyrosine 146, histidine 176, Glutamine 181, Isoleucine 182, Alanine 183, Alanine 186, Threonine 174, Alanine 178, Glutamine 184, Phenylalanine 185 and Proline 187 of NCp (Figures 5A and 6A) and also with the phosphorylation rich domain of NCp (Figure 5B and 6B). NCp has been shown to be mutated at several phosphorylation sites and phosphorylation recognition sites within the phosphorylation domain of NCp, including serine 186 (S186F), serine 197 (S197L), serine 202 (S202N), Arginine 203 and Glycine 204 (RG203/204KR and RG203/204) [33,38]. It was previously proposed that cells infected with SARS-COV-2 possess a cellular response mechanism for the binding and sequestration of NCp by Protein 14-3-3 involving multi sites phosphorylation by a variety of cellular protein kinases, and in counterpart, SARS-COV-2 has evolved to evade the cellular response mechanism through mutations of at least 3 phosphorylation sites, serines 186, 197 and 202 and 2 phosphorylation recognition sites (RNpSTP and RGTpSP), serine 197, arginine 203 and glycine 204 [33,38]. It was therefore necessary to determine whether Chlorophyll A can bind all the NCp mutants with high affinities. The dissociation constants (KD) for the binding of Chlorophyll A to wild type and mutated monomeric and dimeric NCps are summarized in Tables 1 and 2. These results show that Chlorophyll A bound monomeric and dimeric wild type and mutant NCps with very similar high affinities.

fig 1

Figure 1: A: Ribbon structure (blue) of SARS-COV-2 Nucleocapsid protein monomer (NCp monomer), rendered as described in Method Section. The amino acid sequence of the phosphorylation rich domain is depicted as spheres in pink. B: Realistic rendering of SARS-COV-2 Nucleocapsid protein (NCp) monomer.

fig 2

Figure 2: A: Ribbon structure (blue) of SARS-COV-2 Nucleocapsid protein dimer (NCp dimer), rendered as described in Method Section. The amino acid sequence of the phosphorylation rich domain is depicted as spheres in pink. B: Realistic rendering of SARS-COV-2 Nucleocapsid protein (NCp) dimer.

fig 3

Figure 3: Structures of SARS-COV-2 Nucleocapsid protein monomer (NCp monomer) with bound chemical ligands. A; Halothane (Red); B: Tetraethylene glycol monooctyl ether (Red); C: Chlorophyll (Red), The dissociation constants (KDs) for the binding of NCp monomer-chemical ligands were  66 µM, 78 nM and 28 pM for Halothane, Tetraethylene glycol monooctyl ether and Chlorophyll A respectively.

fig 4

Figure 4: Structures of Dimeric SARS-COV-2 Nucleocapsid protein monomer (NCp monomer) with bound chemical ligands. A; Halothane (Red); B: Tetraethylene glycol monooctyl ether (Red); C: Chlorophyll (Red), The dissociation constants (KDs) for the binding of NCp dimer and chemical ligands were  66 µM, 24 µM and 28 pM for Halothane, Tetraethylene glycol monooctyl ether and Chlorophyll A respectively.

fig 5

Figure 5: A: Ribbon structure (blue) of SARS-COV-2 Nucleocapsid protein monomer (NCp monomer) bound by Chlorophyll A (Red), rendered as described in Method Section. The contact points in NCp are shown in Black. B: Ribbon structure (blue) of SARS-COV-2 Nucleocapsid protein monomer (NCp monomer) bound by Chlorophyll A (Red), The contact points in NCp are shown in Black. The phosphorylation rich domain of NCp is depicted as spheres in pink.

fig 6

Figure 6: A: Ribbon structure (blue) of SARS-COV-2 Nucleocapsid protein dimer (NCp dimer) bound by Chlorophyll A (Red), rendered as described in Method Section. The contact points in NCp are shown in Black. B: Ribbon structure (blue) of SARS-COV-2 Nucleocapsid protein dimer (NCp dimer) bound by Chlorophyll A (Red), The contact points in NCp are shown in Black. The phosphorylation rich domain of NCp is depicted as spheres in pink.

Table 1: Dissociation constants (KDs) for the binding of Chlorophyll A, Halothane and Tetraethylene glycol monooctyl ether (TGME) to NCp monomer and NCp dimer, and dissociation constants (KDs) for NCp-NCp interactions in the presence of Chlorophyll A, Halothane and Tetraethylene glycol monooctyl ether (TGME).

Dissociation constant (KD) for Ligands binding (pM)

Dissociation constant (KD) for NCp-NCpinteractions in NCp dimer (pM)

-NCp

~00.0

NCp-NCp Complex

~7.2

-NCp-Chlorophyll A complex

~28.0

-NCp-NCp-Chlorophyll A Complex

~12

~1000000

-NCp-Halothane Complex

~66000000

-NCp-NCp-Halothane Complex

~66000000

~150

-NCp-TGME Complex

~2400000

-NCp-NCp-TMGE Complex

~78000

~4000

Table 2: Dissociation constants (KD) for the binding of Chlorophyll A to NCp monomer, NCp monomer mutants, NCp dimer and NCp dimer mutants.

 

Dissociation constants (KDs) for Chlorophyll A binding (pM)

Wild Type-NCp-Chlorophyll A complex

~28.0

S186F mutant-NCp-Chlorophyll A complex

~6.0

S197L mutant-Chlorophyll A Complex

~6.1

S202N mutant-NCp-Chlorophyll A complex

~27.8

RG203/204KR mutant-NCp-Chlorophyll A complex

~27.8

RG203/204KT mutant-NCP Chlorophyll A complex

~6.1

Wild Type-NCp-NCp complex

Wild Type-NCp-Chlorophyll A-Wild Type NCp complex

~12.0

S186F mutant-NCp-Chlorophyll A-S186F mutant-NCp complex

~1.0
S197L mutant-NCp-Chlorophyll A-S197L mutant-NCp Complex

~6.1

S202N mutant-NCp-Chlorophyll A-S202N mutant-NCp complex

~27.8
RG203/204KR mutant-NCp-Chlorophyll A-RG203/204KR mutant-NCp complex

~27.8

RG203/204KT mutant-NCp Chlorophyll A-RG203/204KT mutant-NCp complex

~2.2

Discussion

There is currently no effective means to control SARS-COV-2 infection and virulence. There is also no cure for COVID-19, the disease(s) caused by SARS-COV-2. Although, there are press releases describing 3 candidate vaccines to be over 90% effective, it is clear that they do not prevent SARS-COV-2 infections [7-11]. Full disclosure of the clinical trials of the effectiveness of the 3 candidate vaccines is required before it can be determined scientifically with some degree of certainty that they are indeed effective as prophylactics. The effective control of SARS-COV-2 infection and virulence can be achieved through a disruption of the replication, transcription and packaging of the SARS-COV-2 genome. NCp is an essential co-factor in the replication, transcription and packaging of the SARS-COV-2 genome [12-23]. Inhibiting the function of NCp is therefore a very attractive way to prevent the viability, transmission, infection and virulence of SARS-COV-2 [24-26]. Dimerization and oligomerization are prerequisite steps that enable NCp to act as an essential co-factor in the replication, transcription and packaging of the SARS-COV-2 genome [12-23]. In the present work, a number of chemical ligands, including Halothane, Tetraethylene glycol monooctyl ether (TGME) and Chlorophyll A have been identified and shown to inhibit the dimerization of NCp. All 3 molecules bind to NCp with high affinities, with dissociation constants (KDs) of 66 µM, 78 nM and 28 pM respectively, and disrupts the interaction between NCps by causing the dissociation constant (Kd) of the NCp-NCp interaction to significantly increase from ~7.2 pM to 150 pM, 4000 pM and 1000000 µM respectively. The effects of the chemical ligands on the dimerization of NCp are therefore profound. It was previously shown that NCp becomes mutated at key phosphorylation sites, including serine 186 (S186F), serine 197 (S197L) and serine 202 (S202N) and phosphorylation recognition sites, serine 197 (S197L), arginine 203 and glycine 204 (RG203/204KR and RG203/204KT) within the motifs, RNpSTP and RGTpSP that are located in the linker region of NCp [33,38]. It has been proposed that cells infected with SARS-COV-2 possess a cellular response mechanism for the binding and sequestration of NCp by Protein 14-3-3 involving multi sites phosphorylation by a variety of cellular protein kinases and in counterpart, SARS-COV-2 has evolved to evade the cellular response mechanism through mutations of at least 3 phosphorylation sites, serines 186, 197 and 202 and three phosphorylation recognition sites (RNpSTP and RGTpSP), serine 197, arginine 203 and glycine 204 [33,38]. In the present work, it is shown that Chlorophyll A bound to all the relevant NCp mutants with high affinities. It is submitted that Chlorophyll A is a very potent inhibitor of the dimerization of SARS-COV-2 encoded Nucleocapsid protein (NCp). Because of its relative abundance, good absorption properties and near zero toxicity, the development of Chlorophyll A as a prophylactic and therapeutic for the control of SARS-COV-2 infections and virulence is warranted. In contrast to candidate vaccines that can only prevent symptoms of COVID-19 [7-11], Chlorophyll A will not only act as a prophylactic and therapeutic to cure COVID-19, it will also prevent SARS-COV-2 infection and viability because it acts via inhibition of the dimerization of NCp, an essential and prerequisite step in the initiation and control of the replication, transcription and packaging of the SARS-COV-2 genome.

Acknowledgement

This work was supported by the Nacbraht Biomedical Research Institute Fund.

Author Contribution

H.Y. Lim Tung came up with the concept and the questions, performed the experiments with Pierre Limtung, analyzed the results with Pierre Limtung and wrote the paper with Pierre Limtung.

Pierre Limtung performed the experiments with H.Y. Lim Tung, analyzed the results with H.Y. Lim Tung and wrote the paper with Pierre Limtung.

Conflict of Interest

The authors have no conflict of interest to declare.

References

  1. Zhu N, Zhang D, Wang W, Li X, Yang B, et al. (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382: 727-733. [crossref]
  2. Lu R, Zhao X, Li J, Niu P, Yang B, et al. (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395: 565-574. [crossref]
  3. Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579: 270-273. [crossref]
  4. Wu F, Zhao S, Yu B, Chen YM, Wang W, et al. (2020) A new coronavirus associated with human respiratory disease in China. Nature 579: 265-269. [crossref]
  5. Tung HYL (2020) SARS-COV-2 infection and virulence: classification of seven groups of countries. IP 10000 (infectivity) and DP 10000 (virulence) are influenced by temperature, humidity and far infrared irradiation. New Biomed Sci 1: 71-85.
  6. Tung HYL (2020) Faulty response strategy and abject failure to control SARS-COV-2 infections and virulence. J Invest Cri Pub Sci
  7. Pfizer (2020) Pfizer and BioNTech announce vaccine candidate against COVID-19 achieved success in first interim analysis from phase 3 study. Pfizer.com, Issue of November 9, 2020.
  8. Calloway E (2020) What Pfizer’s landmark COVID vaccine results mean for the pandemic. Nature, Issue of November 9, 2020.
  9. Cohen J (2020) Champagne and question greet first data showing that a COVID-19 vaccine works. Science, Issue of November 9, 2020.
  10. Cohen J (2020) ‘Just beautiful’: Another COVID-19 vaccine from newcomer Moderna, succeeds in large scale trial. Science Issue of November 16, 2020.
  11. Calloway E (2020) COVID Vaccine excitement builds as Moderna reports third positive result. Preliminary data show that the immunization is 94% effective and seems to prevent severe infections. Nature, Issue of November 16, 2020.
  12. Stertz S, Reichelt M, Spiegel M, Kuri T, Martínez-Sobrido, et al. (2007) The intracellular sites of early replication and budding of SARS coronavirus. Virology 361: 304-315. [crossref]
  13. Verheije MH, Hagemeijer MC, Ulasli M, Reggiori F, Rottier PJ, et al. (2010) The coronavirus nucleocapsid protein is dynamically associated with the replication-transcription complexes. J Virol 84: 11575-11579. [crossref]
  14. Cong Y, Ulasli M, Schepers H, Mauthe M, V’kovski P, et al. (2020) Nucleocapsid Protein Recruitment to Replication-Transcription Complexes Plays a Crucial Role in Coronaviral Life Cycle. J Virol DOI: 10.1128/JVI.01925-19.
  15. Tylor S, Andonove A, Cutts T, Cao J, Grudesky E, et al. (2009) The SR-rich motif in SARS-CoV nucleocapsid protein is important for virus replication. Can J Microbiol 55: 254-260. [crossref]
  16. Chen CY, Chang CK, Chang YW, Sue SC, Bai, et al. (2007) Structure of the SARS coronavirus nucleocapsid protein RNA-binding dimerization domain suggests a mechanism for helical packaging of viral RNA. J Mol Biol 368: 1075-1086. [crossref]
  17. Hsieh PK, Chang SC, Huang CC, Lee TT, Hsiao CW, et al. (2005) Assembly of severe acute respiratory syndrome coronavirus RNA packaging signal into virus-like particles is nucleocapsid dependent. J Virol 79: 13848-13855. [crossref]
  18. Yu IM, Gustafson CLT, Diao J, Burgner II, et al. (2005) Recombinant Severe Acute Respiratory Syndrome (SARS) Coronavirus Nucleocapsid Protein Forms a Dimer through Its C-terminal Domain. J Biol Chem 280: 23280-23286. [crossref]
  19. He R, Dobie F, Ballantine M, Leeson A, Li, Y, et al. (2004) Analysis of multimerization of the SARS coronavirus nucleocapsid protein. Biochem Biophys Res Commun 316: 476-483. [crossref]
  20. Peng TY, Lee, K.-R,Tarn WY (2008) Phosphorylation of the arginine/serine dipeptide‐rich motif of the severe acute respiratory syndrome coronavirus nucleocapsid protein modulates its multimerization, translation inhibitory activity and cellular localization. FEBS J 275: 4152-4163. [crossref]
  21. Surjit M, Liu B, Kumar P, Chow VT, Lal S K (2004) The nucleocapsid protein of the SARS coronavirus is capable of self-association through a C-terminal 209 amino acid interaction domain. Biochem Biophys Res Commun 317: 1020-1036. [crossref]
  22. Luo H, Ye F, Sun T, Yue L, Peng S, et al. (2004) In vitro biochemical and thermodynamic characterization of nucleocapsid protein of SARS. Biophys Chem 112:15-25. [crossref]
  23. Chang CK, Sue SC, Yu TH, Hsieh CM, Tsai CK, et al. (2005) The dimer interface of the SARS coronavirus nucleocapsid protein adapts a porcine respiratory and reproductive syndrome virus-like structure. FEBS Lett 579: 5663-5668. [crossref]
  24. Kang S, Yang M, Hong Z, Zhang L, Huang Z, et al. (2020) Crystal structure of SARS-CoV-2 nucleocapsid protein RNA binding domain reveals potential unique drug targeting sites. Acta Pharma Sinica 10: 1228-1238. [crossref]
  25. Mukherjee D, UPASANA R. (2020) SARS-CoV-2 Nucleocapsid Assembly Inhibitors: Repurposing Antiviral and Antimicrobial Drugs Targeting Nucleocapsid-RNA Interaction. chemRxiv, DOI: 10.26434/chemrxiv.12587336.v2.
  26. Yadav R, Imran M, Dhamija P, Kapil Suchal K, Handu, S. (2020) Virtual screening and dynamics of potential inhibitors targeting RNA binding domain of nucleocapsid phosphoprotein from SARS-CoV-2. J Biomol Struc Dynamics, DOI: 1080/07391102.2020.1778536. [crossref]
  27. Gao W, Tamin A, Soloff A, D’Aiuto L, Nwanegbo E, et al. (2003) Effects of a SARS-associated coronavirus vaccine in monkeys. Lancet 362:1895-1896. [crossref]
  28. Liu SJ, Leng CH, Lien SP, Chi HY, Huang CY, et al. (2006) Immunological characterizations of the nucleocapsid protein based SARS vaccine candidates. Vaccine 24: 3100-3108.
  29. Lin Y, Shen X, Yang RF, Li YX, Ji YY, et al. (2003) Identification of an epitope of SARS-coronavirus nucleocapsid protein. Cell Res 13: 141-145. [crossref]
  30. Peng H, Yang LT, Wang LY, Li J, Huang J, et al. (2006) Long-lived memory T lymphocyte responses against SARS coronavirus nucleocapsid protein in SARS-recovered patients Virology 351: 466-475. [crossref]
  31. Kalita P, Padhi AK, Zhang K, Tripathi, T. (2020) Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2. Microbial Pathogenesis, DOI: 10.1016/j.micpath.2020.104236
  32. Dutta NK, Mazumdar K, Gordy JT (2020) The Nucleocapsid Protein of SARS–CoV-2: a Target for Vaccine Development. J Virol 94. [crossref]
  33. Tung HYL, Limtung Pierre (2020) Mutations in the phosphorylation sites of SARS-CoV-2 encoded nucleocapsid protein and structure model of sequestration by protein 14-3-3. Biochem Biophys Res Commun 532: 134-148. [crossref]
  34. Surjit M, Kumar R, Mishra RN, Reddy MK, Chow VT, et al. (2005) The severe acute respiratory syndrome coronavirus nucleocapsid protein is phosphorylated and localizes in the cytoplasm by 14-3-3-mediated translocation. J Virol 79:11476-11486. [crossref]
  35. CH Wu, SH Yeh, YG Tsay, et al. (2009) Glycogen synthase kinase-3 regulates the phosphorylation of severe acute respiratory syndrome coronavirus nucleocapsid protein and viral replication. J Biol Chem 284: 5229-52239. [crossref]
  36. Wu CH, Chen PJ, Yeh SH (2014) Nucleocapsid phosphorylation and RNA helicase DDX1 recruitment enables Corona virus transition from discontinuous transcription. Cell Host and Microbe 16: 462-472. [crossref]
  37. Carlson CR, Asfaha JB, Ghent CM, Howard CJ, Hartooni N, et al. (2020) Phosphorylation modulates liquid-liquid phase separation of the SARS-CoV-2 N protein. bioRxiv, DOI: 10.1101/2020.06.28.176248..
  38. Limtung P, Tung HYL (2020) Structure Model Analysis of the Effects of Mutations in the Phosphorylation Sites of SARS-CoV-2 Encoded Nucleocapsid Protein. Sci J Biol, DOI: 37871/sjb.id19.
  39. Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80: 1715-1735. [crossref]
  40. Xu D, Zhang Y (2013) Toward optimal fragment generations for ab initio protein structure assembly. Proteins 81: 229-239. [crossref]
  41. Yang J, Roy A, Zhang Y (2013) BioLiP: a semi-manually curated data base for biologically relevant ligand protein interactions. Nucleic Acids Res 41:1096-1103. [crossref]
  42. Yang J, Roy A, Zhang Y (2013) Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 20: 2588-2595. [crossref]
  43. Rath E (2010) https://canoz.com/sdh/renamepdbchain.pl.
  44. Guerois R, Nielson JE, Serrano L (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320: 369-387. [crossref]
  45. Joost Schymkowitz J, Jesper Borg J, Francois Stricher F, Robby Nys R, Frederic Rousseau F, et al. (2005) The FoldX web server: an online force field. Nucleic Acids Res 33: W382-W388. [crossref]
  46. Pierce BG, Wiehe K, Hwang H, Kim BH, Vreven T, et al. (2014) ZDOCK Server: Interactive Docking Prediction of Protein-Protein Complexes and Symmetric Multimers. Bioinformatics 30: 1771-1773. [crossref]
  47. McNicholas S, Potterson E, Wilson KS, Noble MEM (2011) Presenting your structures: the CCP4mg molecular-graphics software. Acta Cryst D67: 386-394. [crossref]
  48. Garden DP, Zhorov BS (2010) Docking flexible ligands in proteins with a solvent exposure- and distance-dependent dielectric function. J Comp Aided Mol Des 25: 91-105. [crossref]
  49. Vangone A, Schaarschmidt J, Koukos P, Geng C, Citro N, et al. (2018) Large-scale prediction of binding affinity in protein–small ligand complexes: the PRODIGY-LIG web server. Bioinformatics 35: 1585-1587.
  50. Kurkcuoglu Z, Koukos PI, Citro N, Trellet ME, Rodrigues JPGLM, et al. (2018) Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2. J Comp Aid Mol Des 32:175-185.
  51. Vangone A, Bonvin AMJJ (2015) Contact based prediction of binding affinity in protein-protein complexes. eLife [crossref]
  52. Xue L, Rodrigues J, Kastritis P, Bonvin AMJJ, Vangone A (2016) PRODIGY: a webserver for predicting the binding affinity in protein-protein complexes. Bioinformatics 32: 3676-3678. [crossref]

Exercising and Improving the Mind of Youth: Critical Thinking Following a Time-Honored Approach

DOI: 10.31038/PSYJ.2021313

Abstract

We present a novel way to increase the ability of students to think creatively and critically. We follow the approach used by students of the Jewish Talmud. The students are presented with a case or topic (an ANIMAL which wanders around destroying), instructed to create four questions relevant to the topic, and then to provide four answers (elements) to each question. The elements are mixed into vignettes according to an underlying experimental design, ensuring that each respondent evaluates a unique set of 24 vignettes. Each respondent is told to adopt one of two judgment criteria, to be lenient or to be stringent in terms of evaluating the evidence presented by the vignette. Each vignette is rated on a 5-point scale, ranging from innocent to guilty. External respondents evaluate the vignettes, and the data presented in immediately analyzed form. The process provides a structured path for students to think in a creative manner when setting up the study and when they discuss the results from the real experiment. The students emerge as creative critical thinkers and experimenters. The opportunity now exists for students to grow in their thinking, with concrete results, and exciting, ‘new-to-the-world’ discoveries, both motiving the student at the time of the ‘experiment’, as well as generating a student-created portfolio of studies showcasing the ability to think at a deeper level.

Introduction – Today’s Problem with Education

We live in a metric society, a society which measures all aspect of life, especially in the world of education [1]. The result is the sense that everything can be understand and redirected through these measures. The folk wisdom from many years is ‘that which is measured is done.’ The folk wisdom is right, because it seems to be the repeated observations that people move towards satisfying the demand of measurement. Somehow, it appears that anything that can be measured can become a way to set goals. It may just be that what is measured gives a specific value towards which one can aim. One may not be able to perform to reach a general goal, described by a paragraph, but it is extremely easy to put out a measure, and to measure how close people are to the measure. Eventually the measure itself becomes hallowed, and people stop thinking about what the measure means.

About two thousand years, in both Jerusalem and in Babylonia, after the destruction of the Second Temple, the rabbis who were to found today’s Rabbinic Judaism, the traditional Judaism was we know it, were concerned that the foundations of much of Jewish practice and religious thought would be brought to its end through the seemingly irresistible might of the Roman Empire. The movement was afoot to preserve Judaism in study halls with student and their teachers arguing the fine points of the law. Emerging from this give and take was the Talmud, the so-called ‘Oral Law’, comprising both legal discussion and stories, ‘halacha’ (the way), and ‘midrash’ (the exposition) [2,3].

What is important here is that the students were both taught the ‘law’ as immutable, but the specifics of the law were to be deduced by discussions, by back and forth, by critical and creative thinking, respectively. And so emerged schools of law and practice, the Yeshivot, in which students would sharpen their minds by study in groups he holy writings and the law and law commentaries. The learning was not rote, but demanded the discussion, the understanding of ‘why,’ and the ability to bring forth supporting prooftexts for any point of view. This type of learning is relevant today) [4-6].

As we move forward to today, we see a different world of education, one which is accused of failing the student. The failure of K-12 education world-wide, presented in paper after paper [7-9] suggest the opportunity and the need need for students to re-learn the art and practice of how to think creatively and critically. We live in a time when students must once again learn to argue, discuss, think, create, deduce, and so forth, cogently, effectively, and most of all correctly. In a world information is abundant, easily opened by typing or evening asking Apple’s Siri, or Google’s Assistant, more than ever the need is to return to creating students, not simply automata who can find any fact through the deft use of search engines. Knowledge may be easy to obtain today, thinking less so, critical, creative and exciting thinking far less so.

Doing an Experiment With Ideas

The notion of ‘experiment’ and ‘Talmud’ seem to be at odds with each other. We are accustomed, whether explicitly or implicitly, to hold that the word of Torah is immutable, subject to interpretation in accordance with ‘revealed wisdom.’ There is a ‘right’ way. The goal of Talmudic discourse is to find that right way, by bringing together prooftexts, using allowed principles of interpretation (e.g., the 13 hermeneutic principles of Rabbi Ishmael; Yadin, 2003) [10]. Once these principles have been understood, one can proceed to interpret, always being sure, however, of remaining within revealed knowledge, and accepted practice. The highest goal is for the student to contribute penetrating questions, as well as insights, accepted novella, a goal reached by few, and a goal which eludes many others, the more typical students whom one encounters.

How can we impart the excitement of learning, the joy that must have been experienced by scholars during the last two millennia, scholars about whom we read, but whose very enjoyment of learning is shared by so few? Can today’s technology reignite love of learning, real love, and not just perfunctory expression?

Can we make the methods of the Talmud, but for a secular world, exciting to the student, engaging, and thrilling when the student matures into creative thinking? In the Talmud creativity stops is founded on interpreting the written text, making itself evident in with the discussions, and of course with the written commentaries. Here we want to reproduce the joy of learning, the joy of becoming creative in learning, not for the scholar but for the every-day student, who is only beginning the journey of learning. The pattern be modeled on Talmud study but generalized to the from the mind of the student, living in today’s world, working with holy or the secular, the special or the quotidian, the mundane.

Let us transform ourselves, becoming scholars in the ways the ancient students became scholars, through the study of cases, the and the discussion of these cases. Let us rewrite the past, recreating old ways of learning, recasting them for today’s world, focusing on today’s needs. Let us take the cases of the Talmud, update them to today’s world but in the same form, expand these cases into different features, combine the features of these case in different ways, creating different ‘what if’s’, and telling different stories. But let’s not stop there… the effort is only half done. Now continue. Present to a panel of judges these new cases, developed by the students, instructing the panel of judges give a so-called in Hebrew a ‘psak’ a judgment on each respective case. Do this process for topics far and wide, topics from the Talmud perhaps, but then topics of any sort which engage the student [11,12].

What happens when the student who develops these cases discovers exactly how the judgments from ordinary people are ‘driven by’ each idea in the case, each idea developed by the student who creates this updated case? Can we create a new, engaged spirit for learning, one in which today’s student is an active creator of knowledge, and by doing so, become a deeply involved student?

This approach did not emerge by accident. It came about through a deep study of psychology of experimental design, statistics, consumer research, and the law. For many years, really a half century, these approaches have been involved in business, creating new products, helping schools (even yeshivas) understand how to communicate, and being a scientist. The approach has been used almost 30 years ago at first to create the cash back credit card in 1993 for Discover Card in Chicago, and the Oral B Mechanical Toothbrush in 1992 for Oral B Inc. in California. These same principles are now being approached to guide the student to a new level of understanding, encouraging creative thinking, and critical thinking [13].

So, what is this Approach Really, and What does it Deliver?

One of author HRM’s favorite sections of the Talmud is Shor Shenagach, The Ox Which Gored. Why? Because despite the topic, unusual to city-folk, is in essence real, quirky, and has aspects of ‘fun’ when the different aspects of the case are elaborated by students using their imagination. The topic, the Ox Which Gored, is one of the first sections taught to beginning students. The facts of the case are concrete, and it is simple, understandable, and can be easily identified with. So why not a ‘riff’ on that topic, to teach thinking to a new world, new people taught old problems, with creative methods?

The problem was how to drive creative thinking by the judicious application of experimentation to this age-old topic, the Ox which Gored, or more realistically the legal issue of someone destroying the property of another through a third agent (the ox). We wanted to be both respectful of the original topic — have it relate to the section of the Talmud, but also be creative — moving outside to explore ideas such as the behavior in the court, and whether it made any difference. To do so we enlisted the help of a student in a Jewish School (yeshiva) to ‘fill in the blanks,’ thus making the issues relevant for exploration using today’s computer technology.

We move now to a technology which explores the mind in a systematic way Mind Genomics. In its broader scope, Mind Genomics is simple, powerful, emerging science, one which makes it possible to understand how people think about the topics of the everyday. Mind Genomics is fast, simple, teaches, and encourages creative and critical thinking [14,15].

The Method Behind Mind Genomics

Let us apply Mind Genomics to the case of the ‘Ox Which Gored.’ Imagine we want to find out whether a person is to be let off, scot-free, or judged guilty and even fined in a case … a case recreated to follow a topic in the Talmud but recreated by the students based on their own thinking. We follow the steps below. By the way, they are the same steps as we follow for studying a teacher, studying a topic in business like what makes a good businessperson, what makes two countries fright with each other, and so forth.

The ideal here is to have a group of 2-4 students collaborate, a chevruta of students who will imagine, create, experiment, learn, and together build a portfolio of experiments, perhaps experiments finding their topics in the historical books, such as the Talmud, but brought up to date, and expanded using their minds and imaginations.

The specific process is templated. Figure 1 shows an example of the set-up for the study, with four panels. The BimiLeap program guides the researcher through the study, with the typical set-up taking about 15-20 minutes, after one is one familiar with the program. The BimiLeap program has been set up to guide users, step by step, in the process, without suggesting too much. The underlying world view is that with one or two attempts, the thinking will emerge from the mind of the user, not from the program

fig 1

Figure 1: The four steps for the setup of the Mind Genomics ‘experiment’ about the ‘Ox which gored’.

Step 1 – Pick the Topic

Here it is a recreation of the Mishna ‘Shor Shenagach’, the Ox Which Gored. As noted above, the topic can be anything. For this example, selecting a topic used to introduce students to the study of the Talmud seems appropriate, because we can show how old methods of learning can be updated to become fun, with an aspect of the ‘daringly new,’ not known to teachers or fellow as students, at least as of this writing.

Step 2 – Ask Four Questions Which are Relevant to the Topic, Questions Which Tell a Story

It is a bit of effort, but the exercise is a powerful, effective way to teach one how to think. It is at this point that students find the approach difficult. They are accustomed to learning facts, and even deep reasoning but we are asking to think about the topic, to structure an inquiry, i.e., to organize without any information yet to be organized.

Step 3 – For Each Question in Step 2, Give Four Answers, Each Answer a Phrase, not Just a Word

Table 1 shows us the four questions ‘which tell a story’ (the case), and the four answers to each question, the answers providing specifics. The answers (elements) are easy to create once the questions are selected.

Table 1: The four new questions and the four new answers to each question.

Question 1 – What happened?
A1 ANIMAL walked in on a tomato field and crushed it
A2 The WORKER paved a fresh road and the ANIMAL started making a real mess
A3 The ANIMAL broke the fence of the field
A4 The OWNER paved a fresh road and the ANIMAL started making a real mess
Question 2 – What got damaged?
B1  Tomatoes got ruined to a value of $200
B2  Tomatoes got ruined to a value of $600
B3  Owner must rebuild an entire fence
B4  ANIMAL broke the main water line …crops dried up and died
Question 3 – What happened in court?
C1  Everyone was screaming at each other
C2  Owner attempted to kill the ANIMAL in his anger
C3  Person with the ANIMAL showed a document allowing ANIMAL walking
C4  Everyone left… not talking to each other … but mad
Question 4 – Who was doing the judging?
D1  Judges are local farmers who know the land
D2  Judges are local magistrates (judges) who always are ‘in session’
D3  Judges are regular people … local businesspeople
D4  Judges are clergy from the religious groups in the town

Step 4 – Mix the Answers To Create A Simulated Case, Also Known as A Vignette

This is done by a computer program (BimiLeap; Big Mind Learning App). The strategy is to create 24 such simulated cases, each case comprising 2-4 answers or element in bare form, one line atop the other. The simulated cases look like a ‘blooming, buzzing confusions’ in the words of the famous psychologist, William James, who was asked to describe the sense of the world to a newborn child. Nothing could be further from the truth, however. The 24 elements are combined in a manner strictly defined, so that each element is statistically independent of every other element, allowing for high level analyses later, when the data are analyzed. Each of the 16 elements appears an equal number of times, and statistically ‘independent’ from the other 15 elements. Finally, each respondent evaluates a totally unique set of 24 combinations, combinations which follow the same structure. The difference across respondents is that the underlying design is ‘permuted’ to maintain the basic design, the combinations are different. This approach, the permuted experimental design ensures that the study covers a great number of the possible cases [16,17].

The process is summarized for this study in Figure 1, as noted above. Figure 1 comprises four panels, taken from screen shots of the study. The computer was told that this would be ‘Shor Shimmy2 BS’ (short for Shor Shenagach, the Ox Which Gored, by Shimmy), second study, with respondent instructions to be strict, the ‘law is the law.’ (viz., like School of Shammai).

Panel A shows the first step. Students pick a name for the topic. For this study the students (under a bit of guidance) chose the topic of ‘Shor Shenegach,’ the Ox Which Gored. Picking a topic is easy, although one might be surprised that without a bit of encouragement many students feel that they cannot ‘choose’ a topic. The terms BS refers to the ‘School (Bais) of Shammai’, a group of students and teachers who were notoriously strict interpreters of the law.) The instructions will tell the respondents to be ‘strict with their consideration,’ without bringing up any connections with historical situations at the time of the Talmud.

Panel B shows the four questions. Here is the hard part. The user must generate exactly four questions which tell a story, a task which seems easy at first, yet to the newcomer a task which often is daunting. We are not taught to think this structured way, to deconstruct a situation into a set of four questions which tell a story. Yet that talent, that ability, will be valuable in thinking creatively and critically. The four questions eventually emerge, often taking 10-15 minutes, as the students grapple with the topic, and try to come up with a ‘story in questions.’ Our four questions may not be the best, but they are those chosen by the students. Note: Almost everyone who starts on this process asks ‘Am I doing it right? Are these the right questions and the right answers? There is no right nor wrong questions. With practice the questions will be better, as will the answer. The group or individual doing this set-up will become more efficient with practice; each student will think better.

Panel C shows the four answers to the first question, these four answers also provided by the same students. Observations over the past several years suggest that the questions are harder to develop, whereas the answers to the questions are much easier. The answers are expressed in English, in simple phrases, with few if any subordinate phrases. Table 1 above shows the full set of questions and answers. Panel D shows an example of an introduction to the vignette, a vignette, instructions how to rate the vignette, and one of the 24 vignettes, this vignette with only three answers, one answer each from three questions. Behind the scenes the Mind Genomics program works with a recipe book, creating these combinations at presenting them.

Two procedure questions always emerge…. WHAT does the respondent see, and WHO evaluates the vignette? To answer the first question, the questions never appeared in the vignette, only the answers did. The questions are selected to motivate the answers. The WHO are people from a panel (here Luc.id, a company specializing in these studies), or the respondents can be from two groups in a single classroom, who compete.

Step 5 – Select an Orientation and a Rating Question, for this Case a Judgment of the Case

The experiment comprised two smaller cells, one cell with the respondent instructed ahead of time for each vignette to be lenient (Cell 1), and the second with the respondent instructed ahead of time for each vignette to be strict (Cell 2).

Panel D Shows the instructions to the stringent group (BS, School of Shammai) The text is You are a person who judges everything by what the law says … the law is the law…and made for everyone … no exceptions … no blind eyes… justice..that’s what’s important.

Not shown are the complementary instructions to the lenient group (BH, School of Hillel). The text to the lenient group is: You are a person who judges everything leniently…and gives everyone a fair shake…. no matter what… and turns a bit of a blind eye when needed.

The rating scale comprises five points, anchored at 1 and 5, respectively.

1= Innocent, No need to pay…. 5 = Guilty, Pays a fine for damages.

Step 6 – Invite Respondents to Participate, and Run the Study Usually Lasting 3-4 Minutes

This is easiest with a panel of respondents who are accustomed to do surveys such as this one. Fellow students can participate as well, but the results come back far more quickly with an online panel (minutes, rather than hours and days).

Each person who participates receives an email, and instructions to read the vignettes, participates in an experiment on the web lasting 3-4 minutes. The respondent spends about 4-6 seconds on each case, the above-mentioned vignettes, each comprising 2-4 statements and a rating scale.

A total of 90 respondents participated, all provided by Luc.id. 60 respondents participated with instructions to leniently (BH), 30 respondents participated with instructions to judge stringently (BS).

Making Sense of the Results

One of the recommended steps to ‘understand’ one’s data is to plot the data, in colloquial terms to ‘get dirty with the data, roll around, and get a feel of what are the results.’ In an era of rapid analysis, it is tempting to forget that, rushing immediately to the high-level analysis, without having a sense of what the data are saying at a very preliminary level. The effort of digging into the raw data is worth it, in terms of understanding, however.

Figure 2 shows two panels of the graphs each. Each graph on the top panel is a histogram, showing the number of ratings on the scale 1-5 (5 columns, one per point). Each graph on the bottom panel is a response time. The response times distribute from 0 seconds to 9 seconds. The bottom graph shows 10 columns, spaced out in a so-called logarithmic scale. That scale is used because most of the response times are shorter than 2 seconds.

fig 2

Figure 2: Distribution of ratings and responses times for the total panel and for respondents instructed to judgment leniently (BH) and for respondents instructed to judge stringently (BS).

The top set of graphs shows that the rating 3 is the mode, the most frequently selected rating. There is another observation which is lurking right below that. The first observation is that despite the instructions to judge ‘leniently,’ the respondents tend to judge the cases on the harsh side! We see this in the middle graph at the top, showing the ratings on the 5-point scale. There are many more ratings on the 5-poiont scale, the side of punishment, than there on are the side of the scale towards innocence. Our first conclusion is that it is probably easy to instruct people how to judge, viz., lenient or stringent, but hard for people to alter their way of thinking and judging, simply based on external instructions. This insight is our first, and a topic which should stimulate some discussion about ‘why is this the case?’

The second observation comes from the response times. The third graph on the right shows the distribution of response times for the respondents instructed to be stringent. The distribution of response times suggests that most of the response times are short. That is, it is easy to make judgments when the respondents are told to judge stringently. This is a normal way of looking at the case. When the respondents are instructed to judge leniently, the response times are longer, as if the respondents have to think about their judgments, because the tendency to judge leniently is not a natural one.

Going Deeper Into the Data To Really Understand The Judge’s Mind

Plotting out the distribution of ratings across the vignettes starts to give us hints about the mind of the respondent. At this point, we are at the stage where the results start to become interesting, and hopefully intrigue the student. We have learned that instructing the respondent does not necessarily work. That’s a discovery a student can ‘own,’ and talk about with others.

There is more. The experimental design mixes and matches the messages. The respondent evaluates the combinations, almost in a fashion that we might consider ‘intuitive,’ if we were generous, or better ‘indifferent’ if we were to be accurate. Indeed, the term indifferent sums up the way most people approach the typical events of their lives, the ordinary, quotidian events which become the fabric of daily life (Kahneman, 2011) [18]. Creating our mixtures of elements through the statistics of experimental design enables deconstructing the rating of these vignettes, to discover the ‘driving’ power of each element. That becomes another source of excitement to the student,

After one of these studies, it makes sense to ‘interview’ the respondents, to ask what they were thinking, what did they feel, and so forth. Most respondents in this so-called ‘exit interview’ confess, often guiltily, that they ended up these judgments without thinking, almost automatically, even though they had started the experiment with the intention of ‘doing it right, giving the right answer’. They simply could not. There was too much information. So, the respondents often admit that they simply assigned numbers at a gut level, without thinking. We soon will find patterns in the data which tell us, but more important, tell the student researchers, student experimenters, that despite what they hear, there ‘gold in them hills.’ It’s a matter of systematic analysis, a templated approach which is automated, relieving the student of the hard and grinding work.

The next step in the analysis is to deconstruct the ratings, originally on a 1-5 scale, into two new scales, both having just two points, 0 and 100, respectively. The rationale for the transformation comes from decades of experience by political pollsters and market researchers. Although the data from a 5-point scale tends to be more sensitive to differences, the frank reason is that most users of the data do not know how to interpret the results. The user of the data will readily admit that although the scale seems reasonable, the user does not need the precision to make use of the results. The user simply wants to know ‘what is good’ and ‘what is bad’ in terms of a meaningful criterion (viz., guilty or innocent, respectively, in our study.)

To make the data simpler to understand we perform two transformations on the data:

Guilty (Top2)

Ratings 1-3 transformed to 0 to denote not guilty; ratings 4-5 transformed to 100 to denote guilty, Afterwards, a small random number is added to each binary transform so that the binary rating has some modest degree of variation, necessary for the statistical analyses which follow.

Innocent (Bot2)

Ratings 1-2 transformed to 100; ratings 3-5 transformed to 0, and a small random number added to each binary transform)

Over the past century or more there has been an ongoing debate, albeit an informal one not often surfaced, that the graded scale, the 1-5 scale is probably more sensitive than a binary scale. That is true because the graded or Likert scale is more inherent granular, and more sensitive to small details. The problem with the granular scale is that people who use the results often ask about the specific meaning of each scale point, a question not easily answered. Thus, the resort to analyzing data with the the less sensitive but easier to understand scale, guilty or not guilty, innocent or not innocent, respectively. They are not opposites, since there is a middle point not counted for guilty or for innocent respectively, scale point 3.

After the transformation, which is done automatically by the BimiLeap program, ‘behind the scenes,’ the actual workhorse analysis is done, also behind the scenes. The analysis is the well-known method of OLS (ordinary least-squares) regression, also known as curve-fitting. The process fit an equation to the data, so that the equation predicts the dependent variable based upon known levels of the independent variables.

For our study there are 16 independent variables, small, structured combinations of which become the vignettes, evaluated as a single set of ideas. Regression related the presence/absence of these 16 variables, the elements, to the transformed rating (Guilty or Not Guilty; Innocent or Not Innocent, respectively.)

The independent variables take on one of two values, the value ‘0’ when the element is absent from the vignette, and the value ‘1’ when the element is present from the vignette. Most of the predictor values will be simply 0, based upon the experimental design.

Each data row, corresponding to a vignette, comprises two, three, or four values of ‘1’, and the remaining 14, 13 or 12 ‘0’s’, respectively.

In turn, the dependent variables take only on only one of two values, 0 or 100. The specific transform depends upon the rating assigned, and the rule for transformation. The data are now prepared for virtually instantaneous analysis by OLS regression, also done behind the scenes, so that the student can enjoy the experience of thinking, experimenting, and discovering in almost 0 time (viz., 1-2 hours for most effort).

The OLS regression analysis shows the coefficients from the equation, in the form of a table. Table 2 shows the model for INNOCENT, Table 3 shows the model GUILTY. The equation below says that the likelihood of a guilty verdict (Top2) is a constant and 16 weighting factors, one for each of the 16 elements, A1-D4. Note that the exact same interpretation applies to the equation relating the innocent verdict (Bot2) to the elements. The numbers in the equation, called parameters, are returned by the BimiLeap program in the form of a simple set of tables, easy to read.

Table 2: The models for ‘INNOCENT’ for total panel, School of Hillel (BH) and School of Shammai (BS), and two complementary mind-sets clustered and created using judgments of INNOCENT. Only the positive coefficients appear in the table.

table 2

Table 3: The models for ‘GUILTY’ for total panel, School of Hillel (BH) and School of Shammai (BS), and two complementary mind-sets clustered and created using the judgments for GUILTY.

table 3

Dependent Variable (Top2, Guilty) = k0 + k1(A1) + k2(A2) … k16(D4)

a. The additive constant k0, is, metaphorically the ground floor. Thus, when the regression comes back with an additive constant of 28 for the variable Top2 (guilty), we interpret that to mean that in the absence of any elements we estimate the likely proportion of ratings of 4,5 (guilty) to be 28%. The additive constant may be interpreted as a sense of basic likelihood to find the defendant guilty in the absence of facts (Top2) or find the defendant innocent in the absence of facts (Bot2). Just knowing the value of the additive constants for a group of respondents is sufficient material to ignite a discussion among the students as to how one can be instructed to pay attention just to the facts and ignore a predisposition when making judgments.

b. The additional guilt (or reduction of guilt) for each element, metaphorically the height of each part of the building beyond the ground floor. There can be negative values as well such as when the element reduces guilt. Thus, we the regression suggests a coefficient of + 6, we interpret that to mean that beyond the additive constant (viz., the 28%), we expect to see an additional 6% of the responses be 4 or 5, respectively, for a value of 24. In the interests of simplicity, Tables 2 and 3 show only positive coefficients, elements which directly drive the verdict of Innocent (Table 2), or elements which directly drive the verdict of Guilty (Table 3).

c. The students discussing the results can reconstruct arguments (combinations of elements and additive constant), which either drive a verdict of Innocent, or drive a verdict of Guilty. The output is a sum of additive constant and coefficient, a number which provides the student with a tool for deeper understanding and excitement to look for patterns in the numbers. The only caveats are that the newly constructed vignette must comprised a minimum of two elements, a maximum of four elements, and at most one element from each question. This caveat reproduces the way the original vignettes were created. The ability to ‘know’ the underlying algebra of the mind of the ‘electronic jurors’ who participated provides the student with the tools to discuss simulated cases, with new combinations of elements. The student can easily construct new combinations and estimate the percent of responses to that combination, either in terms of guilty (estimated value of Top2), or in terms of innocent (estimated value of Bot2).

Up to now we have discussed only the analysis of the ratings themselves, presumed to be under the conscious control of the respondent. The Mind Genomics process further measures the time between the appearance of the vignette and the response. This total response time is deconstructed into the contributions of the different elements. Now the student can understand ‘engagement,’ viz., the number of estimated seconds occupied by the respondent reading the element and thinking about it. Again, the objective is to understand the mind of the respondent, bringing in new ways of thinking to traditional topics, with the BimiLeap programming doing all the hard ‘grunt’ work, and emerging with a table of results ready for discussion. The analysis of response times is similar to the deconstruction of the ratings. The differences the dependent variable (response time in seconds), and the absence of an additive constant. The additive constant is presumed to be 0 for the response time model, because in the absence of a stimulus vignette there is no underlying ‘tendency’ to respond. The equation is written below, and estimated by the same approach, OLS (ordinary least-squares regression).

Dependent Variable (Response Time = k1(A1) + k2(A2) … k16(D4)

Two Different Ways to Divide Our Respondents – By What We Instruct, By How They Think

It is when we divide our respondents in different ways, and look at how they make their judgments, that we can excite many students. It is at this point, in the study of individual differences, that the student’s imagination may be further fired up.

When we set up the experiment, we divided the respondents into two groups, based upon the instructions about how to judge the case (BH – lenient; BS – stringent). These instructions are imposed from the outside. We know the instructions provided to each respondent, whether the respondent was assigned to the BH group or the respondent was assigned to the BS group.

We can divide our respondents in a new fashion, by mind-sets, different and clear ways that the respondents weigh the information to drive a rating. Experimental psychologists and market researchers, and especially those working with Mind Genomics, have found that people differ profoundly and organically in the way they think about a topic. That is, the differences seem to be built in, and not a function of who the people ARE, what they people SAY they believe, or even how the people have previously BEHAVED. These differences, called mind-genomes, are empirically discovered ways by which people differ in their judgments for specific, granular situations (Moskowitz, 2012; Moskowitz et. al., 2006) [14,15].

The mind-sets are created by considering the 16 coefficients for Top2 (guilty). Each respondent generated a separate equation, estimated once again ‘behind the scenes.’ The set of coefficients is analyzed by a program called a cluster program (Dubes and Jain, 1980) [16]. Respondents are put into two or three groups (here two groups), based upon the pattern of their 16 coefficients for Top2. We find that the people naturally divide into a limited number of groups, clearly interpretable, groups specific to the topic. A good metaphor is the set of ‘primary colors’ (red, blue, yellow) for the particular topic being studied. We then create two pairs of models, one pair based on clustering using the Bot2 coefficients (MS1 vs MS2), and the other pair based on clustering using the Top2 coefficients (MS3 vs MS4). By having the BimiLeap program do all work ‘behind the scenes,’ and by having the results returned to the student in the form of a PowerPoint to be shared, discussed, and presented, the Mind Genomics system now enjoys the further potential of exciting the student.

Looking at the Data for Innocent (Bot2), Guilty (Top2) and Engagement (Response time)

We begin with the detailed analysis, shown in Table 2 (Innocent), Table 3 (Guilty), and Table 4 (response time). Each table will be set up similarly. The elements will be on the left, the first data column will correspond to the total panel, the second and third data columns will correspond to the two sets of instructions (lenient vs stringent, respectively), the fourth and fifth data columns will correspond to the two mind-sets created using the appropriate model.

Table 4: The models for ‘response time’ (engagement with the element) for total panel, School of Hillel (BH) and School Shammai (BS), and two complementary mind-sets based upon the patterns of response times.

table 4

We look at the three sets of models, one set at a time, to identify interesting points.

Innocent: Table 2 shows the parameters for the five models.

    1. The additive constants are 31-38, meaning about one in three responses (viz., verdicts) are likely to be Innocent the absence of elements, viz., a one in three proclivity to leniency.
    2. Paradoxically, instructing a respondent to be lenient generates a person who is less lenient (BH, additive constant 31), and instructing a respondent to be stringent (BS, additive constant 38).
    3. Instructions to the respondent regarding lenience vs stringency , do not make much of a difference. The coefficients for BH (drive to leniency) are low, as are the coefficients for BS (drive to stringency)
    4. Differences emerge when one clusters the respondents based upon the pattern of the coefficients for lenience Mind-Set 1 is lenient when the defendant must make total amends, or when it appears that the legal system is stacked against him. Mind-Set 2 is lenient when the story emphasizes an ANIMAL.
    5. Overall, there are remarkably few ratings of ‘Innocent,’ even when the respondents in one of the groups (BH) are specific instructed to be lenient in their judgments.

Table 3 shows the parameters of the models for GUILTY (Top2). The two mind-sets, Mind-Set 3 and Mind-Set 4, emerged from clustering the coefficients based upon the individual models for Top2 (Guilty). The patterns emerging from Table 3 are quite different. That is, when we look at the data from the perspective of judgments of Guilty, we see different elements emerging, elements which are much stronger performers.

      1. The additive constants are 37-47, meaning about two in five responses are likely to be Guilty in the absent of elements. It appears that people are more ready to judge the defendant to be guilty, rather than innocent, in the absence of information. For the total panel, as an example, the additive constant for innocent (Table 2) is 33, whereas the additive constant for guilty (Table 3) is 44. This is a topic for the students to discuss.
      2. Instructions to the respondents regarding leniency vs stringency (BH vs BS) again show an unexpected reversal when we look at ratings of Guilty. BH, instructed to be lenient shows an additive constant for guilty of 47. BS, instructed to be stringent, shows an additive constant for guilty of 37! This is a dramatic reversal in what we expect and should lead to questions about the instructions to the jury by those in authority.
      3. The two new emergent mind-sets differ in what messages or elements drive them to assign a verdict of guilty. Mind-Set 3 votes guilty when the case talks about the actual damage. Mind-Set 4 is most stringent when the case talks about the emotional response of the litigants.
      4. Overall, there are many elements driving stringency in the ratings, and 13 elements out of a possible 80 show a significant level of guilty (viz., 5 or higher, based upon a standard error of 5 for the coefficient, from statistical tests.)

Table 4 shows the results for response time. Response times need not have anything to do with judgments of innocent or guilty, but rather measure the time to ‘process’ the information in the elements. Table 4 shows the same sets of groups, Total Panel, lenient vs stringent instruction (BH vs BS), and two newly created mind-sets based upon the pattern of response times. As noted above there is no additive constant in the model. The data from the total panel are shown for all 16 elements. For the four groups, and in the interest of readability of the results, only the response times of 1.2 seconds or longer are shown, considered to be those elements to which the respondent paid attention. These long response times are shaded as well.

        1. The data are sorted from high to low using the coefficients from the Total Panel. These response times give a sense of the range of times across the 16 elements. The longest response time is 1.2 second B2: Tomatoes got ruined to a value of $600. The other element, with almost as long a response time is: A4: The OWNER paved a fresh road and the ANIMAL started making a real mess. Both these elements deal with facts. The remaining response times are shorter, down to 0.6 seconds, D3: Judges are regular people … local businesspeople.
        2. When the respondent is instructed to be lenient (BH), the response times are substantially longer, with five of the response times being 1.2 seconds or longer.
        3. When the respondent is instructed to be stringent (BS), the response times are universally shorter. It may well be that the mind-set is to judge, and to judge means to judge stringently, not to judge leniently.
        4. Dividing the respondents by the pattern of response times means not paying attention to long response times versus short response times, but rather focusing on the similarity of patterns of response times across the 16 elements. Considered from that perspective, Mind-Set 5 shows universally short response times. Mind-Set 6 shows seven of the 16 elements driving long response times. Mind-Set 6 engages with the information, whereas Mind-Set 5 does not.

Deeper Understanding by Plotting Coefficients

We began the analysis by plotting the distribution of ratings, an exercise which gave us some idea of the differences among groups. That plot was equivalent to looking at a world from the outside, seeing ‘stuff move around in different ways.’ We could sense that there were differences, and if the mood suited us, then we could have created some hypotheses.

We learned a great deal more by creating equations, models, relating the presence/absence of the elements to either the ratings, or the response time. Our learning was enhanced because the elements convey messages. We moved close in, to understand the ‘case’ from the inside out, from the facts, and from the mind of the judges.

We now move back, to look at general patterns, this time by plotting coefficients against each other. We are looking at the outside, after having dived into the minds of the respondents. In the world of science, ‘plotting the data’ is one of the exercises inculcated into young researchers. ‘Playing with data’ may like something which is frivolous, but nothing is further from the truth. It is playing with data, plotting it, average it, testing out ideas, looking for insights, for new patterns, for something to discover, those are the behaviors which teach one how to become a scientist, and how to think both creatively and analytically.

The first plot in our ‘deeper analysis’ looks at the coefficients for BH (instructed to be lenient) vs the coefficients for BS (instructed to be stringent). The plot comprises three scatterplots, shown in Figure 3, one for each key dependent variable (innocent, guilty, response time). The left panel plots the coefficients for innocent (Bot2), the middle panel plots the coefficients for guilty (Top2), and the right panel plots the coefficients for engagement (Response Time). Each filled circle corresponds to one of the 16 elements. It is the pattern which interests us here, not the specific elements.

fig 3

Figure 3: Scattergram showing the pattern of 16 coefficients for Innocent (Bot2), Guilty (Top2) and Engagements (Response Time). The abscissa shows the data from the School of Hillel (instructed to be lenient), the ordinate shows the data from the School of Shammai (instructed to be stringent).

The two groups differ in the pattern of their coefficients for all three measures, innocent, guilty and response time. That is, the instructions given to the respondents at the start of the experiment make a difference. The same element judged on the same scale (e.g., innocent) can generate two radically different coefficients, depending upon how the respondent is instructed. The impact of instruction is even more dramatic when we look at the response times. The response times show greater element-to-element variation when the respondent is instructed to judge stringently (BS), and lesser element-to-element variation when the response in instructed to judge leniently (BH).

The second plot in our deeper analysis looks at the three pairs of complementary mind-sets, for innocent, for guilty, and response times, respectively. The clustering had generated two groups of respondents for each dependent variable, respectively, based upon the pattern of the coefficients. Presumably, the patterns should be quite different.

Figure 4 shows the mind-sets to be most different for the mind-sets generated from response times. The mind-sets created for judgments of innocent and guilty differ but are correlated. For judgments in this topic, there are not groups of individuals who think about the same problem, but in different ways. It is a matter of degree, of focus on some elements but not others, when we deal with verdicts of innocent versus guilty (left and middle panels, respectively.) Once again, the discoveries here should excite the student to discuss the ‘what’ (what has been discovered), the ‘why’ (why would this be the case), and the ‘next’ (what would be a good legal case to discover more radically different mind-sets.)

fig 4

Figure 4: Scattergram showing the pattern of 16 coefficients for pairs of complementary mind-sets, based upon coefficients for judgments of innocent (Bot2), for judgments of guilty (Top3), and for measured engagement (response time), respectively.

Going Forward – What does this Mean for the Critical Thinking for Students

There are different ways to study critical topics, such as the law. The tools just presented show that one can take old texts, old problems, topics that often were studied without joy and enthusiasm, and transform them to topics relevant to today. The objective is not only to excite students who study the Talmud, generally limited to a small cadre of younger people of Jewish faith, but rather to use the topics on which they are trained, bringing those topics into the modern-day world as exemplars. In this 21st century of the common era, the notion of studying to develop creative and critical thinking requires moving beyond the simple, limited, strictures of remembering, reciting, and answering questions. The objective is to think, to explore, to create, and to add to the body of knowledge. All this from students age 10 and above! The approach of Mind Genomics may provide just so a new direction, consistent with ethical and religious values held by the student but brought to life by the spirit of the times, the Zeitgeist of this new day.

References

      1. Almog T, Almog O (2019) Academia: All the Lies.
      2. Dolgopolski S (2013) The Open past: Subjectivity and remembering in the talmud: fordham Univ Press.
      3. Guzmen-Carmeli S (2020) Texts as Places, Texts as mirrors: Anthropology of judaisms and jewish textuality. Contemporary Jewry 1-22.
      4. Alexander ES (2009) Why study talmud in the twenty‐first century?: The relevance of the ancient jewish text to our world 11-24.
      5. Block AA (2004) Talmud, curriculum, and the practical: Joseph schwab and the rabbis (Vol. 2). Peter Lang.
      6. Shulman LS (2008) Pedagogies of interpretation, argumentation, and formation: From understanding to identity in Jewish education. Journal of Jewish Education 74: 5-15.
      7. Hilty EB (2018) The professionally challenged teacher: Teachers talk about school failure. In Thinking about Schools. Routledge.
      8. McLaughlan R, Lodge JM (2019) Facilitating epistemic fluency through design thinking: A strategy for the broader application of studio pedagogy within higher education. Teaching in Higher Education, 24: 81-97.
      9. Pithers RT, Soden R (2000) Critical thinking in education: A review. Educational research, 42: 237-249.
      10. Ingall CK (2003) Cooperative or collaborative learning. The ultimate Jewish teacher’s handbook, pp: 351-362.
      11. Yadin A (2003) The hammer on the rock: polysemy and the school of Rabbi Ishmael. Jewish Studies Quarterly 10: 1-17.
      12. Lehman, Kanarek J (2011) Talmud: Making a case for Talmud pedagogy-the Talmud as an educational model. In International Handbook of Jewish Education (pp. 581-596). Springer, Dordrecht.
      13. Moskowitz HR, Gofman A (2007) Selling blue elephants: How to make great products that people want before they even know they want them. Pearson Education.
      14. Moskowitz HR (2012) ‘Mind genomics’: The experimental, inductive science of the ordinary, and its application to aspects of food and feeding. Physiology & behavior, 107: 606-613. [crossref]
      15. Moskowitz HR, Gofman A, Beckley J, Ashman H (2006) Founding a new science: Mind genomics. Journal of sensory studies, 21: 266-307.
      16. Dubes R, Jain AK (1980) Clustering methodologies in exploratory data analysis. In Advances in computers 19: 113-228. Elsevier.
      17. Gofman A, Moskowitz H (2010) Isomorphic permuted experimental designs and their application in conjoint analysis. Journal of Sensory Studies 25: 127-145.
      18. Kahneman D (2011) Thinking, fast and slow. Macmillan.

Low-Dose 17β-Estradiol Supplemented with Andrographis Paniculata Improved Glucose and Lipid Homeostasis in a Type-2 Diabesity Mice Model

DOI: 10.31038/EDMJ.2020453

Abstract

Estrogens play an important role in metabolic homeostasis. However, its risk of uncogenecity and cardiovascular adverse effects underscores its therapeutic benefits. This study investigated the metabolic effect of low dose estrogen supplemented with Andrographis paniculata on type-2 diabesity mice model. The experimental animals maintained on high fat diet were induced diabetes with streptozotocin (100 mg/kg) after intraperitoneal injection of 50 mg/kg nicotinamide. Low dose estrogen (0.02 mg/kg) was administered alone as well as in combination with 50, 150 and 500 mg/kg of the ethanol extract of A. paniculata. These doses of the extract, vehicle (5 ml/kg distilled water) and two reference standards-pioglitazone (30 mg/kg) and metformine (100 mg/kg) were used as controls. Oral glucose tolerant test was used to determine the effect of treatment on pancreatic β-cell function and insulin sensitivity following oral glucose load of 2 g/kg. Lipid profile tests and blood glucose measurements were used to evaluate effect of treatment on lipid homeostasis and chronic diabetes respectively. Combination of low dose estradiol with 150 and 500 mg/kg of the extract showed significant (P<0.05) reduction in blood glucose when compared to their individual monotherapeutic effects. Co-administration of the extract with estradiol at all doses of the extract produced significant (P<0.05) improvement in oral glucose tolerance as depicted by smaller AUC when compared to either the extract or estradiol alone. Low dose estradiol was unable to significantly improve diabesity associated lipid profile abnormalities. However, combination of both low doses of the extract (50 mg/kg) and estradiol showed significant (P<0.05) reduction in serum TG and LDL-cholesterol as well as significant (P<0.05) increase in HDL compared to vehicle control group. These findings established that augmentation of low-dose estrogen with A. paniculata resulted in the improvement of glucose and lipid homeostasis in a type-2 diabesity mice model compared to their individual effects. The low-dose estrogen augmentation is expected to reduce the side effects of estrogen monotherapy while at the same time exploiting its metabolic potentials in glucose and lipid homeostasis.

Keywords

Estrogen, Metabolism, Angrographis paniculata, Diabesity

Background

Diabesity is a term describing diabetes in the context of obesity and sometimes referred to as obesity-dependent diabetes [1]. It is the continuum of progressive abnormal biology, which ranges from mild insulin resistance to full-blown type-2 diabetes [2]. Obesity-dependent diabetes has been recognized as a major public health challenge that is evolving to become an epidemic [3]. According to the report by Zambard et al. [4], diabesity and cardiovascular disease share many common risk factors including central obesity, hyperinsulinaemia, hyperglycaemia, elevated blood pressure and dyslipidaemia.

Beyond the well-recognised role of estrogen in the reproductive system, estrogens are important participants in metabolic regulation [5]. A strong correlation between estrogen deficiency and metabolic dysfunction has also been established [6]. This is consistent with studies demonstrating accelerated development of insulin resistance and type-2 diabetes in postmenopausal women with reduced estrogen production [7]. Estrogen therapy due to its risk of oncogenecity underscores its therapeutic benefits in the maintenance of glucose and lipid homeostasis [8]. This potential risk factor can be averted by maintaining a low-dose estrogen therapy with possible augmentation of its therapeutic benefits by combining it with other bioactive compounds. This approach may provide superior benefits in glucose and lipid metabolism while at the same time keeping the risk of estrogen therapy in check.

The plant Andrographis paniculata (Family Acanthaceae) is one of the most popular medicinal plants used traditionally for the treatment of array of diseases including diabetes [9]. In more recent studies, compounds isolated from the alcoholic extract of the plant showed great potential to ameliorate diabetic nephropathy in MES-13 cells [10], while the ethanol extract significantly reduced blood glucose level in streptozotocin-induced hyperglycaemic rats [11]. Given the acclaimed blood glucose-lowering potentials of this plant, little or nothing has been documented about its effectiveness in diabesity presenting classical features of insulin resistance with consequent hyperglycaemia and hyperlipidaemia. Also the metabolic potential of low-dose 17β-estradiol (E2) suggested to reduce hepatic glucose output compromised in insulin resistance has not been fully exploited especially when combined with medicinal plants.

It is to this end that this study was set to investigate the contributions of low-dose 17β-estradiol (E2) augmentation on glucose and lipid homeostasis in male type-2 diabesity mice model treated with Andrographis paniculata.

Materials and Methods

Plant Collection and Extraction

The aerial part (leaves, seeds and stem) of A. paniculata was collected from the botanical garden of the Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Agulu. The plant was air dried at room temperature and pulverized into coarse powder. The powdered plant (200 g) was macerated in 2 L of ethanol for 72 h with intermittent shaking, filtered and concentrated using rotary evaporator at 50°C. The resulting extract was stored at 0 – 4°C in the refrigerator till further use.

The percentage yield of the extract was calculated using the following formula:

FORMULA

Animals

Swiss male Albino mice (25-30 g) were used for this study. The animals were obtained from the Animal House of the Department of Pharmacology/Toxicology, Nnamdi Azikiwe University, Awka. The animals were housed in standard laboratory condition. All animal studies were performed in accordance with NIH guidelines outlined in the Guide for the Care and Use of Laboratory Animals, as described in protocols reviewed and approved by the NnamdiAzikiwe University institutional Animal Care and Use Committee.

Phytochemical Analysis

The extract was subjected to qualitative determination of alkaloids, saponins, tannins, flavonoids, terpenoids and cardiac glycosides as well as quantitative determination of terpenoids, saponins, flanonoids and tannins were using standard procedures described by Odoh et al. [12].

Acute Toxicity Study

Acute toxicity analysis of the extracts was performed using Lorke’s method as described by Agyigra et al. [13]. This first phase comprised of nine mice randomized into three groups of three mice each. Each group of animals was administered different doses (10, 100 and 1000 mg/kg) of the extracts. The mice were observed thereafter for 24 hours for signs of toxicity as well as mortality. The second phase was made up of four groups of one mouse each. Based on result of the first phase, they were administered 2000, 3000, 4000 and 5000 mg/kg of the extract respectively. Observations for toxicity and death were also done for 24 h post administration.

Formulation of High Fat Feed

High fat feed was formulated as described by Mbagwu et al. [14]. The diet was composed of 45% fat, 35% carbohydrate and 20% protein having total caloric energy value of 4057 Kcal/kg (Animal Care Feeds, Asaba, Nigeria) against normal mice diet that was found to composed of 10% fat , 70% carbohydrate and 20% crude protein with the same total caloric energy value of 4057 Kcal/kg(Animal Care feeds, Asaba, Nigeria).

Effect of the Extract on High-Fat Diet Streptozotocin-Nicotinamide-induced Type 2 Diabetic Mice

A total of 100 mice were used for this study. The animals were maintained on high fat diet with free access to water ad libitum for 4 weeks. Prior to induction of diabetes, 50 mg/kg of nicotinamide was injected intraperotoneally to provide partial protection of the beta cells from complete pancreatectomy. Thereafter, streptozotocin (100 mg/kg) was administered intraperitoneally within an interval of 15 min as described by Tahara et al. [15]. After 5 days, animals were assessed for successful induction of diabetes (fasting blood glucose >160 mg/dl). The diabetic animals were divided into 10 groups of 10 animals with mean blood glucose of 232 ± 2 mg/dl per group. The grouping was as described below:

Group 1: 5 ml/kg distilled water

Group 2: 0.02 mg/kg of estrogen

Group 3: 50 mg/kg extract + 0.02 mg/kg estradiol

Group 4: 150 mg/kg extract + 0.02 mg/kg estradiol

Group 5: 500 mg/kg extract + 0.02 mg/kg estradiol

Group 6: 50 mg/kg extract

Group 7: 150 mg/kg extract

Group 8: 500 mg/kg extract

Group 9: 30 mg/kg pioglitazone

Group 10: 100 mg/kg metformin.

In each group, 5 animals were used to monitor effect of treatment on lipid metabolism while the other half was used to monitor effect of treatment on glycermic control. Treatment lasted for 4 weeks while the animals were still maintained on high fat diet.

Effect of Treatment on Chronic Diabetes

Blood samples were drawn from tail vain of the diabetes animals in all the groups for the determination of pre-treatment fasting blood glucose concentration using One Touch Glucometer (Lifeshield, Johnson & Johnson, California). After 4 weeks treatment, blood samples were obtained again from the animals for the determination of post-treatment fasting blood glucose concentration.

Effect of Treatment on Oral Glucose Tolerance Test (OGTT)

Prior to the test, the animals were fasted overnight and fasting blood glucose determined. The mice were given 2 g/kg oral glucose solution. At 15, 30, 45, 60, and 120 min after the administration of glucose, blood samples were collected by tail milking and the glucose concentration estimated. The Area under the curve (AUC) of the plot of blood glucose against time was used to determine the oral glucose tolerance.

Effect on Lipid Parameters

Lipid parameters (total cholesterol, triglyceride, LDL-Cholesterol, and HDL-Cholesterol) were assayed using standard serum lipid assay kits (Randox). The procedure was followed as prescribed by the manufacturer.

Statistical Analyses

Statistical analyses was done using SPSS software (version 18). The data obtained was expressed as mean ± SEM, analysed by Kruskal-Wallis ANOVA test. The differences between various groups were determined by multiple comparisons of mean ranks for all groups. In all cases, a probability error of less than 0.05 was selected as the criterion for statistical significance.

Result

Yield and Phytochemical Content

The concentration extract weighed 10.4 g and the yield was calculated to be 5.2%. Qualitative phytochemical analysis showed positive test for all the phytocompounds tested. Further quantitative analysis showed that terpenoids, saponins, flavonoids and tannins were 30.8, 11.8, 8.6 and 6.9% respectively.

Acute Toxicity Study

Administration of the extract at 10 – 5000 mg/kg did not produce mortality or obvious signs of toxicity throughout the period of observation. Reduction in physical activities and eating were however observed after drug administration but normalized 30 minutes post administration.

Effect of Supplementation of Low Dose Estradiol with A. paniculata on Chronic Diabetes

Result of the pre-treatment blood glucose concentration showed no significant (P>0.05) differences across groups. However, after 4 weeks treatment, significant (P<0.05) reductions in blood glucose were recorded across the treatment groups when compared with vehicle control post-treatment value (Figure 1). Compared with individual group pre-treatment values, low doses of the extract (50 mg/kg) and estradiol (0.02 mg/kg) as monotherapy showed significantly (P<0.05) increased blood glucose concentration just like the vehicle control group. However, this significant increase was offset when these low doses were given as combination therapy. Combination of low dose estradiol with 500 mg/kg of the extract produced significant (P<0.05) reduction in blood glucose just like the reference standards pioglitazone (30 mg/kg) and metformine (100 mg/kg) when compared with their pre-treatment diabetic values. Also combination of low dose estradiol with 150 and 500 mg/kg of the extract showed significant (P<0.05) reduction in blood glucose when compared to their individual monotherapeutic effects.

fig 1

Figure 1: Pre-treatment and post-treatment blood glucose concentration.
*P<0.05 compared to pre-treatment; #P<0.05 compared to post-treatment 5 ml/kg distilled water (vehicle control); a = P<0.05 compared to extract/estradiol alone post-treatment; b = P<0.05 compared to pilocarpine/metformine post-treatment.

Effect of the Supplementation of Low Dose Estrogen with A. paniculata on Oral Glucose Tolerance

The plasma glucose levels of the diabetic animals in each group peaked at 15 minutes post oral glucose load (Figure 2). However, animals treated with the extract and estradiol either alone or in combination produced lower blood glucose peak level in comparison to the vehicle control group (5 ml/kg distilled water). Oral glucose tolerance of the treated animals showed significant (P<0.05) improvement when compared to the vehicle control group (Figure 3). Co-administration of the extract with estradiol at all doses of the extract produced significant (P<0.05) improvement in oral glucose tolerance as depicted by smaller AUC when compared to either the extract or estradiol alone. The combination effect of the extract was dose dependent and at 150 and 500 mg/kg produced better oral glucose tolerant effect than low dose estradiol (0.02 mg/kg). However, the combination of the least dose (50 mg/kg) of the extract with estradiol produced similar effect as the highest dose of the extract (500 mg/kg). Combination effect of 150 mg/kg extract and estradiol was similar to the reference standard pioglitazone (30 mg/kg) as depicted by non-significant difference (P>0.05) in their AUC while at 500 mg/kg of the extract, the combination effect was significantly (P<0.05) better than pioglitazone.

fig 2

Figure 2: Plasma glucose concentration curve for 2 h oral glucose tolerance test.

fig 3

Figure 3: Area under the curve of oral glucose tolerant test.
*P<0.05 compared to 5 ml/kg distilled water (vehicle control); the alphabets a – e represents improved glucose tolerance in increasing order. Bars with different alphabets in each category indicates significant (P<0.05) difference.

Effect of the Supplementation of Low Dose Estrogen with A. paniculata on Lipid Profile

From Figure 4, it was evident that low dose estradiol was unable to significantly improve diabesity associated lipid profile abnormalities. Similarly, low dose of A. paniculata extract (50 mg/kg) among other lipid parameters showed significant (P<0.05) reduction only in serum triglyceride (TG). Combination of both low doses of the extract and estradiol showed significant (P<0.05) reduction in serum TG and LDL-cholesterol as well as significant (P<0.05) increase in HDL compared to vehicle control group. Compared with low dose estradiol, combinations with the extract at 150 and 500 mg/kg produced significant (P<0.05) reduction in serum TG, LDL and increased HDL while combination with 50 mg/kg of the extract only showed significant (P<0.05) difference on serum TG and LDL. Compared with the extract monotherapy, combination of estradiol with the extract at all the tested doses showed improvement in lipid profile with significant (P<0.05) reduction and increase recorded for LDL and HDL respectively. Combination of estradiol with the extract at 50 mg/kg produced similar effect on TG, LDL and HDL when compared to the reference standard pioglitazone (30 mg/kg). The monotherapeutic effects of low doses of the extract (50 mg/kg) and estradiol (0.2 mg/kg) on TG, LDL and HDL are significantly (P<0.05) lower than the reference standard pioglitazone. However, similar effects like pioglitazone were recorded on these lipid parameters when both treatments were given as combination therapy. Combination of low dose estradiol with 500 mg/kg of the extract produced significant (P<0.05) reduction in LDL and increase in HDL when compared to the reference standard metformine (100 mg/kg).

fig 4

Figure 4: Effect of treatment on lipid profile.
D. water = distilled water, E2 = extradiol, A.P = A. paniculata extract, HDL = High Density Lipoprotein; * = P<0.05 compared to 5 ml/kg distilled water (vehicle control); #P<0.05 compared to estradiol (0.2 mg/kg); b = P<0.05 compared to 30 mg/kg pioglitazone; c = P<0.05 compared to 100 mg/kg metformine; d = P<0.05 compared to extract alone.

Discussion

One promising but yet poorly explored aspects of the regulation of glucose and lipid homeostasis is the use of estrogen. There is increasing evidence both in humans and rodents linking estrogen to the maintenance of glucose and lipid homeostasis [16]. Estrogen deficiency clearly predisposes males to increased adiposity and metabolic dysregulation [17]. In apparent contrast, however, obesity in men has been associated with hyperestrogenemia, and further excessive estradiol exposure has been postulated to play an exacerbating role in the progression of obesity and attendant metabolic dysregulation [18]. This study was designed to investigate the effect of low-dose 17β-estradiol supplemented with Andrographis paniculata on glucose and lipid homeostasis in a type-2 diabesity mice model.

High-fat diet-fed/STZ-NAD induced type 2 diabetes rats are a well-documented model of obesity-induced diabetes used for the screening antidiabetic agents. STZ preferentially accumulates in the β-cells via GLUT2 glucose transporter and induces the DNA strand breakage in β-cells causing a decrease in endogenous insulin release [19]. Many studies have reported that a long-term high-fat diet leads to insulin resistance and hyperinsulinaemia [20,21]. Intraperitonial administration of nicotinamide provides partial protection of the beta cells from complete STZ induced chemical pancreatectomy [14]. In other words, the high-fat diet combined with STZ-NAD induced diabetic rats have the characteristics of later-stage T2DM including hyperglycaemia, moderate impairment of insulin secretion, abnormalities in lipid metabolism, destruction of islet cells and reduced glycogen synthesis [22].

Dyslipidemia is a common abnormality associated with HFD consumption. Accumulation of excess fatty acid from lipid metabolism in non-adipose tissues (liver, pancreas and muscle) is a predominat feature of metabolic diseases like obesity and diabetes [23]. Subsequent metabolism of these fatty acids leads to decreased insulin-stimulated glucose uptake in skeletal muscle, unsuppressed hepatic glucose production and altered glucose-stimulated insulin release from B-cells [24]. Hyperglycermia resulting from these dysregulations in addition to FFA combine to generate major oxidative stress in tissues, further aggravating insulin resistance and deficiency. Estrogen modulates lipid concentration in plasma by regulating lipogenesis in adepocytes and hepatocytes [25]. The reduction in LDL and cholesterol level by low dose estrogen administration was probably as a result of estrogen induced accelerated conversion of hepatic cholesterol to bile acids and increased expression of LDL receptors on cell surfaces, resulting in augmented clearance of cholesterol and LDL from the plasma [26]. Other documented beneficial roles of estrogen on lipid metabolism include increase in lipoprotein lipase expression, increased fat oxidation and the regulation of acetyl-CoA oxidase as well as uncoupling proteins (UCP2-UCP3), which enhances fatty acid uptake without lipid accumulation [6].

A paniculata has also hyperlipidemia-lowering effect profile. One of its active compounds – Andrographolide has been reported to reduce serum cholesterol, triglycerides and LDL-cholesterol in hypercholesterolaemic patients and high-fat diet animals [27-28]. The combined effects of estrogen and A. paniculata on the same lipid homeostatic targets and separately on different regulatory targets may account for the improved lipid lowering effect of low estrogen supplemented with A. paniculata compared to effects recorded when they were administered separately.

Among the series of indices for testing β-cell function and insulin sensitivity, oral glucose tolerance test (OGTT) has emerged as a simple method that provides a reasonable approximation of whole-body insulin sensitivity [29]. The index of insulin sensitivity obtained from the oral glucose tolerance test has also been documented to be applicable to advanced type-2 diabetes [30]. Based on these documented evidences, we chose OGTT as our index for estimating insulin secretion and insulin sensitivity.

E2 regulates insulin action directly via actions on insulin-sensitive tissues or indirectly by regulating factors like oxidative stress which contributes to insulin resistance. In skeletal muscles, E2 via ERα have positive effect on insulin signalling and GLUT4 expression [31]. E2 also suppresses oxidative stress via both non genomic and genomic actions, by activating pathways that prevent generation of reactive oxygen species and increasing efficient scavenging of ROS [32]. The enhanced tolerance to oral glucose load by low-dose estrogen administration may have resulted from estrogen-mediated increase in sensitivity of skeletal muscle to insulin-stimulated glucose uptake. This enhanced response can account for improvement of the diabetic state in the partially pancreatectomized animals since small amount of endogenously insulin are likely to be secreted by the pancreatic remnant. Although estrogen is not an insulin secretagogue, it has however been reported to induces pancreatic beta-cell proliferation which may represent additional mechanism of improved glucose tolerance in this diabesity model [33].

Phytocompounds of A. Paniculata has been found to induce the mRNA and protein levels of GLUT4 – increasing glucose uptake in a time- and dose-dependent manner [34]. They also increase insulin secretion, acting as insulin secretagogue, as well as preventing loss of β-cells and/or their dysfunction through inhibition of ROS production and cytokine-stimulated NF-KB activation which are part of the mechanisms through which HFD and STZ damage the β-cells [35]. Supplementing estradiol with A. paniculata may have contributed to increase in insulin secretion with complementary stimulation of more insulin-mediated glucose uptake.

Conclusion

Augmentation of low-dose estrogen with A. paniculata resulted in the improvement of glucose and lipid homeostasis in a type-2 diabesity mice model compared to their individual effects. The low-dose estrogen augmentation is expected to reduce the side effects of estrogen monotherapy while at the same time exploiting its metabolic potentials in glucose and lipid homeostasis. A. paniculata augmentation with low-dose estrogen elicited a better control of glucose and lipid parameters associated with diabesity.

References

  1. Servan PR (2013) Obesity and diabetes. Nutr Hosp 28(5): 138-143. [crossref]
  2. Haslam D (2010) Obesity and diabetes: the links and common approaches. Prim Care Diabetes 4(2): 105-12. [crossref]
  3. Tabish SA. Is Diabetes Becoming the Biggest Epidemic of the Twenty-first Century? Int J Health Sci (Qassim) 1(2): 5-8. [crossref]
  4. Zambard SP, Tuli D, Mathur A, Ghalsasi SA, Chaudhary A, Deshpande SS, Gupta RC, Chauthaiwale V, Dutt C, et al. (2013) TRC210258, a novel TGR5-agonist, reduces glycaemic and dyslipidaemic cardiovascular risk in animal models of diabesity. Diabetes Metab Syndr Obes 7: 1-14. [crossref]
  5. Gupte AA, Pownall HJ, Hamilton DJ (2015) Estrogen: an emerging regulator of insulin action and mitochondrial function. J Diab Res 7: 1-9. [crossref]
  6. Lizcano F, Guzmán G (2014) Estrogen Deficiency and the Origin of Obesity during Menopause. Biomed Res Int 2014: 757461. [crossref]
  7. Louet J-F, LeMay C, Mauvais-Jarvis F (2004) Antidiabetic actions of estrogen: insight from human and genetic mouse models. Curr Atheroscler Rep 6: 180-185. [crossref]
  8. Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, et al. (2002) Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the women’s health initiative randomized controlled trial, J Am Med Ass 288(3): 321-333. [crossref]
  9. Okhuarobo A, Falodun JE, Erharuyi O, Imieje V, Falodun A, Langer P (2014) Harnessing the medicinal properties of Andrographis paniculata for diseases and beyond: a review of its phytochemistry and pharmacology. Asian Pac J Trop Dis 4(3): 213-22.[crossref]
  10. Lee MJ, Rao YK, Chen K, Lee YC, Chung YS, Tzeng YM (2010) Andrographolide and 14-deoxy-11,12-didehydroandrographolide from Andrographis paniculata attenuate high glucose-induced fibrosis and apoptosis in murine renal mesangeal cell lines. J Ethnopharmacol 132: 497-505. [crossref]
  11. Zhang XF, Tan BK (2000) Anti-diabetic property of ethanolic extract of Andrographis paniculata in streptozotocin-diabetic rats. Acta Pharmacol Sin 21(12): 1157-64. [crossref]
  12. Odoh UE, Obi PE, Ezea CC, Anwuchaepe AU (2019) Phytochemical methods in plant analysis. Pascal communications, Nsukka, Enugu State. 46. [crossref]
  13. Agyigra I, Ejiofor J, Magaji M (2017) Acute and subchronic toxicity evaluation of methanol stem-bark extract of Ximenia americana Linn (Olacaceae) in Wistar rats. Bulletin of Faculty of Pharmacy Cairo University,55(2): 263-267.
  14. Mbagwu IS, Akah PA, Ajaghaku DL (2020) Newbouldia laevis improved glucose and fat homeostasis in a type-2 diabesity mice model. J Ethnopharmacol 251: 112555. [crossref]
  15. Tahara A, Mastuyama-Yokono A, Shibasaki M (2011) Effects of antidiabetic drugs in high-fat diet and streptozotoain-nicotinamide-indiced type 2 diabetic mice. Eur J Pharmacol 655(1-3): 108-116. [crossref]
  16. Mauvais-Jarvis F, Clegg DJ, Hevener AL (2013) The role of estrogens in control of energy balance and glucose homeostasis. Endocr Rev 34(3): 309-38. [crossref]
  17. Rubinow KB (2017) Estrogens and Body Weight Regulation in Men. Adv Exp Med Biol 1043: 285-313. [crossref]
  18. Mauvais-Jarvis F (2017) Sex and gender factors affecting metabolic homeostasis, diabetes and obesity, vol 1043. Cham: Springer International Publishing; https://link.springer.com/book/10.1007/978-3-319-70178-3.
  19. Lenzen S (2008) The mechanisms of alloxan- and streptozotocin-induced diabetes. Diabetologia 51(2): 216-26. [crossref]
  20. Chalkley SM, Hettiarachchi M, Chisholm DJ, Kraegen EW (2002) Long-term high-fat feeding leads to severe insulin resistance but not diabetes in Wistar rats. Am J Physiol Endocrinol Metab 282(6):E1231-8. [crossref]
  21. Feng X, Scott A, Wang Y, Wang L, Zhao Y, Doerner S, Satake M, Croniger CM, Wang Z (2014) PTPRT regulates high-fat diet-induced obesity and insulin resistance. PLoS One 20;9(6):e100783. [crossref]
  22. Whitton PD, Hems DA (1975) Glycogen synthesis in the perfused liver of streptozotocin-diabetic rats. Biochem J 150: 153-165. [crossref]
  23. Castro AVB, Kolka CM, Kim SP, Bergman RN (2014) Obesity, insulin resistance and comorbidities ? Mechanisms of association. Arq Bras Endocrinol 58( 6 ): 600-609. [crossref]
  24. Honka MJ, Latva-Rasku A, Bucci M, Virtanen KA, Hannukainen JC, Kalliokoski KK, Nuutila P, et al. (2018) Insulin-stimulated glucose uptake in skeletal muscle, adipose tissue and liver: a positron emission tomography study. Eur J Endocrinol 178(5): 523-531. [crossref]
  25. Kim JH, Cho HT, Kim YJ (2014) The role of estrogen in adipose tissue metabolism: Insights into glucose homeostasis regulation. Endocr J 61: 1055-1067. [crossref]
  26. Guetta V, Cannon RO (1996) Cardiovascular effects of estrogen and lipid-lowering therapies in postmenopausal women. Circulation 93(10): 1928-1937. [crossref]
  27. Reyes B, Bautista N, Tanquilut N, Anunciado R, Leung A, Sanchez G, et al. (2006) Anti-diabetic potentials of Momordica charantia and Andrographis paniculata and their effects on estrous cyclicity of alloxan-induced diabetic rats. J Ethnopharmacol 105: 196-200.
  28. Nugroho AE, Lindawati NY, Herlyanti K, Widyastuti L, Pramono S (2013) Anti-diabetic effect of a combination of andrographolide-enriched extract of Andrographis paniculata (Burm f.) Nees and asiaticoside-enriched extract of Centella asiatica in high fructose-fat fed rats. Indian J Exp Biol 51: 1101-1108. [crossref]
  29. Matsuda M, DeFronzo RA (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22(9): 1462-70. doi: 10.2337/diacare.22.9.1462. PMID: 10480510. [crossref]
  30. Kanauchi M (2002) A new index of insulin sensitivity obtained from the oral glucose tolerance test applicable to advanced type 2 diabetes. Diabetes Care 25(10): 1891-1892. [crossref]
  31. Mancini A, Raimondo S, Persano M, Di Segni C, Cammarano M, Gadotti G, Silvestrini A, Pontecorvi A, Meucci E (2013) Estrogens as antioxidant modulators in human fertility. Int J Endocrinol 2013: 607939. [crossref]
  32. Wu T, Xu J, Xu S, Wu L, Zhu Y, Li G, Ren Z (2017) 17β-Estradiol Promotes Islet Cell Proliferation in a Partial Pancreatectomy Mouse Model. J Endocr Soc 5;1(7): 965-979. [crossref]
  33. Yu B, Hung C, Chen W, Cheng J (2014) Antihyperglycemic effect of Andrographolide in streptozotocin-induced diabetic rats. Planta Med 69: 1075-1079. [crossref]
  34. Li Y, Yan H, Zhang Z, Zhang G, Sun Y, Yu P, Wang Y, Xu L (2015) Andrographolide derivative AL-1 improves insulin resistance through down-regulation of NF-κB signalling pathway. Br J Pharmacol 172(12): 3151-8. [crossref]
  35. Barros RP, Machado UF, Warner M, Gustafsson JA (2006) Muscle GLUT4 regulation by estrogen receptors ERbeta and ERalpha. Proc Natl Acad Sci U S A 103(5): 1605-8. [crossref]