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Abstract

Anger is one of the possible reactions to cancer. There are mutual influences between cancer and psychological status, with repercussions on the immune system. The aim of this study was to analyze differences in the experience, expression and control of anger by gender and to measure the relationship between anger, anxiety, depression, quality of life, and progression of the disease. We have conducted a cross-sectional study assessing 281 cancer patients, using the STAXI-2, HADS and a visual analogue scale to measure Quality of Life. Results: Females reported significantly higher State and Trait Anger scores and lower Anger Control Out scores than males. In the whole sample the anger subscale scores increased with levels of anxiety or depression. In males, high State, Trait and Expression Anger subscale scores resulted associated with low Quality of Life, among females, this relationship seems to be weak. No differences emerged on STAXI-2 scales and subscales between patients in progression of disease. Conclusions: Anxiety, depression and anger seem to be organized into a pattern of a general emotional reaction. Since immunotherapy is the anticancer treatment that increases the body’s natural defenses to fight disease, a balanced immune system has become the main concern. In conclusion, clinicians could gain important insights about their patients by looking at the result of validated self-report patient questionnaires, to identify patients with inadequate expression of emotion or too high levels of emotional reaction in order to improve quality of care and response to treatment.

Keywords

anger scale, cancer management, depression assessment, immunological characteristics quality of life

Introduction

Anger is an emotion that in these present times is making itself noticed in many contexts and in everyday life. It is the reaction to the frustration that people are experiencing due to the poor prospects of economic development, the lack of work, for young and old, etc. In oncology, clinicians are led to think that anger is the emotional reaction that characterizes the moments behind the diagnosis to then give way to emotional experiences of anxiety and then later depression. For a long time it was hypothesized that there could be a sequence of psychological reactions conditioned by the distance from the diagnosis. Today the experience and manifestations of anger must be studied in a more specific way, especially in light of the evidence that has shown a sex-specific differences in activity of emotion processing regions [1,2]. In medicine we know that sex hormones influence the onset and severity of various immuno-modulate disease [3,4], and the role of the psychological state on the disease and vice versa is known. Different diseases could be influenced by the psychological status and gender, just as diagnoses influence the psychological status of the patient, perhaps differently in the two sexes. If on the one hand the knowledge on prevalence of anxiety and depression is now consolidated [5], on the other, knowledge about the differences between the sexes in anger is still limited. Common sense leads us to think that the expression of anger outward is more frequent among males, although Aghaei [6] has found very similar anger scores between males and females in a sample of cancer patients admitted to the hospital for surgery. There is a need to study in depth the complex relationships between sex, psychological distress and disease, especially today that the use of immunotherapy treatments makes the picture more complicated. Scientific literature has proposed to distinguish among different components of anger. There are differences between expressing or inhibiting anger, between controlling it by adopting relaxation methods or rather strategies to shift attention towards other stimuli. Studies that investigated the aspects of suppression, repression, or restraint of anger [7] found that the suppressed anger was associated with health risk factors and early mortality for all-cause, included cardiovascular and cancer mortality, in women [6,8]. The inhibition of negative emotions plays an important role in how cancer patients adjust to the disease [9,10]. Low levels of anger were associated with maladaptive coping strategies to cancer [3,4]. Differently other researchers have found that a reaction of anger expression may be an important factor in the fight against cancer [11]. However, patients with high levels of intensity of anger should be evaluated carefully to determine whether the risk of acting out their anger represents a potential danger to themselves and others. Differently, moderate anger experiences may guide problem-solving behaviour and people may be more assertive than aggressive.

Anyways anger is a human emotion that can vary in component, intensity, expression and control [12] and each pattern can have different meaning and relationship with other emotional feeling and behavioural experiences for people. To study the multifaceted construct of anger the State-Trait Anger Expression Inventory-2TM [13,14] has been developed and validated. The questionnaire has been developed to recognizes 11 components of experience, expression and control of anger. The State Anger refers to the intensity of the individual’s angry feelings at a specified time or at the time of testing. The State Anger splits into 3 domains: Feeling Angry, to Feel like Expressing Anger Verbally and Feel like Expressing Anger Physically. The first is related with annoyance, irritation, anger or fury. The second, with the desire to express anger by swearing, cursing, yelling or screaming. The third, reflects the inclination to hit someone or to break things. Trait anger measures a predisposition to become angry and is critical for understanding how often a person becomes angry. Anger experienced quickly and with little provocation reflects an Angry Temperament, as a predisposition, while the tendency to become angry when people receives negative feedback is Angry Reaction. Anger Expression Out and Anger Expression In describe the extent to which people expresses emotional experiences of anger in an outwardly negative and poorly controlled way or vice versa to be under-reactive, suppressing, repressing, or denying anger feelings because uncomfortable. Anger Control Out involves the expenditure of energy to monitor and control the expression of angry. People with high Anger Control Out may not be in touch with their emotions and may not recognize the need to act on solving the problem causing the anger. Last, Anger Control In reflects how a person attempts to relax and reduce angry before got out of control. The aim of this study was to measure the sexes differences among cancer patients related to: (1) experience, expression and control of anger; (2) relationship between anger components and anxiety, depression, quality of life, and progression of disease.

Materials and Methods

142 male cancer patients (mean age= 65.19; standard deviation, SD = 12.53) and 139 female cancer patients (mean age = 62.57; SD = 11.47) admitted for oncological treatment at the Day Hospital were consecutively enrolled in this study. Informed consent was obtained for all patients at the enrolment in the study. The Committee of Ethics of the hospital approved the study protocol (Prot. n. 39/CE/2017, 04 Oct 2017).

Questionnaires

State-Trait Anger Expression Inventory-2TM (STAXI-2) [13,14]. The 57-item STAXI-2 consists of six main scales (State Anger, Trait Anger, Anger Expression Out, Anger Expression In, Anger Control Out and Anger Control In) and five subscales (for State Anger: Feeling Angry, to Feel like Expressing Anger Verbally and Feel like Expressing Anger Physically; for Trait Anger: Angry Temperament and Angry Reaction) to measure experience, expression and control of anger. The State Anger scale assesses the intensity of anger as an emotional state at a data time. The Trait Anger scale measures how often angry feelings are experienced over time. The Anger Expression Out and Anger Expression In, Anger Control Out and Anger Control Out In scales assess four independent anger-related traits: expression of anger toward other persons or objects in the environment; holding in or suppressing angry feelings; controlling angry feelings by preventing the expression of anger toward other persons or objects in the environment; controlling suppressed angry feelings by calming down or cooling off, respectively. Each item has 4 options and the subjects graded themselves by using a 4-point Likert scale (from 1=almost never to 4=usually). In this study, internal consistency was good to excellent for all STAXI-2 scales, with Cronbach’s alpha coefficients ranging from 0.69 (Anger Expression Out) to 0.93 (State Anger) for males, and from 0.72 (Anger Expression Out) to 0.93 (State Anger) for females.

Hospital Anxiety and Depression Scale (HADS) [15]. The HADS is a 14-item screening instrument for anxiety (7 items) and depression (7 items) in a non-psychiatric setting. Each item is scored from 0 (not present) to 3 (highly present). Separately for each scale (range 0–21), scores <8 suggest absence of disease, scores from 8 to 10 are considered borderline, and those >10 identify probable cases of anxiety or depression [16]. In this study, Cronbach’s alpha coefficients were 0.78 and 0.79 for males and 0.82 and 0.75 for females, respectively for anxiety and depression.

Quality of life (QoL) and perception of severity and curability of the disease [17] were self- assessed by the patients using a 10-point rating scale (bad/excellent quality of life, very/not very severe, difficult/very easy to cure). Sociodemographic data and clinical information were also collected.

Statistical Analyses

For descriptive purpose we divided patients into two categories according to differences in education (<=13yrs of school versus >13yrs). Marital status was dichotomized into persons living alone (single, divorced, widowed) and into those cohabiting (married, cohabiting). Data about presence of metastasis, lymphectomia, and progression of disease were also dichotomized (absent versus present). Cancer type with less than 5 patients were grouped together and labeled “other”. For sub-analyses purpose, data about HADS anxiety and depression were divided into three groups (no disease, score 0–7; borderline, score 8–10, cases 11–21). All the raw questionnaire and 10-point rating scale scores were linearly transformed to 0–100 scales to allow comparability, using the

Formula:

Tscore = (X – Xmin)/X range)*n

where Tscore is the adjusted variable, X is the original variable, Xmin is the minimum observed value on the original variable and Xrange is the difference between the maximum potential score and the minimum potential score on the original variable and n is the upper limit of the rescaled variable. The Tscores are characterized by a distribution with a mean of 50 and a standard deviation (SD) of 10, with 0 and 100 assigned to the lowest and highest possible values, respectively. Continuous variables were reported as medians (and interquartile ranges [IQRs]) and as means (and SD or 95% Confidence Intervals, 95%CIs), and inferences were tested with parametric or non- parametric tests accordingly with their distribution. Even if the sample distribution of the values was asymmetric, the data were also presented as means to allow better visualization of the results, since the median often coincided with the quartiles. Categorical variables were reported as proportions and chi-squared or Fisher’s exact test were used for comparison. Cronbach’s alpha was adopted to evaluate internal consistency of questionnaires scales and subscales. The analyzes were conducted separately from gender. All statistical analyses have been performed using STATA, version 11.0 (StataCorp, College Station, Tex).

Results

A total of 281 cancer patients, 142 males and 139 females, aged from 31 to 88 years, were enrolled in the study. Sociodemographic and clinical features are reported in Table 1, separately for gender. Males were older at diagnosis (p=0.016), they had more frequently metastasis (p<0.001) and an advanced cancer stadium (p<0.001), a shorter length of disease (p=0.038) and less chemotherapeutic treatments (p=0.001), compared to females. In Table 2 mean values and 95%CIs of STAXI-2 and HADS domains, perception of qol, severity and curability of the disease are shown separately for males and females. Males reported lower mean scores in State (p=0.019) and Trait Anger (p=0.030), and higher mean scores in Anger Control In, than females. No differences emerged in Anger Expression, Out and In, and in Anger Control In between gender.

Table 1. Sociodemographic and clinical characteristics of the sample, separately for gender.

		Males (N=142; 50,53%)		Females (N=139; 49,47%)
		N	%	N	%	pvalue*
age, years (mean; 95%CI)		65,19	63,11–67,27	62,57	60,65–64,49	0,069
education	<13yrs	113	79,58	105	75,54	0,417
	>13yrs	29	20,42	34	24,46
married	no	19	13,48	28	20,59	0,115
	yes	122	86,52	108	79,41
age at diagnosis (mean; 95%CI)		63,13	60,96–65,30	59,43	57,34–61,52	0,016
length of disease, years		2,06	1,41–2,70	3,17	2,33–4,00	0,038
	0–1yrs	98	69,01	79	56,83
	2–8 yrs	34	23,94	41	29,50
	9+ yrs	10	7,04	19	13,67	0.065
cancer type	colon	37	26,06	22	15,83	<0.001
	melanoma	26	18,31	24	17,27
	lung	34	23,94	14	10,07
	breast	0	0,00	46	33,09
	vescica	10	7,04	3	2,16
	ovaio	0	0,00	12	8,63
	gastrico	7	4,93	3	2,16
	other	28	19,72	15	10,79
cancer stadium	I	4	2,92	6	4,65	<0.001
	II	2	1,46	22	17,05
	III	26	18,98	23	17,83
	IV	105	76,64	78	60,47
linfectomia	no	67	47,18	33	23,74	<0.001
	yes	68	47,89	101	72,66
	missing value	7	4,93	5	3,60
metastasis at diagnosis	no	72	50,70	104	74,82	<0.001
	yes	69	48,59	35	25,18
	missing value	1	0,70	0	0,00
radiotherapy	no	111	78,17	91	65,47	<0.001
	adjuvant	5	3,52	33	23,74
	palliative	24	16,90	14	10,07
	esclusiva	2	1,41	1	0,72
CHT	no	103	72,54	63	45,32	<0.001
	neoadjuvant	4	2,82	3	2,16
	adjuvant	35	24,65	73	52,52
pre-CHT line (mean; 95%CI)		2,30	1,70–2,91	4,03	3,33–4,72	<0.001
progression of disease	no	28	19,72	45	32,37	0,016
	yes	114	80,28	94	67,63

* Chi2 test; Student t test

Table 2: Descriptive statistics for STAXI-2 scales, HADS scales, patient perception of quality of life (QOL), cancer severity and curability.

	males		females
	(N=142; 50,53%)		(N=139; 49,47%)
	mean	95%CI	mean	95%CI	p-value*
STAXI
Anger state	7,84	5,47–10,21	11,45	8,71–14,18	0,019
feeling angry	12,35	9,46–15,24	17,55	13,99–21,12	0,040
verbal anger	7,56	4,50–10,62	12,09	8,46–15,72	0,023
physical anger	3,62	1,56–5,67	4,70	2,61–6,69	0,472
Ange trait	24,88	21,50–28,27	29,34	25,93–32,74	0,030
temperament	19,07	15,59–22,56	25,00	21,61–28,39	0,002
reaction	31,87	27,70–36,04	35,13	31,05–39,21	0,145
Anger expression
out	23,88	21,26–26,51	24,85	22,16–27,54	0,566
in	34,33	31,12–37,54	33,69	30,10–37,29	0,577
Anger control
out	66,33	62,15–70,51	60,19	55,96–64,43	0,036
in	58,51	54,56–62,46	57,27	53,23–61,28	0,723
HADS
anxiety	26,89	24,02–29,77	35,46	32,28–38,63	<0.001
depression	27,20	24,16–30,23	31,41	28,35–34,48	0,054
QOL	6,81	6,45–7,17	6,40	6,03–6,78	0,091
Cancer severity perception	5,13	4,70–5,56	4,96	4,53–5,38	0,539
Cancer curability perception	6,38	5,99–6,78	6,20	5,79–6,62	0,517

* Kruskal-wallis test

Males also reported lower mean score in HADS-Anxiety (p<0.001) and Depression scale (p=0.054). No significant differences emerged in perception of QoL, severity and curability of cancer between gender. Sixteen males (11.27%) and 32 females (23.02%) resulted probably-cases of anxiety (HADS- Anxiety score>10); anxiety was absent (score<8) in 76.76% of males and 56.12% of females, and borderline (scores between 8 and 10) in 11.97% of males and 20.86% of females. Eighteen males (12.68%) and 17 females (12.23%) resulted probably-cases of depression (HADS-Depression score>10); depression was absent (score<8) in 70.42% of males and 64.75% of females, and borderline (scores between 8 and 10) in 16.90% of males and 23.02% of females. STAXI-2 scale and subscale descriptive statistics, separately for categories of HADS-anxiety (absent, borderline, cases) and gender are shown in Table 3. Independently of gender, the anger subscale scores increased with levels of anxiety, excepted for both the Anger Control subscales with the exception for males for which the Anger Control Out scores decreased from absence to cases of anxiety. A similar pattern of associations (shown in Table 4) emerged respect to categories of HADS-depression (absent, borderline, cases). No relationship has been found between levels of Depression and Anger Control Out. The higher anger scores resulted in Anger Control In, Reaction and Anger Expression In compared to the other STAXI-2 subscale scores. STAXI-2 scale and subscale descriptive statistics, separately for Quality of Life levels (low, medium, high) and gender are shown in Table 5. For males, a low QoL score was associated with high anger subscale scores, excepted for Anger Control subscales (high scores are associated with high QoL). Among females, the relationship between anger and QoL seems to be weak, excepted for the Anger Control subscales, which elevated scores are associated with good QoL. No differences emerged between STAXI-2 scales and subscales and progression of disease (PD) among females. Males in PD feel annoyance, irritation, anger or fury (p=0.051). The predisposition to become angry is not different between those with PD and no-PD. Differently, anger experienced quickly and with little provocation was higher among patients with PD than no-PD (see Table 6).

Table 3: Descriptive statistics of STAXI-2 scales and subscales, separately for categories of HADS-Anxiety and Gender

HADS-Anxiety Males, N=142		absent (<=7) N=109		borderline (8–10) N=17		cases (>=11) N=16		pvalue
Anger, state	mean, SD	4,38	8,07	10,59	13,30	28,47	26,66
Anger, state	median, IQR	0,00	0–4,44	6,67	4,44–13,33	17,78	6,67–53,33	<0,001
feeling	mean, SD	7,77	11,95	18,82	14,76	36,67	27,65
feeling	median, IQR	0,00	0–13,33	20,00	13,33–26,67	30,00	13,33–63,33	<0,001
verbal anger	mean, SD	3,79	10,48	8,63	22,58	32,08	33,40
verbal anger	median, IQR	0,00	0–0	0,00	0–6,67	16,67	3,33–60	<0,001
physical anger	mean, SD	1,59	5,73	4,31	14,52	16,67	27,76
physical anger	median, IQR	0,00	0–0	0,00	0–6,67	0,00	0–23,33	0,002
Anger, trait	mean, SD	21,37	17,80	33,33	25,32	39,81	23,17
Anger, trait	median, IQR	14,81	7,41–29,63	25,93	14,81–44,44	38,89	20,37–53,70	0,002
temperament	mean, SD	15,60	18,95	29,90	25,01	31,25	22,87
temperament	median, IQR	8,33	0–25	25,00	8,33–50	33,33	8,33–41,67	0,002
reaction	mean, SD	28,44	22,60	38,24	29,76	48,44	29,85
reaction	median, IQR	25,00	8,33–41,67	25,00	25–33,33	37,50	29,17–79,17	0,017
Expression Out	mean, SD	22,13	14,86	25,74	16,35	33,85	18,81
Expression Out	median, IQR	20,83	8,33–33,33	25,00	12,5–33,33	31,25	22,92–43,75	0,038
Expression IN	mean, SD	31,15	18,73	42,89	17,85	46,88	18,35
Expression IN	median, IQR	29,17	16,67–41,67	45,83	29,17–54,17	45,83	35,42–62,5	0,001
Control Out	mean, SD	69,46	24,68	60,50	19,47	51,19	28,70
Control Out	median, IQR	76,19	57,14–90,48	57,14	42,86–71,43	50,00	30,95–73,81	0,016
Control In	mean, SD	59,82	24,03	58,82	17,61	49,22	26,93
Control In	median, IQR	62,50	45,83–79,17	54,17	50–66,67	43,75	33,33–72,92	0,289

HADS-Anxiety Females, N=139		absent (<=7) N=78		borderline (8–10) N=29		cases (>=11) N=32		pvalue
Anger, state	mean, SD	5,36	8,55	11,57	15,53	25,56	22,01
	median, IQR	2,22	0–6,67	4,44	2,22–20	18,89	8,89–43,33	<0,001
feeling	mean, SD	9,40	14,71	16,78	15,80	36,25	25,34
	median, IQR	6,67	0–13,33	13,33	6,67–20	36,67	10–56,67	<0,001
verbal anger	mean, SD	5,56	12,73	12,41	21,58	27,71	30,06
	median, IQR	0,00	0–6,67	0,00	0–20	20,00	0–46,67	<0,001
physical anger	mean, SD	1,11	3,47	5,52	14,04	12,71	19,63
	median, IQR	0,00	0–0	0,00	0–0	0,00	0–20	<0,001
Anger, trait	mean, SD	22,27	16,16	32,57	23,31	43,63	18,69
	median, IQR	18,52	11,11–33,33	29,63	14,81–48,15	44,44	29,63–55,55	<0,001
temperament	mean, SD	17,95	15,90	28,16	20,94	39,32	21,30
	median, IQR	16,67	8,33–25	25,00	8,33–41,67	33,33	25–54,17	<0,001
reaction	mean, SD	27,99	21,36	38,51	27,59	47,48	21,58
	median, IQR	25,00	8,33–41,67	33,33	16,67–58,33	50,00	33,33–62,5	<0,001
Expression Out	mean, SD	21,63	14,50	25,86	14,23	31,77	19,10
	median, IQR	20,83	12,5–29,17	25,00	16,67–37,5	27,08	20,83–39,58	0,016
Expression In	mean, SD	28,79	19,83	30,89	16,20	48,18	23,45
	median, IQR	25,00	12,5–45,83	29,17	20,83–41,67	47,92	29,17–60,42	<0,001
Control Out	mean, SD	62,82	25,87	55,67	23,64	57,89	25,11
	median, IQR	66,67	42,86–85,71	52,38	38,09–76,19	57,14	40,48–80,95	0,280
Control In	mean, SD	59,56	24,82	52,30	21,20	56,12	24,20
	median, IQR	62,50	41,67–75	54,17	37,5–66,67	62,50	33,33–75	0,211

* Kruskal-wallis test

Table 4: Descriptive statistics of STAXI-2 scales and subscales, separately for categories of HADS-Depression and gender

HADS, Depression Males, N=142		absent (<=7) N=100		borderline (8–10) N=24		cases (>=11) N=18		p-value
Anger, state	mean, SD	4,09	8,10	15,28	21,48	18,77	19,97
	median, IQR	0,00	0–4,44	6,67	3,33–18,89	10,00	6,67–31,11	<0,001
feeling	mean, SD	6,60	9,11	23,61	23,91	29,60	24,21
	median, IQR	0,00	0–13,33	20,00	6,67–30,00	23,33	13,33–40	<0,001
verbal anger	mean, SD	3,93	12,72	14,17	26,25	18,89	25,90
	median, IQR	0,00	0–0,00	0,00	0–13,33	6,67	0–33,33	<0,001
physical anger	mean, SD	1,73	7,62	8,06	19,75	8,15	18,37
	median, IQR	0,00	0–0,00	0,00	0–6,67	0,00	0–6,67	0,006
Anger, trait	mean, SD	21,19	18,52	31,64	18,85	36,42	26,17
	median, IQR	14,81	7,41–29,63	29,63	22,22–50,00	24,07	18,52–62,96	0,003
temperament	mean, SD	15,25	19,21	27,78	21,80	28,70	24,12
	median, IQR	8,33	0–25,00	33,33	4,17–41,67	20,83	8,33–50,00	0,003
reaction	mean, SD	28,33	23,21	36,46	25,63	45,37	30,28
	median, IQR	25,00	8,33–33,33	33,33	12,5–54,17	37,50	25–83,33	0.024
Expression Out	mean, SD	21,88	14,57	28,65	16,95	28,70	19,43
	median, IQR	20,83	8,33–31,25	27,08	16,67–41,67	27,08	16,67–41,67	0,105
Expression IN	mean, SD	30,83	19,27	41,32	17,55	44,44	16,85
	median, IQR	27,08	16,67–41,67	41,67	29,17–54,17	45,83	33,33–54,17	0,002
Control Out	mean, SD	70,90	24,48	59,92	20,30	49.47	27,14
	median, IQR	76,19	57,14–90,48	59,52	42,86–76,19	45,24	33,33–71,43	0,001
Control In	mean, SD	62,33	23,42	52,78	21,02	44,91	23,94
	median, IQR	66,67	47,92–79,17	54,17	43,75–66,67	43,75	29,17–58,33	0,004

HADS, Depression Females, N=139		absent (<=7) N=90		borderline (8–10) N=32		cases (>=11) N=17		p-value
Anger, state	mean, SD	7,41	12,64	17,78	20,59	19,74	18,34
	median, IQR	2,22	0–8,89	13,33	0–22,22	17,78	2,22–24,44	<0.001
feeling	mean, SD	12,22	17,20	23,75	23,34	30,59	25,06
	median, IQR	6,67	0–13,33	20,00	0–43,33	33,33	6,67–53,33	0.002
verbal anger	mean, SD	7,48	17,04	19,58	27,09	22,35	25,71
	median, IQR	0,00	0–6,67	10,00	0–26,67	13,33	0–33,33	0.001
physical anger	mean, SD	2,52	8,75	10,00	17.92	6,27	14,43
	median, IQR	0,00	0–0,00	0,00	0–16,67	0,00	0–0,00	0,038
Anger, trait	mean, SD	26,01	18,61	31,60	23,48	42,70	17,23
	median, IQR	22,22	11,11–37,04	29,62	12,96–44,44	40,74	33,33–55,56	0,004
temperament	mean, SD	21,85	17,47	26,56	23,99	38,72	21,44
	median, IQR	16,67	8,33–33,33	25,00	8,33–41,67	33,33	25–58,33	0,012
reaction	mean, SD	31,57	23,62	37,05	25,49	49,51	21,14
	median, IQR	25,00	8,33–50,00	33,33	16,67–58,33	50,00	33,33–66,67	0,013
Expression Out	mean, SD	22,96	14,54	26,95	17,13	30,88	20,20
	median, IQR	20,83	12,5–33,33	22,92	14,58–37,50	25,00	20,83–37,50	0,227
Expression In	mean, SD	30,60	20,68	32,29	16,93	52,70	24,24
	median, IQR	25,00	16,67–45,83	31,25	20,83–41,67	50,00	33,33–66,67	0,003
Control Out	mean, SD	63,76	23,89	52,83	26,43	55,18	27,72
	median, IQR	66,67	47,92–80,95	50,00	33,33–76,19	61,94	38,10–76,19	0,083
Control In	mean, SD	60,93	23,50	50,39	23,00	50,74	25,44
	median, IQR	64,58	45,83–75,00	45,83	35,42–66,67	54,17	29,17–75,00	0,029

* Kruskal-wallis test

Table 5: Descriptive statistics of STAXI-2 scales and subscales, separately for Quality of Life and gender

Quality of Life Males, N=142		low (1–3) N=100		medium (4–7) N=24		high (8–10) N=18		pvalue
Anger, state	mean, SD	21,11	24,72	8,01	13,27	5,09	11,21
	median, IQR	7,78	2,22–41,11	4,44	0–8,89	0,00	0–4,44	0,002
feeling	mean, SD	30,56	30,01	13,73	16,53	7,31	12,11
	median, IQR	16,67	6,67–56,67	6,67	0–20,00	0,00	0–13,33	0,001
verbal anger	mean, SD	25,00	33,77	6,27	14,66	5,59	16,69
	median, IQR	6,67	0–50,00	0,00	0–6,67	0,00	0–0,00	0,017
physical anger	mean, SD	7,78	14,17	4,02	13,97	2,37	9,96
	median, IQR	0,00	0–10,00	0,00	0–0,00	0,00	0–0,00	0,040
Anger, trait	mean, SD	41,36	23,79	26,31	20,99	20,13	17,22
	median, IQR	44,44	24,07–55,56	20,37	11,11–37,04	14,81	7,41–25,93	0,006
temperament	mean, SD	34,72	22,43	20,96	21,57	13,98	18,47
	median, IQR	33,33	12,5–54,17	16,67	0–33,33	8,33	0–25,00	0,004
reaction	mean, SD	46,53	27,40	33,46	26,33	27,28	22,29
	median, IQR	41,67	29,17–62,50	25,00	16,67–45,83	25,00	8,33–33-33	0,032
Expression Out	mean, SD	31,60	22,29	24,94	14,32	21,24	15,68
	median, IQR	29,17	14,58–39,58	20,83	14,58–33,33	16,67	8,33–29,17	0,131
Expression IN	mean, SD	38,54	19,15	39,52	18,37	27,82	18,81
	median, IQR	35,42	29,17–47,92	41,67	25–52,08	25,00	8,33–41,67	0,001
Control Out	mean, SD	40,87	19,63	70,03	22,77	67,20	26,17
	median, IQR	38,10	28,57–52,38	73,81	52,38–90,48	71,43	52,38–85,71	0,001
Control In	mean, SD	36,46	15,19	60,23	21,48	60,89	25,59
	median, IQR	41,67	29,17–45,83	60,42	45,83–77,08	66,67	45,83–79,17	0,001

Quality of Life Females, N=139		low (1–3) N=12		medium (4–7) N=77		high (8–10) N=50		pvalue
Anger, state	mean, SD	12,41	10,05	13,33	17,38	7,91	15,44
	median, IQR	15,56	2,22–17,78	6,67	2,22–20,00	2,22	0–6,67	0,008
feeling	mean, SD	25,56	20,66	19,05	20,34	12,13	20,90
	median, IQR	20	6,67–46,67	13,33	0–26,67	6,67	0–13,33	0,006
verbal anger	mean, SD	8,89	11,84	15,57	24,49	7,33	17,68
	median, IQR	0	0–20,00	0	0–20,00	0,00	0–0,00	0,023
physical anger	mean, SD	2,78	6,64	5,28	13,86	4,27	11,33
	median, IQR	0	0–0,00	0	0–0,00	0,00	0–0,00	0,868
Anger, trait	mean, SD	34,26	23,64	32,28	21,62	23,63	16,05
	median, IQR	27,78	16,67–55,56	29,63	14,81–44,44	22,22	11,11–33,33	0,078
temperament	mean, SD	31,94	25,08	27,16	21,90	20,00	14,96
	median, IQR	25	16,67–50,00	25	8,33–41,67	16,67	8,33–33,33	0,194
reaction	mean, SD	36,81	29,4	38,53	25,22	29,50	20,91
	median, IQR	29,17	12,5–66,67	33,33	16,67–58,33	25,00	16,67–50,00	0,181
Expression Out	mean, SD	26,74	24,58	25,38	14,77	23,58	15,76
	median, IQR	18,75	10,42–35,42	25	12,5–33,33	20,83	12,5–33,33	0,618
Expression IN	mean, SD	30,9	21,35	37,12	19,78	29,08	23,34
	median, IQR	27.08	18,75–37,50	33,33	20,83–50,00	25,00	12,5–41,67	0,037
Control Out	mean, SD	44,84	28,31	62,77	23,62	59,90	26,14
	median, IQR	42,86	28,57–64,29	66,67	42,86–80,95	61,90	38,09–80,95	0,102
Control In	mean, SD	37,15	23,13	59,63	22,76	58,42	24,22
	median, IQR	33,33	18.75–52,08	62,50	41,67–75,00	62,50	41,67–75,00	0,017

* Kruskal-wallis test

Table 6: Descriptive statistics of STAXI-2 scales and subscales by progression of disease and gender

Progression of disease Males, N=142			No N=28		Yes N=114	p-value
Anger, state	mean, SD	4,21	7,61	8,73	15,38
	median, IQR	0,00	0–5,56	4,44	0–8,89	0.079
feeling	mean, SD	7,14	11,61	13,63	18,40
	median, IQR	0,00	0–13,33	6,67	0–20,00	0,051
verbal anger	mean, SD	5,00	12,65	8,19	19,59
	median, IQR	0,00	0–0,00	0,00	0–6,67	0,382
physical anger	mean, SD	0,48	1,75	4,39	13,71
	median, IQR	0,00	0–0,00	0,00	0–0,00	0,125
Anger, trait	mean, SD	20,11	19,74	26.06	20,46
	median, IQR	14,81	5,56–25,93	22,22	11,11–37,04	0,120
temperament	mean, SD	12,80	20,22	20,61	21,01
	median, IQR	8,33	0–12,50	16,67	0–33,33	0,032
reaction	mean, SD	27,68	22,46	32,89	25,74
	median, IQR	25,00	8,33–33,33	25,00	16,67–41,67	0,442
Expression Out	mean, SD	23,21	14,80	24,05	16,15
	median, IQR	20,83	12,5–33,33	22,92	12,5–33,33	0,918
Expression IN	mean, SD	33,18	20,27	34,61	19,23
	median, IQR	31,25	18,75–45,83	33,33	20,83–45,83	0,721
Control Out	mean, SD	73,13	19,58	64,66	26,20
	median, IQR	73,81	59,52–90,48	71,43	42,86–85,71	0,183
Control In	mean, SD	65,33	20,26	56,83	24,37
	median, IQR	64,58	52,08–83,33	58,33	41,67–75,00	0,113

Progression of disease Females, N=139		No N=45		Yes N=94		p-value
Anger, state	mean, SD	9,98	12,69	11,94	17,80
	median, IQR	6,67	2,22–13,33	2,22	0–17,78	0,530
feeling	mean, SD	18,07	19,63	16,67	21,48
	median, IQR	13,33	6,67–20,00	6,67	0–26,67	0,271
verbal anger	mean, SD	8,44	16,66	13,83	23,55
	median, IQR	0,00	0–13,33	0,00	0–20,00	0,261
physical anger	mean, SD	3,41	9,06	5,32	13,81
	median, IQR	0,00	0–0,00	0,00	0–0,00	0,984
Anger, trait	mean, SD	29,79	14,71	29,12	22,56
	median, IQR	29,63	18,52–40,74	22,22	11,11–44,44	0,362
temperament	mean, SD	23,33	15,65	25,80	22,13
	median, IQR	25,00	8,33–33,33	25,00	8,33–33,33	0,875
reaction	mean, SD	37,41	19,75	34,04	26,29
	median, IQR	33,33	25–50,00	29,17	8,33–58,33	0,227
Expression Out	mean, SD	24,81	12,96	24,87	17,40
	median, IQR	20,83	16,67–33,33	20,83	12,5–33,33	0,567
Expression IN	mean, SD	33,89	18,65	33,60	22,76
	median, IQR	33,33	20,83–45,83	29,17	16,67–50,00	0,650
Control Out	mean, SD	60,11	23,31	60,23	26,26
	median, IQR	61,90	42,86–76,19	61,90	42,86–80,95	0,869
Control In	mean, SD	57,41	22,27	57,18	24,87
	median, IQR	62,50	41,67–75,00	62,50	41,67–75,00	0,941

* Kruskal-wallis test

Discussion

Among patients with a diagnosis of cancer, anger is one of the “expected” emotional reactions. Surprisingly we failed to found relationships between the characteristics of anger, its expression and control, and the clinical objective characteristics of the cancer as type, stadium, progression of disease, years from diagnosis, and being in chemotherapeutic treatment. Neither association have been found with patients’ sociodemographic characteristics, with the exclusion of years of education. To have less than 14 years of school is associated with high scores in State and Trait Anger, while having more than 13 years of school is associated with more Anger Control In, males and females attempted to relax them to reduce angry. In this sample we have found low scores related to the intensity of the individual’s angry feelings (Anger State), especially among males, and more high scores in the tendency to become angry when people faced with a negative or difficult to control situation (Anger Reaction). At the same time, the Anger Expression scores, especially “In”, reflect that these patients tend to suppress, repress, deny anger feelings. Together with high Anger Control scores reflecting the expenditure of energy that people do to monitoring and control the expression of angry. Generally, people emotionally over-reactive with a high control of emotions experienced an internal conflict and become anxious and depressed especially if the situation causing anger persists. In this sample, males and females showed differently their emotional state of anger. Males tend to repress anger and show a lack of control of anger towards the outside, more than females. Adopting anger repression as the preferred mode of expression of anger could lead to depression and directly affects the immune system.

Nonetheless responses of anxiety, depression and anger seem to be organized into a pattern of a general emotional reaction. A first hypothesis is that worsening mood is associated with increased communication between the amygdala and hippocampus, which are linked to emotion and memory respectively. Probably due to individual and gender differences. Testosterone has been associated with increased emotional reactivity in the brain [18] and patient’s personal history could explain why an anger mood (in the amygdala) became a trigger for the recollection of sad memories (in memory). In this framework patient’s ability to control anger could be the defense that consent patient to feel better. And we have seen that this ability differs in males and females. From an immunological point of view, we know that there are different interconnections between emotions and immune response and between gender and immune response. Gender differences have also been reported recently in the response to immunotherapy treatments by Botticelli [19]. Many genes involved in the immune response are located on the X chromosome and gender and diet are implicated in differences in the function of microbiota in the modulate of the immune system. The study of specific differences between the sexes in the processing and reactions to emotions could have important implications for immunotherapy treatment use in cancer due to its different efficacy in males and females.

From an oncological point of view some serious considerations are to be taken on commitment at this time that immunotherapy is becoming an important cancer treatment. If we want to boost the body’s natural defenses to fight cancer therefore the healthy immune systems became our primary concern. In conclusion, staff working in oncological day hospital should be trained to identify patients with emotional difficulties and facilitate referral for treatment. Today there is a growing interest in the use of patient-reported outcome measures that could capture some peculiar aspects that are not normally understood during the doctor-patient visit. Clinicians could gain important insights about their patients by looking at the result of validated self-report patient questionnaires, to identify patients with inadequate expression of emotion or too high levels of emotional reaction in order to improve quality of care and a better response to treatment.

Abbreviations: CI, Confidence Interval; HADS, Hospital Anxiety and Depression Scale; IQR, interquartile range; PD, progression of disease; QoL, quality of life; SD, standard deviation; STAXI, State-Trait Anger Expression Inventory.

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Acute Massive Aortic Dissection Secondary to Vascular Endothelial Growth Factor Inhibitor: An Uncommon Presentation of Drug-Induced Cardiovascular Toxicity

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DOI: 10.31038/JCCP.2020316

Introduction

In a patient presenting with severe acute chest or back pain, aortic dissection is a concerning etiology and requires immediate attention due to its profound morbidity and mortality. Less common presentations include altered mental status, stroke symptoms, and syncope, which represent less than 30% of aortic dissections. Risk factors have been well described, and include history of uncontrolled hypertension, connective tissue disease, and vasculitis. We present a case of acute Stanford Type A aortic dissection in a patient without risk factors but with recent history of bevacizumab treatment, who also presented with atypical symptoms.

Case Description

The patient is a 60 year old female with past medical history of colon cancer metastatic to liver and lung, who woke from sleep with severe chest pain, followed by right facial droop and right upper extremity weakness. Stroke code was called and Glasgow Coma Scale was 5 on admission, but CT Head was negative for hemorrhage or large-territory infarct. CT angiography of the brain and neck partially showed a Stanford type A aortic dissection, and CT aneurysm study confirmed the dissection, which extended from the aortic valve to the femoral arteries and included the great vessels, celiac trunk, and superior mesenteric artery. She was in shock on admission, was intubated and sedated. Based on the patient’s frailty she was a poor surgical candidate, and the family stated she would have declined repair. She lived for another day in the ICU before transitioning to comfort care.

Postmortem review revealed no classic risk factors for aortic dissection. The patient had no history of hypertension, connective tissue disease, or vasculitis, prompting further evaluation of her extensive history of chemotherapy. She was originally diagnosed with moderately-differentiated colon cancer in 2012 and subsequently underwent multiple resections of the primary tumor and metastasis (liver and peritoneum). Bilateral pulmonary nodules were found and treated medically. Multiple antitumor agents were used since 2012, the majority of which involved many iterations of fluorouracil and bevacizumab. The last chemotherapy regimen consisted of FOLFIRI and bevacizumab, which was resumed four months prior to the dissection.

Discussion

This case illustrates the broad range of presentations of aortic dissection, including altered mental status or stroke symptoms, as well as devastating complications of certain antitumor regimens. Cardiovascular side effects can be the most profound, especially in the use of fluoropyrimidines (e.g. fluorouracil) and vascular endothelial growth factor (VEGF) pathway inhibitors (e.g. bevacizumab). In the absence of typical risk factors, repetitive vascular toxicity from these agents likely represents the causative pathophysiology for the acute dissection. While aortic dissection is an uncommon complication of these therapies, it is incumbent on providers to recognize this possibility and recall the variable presentations of the pathology.

JCCP_2020-Joey Saliba L-f1

Figure 1. Sagittal view showing Stanford type A aortic dissection extending from aortic valve
into abdomen

JCCP_2020-Joey Saliba L-f2

Figure 2. Axial view showing aortic dissection involving arch and all three arch arterial
branches.

Splenic Changes in Heart Surgery with Circular Circulation

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DOI: 10.31038/JCCP.2020315

Summary

Cardiac surgery-related morbidity and mortality may be higher in patients with malignant neoplastic disease. Inflammatory phenomena and immunological alterations secondary to the use of cardiopulmonary bypass may also increase tumor recurrence. The aging population favors the early diagnosis of cancer and new therapies that increase the survival of patients with malignant neoplasms. emergence of cancer patients who at the same time have a cardiomyopathy that requires surgery. The benefits, especially in terms of long-term survival and risks, have not yet been clearly established. This study evaluated the characteristics of cancer patients undergoing cardiac surgery with Cardiopulmonary Bypass (CPB) indicated for a different cause of the tumor: type of procedure required, morbidity and mortality, long-term survival and incidence of tumor recurrence. To carry out this study, we used bibliographic research, performing a review on the topic.

Keywords

Cardiac surgery, Extracorporeal circulation, Splenic changes

Introduction

Controversy over the appropriateness of cardiac surgery in cancer patients, especially if they are not in complete remission, is increasing in daily clinical practice. As there are no objective reasons to justify higher mortality in these patients, the interest of the procedure focuses on the prospects for long-term survival and the risks arising from the use of CPB through direct alteration of the immune system, may exacerbate the tumor and favor dissemination and / or relapse [1]. Since the 1990s, studies on cancer patients undergoing cardiac surgery have been published. Some exclude newly diagnosed or untreated cancers and others refer exclusively to the association of cardiac surgery and the simultaneous or deferred surgical excision of lung tumors. A third group describes the results of surgical treatment of cancers that affect the heart as a primary tumor or metastasis and require CPB for resection [2]. Finally, a final group includes patients affected by tumors with special characteristics, such as hematologic malignancies. The publications are few and far between and the series are limited and heterogeneous due to the low number of operated patients. The risk of selection bias is difficult to avoid and, in addition, the different origins, stages and degrees of tumor differentiation and spread make it difficult to compare results [3]. Although clinical treatment of heart disease progresses from year to year and the less invasive approach is expanding rapidly, cardiac surgery is the preferred intervention in some cases of heart disease [4]. Therefore, this study deals with cardiopulmonary bypass and cardiac surgery, considering the main theoretical findings on the subject.

Cardiopulmonary Bypass

Supportive cardiopulmonary bypass during cardiac surgery is unique because blood exposed to foreign surfaces of nonendothelial cells is collected and continuously recirculated throughout the body. This contact with synthetic surfaces within the perfusion circuit, as well as open tissue surfaces within the wound, trigger a defensive reaction involving at least five plasma protein systems and five circulating blood cell types [5]. This inflammatory response to cardiopulmonary bypass initiates a powerful thrombotic stimulus and the production, release, and circulation of vasoactive and cytotoxic substances that affect all organs and tissues of the body. Because of this, open-heart surgery using cardiopulmonary bypass is not possible without anticoagulation, which is usually with heparin; thus, the inflammatory response to cardiopulmonary bypass involves the consequences of exposure of heparinized blood to foreign surfaces uncoated with endothelial cells [6]. During Cardiopulmonary Bypass (CPB) for cardiac surgery, blood is typically severely drained into the venous reservoir of the heart-lung machine through cannulae placed in the superior and inferior vena cava or a single cannula placed in the right atrium. Specialized cannulae can also be placed in the inferior vena cava through a femoral approach [7].

The blood from the reservoir is then pumped through a hollow fiber oxygenator after appropriate gas exchange that occurs in the systemic arterial system through a cannula placed in the distal ascending aorta, femoral artery or axillary artery. This basic extracorporeal perfusion system can be adapted to provide partial or full circulatory and respiratory support or partial support for the left or right heart or lungs separately [8]. That is, Cardiopulmonary Bypass (CPB) is used to maintain the patient’s blood circulation and / or pulmonary function outside the body. Oxygen-depleted blood is diverted out of the body and enriched with oxygen, passing it through an artificial lung (oxygenator) before being pumped back into the patient’s circulation for up to six hours [9].

Results Analysis and Discussion

Heart Disease And Cardiopulmonary Bypass (CPB)

In order to perform different types of cardiac surgeries, Cardiopulmonary Bypass (CPB) remains a frequent procedure, aiming to provide a clean surgical field, preserve the functional characteristics of the heart and provide safety to the surgical team [10]. Cardiopulmonary Bypass (CPB) is a form of circulation in which the patient’s blood is diverted from the heart and lungs out of the body. The normal physiological functions of the heart and lungs, including blood circulation, oxygenation and ventilation, are temporarily taken over by the ECC machine [7,11]. In most cases, the heart is also separated from the circulation (eg, aortic clamping) and the cardioplegia solution is administered to allow the cardiac surgeon to operate on a heart not beating in a bloodless field while other end organs they are adequately oxygenated and perfused [12].

General Principles

Equipment and Physiology

CPB components include pumps, tubing, and gas exchange (oxygenator) and heat exchange units. Modern CPB machines are also equipped with systems that continuously monitor line or circuit pressure, temperature and blood parameters (eg, oxygen saturation, blood gases, hemoglobin [Hgb], potassium) as well as safety features such as air and fluid level detection. Systems and a blood filter in the arterial line [10,12,13]. During cardiopulmonary bypass, venous blood is drained from the right atrium and is diverted through the venous line of the CPB circuit to a venous reservoir [14]. CPB machines are typically equipped with vacuum assisted technology that facilitates drainage to maintain a bloodless surgical field and allows the use of smaller venous cannulas and reduced CPB circuit volumes. The arterial pump acts as an artificial heart by drawing blood from this reservoir and propelling it through a heat exchanger, an artificial lung (oxygenator or gas exchanger) and, finally, an arterial line filter [15]. The blood is then returned to the patient through an arterial cannula positioned in the ascending aorta or other major artery. Additional cardiopulmonary bypass pumps or other components are employed as needed to suck blood from the surgical field, provide cardioplegia solution to produce cardiac electromechanical silence, decompress the heart through a vent and remove fluid (ultrafiltration) [9,16]. Thus, the cardiopulmonary machine temporarily assumes the functions of the heart, lungs and, to a lesser extent, the kidneys [17].

The contact of blood with non-endothelial surfaces of the CPB circuit induces an intense inflammatory response. This results in platelet activation and initiation of the coagulation cascade with decreased levels of circulating coagulation factors. Endothelial cells and leukocytes are activated, releasing mediators that may contribute to capillary leakage and tissue edema. Many of the challenges encountered during weaning from CPB and in the postoperative period are considered consequences of this inflammatory sequence [17]. In addition, the CPB circuit priming solution (typically 1 to 2 liters of a balanced crystalloid solution) results in hemodilution with anemia and temporary or persistent coagulopathy [18].

Protocol

Surgical procedures that require CPB follow a predictable sequence of events including CPB circuit initiation and testing, anticoagulation, vascular cannulation, CPB initiation and maintenance, and finally CPB weaning and termination. Myocardial arrest with myocardial protection and myocardial reperfusion are additional considerations when a stationary heart and a bloodless field are desired. Protocols established for the management of CPB parameters approaching normal physiology [19].

In adults, the target flow rate during CPB is 2.2 to 2.4 Liters / min / m² in normothermic patients to approach a normal cardiac index; cardiac index is appropriately decreased if hypothermia is induced [6,20].
Mean arterial pressure (MAP) is usually directed to ≥65 mmHg, but the goal may be higher in elderly patients and those with cerebrovascular disease. MAP should not exceed 100 mmHg [20,21].
The adequacy of target organ perfusion is determined by the analysis of arterial blood gases and mixed venous oxygen saturation (SvO2), which is continuously monitored and maintained ≥75 percent throughout CPB. Arterial blood gas analysis, baseline deficit and lactate levels are intermittently checked (approximately every 30 minutes) [22].
ECB weaning preparations and checklists to ensure readiness for weaning are described separately [9,18].

Cardiopulmonary Bypass

Objectives during CPB include maintenance of general anesthesia, anticoagulation, and parameters that approximate normal physiology for optimal end-organ function [23]. Arterial oxygenation, ventilation and blood gas – Arterial pO 2 is maintained at 150 to 250 mmHg during CPB. A continuous arterial blood pressure monitoring system is located on the arterial line of the CPB circuit, and a continuous venous oximeter is located on the venous return line. Arterial blood gas values are checked in the laboratory or by point-of-care testing approximately every 30 minutes, which also allows intermittent recalibration of the continuous blood gas monitor in the arterial line [24]. More specific oxygenation strategies (eg targeting hyperoxia) have not been shown to be clinically beneficial. A 2018 systematic review of 12 randomized trials noted little evidence of differences in the outcome of a hyperoxic rather than normoxic oxygenation strategy used during cardiac surgery, but the studies were small and heterogeneous [21,22,8]. Two studies [9,25] reported a reduction in postoperative myocardial enzymes and one study reported a reduction in mechanical ventilation time in normoxic groups. Alpha-stat management of arterial blood gases without temperature correction is used to maintain a normal range for pCO 2 (35 to 45 mmHg [4.7 to 6 kPa]) and pH (7.35 to 7.45) [25]. Maintaining PaCO 2 and pH within this physiological range during CPB is important to preserve cerebral self-regulation [26].

Ventilation of the lungs during CPB has not been shown to improve lung function and may increase the technical difficulty for the surgeon. Some doctors use continuous positive airway pressure (CPAP) during CPB. In a meta-analysis of seven small randomized trials, the use of 5 to 15 cmH20 CPAP improved the postoperative alveolar-arterial oxygen gradient (Aa gradient) by a weighted average difference of approximately 31 mmHg [23]. Pump flow and mixed venous oxygen saturation – CPB flow rates are set at 2.2 to 2.4 Liters / min per m² in a normothermic patient to provide adequate blood flow for optimal brain and other end organ perfusion. . These rates may be slightly decreased if hypothermia is employed [27]. Acute decreases in Mean Arterial Pressure (MAP) or increases in Central Venous Pressure (CVP) may indicate a sharp reduction in venous return due to the surgeon’s lifting of the heart (causing a reduction in flow), a misplaced or twisted arterial or venous cannula, or obstruction of blood flow by an air blockage. With severe reduction in venous return, the perfusionist may need to administer volume to the CPB reservoir or reduce arterial flow. Persistent reductions in arterial line flow and / or venous return should be urgently addressed, identifying and correcting the cause [2,5,24]^.

Mixed venous oxygen saturation (SvO2) is maintained ≥75% throughout CPB as a peripheral perfusion adequacy monitor. Persistent SvO 2 values <75% may indicate inadequate oxygen supply and are associated with worse outcomes, including postoperative delirium and decreased long-term survival [27]. In addition, lactate and baseline deficit values are measured when arterial blood gases are obtained approximately every 30 minutes. Although absolute lactate values are multifactorial, increasing levels during CPB represent anaerobic metabolism at the cellular level due to inadequate tissue oxygen delivery and may reflect hypoperfusion during CPB, particularly if maintained or associated with low mixed venous saturation. oxygen (SvO 2 <70%) [28]. Treatment for SvO 2 <75 percent, baseline deficit less than -5, or lactate level> 4 mEq / L is directed to increasing CPB flow rate as well as ensuring that blood gas and hemoglobin levels ( Hgb) are suitable. It is common practice to administer sodium bicarbonate for a baseline deficit below -5, or lactate level> 4 mEq / Liter, but excessive administration of sodium bicarbonate may cause postoperative hypernatremia [19,20]. Although increased lactate level during CPB is more often the result of inadequate tissue perfusion, persistent postoperative lactic acidosis may occur due to other factors (eg, beta-adrenergic metabolic effects of epinephrine infusion) [28]. Mean blood pressure – In the context of acceptable CPB flow rates, MAP is generally directed to ≥ 65 mmHg; a higher target may be selected in elderly patients and in those with cerebrovascular disease [28].

In a retrospective study30 of nearly 7500 patients, postoperative stroke was associated with prolonged periods with MAP <65 mmHg, with an adjusted odds ratio (OR) of 1.13 for each 10-minute period that was 55 to 64 mmHg during and after surgery. After CPB (95% CI 1.05–1.21) and adjusted OR of 1.16 for each 10-minute period when MAP was <55 mmHg (95% CI 1.08–1.23) Other factors associated with stroke in this study were advanced age, history of hypertension, combined procedures of valvular myocardial revascularization, prolonged CPB duration, emergency surgery, and new onset of postoperative atrial fibrillation [11,29]. However, a randomized study in 197 patients showed no reduction in the number or volume of cerebral infarcts in those who received vasopressor therapy to maintain a nearly physiological MAP target (70 to 80 mmHg) compared to those who had a smaller target. MAP (40 to 50 mmHg) [30]. Episodes of low or high blood pressure during CPB have been associated with the risk of brain events and other adverse outcomes, although a causal relationship has not been established [21]. A retrospective study [31] observed that impaired cerebral self-regulation is common, occurring in almost one third of patients undergoing cardiac surgery with CPB. In this study, impaired self-regulation was associated with small brain vessel disease (identified with brain magnetic resonance imaging) but was not associated with large vessel disease (identified by transcranial Doppler). However, it is not yet clear how these data inform the management of blood pressure in individual patients.

During attempts to increase MAP, it is important to ensure adequate pump flow and to maintain clear communication between the anesthesiologist (who may be adjusting the systemic vascular resistance of the vasopressor patient) and the perfusionist (who may be adjusting the flow rate) effective heart rate with changes in pump flow) [34]. MAP should not exceed 100 mmHg, in most patients a range of MAP from 65 to 100 mmHg allows for self-regulation of cerebral circulation and other target organs throughout CPB, although it is not possible to determine an accurate range of self-regulation for each patient. Individual [3,25].

Hypotension

Moderate hypotension – In the context of acceptable CPB flow rates, if MAP is below the target range, the perfusionist may increase pump flow (equivalent to increasing cardiac output), particularly if <2.4 L / min / m². If hypotension persists after increased pump flow, a vasopressor may be administered as an intravenous (IV) bolus or by continuous infusion. In many institutions, small bolus doses of phenylephrine (eg 40 to 100 mcg) are administered directly into the CPB reservoir to treat hypotension. Phenylephrine infusions at 10 to 200 mcg / min, vasopressin at 0.04 units / min or norepinephrine at 0.02 to 0.06 mcg / kg / min are also commonly employed [22]. Severe vasoplegia – systemic vasodilation, characterized by markedly decreased systemic vascular resistance (SVR) and low MAP during and after CPB, occurs in 5 to 25 percent of patients undergoing cardiac surgery. Risk factors include the preoperative use of agents such as angiotensin converting enzyme (ACE) inhibitors, heparin or calcium channel blockers, as well as hemodynamic instability before passage [17]. Before treating low blood pressure near the end of the CPB period, it is important to verify that radial blood pressure is not markedly underestimating central aortic pressure. A significant central pressure gradient associated with end-CPB rewarming is often present during cardiac surgery. Attaching a pressure transducer to the side port of the aortic cannula after CPB is complete, or the use of a surgeon-inserted femoral intraarterial catheter in the field will usually provide an accurate estimate of true central aortic pressure [33].

If hypotension due to vasoplegia (low SVR) is confirmed, a continuous vasopressor infusion is usually necessary. Although an optimal approach for vasopressor selection during CPB has not been established, observations in the distributive shock scenario suggest that administration of vasopressin or a combination of vasopressin with noradrenaline or phenylephrine is associated with lower atrial fibrillation rates compared to administration. isolated from norepinephrine [34]. If these agents are ineffective, methylene blue 1 to 2 mg / kg IV for 20 minutes may be administered to reduce the responsiveness of nitric oxide resistance vessels. Notably, methylene blue should be avoided in patients receiving chronic serotonin therapy (eg, fluoxetine) because of the risk of serotonin syndrome and may interfere with monitors employing oximetry to measure oxygen saturation (pulse oximetry and cerebral oximetry) [5,29]. Hypertension – If MAP increases to> 90 mmHg during CPB, treatment includes increasing the concentration of volatile anesthetic administered through the CPB circuit and / or administering additional IV anesthetic. Occasionally, administration of a vasodilator may be required. For brief periods, pump flow may be reduced while these pharmacological interventions are effective [35]. Anticoagulation Maintenance – The adequacy of heparin anticoagulation is measured with point-of-care tests such as activated whole blood clotting time (ACT) every 30 minutes to maintain a target value during CPB (typically above 480 seconds). If available, plasma heparin concentrations may also be determined by treatment point assays such as Hepcon with target heparin concentration ≥4 units / mL. Protocols in some institutions emphasize the treatment of heparin concentrations <4 units / mL, even if ACT values are adequate [36].

Special Population Management

Specific management strategies are employed during CPB for patients with aortic insufficiency, cerebrovascular disease, renal failure, or vasoplegia and for those undergoing a period of elective deep hypothermic circulatory arrest (DHCA) [6,30]. Pre – existing Aortic Regurgitation (AR) can be diagnosed and its severity characterized by intraoperative Transesophageal Echocardiography (TEE) examination in the pre – reception period. During CPB, the presence of AR may limit the effectiveness of administration of anterograde cardioplegia solution to the coronary artery ostia after the ascending aorta is crossed [37]. Much of the cardioplegia solution will regurgitate through the incompetent aortic valve to the Left Ventricle (LV). The severity of RA can be influenced by the increased aortic root pressure that occurs during attempted delivery of anterograde cardioplegia and surgical manipulations that further distort the normal aortic root and valve geometry [38]. In addition, cardioplegia solution that flows back through the incompetent aortic valve may cause LV distention as the ventricle is not ejecting regularly due to bradycardia, asystole or ventricular fibrillation. Distension causes increased LV wall tension. In combination with inadequate coronary delivery of anterograde cardioplegia solution, this may result in inadequate myocardial protection and severe LV dysfunction. In this situation, a LV ventilator is placed by the surgeon to keep the ventricle in an uncompressed state [4,15,28].

Correct ventilation placement and effective LV decompression are confirmed with the TEE examination. Subsequently, continuous monitoring of TEE and Pulmonary Artery Pressure (PAP) supplement the surgical detection of a displaced LV output or recurrence of LV distension [28]. If the administration of anterograde cardioplegia is inadequate due to RA, the cardiac surgeon usually minimizes the attempted delivery by this route, opting for retrograde cardioplegia provided by the coronary sinus. In selected aortic valve or aortic root procedures, it may be necessary to implant cardioplegia solution directly into the coronary ostia after aortic clamping and opening of the aortic root [36]. Rapid and severe LV distension may occur in a patient with significant RA, even before aortic clamping, if ventricular fibrillation occurs at the onset of CPB and on cooling. In this situation, the CPB flow rate may be temporarily reduced to allow the surgeon to manually decompress the LV, since defibrillation is more likely if the heart is empty (not distended). Defibrillation is performed with internal paddles applied directly to the heart to provide 10 to 20 joules of electricity. Subsequently, if LV distension recurs after the application of aortic cross forceps, a left ventricular opening may be inserted [39].

Cerebrovascular disease is common in patients undergoing cardiac surgery. A study that used preoperative magnetic resonance imaging observed a major brain vessel disease in 25% and small vessel disease present in 35% of 346 patients undergoing cardiac surgery with CPB. Considerations for patients with known cerebrovascular disease and / or evidence of severe aortic atherosclerosis include maintaining a higher mean arterial pressure than patients without these comorbidities, with careful attention to maintaining hemoglobin (Hb) and hematocrit (Hct) levels. and the prevention of cerebral hyperthermia. The use of intraoperative oximetry and the maintenance of regional cerebral oxygen saturation (rSO2) in 20% of baseline values has been advocated by patients at high risk of adverse neurological outcomes, including those with known cerebrovascular disease [23,39].

Conclusion

Cardiopulmonary Bypass (CPB) is a form in which the patient’s blood is diverted from the heart and lungs out of the body. Normal physiological functions of the heart and lungs, including blood circulation, oxygenation, and ventilation, are temporarily taken over by the CPB machine. Most studies do not show a significant increase in the morbidity and mortality of CPB cardiac surgery in cancer patients, even at an active stage, compared to the normal population. It appears that the survival of cancer patients undergoing cardiac surgery is more related to tumor progression than to the surgical procedure. Medium-term survival is acceptable, although less in active cancer patients at the time of intervention and in those <2 years between cancer diagnosis and surgery. Readmission rates are higher than in the general population and are mainly due to the need for cancer treatment and / or complications arising from it. Typical parameters during CPB in adults include controlled flow rate of 2.2 to 2.4 Liters / min / m², maintenance of mean arterial pressure (MAP) ≥ 65 mmHg and mixed venous oxygen saturation ≥ 75%. Maintaining renal blood flow is achieved by maintaining adequate CPB pump flow throughout CPB to minimize the risk of Acute Kidney Injury (AKI). Inadequate anesthetic depth is treated by increasing the volatile anesthetic concentration administered by the CPB circuit or by the administration of additional Intravenous (IV) anesthetic agents. It is reasonable to monitor processed EEG indices (eg bispectral index) or unprocessed EEG to provide data that may detect inadequate anesthesia during CPB. Decreasing the dose of the selected Neuromuscular Blocking Agent (NMBA) may be adequate during the hypothermic CPB period; however, additional NMBA is usually required during reheat. Individual assessment of each case should consider both the tumor stage and the chances of complete remission (even in active cancers) before discounting cardiac surgery. Larger studies are needed to allow comparison of morbidity and mortality outcomes and survival with and without CPB to identify their influence on these variables and long-term recurrence.

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Cardiac Surgery in Pregnant Women

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DOI: 10.31038/JCCP.2020314

Summary

The need for cardiac surgery during pregnancy is rare. Only 1% to 4% of pregnancies are complicated by maternal heart disease and most of them can be treated with medical therapy and lifestyle changes. Occasionally, whether due to the natural progression of underlying heart disease or precipitated by cardiovascular changes in pregnancy, cardiac surgical therapy should be considered. Cardiac surgery is inherently dangerous for both mother and fetus with mortality rates close to 10% and 30%, respectively. For some conditions, percutaneous cardiac intervention offers effective therapy with much less risk for the mother and her fetus. For others, cardiac surgery, including procedures that require the use of cardiopulmonary bypass, should be considered to save the mother’s life. Given the extreme risks to the fetus, if the patient is in the third trimester, serious consideration should be given to pre-operative delivery involving cardiopulmonary bypass. At earlier gestational ages, when this is not feasible, modifications to the infusion protocol, including higher flow rates, normothermic perfusion, pulsatile flow, and the use of intraoperative monitoring of the external fetal heartbeat should be considered.

Keywords

Cardiac surgical procedures, Cardiopulmonary bypass, Cardiovascular, Surgery, Complications of pregnancy, Heart defects congenital, Pregnancy

Introduction

Cardiovascular adaptations during pregnancy are usually well tolerated in healthy women. However, 2% to 4% of women of childbearing age have some degree of concomitant heart disease, and these changes may compromise cardiac function. Of these, some who do not respond to medical treatment may require surgical correction. In this scenario, the maternal mortality rate improved to levels similar to non-pregnant women. However, the fetal mortality rate remains high (up to 33%). Factors contributing to the high rates of fetal mortality include the timing of the operation, the urgency of the operation, and the fetal / fetoplacental response to cardiopulmonary bypass. Modulation of the fetoplacental response to cardiopulmonary bypass may prevent placental dysfunction and sustained uterine contractions that underlie fetal hypoxia and acidosis. In this article, we reviewed the cardiovascular adaptations to pregnancy and the pathophysiological effects of cardiopulmonary bypass on the mother, fetus and fetoplacental unit, and talked about whether manipulating these responses can help improve fetal outcome. Finally, approaches to perfusion management and cardiac surgical techniques without extracorporeal circulation in pregnancy were made.

Khandelwal et al [1] points out that approximately 2% to 4% of women of childbearing age have concomitant heart disease. According to Abbas [2], Congenital Heart Disease (CHD) is responsible for most structural heart diseases that affect women of childbearing age. Abbas [2] also points out that the acquired heart disease observed during pregnancy is mainly valvular in nature, usually a consequence of rheumatic fever. Although rheumatic fever is decreasing in developed countries, it remains a serious problem in the developing world. Immigrants also form a high-risk population, especially those who are unaware of the risks inherent in heart disease during pregnancy or unaware of the presence of any heart disease. In this regard, mitral stenosis is the most commonly found lesion. Abbas [2] further states that although its incidence is declining, aortic valve disease is less common: aortic incompetence is usually a consequence of endocarditis (1 in 8,000 pregnancies) or aortic dissection, in which case there may be tissue disease. underlying connective tissue such as Marfan syndrome. Thus, significant aortic stenosis is uncommon in this age group. Parry [3] points out that ischemic events during pregnancy are rare. The incidence of myocardial infarction is approximately 1 in 10,000 pregnancies. Mabie [4] states that infarction is usually secondary to underlying coronary artery disease, although spontaneous coronary artery dissection appears to be more common during pregnancy and accounts for 30% of all myocardial infarctions seen in pregnancy.

Maternal and Fetal Predictors

Siu [5] highlights that heart disease in the mother is the leading cause of maternal death during pregnancy. Of these patients, 25% have congenital heart disease. In a large prospective multicenter study on pregnancy outcomes among women with heart disease, adverse cardiovascular events were observed in 13% of patients. Independent predictors of maternal cardiac complications included previous cardiac events, functional class III / IV, left heart obstruction, and left ventricular systolic dysfunction. Neonatal complications were observed in 20% and were associated with functional class or poor cyanosis, left heart obstruction or smoking. Klairy [6] says that a recent prospective observational study in pregnant patients with congenital heart disease found that both cardiac complication and neonatal complication rates were considerable in these women. In mothers, right subpulmonary ventricular systolic dysfunction and severe pulmonary regurgitation were predictors of an adverse fetal outcome

Drenthen [7], in his bibliographic review, describes that the results of 2,491 pregnancies among women with structural heart disease (congenital heart disease), were observed substantial cardiac complications in 11% of pregnancies. Obstetric complications do not appear to be more prevalent except for hypertensive disorders and thromboembolic events. In patients with complex CHD, rates of preterm delivery ranged from 22% to 65%, and neonates were lower than normal for gestational age.

Maternal Cardiovascular Changes During Pregnancy

In the presence of maternal heart disease, circulatory changes in pregnancy may result in decompensation and death of the mother or fetus. Pregnancy produces changes in the cardiovascular system that have profound implications for heart surgery. Cardiac output increases markedly by the end of the first trimester, followed by a more gradual increase of 30% to 40% above baseline until the third trimester partly due to an increase in stroke volume and heart rate. In most patients, this hyperdynamic situation results in mild midsystolic murmur [8]. Diastolic murmurs may be normal, but deserve further investigation. At the end of pregnancy, compression of the lower uterine vena cava in the supine position decreases venous return and reduces cardiac output. Relief of vena cava compression is easily achieved by positioning the patient in the left lateral position. Systemic and pulmonary vascular resistance falls due to the effects of circulating prostaglandins in addition to other hormones and the poor resistance of the placental circulation. This lowers blood pressure during the first half of pregnancy, but pressure tends to increase in the second half [9].

Blood volume increases with a concomitant increase in plasma volume and red blood cell volume. Plasma volume expands by approximately 40% at term, when red cell mass increases by about 20%. Consequently, there is a drop in hematocrit and blood viscosity [10]. Hemoglobin’s oxygen carrying capacity is increased by a right shift on the oxygen dissociation curve. Pregnancy induces a procoagulant state with a 2-fold increase in fibrinogen level and an increase in factors V, VII, VIII, IX, and X, which has teleological advantage as it helps in hemostasis and reduces blood loss. during childbirth. In addition, this state ensures the optimal supply of oxygen and nutrients to the mother and fetus

Responses to Cardiopulmonary Bypass

Maternal

As the circulation of pregnant women has already undergone significant changes, the additional effect of Cardiopulmonary Bypass (CPB) induces a non-physiological hemodynamic state that may adversely affect the mother during cardiac surgery. This effect is amplified by simultaneous changes in the cellular and protein components of the blood. Hemodilution and changes in coagulation, complement activation, release of vasoactive substances by leukocytes, gas embolism and hypotension during CPB are further added to the deleterious effect. However, these effects are relatively well tolerated by the mother, as the maternal mortality rate associated with CPB in pregnant women is similar to that of non-pregnant women undergoing CPB-like cardiac procedures [3].

Uterine

Sustained uterine contractions during cardiac surgery and CPB are accepted as the most important cause of fetal death. Sustained uterine contractions reduce uterine blood flow and interventional perfusion, resulting in fetoplacental insufficiency and subsequent fetal hypoxia. Cardiopulmonary bypass has other potentially deleterious effects on the uterus during pregnancy. Both cooling and reheating phases are associated with increased sustained uterine contractions. This observation is supported by the fact that there is a noticeably higher fetal mortality rate with hypothermic CPB than with normothermic CPB [3]. Uterine muscle excitability is probably increased by hormonal dilution, especially by progesterone dilution. Postoperative progesterone administration successfully eliminated preterm labor.

Fetoplacental shunt

Experimental studies of fetal CPB provided much information on the fetoplacental response, which could shape future management of CPB in pregnant women. Studies in sheep have repeatedly shown that standard non-pulsatile flow perfusion rapidly causes severe placental dysfunction dominated by a strong vasoconstrictor reaction. Vasodilators have been used with some success to overcome this increase in placental vascular resistance, producing improvement in both placental blood flow and acidosis [11]. A study of the human placenta under physiological conditions reveals that an active fetal renin-angiotensin system can modulate placental perfusion in vivo. Endothelial dysfunction is a cause or an effect of excessive renin-angiotensin activity that has yet to be resolved. Lactate levels during CPB pulsatile flow were stable, while a continuous increase is observed with non-pulsatile CPB. Manipulation of the fetal lamb during CPB has been shown to produce significant lactic acidosis; this was considered as a result of metabolic debt of fetal stress, and anesthesia. Since lactate release into the maternal circulation was similar between the beginning and the end of CPB, the increase in fetal lactate probably originated from within the fetus. The placenta plays an important role in regulating circulating lactate levels under hypoxic conditions, thus allowing the fetus to maintain stable lactate levels. However, a reduction in placental perfusion prevents lactate clearance in the fetal circulation [11].

Fetal

The fetal mortality rate during maternal cardiac surgery with CPB ranges from 16% to 33%. Increased gestational age and increased hypothermia are factors known to increase fetal morbidity during CPB. Unfortunately, little experimental evidence is available on the fetal response to maternal CPB. Fetal cardiac monitoring usually shows that the onset of CPB is bradycardia, which in most cases reverts to sinus rhythm immediately after CPB cessation [13]. Alternatively, an initial tachycardia may occur, accompanied by an increase in blood pressure. Fetal bradycardia observed after the onset of CPB is corrected by increased blood flow and the restoration of maternal circulation upon CPB discontinuation. The mechanism of this bradycardic response is unknown, but several causes have been postulated, including fetoplacental dysfunction, fetal hypoxia and acidosis, maternal hypothermia, and drugs that cross the placental barrier, such as β-adrenergic blockers [14]. Fetal hypoxia during CPB may be a consequence of hemodilution, as hemodilution reduces the oxygen content in the maternal blood. Other causes of fetal hypoxia include reduced uterine perfusion pressure and increased uterine vascular resistance [14] Because placental blood vessels are maximally dilated during pregnancy, uterine blood flow is not self-regulating but directly depends on maternal mean arterial pressure and uterine vascular resistance.

Decreased maternal blood pressure may result in fetal bradycardia shortly before the onset of CPB. Maternal hypotension soon after cardiopulmonary bypass is caused by decreased systemic vascular resistance caused by reduced flow rate, hemodilution, and release of vasoactive substances, which may result in a significant reduction in placental perfusion. Cardiac fetal decelerations observed during CPB are probably caused by reduced blood flow to the interventional spaces and the resulting fetal hypoxia. Factors such as non-pulsatile flow, uterine arteriovenous bypass, and obstruction of venous drainage by cannulation of the inferior vena cava, particulate and gas emboli, and spasm of the uterine artery may reduce placental circulation and lead to fetal hypoxia. When CPB is prolonged, there is a significant risk that the fetus will develop a prolonged bradycardic response. Changes in fetal heart rate may be observed even when maternal circulation, acid-base balance and perfusion pressure are stable. Therefore, it has been postulated that these changes are related to the narcotic effect of drugs used during anesthesia. Vasoconstrictors may reduce uteroplacental flow (direct evidence of this is lacking) and should be avoided, although phenylephrine and ephedrine are considered safe during pregnancy.

Surgical Issue

The principles for the management of pregnant patients undergoing cardiac surgery with CPB are similar to those of pregnant women undergoing surgical intervention. These include: attention to maternal well-being, prevention of teratogenic drugs, prevention of intrauterine hypoxia and prevention of premature labor. In addition, strong consideration should be given to the administration of maternal corticosteroids to initiate endothelial membrane stability and fetal lung maturation, which can substantially improve fetal outcome if delivery occurs after CPB. In summary, the main concerns in optimal management of pregnant patients undergoing CPB are temperature control, perfusion pressure, and the nature of bypass flow. Current evidence favors the maintenance of normothermic CPB, avoiding the use of vasoconstrictors (which may have a profound effect on the placental unit) and maintaining high hematocrit rates and high blood flow rates. Fetal hypoperfusion and hypoxia may also be alleviated by the use of pulsatile perfusion. Other adjuncts, such as the use of arterial line filters or leukocyte depletion filtration, have not been evaluated in the specific context of pregnant patients [15].

Extracorporeal Circulation

Aggarwal and coauthors [16] reported that closed mitral valvotomy offers excellent results, comparable to non-surgical treatments; It is still the procedure of choice in certain parts of the world. Myocardial revascularization without cardiopulmonary bypass is a safe and accepted technique for coronary revascularization; However, its role in pregnancy needs further evaluation. The current medical literature contains only 1 documented case of myocardial revascularization during pregnancy – that of a 32-year-old woman at the 22nd week of gestation who had a spontaneous dissection of the left anterior descending artery and subsequently delivered a healthy baby at term. Hemodynamic instability during unsupported myocardial revascularization (CABG) surgery – involving a decrease in mean systemic blood pressure and an increase in mean pulmonary pressure during distal anastomosis construction and heart manipulation to gain access to the lateral and posterior branches – may not be well tolerated by pregnant patients and could cause significant placental malfunction. However, if only one anterior target (such as the anterior descending coronary artery territory) is grafted, this can usually be accomplished with minimal hemodynamic compromise and avoid all risks inherent to CPB, such as hemodilution, systemic inflammatory response to CPB and increased risk of bleeding [17].

To deepen our understanding of the placental and fetoplacental response to off-pump myocardial revascularization surgery, it is useful to look at how other vascular beds, such as splanchnic circulations, behave during off-pump coronary artery bypass grafting (CABG). If we hypothesize that splanchnic and fetoplacental circulations will respond similarly to hemodynamic insults during CPB myocardial revascularization surgery, much can be learned by observing splanchnic and gastrointestinal physiology during cardiac operations. L-lactate concentration has been significantly higher in the intestinal mucosa of patients undergoing myocardial revascularization with CPB than without CPB [17]. This supports the view that without CPB produces less dysfunction in these vascular beds than CPB. When Fiore et al. [18] examined upper mesenteric blood flow during myocardial revascularization, they found that cardiac manipulation to gain access to the lower and lateral walls caused hemodynamic changes that resulted in significant mesenteric hypoperfusion. Such information can help us plan the approach to revascularization we want to perform in pregnant patients who develop spontaneous coronary artery dissection.

Conclusion

Spontaneous single vessel or two vessel dissections, particularly in the anterior descending territory or right coronary artery territory, are probably the best candidates for off-pump surgery: the use of CPB in these instances avoids manipulation of the heart to reach the posterior artery. and inferior, thus avoiding hemodynamic instability and its effects on fetoplacental circulation More observational data should be collected in the context of myocardial revascularization in pregnancy, with particular attention to responses. fetal and maternal cardiovascular diseases, before myocardial revascularization can be safely recommended as the ideal therapy for this challenging group of patients.

References

Khandelwal M, Rasanen J, Ludormirski A, Addonizio P, Reece EA (2015) Avaliação da hemodinâmica fetal e uterina durante a circulação extracorpórea materna. Obstet Gynecol 88: 667–671.
Abbas AE, Lester SJ, Connolly H (2005) Gravidez e sistema cardiovascular. Int J Cardiol 98: 179–189.
Parry AJ, Westaby S (2015) Circulação cardiopulmonar durante a gravidez. Ann Thorac Surg 61: 1865–1869.
Mabie WC, Freire CM (2012) Dor súbita no peito e emergências cardíacas no paciente obstétrico. Obstet Gynecol Clin North Am 22: 19–37.
Siu SC, Sermer M, Colman JM, Alvarez AN, Mercier LA, et al. (2001) Estudo prospectivo multicêntrico dos resultados da gravidez em mulheres com doença cardíaca. Circulation 104: 515–521.
Khairy P, Ouyang DW, Fernandes SM, Lee-Parritz A, Economia KE, et al. (2006) Resultados da gravidez em mulheres com cardiopatia congênita. Circulation 113: 517–524.
Drenthen W, Pieper PG, Roos-Hesselink JW, van Lottum WA, Voors AA, et al. (2007) Desfecho da gravidez em mulheres com cardiopatia congênita: uma revisão de literatura. J Am Coll Cardiol 49: 2303–2311.
Edmonds KD, Dewhurst J (1999) Em: livro de obstetrícia e ginecologia de Dewhurst para pós-graduados. 6a ed. Malden (MA): Ciência Blackwell.
Clark SL, Cotton DB, Lee W, Bispo C, Hill T, et al. (2009) Avaliação hemodinâmica central da gravidez a termo normal. Am J Obstet Gynecol 161: 1439–1442.
Pomini F, D Mercogliano, Cavalletti C, Caruso A, Pomini P (2015) Circulação cardiopulmonar na gravidez. Ann Thorac Surg 61: 259–268.
Vedrinne C, Tronc F, Martinot S, Robin J, Allevard AM, et al. (2000) Melhor preservação da função endotelial e diminuição da ativação da via renina-angiotensina fetal com o uso de fluxo pulsátil durante a derivação fetal experimental. J Thorac Cardiovasc Surg 120: 770–777.
Sabik JF, Heinemann MK, Assad RS, Hanley FL (2011) Esteróides de altas doses impedem a disfunção placentária após o bypass cardíaco fetal. J Thorac Cardiovasc Surg 107: 116–125.
Ralston DH, Shnider SM, DeLorimier AA (2011) Efeitos da efedrina equipotent, metaraminol, mephentermine, e metoxamina no fluxo sanguíneo uterino na ovelha grávida. Anesthesiology 40: 354–370.
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Perner A, Jorgensen VL, Poulsen TD, Steinbruchel D, Larsen B e Andersen LW (2005) Concentrações aumentadas de L-lactato no lúmen retal em pacientes submetidos à circulação extracorpórea. Ir. J Anaesth 95: 764–768.
Fiore G, N Brienza, Cicala P, Tunzi P, N Marraudino, et al. (2006) Superior modificações do fluxo sanguíneo da artéria mesentérica durante a cirurgia coronária sem CEC. Ann Thorac Surg 82: 62–67.
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Surgical Treatment of Pulmonary Arterial Thrombosis

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DOI: 10.31038/JCCP.2020313

Summary

Right Thromboses Floating (RHT) are in transit from the legs to the pulmonary arteries and are therefore a severe form of Venous Thromboembolism (VTE) with a high early mortality rate without treatment. There is a lack of evidence-based recommendations for its management, which is why, motivated the accomplishment of this study, dealing with the subject, addressing the surgical treatment of pulmonary arterial thrombosis, considering professional experiences in the surgical management of the thrombus in transit and Pulmonary Embolism (PE).

Keywords

Pulmonary arterial thrombosis, Pulmonary embolism, Surgical treatment

Introduction

The presence of thrombi in right heart chambers may be a consequence of Venous Thromboembolism (VTE) or may develop in situ as a consequence of cardiac conditions. Transient Thrombus (TT) or floating thrombus is defined as a thrombus temporally located in the right cardiac chambers en route to the Pulmonary Artery (AP). It is, therefore, a severe manifestation of VTE and very often (more than 90%) is associated with Deep Vein Thrombosis (DVT) or Pulmonary Embolism (PE). Given the high mortality rate without treatment (90%) and being very early (in the first 24 hours), monitoring in critical units and urgent treatment (including systemic fibrinolysis or surgical embolectomy, in addition to conventional anticoagulation) is justified. TT is an uncommon manifestation of symptomatic PE, with an approximate frequency of 4%. Currently, there is insufficient evidence on the best treatment option, and the recommendations are based on conclusions drawn from case series. The objective of this study is to discuss theoretical findings and experience of early surgical treatment of patients with TT and PE.

Discussion on the Topic

The presence of thrombi in the right cardiac chambers may be a consequence of VTE or may develop in situ as a consequence of cardiac conditions. This differentiation is of great importance considering that they have different treatment and prognosis [1]. TT or floating thrombus is defined as thrombus temporarily located in the right cardiac chambers en route to the PA. It is, therefore, a severe manifestation of VTE and with very high frequency (more than 90%) is associated with DVT or PE [2]. Thus, the presence of a TT confirms the diagnosis of PE and implies the desire for immediate treatment without the need for additional diagnostic tests. Three types of right thrombus were described using the Transthoracic Echocardiogram (TTE): type A thrombi are the most common and tend to be large, free floating masses with a high propensity for distal embolization; Type B thrombi are small clots of immobile right chambers attached to walls originating in situ; finally, type C thrombi are rare and have great mobility mimicking atrial myxomas. TT is an uncommon manifestation of symptomatic PE, with an approximate frequency of 4%.

Although coexisting Right Heart Thrombus (RHT) does not commonly occur in patients with acute symptomatic PE, studies have validated THR as a predictor of poor prognosis [3]. Despite anticoagulation, the mortality rate has been reported high (23.2-44.7%), especially in the first 24 hours [3]. In a recent meta-analysis, which included 15,220 patients with symptomatic acute PE, patients with detectable RHT concomitantly with echocardiography had a three-fold increased risk of short-term death compared to patients without RHT [4]. Similar results were found in a study of patients with PE from the Computerized Registry of the Thrombotic Infirmary (RIETE), which included 12,441 patients with PE, of which 325 had RHT.

Severe hypoxemia and the occurrence of cardiac arrest were significantly related to in-hospital mortality in patients with concomitant acute and concurrent PH. For these reasons, early diagnosis and treatment are essential. However, some studies have shown that clinical prognostic scores (PESI, simplified PESI) have demonstrated excellent accuracy for the identification of patients with low risk of short-term complications and have shown that coexisting TT in normotensive patients with PE did not lead to greater all-cause mortality. However, the data suggest an increased risk of mortality for patients with TT and RV dysfunction. On the other hand, the prognosis is generally good after discharge.

Most TT is detected by TTE in patients diagnosed or suspected of acute PE. Therefore, TTE is an important tool for early recognition. In case of doubt, it would be necessary to perform a transesophageal echocardiogram, which is also useful in the detection of thrombi in PA and in the coexistence of Patent Foramen Ovale (FOP). The presence of FOP should be taken into account as it is a source of paradoxical embolism (emboli originating within the venous system, which passes through a FOP and enters the systemic circulation) and may be a site for the clot to be lodged. Some authors such as Barrios et al and Bodia favor the surgical removal of these thrombi with simultaneous pulmonary embolectomy due to the imminent risk of displacement, which can lead to massive PE or paradoxical systemic embolism. Despite its prognostic importance, current guidelines do not propose that echocardiography be performed routinely in all patients with acute PE or in all patients with a low-risk PESI assessment. However, they stimulate the evaluation of right ventricular function by echocardiography and / or measurement of cardiac biomarkers if, after clinical evaluation, there is uncertainty about whether patients need more intensive monitoring or thrombolytic therapy [4].

Current guidelines suggest that patients with acute hypotension (ie systolic BP <90 mmHg for 15 min) and no high risk of bleeding should be treated with thrombolytic therapy. The development of hypotension suggests that thrombolytic therapy is indicated. Deterioration that has not resulted in hypotension may also lead to the use of thrombolytic therapy. For example, there may be a progressive increase in HR, a decrease in systolic BP (which remains> 90 mmHg), increased jugular venous pressure, worsening of gas exchange, shock signals (eg, cold sweat, diuresis reduction, confusion ), progressive dysfunction of the right heart on echocardiography, or increase in cardiac biomarkers [4]. However, few studies, only series of cases and retrospective studies, have compared thrombolysis with surgical therapy in acute PE and have recommended considering surgical pulmonary embolectomy in case of hypotension and contraindication for thrombolysis or life-threatening situations such as thrombolysis failure, RV failure, cardiogenic shock and TT.

According to current guidelines, in patients with TT, the therapeutic benefits of thrombolysis remain controversial. Surgical embolectomy is justified in some cases of TT, for example, those with coexistence of FOP. The ideal treatment for concomitant acute and concurrent PH is not currently defined due to the absence of randomized clinical trials and should be considered on a case-by-case basis by multidisciplinary teams following a risk-benefit assessment [4]. Patients who did not receive therapy had a mortality rate of 90 to 100%. In addition, the mortality rate is high (21.1%) in the first 24 hours, which justifies an immediate treatment. Thus, inotropic support with catecholamine infusion should be prepared immediately after the diagnosis of TT and administered as soon as the patient’s blood pressure drops below 100 mm Hg or there is suspicion of cardiogenic shock. It may be preferable to admit patients to an ICU because sudden death is a risk and mechanical ventilation may also be required. Different therapeutic approaches have been reported for acute PE with concomitant TT: anticoagulation with Unfractionated Heparin (UFH), systemic thrombolysis with Recombinant Tissue Plasminogen Activator (RTPA), surgical embolectomy with exploration of the right chambers and pulmonary arteries in complete extracorporeal circulation, and endovascular thrombectomy.

According to Marti (2014), systemic thrombolysis and surgical embolectomy were more likely to survive (81.5% and 70.45%, respectively) than anticoagulation (47.7%). However, there are contradictory data regarding the management of TT; a recent study that included patients with acute RI associated with TT from the RIETE registry showed that there were no significant differences in mortality and bleeding between reperfusion (thrombolysis, surgery) and anticoagulation therapy. Regarding the superiority of thrombolysis or surgical thrombectomy, [4] say that both are effective strategies, with a slight increase in the probability of survival with thrombolysis in some studies. They were, however, plagued by selection bias, small numbers and lack of comparable groups. A prospective, randomized, well-planned study is needed to determine the optimal treatment of acute PD and concomitant TT.

Conclusion

Transit thrombus is an uncommon and severe manifestation of VTE. The high rate of early mortality ensures rapid and effective treatment. Surgical embolectomy in patients with PE and concomitant thrombus in transit may be an effective treatment in selected patients, although the current evidence to support this approach is not definitive.

References

Arboine-Aguirre L, Figueroa-Calderón E, Ramírez-Rivera A (2017) Trombo em trânsito e tromboembolismo pulmonar submassivo tratado com sucesso com tenecteplase. Gac Med Mex 153: 129–133.
Athappan G, Sengodan P, Chacko P (2015) Eficácia comparativa de diferentes modalidades para tratamento de trombos cardíacos direitos em trânsito: uma análise conjunta. Vasc Med 20: 131–138.
Agarwal V, Nalluri N., Shariff MA (2014) Embolia grande em trânsito – um dilema terapêutico não resolvido (relato de caso e revisão da literatura). Coração Pulmão 43: 152–154.
Barrios D, Rosa-Salazar V, Morillo R (2017) Significado prognóstico dos trombos do coração direito em pacientes com embolia pulmonar sintomática aguda: revisão sistemática e metanálise. Baú de 151: 409–416.

Showcase to illustrate how the web-server iRNAMethyl is working

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DOI: 10.31038/JMG.2020322

Short Commentary

In 2015 a very powerful web-server predictor has been established for identifying N⁶-methyladenosine (m⁶A), which is one of the most abundant modifications in RNA [1].

To see how the web-server is working, please do the following.

Step 1: Open the web server at http://lin.uestc.edu.cn/server/iRNA-Methyl and you will see the top page of the iRNA-Methyl predictor on your computer screen, as shown in Fig.1. Click on the Read Me button to see a brief introduction about the predictor and the caveat when using it.

JMG 202-302_Kuo-Chen Chou_F_1

Figure 1. A semi screenshot for the top page of iRNA-Methyl (Adapted from [1] with permission).

Step 2: Either type or copy/paste the query RNA sequences into the input box at the center of Fig.1. The input sequence should be in FASTA format. For the examples of RNA sequences in FASTA format, click the Example button right above the input box.

Step 3: Click on the Submit button to see the predicted result. For example, if you use the query RNA sequences in the Example window as the input, you will see the following shown on the screen of your computer. (1) RNA sequence-1 contains 5 “GAC” (with adenine at its middle) consensus motifs, of which only those at the sequence positions 128 is predicted to be the methylation sites or site, and all the others are not. (2) RNA sequence-2 contains 8 “GAC” consensus motifs, of which only those at the sequence positions 332 is predicted to be the methylation sites, while all the others are not. All these results are fully consistent with the experimental observations.

Step 4: Click on the Data button to download the datasets used to train and test the model.

Step 5: Click on the Citation button to find the relevant paper that document the detailed development and algorithm of iRNA-Methyl.

It is instructive to point out that the web-server predictor has been developed by strictly observing the guidelines of “Chou’s 5-steps rule” and hence have the following notable merits (see, e.g., [2–14] and three comprehensive review papers [15–17]: (1) crystal clear in logic development, (2) completely transparent in operation, (3) easily to repeat the reported results by other investigators, (4) with high potential in stimulating other sequence-analyzing methods, and (5) very convenient to be used by the majority of experimental scientists.

Moreover, it has not escaped our notice that during the development of iRNA-Methyl web-server, the approach of general pseudo amino acid components [18] or PseAAC [19] had been utilized and hence its accuracy would be much higher than its counterparts, as concurred by many investigators [15, 18–266].

It is anticipated that iRNA-Methyl may become a useful high throughput tool for conducting genome analysis as well as drug development.

For the remarkable and awesome roles of the “5-steps rule” in driving proteome, genome analyses and drug development, see a series of recent papers [16, 17, 267–276] where the rule and its wide applications have been very impressively presented from various aspects or at different angles.

References

Chen W, Feng P, Ding H, Lin H, Chou KC (2015) iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition. Analytical Biochemistry 490: 26–33. [Crossref]
Barukab O, Khan YD, Khan SA, Chou KC (2019) iSulfoTyr-PseAAC: Identify tyrosine sulfation sites by incorporating statistical moments via Chou’s 5-steps rule and pseudo components Current Genomics.
Cheng X, Lin WZ, Xiao X, Chou KC (2019) pLoc_bal-mAnimal: predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics 35: 398–406. [Crossref]
Chou KC, Cheng X, Xiao X (2019) pLoc_bal-mHum: predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics 111: 1274–1282. [Crossref]
Chou KC, Cheng X, Xiao X (2019) pLoc_bal-mEuk: predict subcellular localization of eukaryotic proteins by general PseAAC and quasi-balancing training dataset. Med Chem 15: 472–485. [Crossref]
Ehsan A, Mahmood MK, Khan YD, Barukab OM, Khan SA, et al. (2019) iHyd-PseAAC (EPSV): Identify hydroxylation sites in proteins by extracting enhanced position and sequence variant feature via Chou’s 5-step rule and general pseudo amino acid composition. Current Genomics 20: 124–133. [Crossref]
Feng P, Yang H, Ding H, Lin H, Chen W, et al. (2019) iDNA6mA-PseKNC: Identifying DNA N(6)-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 111: 96–102. [Crossref]
Hussain W, Khan SD, Rasool N, Khan SA, Chou KC (2019) SPalmitoylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins. Anal Biochem 568: 14–23. [Crossref]
Hussain W, Khan YD, Rasool N, Khan SA, Chou KC (2019) SPrenylC-PseAAC: A sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. J Theor Biol 468: 1–11. [Crossref]
Ilyas S, Hussain W, Ashraf A, Khan YD, Khan SA, Chou KC (2019) iMethylK-PseAAC: Improving accuracy for lysine methylation sites identification by incorporating statistical moments and position relative features into general PseAAC via Chou’s 5-steps rule. Current Genomics
Jia J, Li X, Qiu W, Xiao X, Chou KC (2019) iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. Journal of Theoretical Biology 460: 195–203. [Crossref]
Khan YD, Batool A, Rasool N, Khan A, Chou KC (2019) Prediction of nitrosocysteine sites using position and composition variant features. Letters in Organic Chemistry 16: 283–293.
Khan YD, Jamil M, Hussain W, Rasool N, Khan SA, et al. (2019) pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J Theor Biol 463: 47–55.[Crossref]
YLu SA, Wang S, Wang J, Zhou G, Zhang Q et al. (2019) An Epidemic Avian Influenza Prediction Model Based on Google Trends. Letters in Organic Chemistry 16: 303–310.
Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review, 5-steps rule). Journal of Theoretical Biology 273: 236–247. [Crossref]
Chou KC (2019) Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Current Medicinal Chemistry 26: 4918–4943. [Crossref]
Chou KC (2019) Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis. Current Topics in Medicinak Chemistry 19: 2283–2300. [Crossref]
Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. Proteins 43: 246–255. [Crossref]
Chou KC (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21: 10–19. [Crossref]
Guo ZM (2002) Prediction of membrane protein types by using pattern recognition method based on pseudo amino acid composition.
Cai YD, Chou KC (2003) Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo amino acid composition. Biochem Biophys Res Comm 305: 407–411. [Crossref]
Chou KC, Cai YD (2003) Predicting protein quaternary structure by pseudo amino acid composition. Proteins 53: 282–289. [Crossref]
Chou KC, Cai YD (2003) Prediction and classification of protein subcellular location: sequence-order effect and pseudo amino acid composition. Journal of Cellular Biochemistry 90: 1250–1260. [Crossref]
Pan YX, Zhang ZZ, Guo ZM, Feng GY, Huang ZD, et al. (2003) Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. Journal of Protein Chemistry 22: 395–402. [Crossref]
Chou KC, Cai YD (2004) Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo amino acid composition. Journal of Cellular Biochemistry 91: 1197–1203. [Crossef]
Wang M, Yang J, Liu GP, Xu ZJ, Chou KC (2004) Weighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition. 17: 509–516.
Cai YD, Chou KC (2005) Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. Journal of Proteome Research 4: 967–971. [Crossref]
Cai YD, Zhou GP, Chou KC (2005) Predicting enzyme family classes by hybridizing gene product composition and pseudo amino acid composition. Journal of Theoretical Biology 234: 145–149. [Crossref]
Gao Y, Shao SH, Xiao X, Ding YS, Huang YS (2005) Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids 28: 373–376. [Crossref]
H Liu, J Yang, M Wang, L Xue, KC Chou (2005) Using Fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types. The Protein Journal 24: 385–389. [Crossref]
Shen HB, Chou KC (2005) Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo amino acid composition to predict membrane protein types. Biochemical & Biophysical Research Communications 334: 288–292. [Crossref]
Shen HB, Chou KC (2005) Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem Biophys Res Comm 337: 752–756. [Crossref]
Cai YD, Chou KC (2006) Predicting membrane protein type by functional domain composition and pseudo amino acid composition. Journal of Theoretical Biology 238: 395–400. [Crossref]
Chen C, Tian YX, Zou XY, Cai PX, Mo JY (2006) Using pseudo amino acid composition and support vector machine to predict protein structural class. J Theor Biol 243 444–448. [Crossref]
Chen C, Zhou X, Tian Y, Zou X, Cai P (2006) Predicting protein structural class with pseudo amino acid composition and support vector machine fusion network. Analytical Biochemistry 357: 116–121. [Crossref]
Du P, Li Y (2006) Prediction of protein submitochondria locations by hybridizing pseudo amino acid composition with various physicochemical features of segmented sequence. BMC Bioinformatics 7: 518. [Crossref]
Mondal S, Bhavna R, Mohan Babu R, Ramakumar S (2006) Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification. J Theor Biol 243: 252–260. [Crossref]
Shen HB, Yang J, Chou KC (2006) Fuzzy KNN for predicting membrane protein types from pseudo amino acid composition. Journal of Theoretical Biology 240: 9–13. [Crossref]
Wang SQ, Yang J, Chou KC (2006) Using stacked generalization to predict membrane protein types based on pseudo amino acid composition. Journal of Theoretical Biology 242: 941–946. [Crossref]
Xiao X, Shao SH, YS Ding SH, Huang ZD, Chou KC (2006) Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 30: 49–54. [Crossref]
Xiao X, Shao SH, Huang ZD, Chou KC (2006) Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. Journal of Computational Chemistry 27: 478–482.
Zhang SW, Pan Q, Zhang HC, Shao ZC, Shi JY (2006)Prediction protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and naive Bayes feature fusion. Amino Acids 30 461–468. [Crossref]
Zhou GP, Cai YD (2006) Predicting protease types by hybridizing gene ontology and pseudo amino acid composition. Proteins 63: 681–684. [Crossref]
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SARS-Cov2: A Review of this Novel Coronavirus

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DOI: 10.31038/IMROJ.2020511

Abstract

Background: Coronaviruses are a group of RNA viruses responsible for respiratory and gastrointestinal illness. Of this group two have been associated with pandemics in recent years. As of December 2019, a new coronavirus (SARS-CoV2) was reported in Wuhan, China which despite best efforts has spread internationally prompting panic and universal public-health measures.

Methods: A literature review was conducted utilising the PubMed database in March 2020. Search terms included ‘Wuhan’ OR ‘Coronavirus’ OR ‘2019-nCoV’ OR ‘SARS-CoV2’ AND ‘pneumonia’ OR ‘Outbreak’ OR ‘Infection’. These were used in isolation and combination to yield results. Papers were selected if they explored the diagnosis, management, transmission and/or treatment of SARS-CoV2. Following these 68 papers were screened of which 48 papers were included in this review.

Results & Discussion: Early studies highlight that SARS-CoV2 has an incubation period of between 3–7 days and commonly presents with fever (93%), cough (69.8%) and dyspnoea (34.5%) and prominent upper respiratory tract symptoms. Patients are predominantly male and there is high prevalence of significant comorbid disease in fatal cases (overall case: fatality ratio-2–3%). Encouragingly, supportive treatments are mainstay with the need for invasive ventilation and extracorporeal life support (ECLS) low. Diagnosis of SARS-CoV2 is made by demonstration of the virus by RT-PCR of throat/lavage specimens; however given the propensity for false negatives, CT-imaging is being used diagnostically with characteristic findings reported and can even detect disease in the asymptomatic phase where transmission is possible. International interventions have been to adhere to 14-day observation periods for suspected cases, the wearing of N95 facemasks alongside social distancing and hand-hygiene due to fomites. While antiviral treatments have been trialled in case-series no clear consensus has been made regarding their use but it remains clear that concurrent antibiotics are mainstay with restrictive fluid replacement. Emerging therapies which may show benefit include chloroquine, remdesivir, tocilizumab, azithromycin and sunitinib with the discontinuation of ACE inhibitors proposed.

Conclusion: SARS-CoV2’s impact is greatest in the elderly and comorbid. Research has indicated key targets which may be important in producing effective treatments and an efficacious vaccine. On-going aims must be to try to alter behaviours and limit viral spread through social distancing, good hand hygiene and Personal Protective Equipment (PPE). Future trials must attempt to prognosticate patients further and understand the role if any of redeployed treatments which have showed some promise. In addition, questions surrounding long term immunity require further investigation.

Introduction

Coronaviruses are a group of spherical positive-strand RNA viruses. Prior to SARS-CoV2 (formerly known as Ncorv-19/covid-19) six coronaviruses were thought to exist, with two causing pandemics in recent times: Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS). This group of viruses are not unique to humans and can be found in birds, domesticated animals, camels and bats who serve as hosts and reservoirs of the disease [1, 2]. It is thought that 30% of annual respiratory illnesses are caused by coronaviruses leading to wide ranging presentations from pharyngitis to pneumonia, with a predominantly respiratory droplet spread. Increasingly, human-human transmission and transmission by asymptomatic carriers of coronaviruses is being appreciated [3, 4].

On 8–9^th December 2019, 7 patients presented within Wuhan, Hubei province, China with a viral pneumonia of unknown origin, but a common exposure to the Huanan seafood market where many wild animals are held in close proximity [1, 5]. As of 31^st December the World Health Organization (WHO) were informed of these novel cases and the first identification of this virus as a coronavirus was on the 9^th January 2020. Complete analysis of the viral genome revealed it is a beta coronavirus with 79% similarity to SARS and 50% similarity to MERS, which are also beta coronaviruses, but is nearly completely homologous with two bat derived SARS who are the likely host and reservoir of the SARS-CoV2. The viral genome was made publicly available from the 12^th January 2020 allowing for the first Reverse Transcriptase polymerase chain reaction (RT-PCR) testing to occur [3, 6]. The structure of a coronavirus is such that there are Spike (S), Envelop (E) and membrane (M) proteins. The (S) protein is the primary determinant of cellular infection and it itself has two domains; S1 which mediates receptor binding, and S2 which allows for virion fusion to the cell membrane. Distinct in SARS-CoV2 is an additional activation loop in the S1/S2 proteins not shared with either MERS or SARS [7]. As of 23^rd March 2020, 170 countries have declared cases, with patient numbers globally standing at >300,000, and over 14,509 deaths with a reported case fatality rate of 2–3% which is much lower than initial reports highlighted [8, 9]. In comparison MERS spread to 27 countries and resulted in 858 deaths with a case fatality rate of 34.4% with SARS affecting 26 countries with 774 deaths: a corresponding case fatality rate of 9.5%. It is because of the inexorable spread of this virus that as of 30^th January 2020 the WHO branded SARS-Cov2 as a global public emergency. This has resulted in a multitude of countries taking enormous steps to limit international travel, and more dramatic interventions here in the UK for example with public transport limited to key workers. This was provoked by by the first wave of mortality here in the UK and quarantine in Italy [10, 11]. Borne from this panic there are increasing reports of discrimination against Chinese people, and mounting hysteria with regards to the possible apocalyptic nature of this outbreak. However, even with the rapid increase in cases noted through means such as a contact tracing and screening, overall case: fatality rate remains 2% with severe disease occurring predominantly in the elderly with attendant comorbidities [12].

In this review we explore the knowns of SARS-Cov2 regarding its prevention, diagnosis, presentation and management and the vast unknowns which have made this disease entity the one of the most globally important of the century so far.

Methods

Literature review was conducted in March 2020. Papers were identified via a literature search using the PubMed database. Papers were selected if they were written in English and published in peer reviewed journals between December and March 2020. Articles were selected if they documented cases of SARS-CoV2 (formerly known as 2019-nCoV/n-cov19) and dealt with the diagnosis, management, transmission and/or treatment of the infection.

Search terms included ‘Wuhan’ OR ‘Coronavirus’ OR ‘2019-nCoV’ OR ‘n-cov19’ OR ‘SARS-CoV2’ AND ‘pnuemonia’ OR ‘Outbreak’ OR ‘Infection’. These terms were used in isolation and combination to locate search results.

This search obtained 78 results from which both authors conducted a screen of their titles, study design and abstracts. Issues highlighted at this stage were addressed by contacting the paper authors. Papers were excluded if they were not written in English, not peer reviewed, did not explore in detail the diagnosis, management, transmission and treatment of SARS-CoV2 leading to 57 papers. Data was extracted from each paper regarding the population, end points, follow up and methods employed. All papers with a conflict of interest or, publication bias and overlapping patient or outcome data were removed. Application of the above criteria yielded 48 papers which were utilised in this review.

Results and Discussion

Following the identification of SARS-CoV2, much has been done to understand the natural history of the condition and its presentation. Early studies revealed that SARS-CoV2 has an incubation period of between 3–7 days with a case series reporting a median of 7 days (4.0–8.0 days) between symptom onset and hospitalisation and from symptom onset to ICU admission 10.5 days (8.0–17.0 days) [5, 13]. In terms of presentation fever (93%) was the most common initial symptom followed by cough (69.8%), dyspnoea (34.5%), sore throat (15%), headache (7.2%) and diarrhoea (6.1%) which corroborates with other studies [5, 6]. These symptoms are common amongst the betacoronaviruses however SARS-CoV2 uniquely also presents with upper respiratory tract infection (URTI) symptoms including sore throat, rhinorrhoea, anosmia and sneezing. Of those patients who went on to die, death occurred between 6–41 days after symptom onset (median 14 days). Unsurprisingly in those patients >70 years old the interval between symptom and onset and death was significantly decreased [13, 15].

To date one of the largest reviews of SARS-CoV2 patients (n=278) utilised data from 3 large scale studies. Of included patients 61.9% were male with an average age of 54.7 years. This gender disparity is described elsewhere in the literature with a male preponderance of up to 70% reported [16]. Among study patients the most common comorbid conditions included hypertension (15–31%), cardiovascular disease (14–40%) and type 2 diabetes mellitus (10–19%). Results from this review revealed that 72 patients required Intensive Care Unit (ICU) admission, 56 of these being diagnosed with Acute Respiratory Distress Syndrome (ARDS). Of these 23 (8% total cohort) required invasive ventilations and 9 (3% total cohort) required extracorporeal life support (ECLS). Overall mortality differed amongst the incorporated studies ranging from 4–14% with inherent differences between study groups [6]. In a review of 99 patients with SARS-CoV2 treated at a major tertiary infectious disease hospital in Wuhan, only 4% required invasive ventilation and 3% required ECLS respectively. Importantly these patients could be predicted to need higher levels of support with existing early warning score models. However, no comparison of patients in these studies receiving such treatments in the context of their MuLBSTA score was undertaken. The MuLBSTA score is a validated tool for assessing the 90-day mortality risk using 6 parameters in the setting of viral pneumonia, somewhat synonymous with the CURB-65 severity score of community acquired pneumonias [17, 18]. Formal studies are required to assess the use of this scoring system and others in predicting outcomes in SARS-CoV2.

Common amongst these multiple reviews is the higher prevalence of disease in middle-aged men with pre-existing cardiovascular disease. One debated risk factor is that of smoking. Interestingly, smoking status in affected patients is both poorly recorded and investigated. Of the one systematic review that could be found on this subject utilizing only 5 papers, a positive association between smoking and both disease progression (OR 1.28) and ICU admission (RR 2.4) was recorded. However no relationship between relative tobacco exposures or e-cigarette use and these outcomes was explored [8, 19]. While larger and further studies are needed it would seem prudent that physicians and health agencies reinforce the need for smoking cessation especially as the public are possibly more motivated to follow health messages at this time. While it is necessary in a minority to utilise invasive ventilation and ECLS, it remains unclear what benefit the latter will offer in SARS-CoV2. Whilst ECLS was utilised in SARS to modest effect, mortality reduction was seen in some cohort studies of MERS [20, 3]. However, experience from the larger H1N1 pandemic of 2009–2011 has cast doubt regarding the efficacy of ECLS in ARDS of viral aetiology, with the EOLIA trial suggesting that 60-day mortality is not altered with this therapy versus invasive ventilation. Recent guidance from the WHO recommends prone ventilation, restrictive fluid practice and lung protective ventilation. Whilst there has been enthusiasm for corticosteroid use in SARS-CoV2 ARDS, there is little evidence for their prescription, and they are not currently recommended having shown no promise in SARS or MERS previously. At present high-flow nasal oxygen therapy is considered a good highly available intervention that can in patients prevent the need for intubation but requires intense monitoring [21–25]. Given the large numbers of patients affected by SARS-CoV2, massive coordinated action must be undertaken by health agencies to increase both ICU beds and expertise amongst non-ICU trained staff on traditional hospital wards. It may be however that the propensity for severe respiratory disease in some patients may be linked to neurological involvement. Evidence from studies of SARS in both human and animal models indicated it could cause severe brainstem infection and by involving the cardiorespiratory centres depress respiration centrally. Whilst this is difficult to reverse, awareness of this possible mechanism of respiratory decline is important for all physicians to consider in the nonresponding patient [26].

As mentioned previously, predicting outcomes in patients is key to allow for health services to deploy resources accordingly. Analysis of patients requiring ICU admission versus those who did not revealed statistically increased total neutrophils, decreased lymphocyte counts and increased serum pro-inflammatory cytokines levels (e.g. IL-6, TNFα), highlighting the role of immune activation and the ‘cytokine storm’ in SARS-CoV2 infection. Moreover, serum IL-6 and TNFα levels have been found to correlate with risk of death in SARS-CoV2 infection [16, 8]. Key to the innate immune systems response to viral infection is the production of interferon following recognition of viral associated pathogen associated molecular patterns (PAMPs) such as viral RNA by toll-like-receptors leading to activation of signalling cascades. It appears clear that there is attenuation of this process in the closely related SARS infection, with immune evasion mechanisms at work including degradation of RNA detecting proteins, which in part explains the prolonged incubation period of SARS-CoV2 versus other common viral infections. For long term control of viral infections Th1 adaptive immune responses are needed with evidence Th2 responses in SARS-CoV2 is linked to negative outcomes. Therefore, a prerequisite for any future vaccines trialled in SARS-CoV2 must be to produce this response with DNA based vaccines more likely to elicit strong durable immune responses of this nature [27].

Currently the diagnosis of SARS-CoV2 requires pathological evidence of the virus through RT-PCR of suitable samples (e.g throat swab/bronchoalveolar lavage (BAL), however radiological imaging is important in determining both severity of disease and ruling out differentials [13]. Imaging studies (CXR/Computer Tomography (CT)) of affected patients demonstrates that SARS-CoV2 presents variably with bilateral ground glass opacifications the most common finding, but focal/lobar consolidation, pleural effusion, mediastinal lymphadenopathy and pneumothorax also being described [4, 28]. In addition CT findings vary in SARS-CoV2 infection depending on the underlying stage of the disease (Table 1). Currently no serological testing is routinely used in SARS-CoV2 diagnosis.

Table 1. CT findings by stage of SARS-CoV2 infection [13].

Stage (Days)	CT Findings
Ultra-Early	Focal / ground glass nodules
Early [1–3]	Congestion of alveolar capillaries, interstitial oedema
Rapid Progression [3–7]	Worsening alveolar and interstitial oedema
Consolidation [7–14]	Multiple patchy consolidation, fibrous alveolar exudates
Dissipation [14–21]	Strip like consolidation

One study assessing the range of radiological findings of SARS-CoV2 in 90 patients (39 men; 51 women; median age 50 years) showed that on baseline noncontrast CT 69/90 (77%) had radiological abnormalities with 53% having multiple lobe involvement with a predilection for the lower lung lobes noted. The most common radiological pattern was ground glass opacification (72%) followed by consolidation (13%), in addition 56% had pleural thickening. It is thought ground glass opacities represent diffuse alveolar damage and their filling with pus or exudate. Of the 52 patients reimaged between 1–6 days post initial CT, 19% had no changes, 73% had disease progression and 3% demonstrared new bilateral ground glass opacities having had normal previous imaging [28]. Interestingly, in a retrospective analysis of 51 confirmed patients with SARS-CoV2 of median age 45 years, while only 36 (71%) had positive RT-PCR throat swabs on first testing with a further 12 testing positive on the second throat swab this compared to 50/51 (98%) on initial noncontrast CT showing signs of viral pneumonia (p<0.001) [29]. This corresponds to a large review of 1014 cases in China where the sensitivity of chest CT was 97%, with 70% of patients with a negative RT-PCR test showing typical CT findings for SARS-Cov2 infection. This highlights an important fact that the total positive rate of RT-PCR of throat swabs is between 30–60% and as such cannot be relied upon by itself for diagnosis and may miss affected patients. Importantly, the time between initial negative to positive RT-PCR testing of throat swabs can be up to 6 days [30]. Moreover, there have been findings of CT changes in keeping with SARS-CoV2 in otherwise asymptomatic patients suggesting that even subclinical disease can lead to pathological changes to the lung space and therefore a low threshold of imaging should be used in patients with suitable exposure to allow for earlier treatment [4]. Therefore, the ongoing focus for diagnosis must be to further optimise the RT-PCR testing process including sample collection, transport and storage to ensure no possible tampering of results. Clinicians must therefore not be deterred by a negative result in the context of a high index of suspicion; although vigilance for superadded bacterial or fungal processes should be both considered and investigated as required.

At present no cases of death have been reported in children, although cases have been reported in children between ages 1.5 months-17 years and appear to manifest in a similar way to adult patients albeit milder with recovery anticipated within 1–2 weeks [31]. It remains unclear the possible mechanism of this protection afforded by youth. Encouragingly there is no evidence of maternal transmission of SARS-CoV2 during pregnancy like both SARS and MERS. However, it is in this patient group that worse outcomes are seen with pneumonia of all types with 25% of those affected needing ventilatory support [1]. While no formal cases of SARS-CoV2 have been identified in pregnant women lessons can be learnt from the SARS and MERS pandemic. In the seven cases of SARS in pregnant women in Hong Kong: four sustained spontaneous miscarriage in the first trimester. Moreover, in case control studies pregnant women with SARS were more likely to suffer renal failure and disseminated intravascular coagulopathy (p=0.006) with 60% and 40% requiring ICU admission and intubation respectively. In MERS of the five documented cases all required ICU, with two mothers dying, and two cases of perinatal death recorded [1]. As such a low threshold for testing and isolating pregnant women is needed, and while no link to the unborn child has been found given the severity of related coronaviruses in this patient group, treatment should be given without delay.

Key to the control of this pandemic is to limit the spread of SARS-CoV2 which is linked to the transmissibility of the virus itself. One key aspect is its reproductive rate (R0 value). This refers to the average number of people who will be infected from one contagious person and is quoted to allow us to predict potential spread of a pathogen. Based on initial data and cases as they appeared, quoted R0 values have included 1.68 (31 December – Jan 2020) 2.24 (10–24^th January 2020) [6], case reports quoting 5.5 with the WHO quoting between 1.4–2.5. This is in keeping with most major viral infections which have resulted in global concern (Table 2) [32, 33]. It remains to be seen if the R0 of SARs-CoV2 continues to change, this is because notable among the coronaviruses is the frequency of both replication errors and recombination events that can take place during its reproductive cycle and can result in quasispecies with increased virulence. So far analysis of the SARS-CoV2 genome shows little evidence of significant recombination. However, with more cases and geographical spread of the virus this may change [33]. One way to decrease the R0 has been to limit both domestic and international travel with at international airports and borders thermal scanning being undertaken to detect possible cases however when this method was evaluated during the H1N1 pandemic a sensitivity of only 5.8% was reported. Regardless however it can be considered important in raising public awareness of the condition and improve overall participation in risk reducing behaviours such as social distancing [2]. Upon analysing the effect of travel restrictions on SARS-CoV2 dissemination since 23^rd January 2020, utilising the Global Epidemic and Mobility Model (GLEAM), it has been shown that even in the instance of 90% travel reductions if the transmission rate of SARS-CoV2 remains constant then the delay achieved in peak number of cases is only by 2 weeks with the peak number of cases in Wuhan expected in March 2020. Therefore, key to any government strategy for SARS-CoV2 is to propagate effective hygiene behaviours and enforce quarantine. This requires considerable investment in easy to access, reputable public information sources and importantly quashing of false information that can now be easily disseminated through social media services. [34] At present common practices include 14 days observation period for exposed and symptomatic persons and to attend hospital if concerned or systemically unwell, wearing of N95 masks by healthcare workers who have had close contact to affected cases and standing at least 1m from other members of the public. These recommendations are supported by the Centres for Disease Control and Prevention (CDC) and have since become internationally adopted [13, 35]. Suspected cases here were defined as those individuals who have been in close contact with confirmed cases in a workplace/study/vehicular environment or have been involved treating a confirmed case. These recommendations are even more relevant given the first reports of human-human transmission (20^th January 2020) which involved a family cluster with a male in Vietnam developing the infection having never travelled to an affected area but only having contact with his parents who had flown from Wuhan some 3 days earlier.[36] Moreover, in the hospital environment reports have circulated of nosocomial outbreaks of SARS-CoV2 highlighting the need for strict hand hygiene and Personal Protective Equipment (PPE) use particularly in aerosol generating procedures. One study assessing 3 patients treated in Singapore in isolation rooms with air sampling, environmental and PPE sampling showed that there was significant environmental and PPE contamination, particularly on the toilet bowel and sinks but not outside of the isolation rooms themselves. This highlights firstly the possibility of faecal shedding of the virus further suggested by the symptoms of diarrhoea in reported cases but also the awareness needed of fomite transmission Emerging evidence suggests the virus can persist on plastic and metal surfaces for up to 3 days. Interestingly after routine decontamination practices these isolation rooms showed no contamination whatsoever proving current procedures are adequate when performed properly and regularly and that adequate PPE resources are mandatory to limit hospital spread [37, 38]. One contentious issue however is mass public masking. This is for several reasons this includes not only a global shortage of masks but also a lack of evidence regarding its effectiveness. While logically their use limits droplet spread, they have never been truly studied in this regard being designed for occupational not environmental exposures. It is because of this the WHO offers rather vague guidance in this regard. However, it must be remembered that if hand hygiene isn’t followed than masks can usher a false sense of security and can easily become contaminated and thereby useless. Regardless clear alternatives are needed given that disposable surgical masks provide huge financial and environmental burdens [39].

Table 2. Relative reproductive rates of commonly encountered viruses [32, 33].

Virus	R0 value
SARs-CoV	2–5
MERS-CoV	<1
H1N1	1.2–1.6
Rhinovirus	6
HIV Measles Influenza Ebola	2–4 12–18 1–2 1.5–2.5

At present there is no consensus on the treatment of SARS-CoV2. Likewise, while many treatments were trialled in the case of SARS/MERS no vaccine was produced. Review of the literature reveals that antibiotics are being trialled in up to 90% with 76–85% receiving antivirals. Other treatments trialled include intravenous immunoglobulin (IVIg) and corticosteroids but none have demonstrated dramatic success [6, 17]. Antiviral choice so far has included among others oseltamivir, ganciclovir [17], lopinavir/ritonavir [2] and while a tendency to improved outcomes and/or reduced viral loads have been reported these have been restricted by their small sample sizes and inadequate methodology. Novel medications that may help in SARS-CoV2 infection include combination chloroquine with remdesivir and hydroxychloroquine. Indeed, in a multiple drug panel analysis looking at half-cytotoxic concentration and selectivity index both chloroquine and remdesivir (adenosine analogue) were able to do so at low micromolar concentrations in in vitro assays and have a proven safety record with the former of these used for some 70 years in clinical medicine [6, 40]. Indeed since this initial report intravenous remdesivir has been given in a case report with a positive outcome [41]. Underlying chloroquine’s antiviral effect is alteration to lysosomal pH which is thought may reduce SARS-CoV2 membrane binding with hydroxychloroquine demonstrating up to 3 times the potency of chloroquine in vitro. Of the few open label clinical trials that exist, one demonstrated that of the 20 patients who received hydroxychloroquine (of which 6 also received azithromycin) a reduction at 6 days in viral carriage of 57.1% and 100% was reported. No evidence of cardiotoxicity was observed in these patients. However, this study was limited by its high dropout rate and limited follow up but does demonstrate some possible means of both prophylaxis and treatment in shedding patients [42]. Common to some of these drugs are their anti-rheumatic properties and it may be that rheumatoid arthritis patients who are on these drugs are less likely to develop SARS-CoV2, although this has not yet been epidemiologically demonstrated. IL-6 blockers (e.g. tocilizumab) have been among rheumatoid drugs shown to have some efficacy in SARS-CoV2 infection and can be assumed to limit the cytokine storm which has been observed in critically ill patients. Small initial retrospective studies in SARS-CoV2 infection patient show that tocilizumab can produce clinical, biochemical and radiological improvements even in severe disease and has now been chosen for phase 2 trials [43]. As we understand more about SARS-CoV2 life cycle so has our identification of potential drug targets. It is now appreciated that SARS-CoV2 utilises the Angiotensin Converting Enzyme 2 (ACE2) receptor to enter mammalian cells via receptor mediated endocytosis with TMPRSS2 a serine protease implicated in this process. The ACE2 receptor is ubiquitous but is particularly expressed within AT2 alveolar epithelial cells which are prone to viral infection and raises two main points. Firstly, Animal studies suggest that ACE inhibitors can significantly increase ACE2 activity, and therefore may potentiate SARS-CoV2 virulence. As such we must determine whether we can limit the risk of severe disease by switching these agents when appropriate. This would be a very easy and inexpensive option that could be pre-emptively done [44, 45]. Secondly, key to infection of these cells is AP2-associated protein kinase 1 (AAK1), which on drug screening is effectively blocked by sunitinib and erlotinib and could represent potential drug targets. It is thought this would offer a means of decreasing virion entry into cells alongside camostate mesylate, a TMPRSS2 inhibitor which has proven in vitro activity in preventing SARS-CoV2 infection in multiple lung cell lines. However, sunitinib and others of the class have significant side-effects such as Posterior Reversible Encephalopathy Syndrome (PRES), Thyroiditis and pancytopenia. Baracitnib, a closely related JAK-kinase inhibitor may avoid these issues however safety concerns with regards to potentiating progression of SARS-CoV2 infection have been raised [44, 46, 47]. While targeting of the S1 subunit of the virus seems ideal considering its role in entering susceptible cells , previous studies in MERS have shown the (E) protein is integral and that when it is deleted both defective viral propagation and beneficial host immune responses to the virus are seen. Considering this (E)-protein based therapies may possibly yield promising results. Suitable animal models could include ferrets as they can replicate some of the URTI symptoms of the condition and therefore the main method by which SARS-CoV2 spreads this, deserves further investigation [3, 48].

Conclusion

SARS-CoV2 represents the 7^th coronavirus and is novel in both its genomics, degree of spread and the fear it has inspired. Thus far its impact is greatest in the elderly and comorbid. Early institution of mechanical ventilation to control droplet spread may be important with consequences for critical care service planning, and in more severe ARDS presentations, there will be increasing demand for invasive therapies, which should primarily be undertaken by experienced centres with high case volume. Research has indicated key targets which may be important in producing an efficacious vaccine, but this may not be available until later waves of disease, as has been seen with prior pandemics. At present current aims must be to try to alter behaviours and limit viral spread, especially as we expect to reach the peak number of cases soon and aware of the viruses stability on surfaces. In established severe disease, lung protective ventilation and prone positioning will be important. Trials should now be undertaken to further prognosticate patients and understand the role of redeployed treatments have in SARS-CoV2 infection as well as the likelihood of possible long-term immunity in those who survive.

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Comprehensive Risk (Genetic and Acquired) Stratification for Primary Prevention of CAD-Genetic Risk of CAD

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DOI: 10.31038/JMG.2020315

Abstract

Coronary Artery Disease (CAD) is the number one cause of death in the world. Epidemiologists’ claim 50 percent of CAD is genetic. The identified first genetic risk variant, 9p21, was discovered in 2007, and subsequently, efforts have led to identifying hundreds of genetic risk variants predisposing to CAD. CAD is preventable based on clinical trials showing reduction in conventional risk factors, such as cholesterol, is associated with 30–40 percent reduction in cardiac mortality and events. Statin therapy, which lowers plasma cholesterol, is very safe and effective. About 50 percent of all Americans living a normal lifespan will experience a cardiac event. The challenge is selecting among asymptomatic individuals, the 50 percent who would benefit most from prevention. Conventional risk factors are age-dependent, while genetic risk variants are independent of age, and can be determined anytime from birth on, since one’s DNA does not change in one’s lifetime. Utilizing a microarray containing the genetic risk variants, and DNA from saliva or blood, studies was performed in over 1 million cases and controls. Genetic risk variants were shown to be relatively independent of conventional risk factors and offer greater discriminatory power in stratifying for CAD risk. Individuals with the highest genetic risk score (GRS) had the highest risk for CAD and benefitted most from statin therapy. A recent study employed the genetic risk in a sample size of 55,685 individuals. Those with a high GRS for CAD (20%) had a 91 percent higher risk for cardiac events. Individuals with a healthy lifestyle and high GRS had a 46 percent lower risk for cardiac events in comparison to those with an unfavorable lifestyle thus, genetic risk can be reduced. Utilizing the GRS to risk stratify for primary prevention of CAD will represent a paradigm shift in halting the spread of this pandemic disease.

Keywords

genetics, coronary artery disease, risk prediction of CAD, prevention of CAD

Comprehensive Risk (Genetic and Acquired) Stratification for Primary Prevention of CAD

Introduction

Coronary artery disease (CAD) is now well established as the number one cause of death in the world, accounting for about 1/3 of all deaths, equally affecting males and females. While, CAD was, until recently, primarily a disease of the western world, it is now pandemic involving low, middle, and high income countries [1]. While, the disease has been decreasing in the west, there has been a marked increase in the east. It is estimated that about 50% of males or females, who live a normal lifespan in the U.S, will be expected to encounter at least one cardiac event [1]. Similar statistics apply to most of the western world. Secondary (already have had a cardiac event) prevention has been very successful by modifying the conventional risk factors predisposing to CAD (cholesterol, hypertension, diabetes, smoking, sedentary lifestyle, obesity, and family history). These studies confirm that CAD is preventable, however to halt the pandemic spread one will have to initiate primary prevention. A major challenge is to identify among asymptomatic individuals those who will benefit most from preventive therapy for CAD. The recent discovery of genetic risk variants predisposing to CAD offers a new tool to select and motivate asymptomatic individuals at risk for CAD.

The Pursuit of Genetic Variant Predisposing to CAD

Epidemiologists have for decades, claimed that approximately 50 percent of predisposition for CAD is inherited [2–4]. Most common chronic diseases are claimed to have similar genetic predisposition. CAD is a polygenic disorder in which genetic predisposition is transmitted by multiple genes, each with only minimal risk. The human genome has 3.2 billion base pairs, referred to as nucleotides. Sequencing studies have shown that, comparison of any two human genomes show a variation in DNA sequence of only 1 percent, with 99 percent being identical [5–8]. Most of this 1 percent difference is due to large structural variants, such as inversions, or copy number variations, with the remainder being due to single nucleotide polymorphisms (SNP). The number of SNPs in the genome is fairly constant, at approximately 5 million per genome [9]. While, the SNPs account for less than 1/10^th of 1 percent of the human genome sequences, they are claimed to be responsible for 70–80 percent [9] of the variation underlying the unique features of human beings (eg color of your hair, predisposition to disease).

To pursue the genetic variants predisposing to CAD, it was necessary to adopt an unbiased approach which would require DNA markers, spanning the whole human genome. The most desirable would be, having markers evenly distributed throughout the genome, at intervals of 3,000 to 6,000 bases, which would require at least one million DNA markers. The millions of SNPs annotated by HapMap [10,11] provided the necessary DNA markers to span the genome. Secondly, in designing the study we adopted the Case Control Association Approach, in which the frequency of markers in the cases would be compared to that of controls [12–13]. This would be referred to as a Genome Wide Association Study (GWAS). Any DNA marker that occurred more frequently in the cases, than in controls, would indicate it is a risk for CAD, or is in close physical proximity to a sequence that increases risk for CAD. This approach of using a million markers would present a major problem if we accepted a p-value of 0.05, in comparing the frequency of a marker in cases versus controls, as it would result in 50,000 false positives. Thus, a statistical correction was necessary. We agreed that the most stringent would be a Bonferroni whereby the p-value of 0.05 would be divided by one million, giving a required p-value of 5×10^–8. This p-value subsequently became known as genome wide significant and further enhanced the need for a large sample size. A further requirement was all SNPs associated with increased risk for CAD of genome wide significance had to be confirmed by replication in an independent population.

Discovery of 9p21 as a Risk Variant for CAD and the Formation of an International Consortium

In our initial attempt to identify the first genetic risk variant predisposing to CAD we hypothesized that CAD as a common disorder would be due to genetic variants that occurred frequently worldwide with each variant conferring minimal risk. We adopted the Case Control Association Approach which had been shown to be successful in diabetes [14] and selected 100,000 SNPs as markers to span the genome. Also SNPs showing statistical significance required replication in one or more independent populations. The discovery of 9p21 was the result of genotyping a total population of over 23,000 individuals from Canada, US, and Denmark [15]. Simultaneously and independently, the Icelandic group also identified 9p21 [16]. Shortly thereafter, 9p21 was confirmed by the Wellcome Trust Group Case Control Consortium (WTGCCC) [17] and subsequently in many other cohorts of Europeans and East Asians [18].

The name, 9p21, refers to being on chromosome 9, the short arm, at band 2.1. 9p21 occurs in about 75 percent of individuals of European ancestry, with each copy associated, with a 25 percent increase in relative risk for CAD. It is of note, that 9p21, was found to be independent of all known risk factors for CAD, implying that risk factors, other than the known conventional risk factors, contributed to the pathogenesis of CAD. The observation that 9p21 imparted only minimal risk confirmed our hypothesis that there would be multiple risk variants, each exerting only minimal increased risk. This further enhanced our need for a large sample size. This lead to the formation of an international consortium, referred to as CARDIoGRAM [19], and subsequently with other additional investigators known as CARDIoGRAMplusC4D. The initial sample size consisted of 22,233 cases and 64,762 controls, with a replication in an independent population of 56,682 [19].

A decade later, the CARDIoGRAMplusC4D investigators, along with other independent investigators, have identified over 200 genetic risk variants predisposing to CAD, which has been summarized in several reviews [20–24]. All of these risk variants are of genome wide significance and have been replicated in an independent population. There were hundreds of other genetic risk variants which did not reach the genome wide significance (P-value of 10^–8), but did satisfy the statistical criteria of false discovery of less than 5 percent. If one combines the risk variants obtained by genome wide association studies using either the corrected 10^–8, or false discovery rate of 5 percent significance, they would account for 30–40 percent of the expected heritability of CAD [24].

Genetic Risk Variants for CAD are Multiple and Occur Commonly

Table 1 shows some of the features of genetic risk variants predisposing to CAD. They are very common, and over 50 percent of them occur in more than 50 percent of the population. This is in keeping with the original hypothesis, that genetic variants predisposing to common diseases would be transmitted by DNA variants that occur commonly. (2) Each genetic risk variant transmits only minimal increase in relative risk, which on the average for CAD, is less than 10 percent. (3) Over 75 percent of the genetic risk variants occur in non-protein coding regions and tend to cluster in regulatory elements. Thus, the influence of these genetic variants is mediated through regulation of protein coding sequences, upstream or downstream (cis acting), and possibly through interacting with protein coding region on other chromosomes (trans acting). Lastly, about two-thirds of the variants do not mediate their risk through any of the known conventional risk factors. Elucidation of the molecular pathways by which these unknown genetic risk variants mediate their risk, will contribute significantly to our understanding of the pathogenesis of CAD and provide targets for development of novel therapy.

Table 1. The Features of Genetic Risk Variants for CAD.

Genetic Risk Variants for CAD Occur Commonly

Each Genetic Risk Variant for CAD Imparts Minimal Risk

75% of Genetic Risk Variants for CAD Occur in Non-Protein Coding Regions

2/3 of Genetic Risk Variants for CAD are Independent of Traditional Risk Factors

Total Genetic Risk Burden of CAD is Proportional to the Number of Risk Variants Inherited

Since, each genetic risk variant contributes only about an 8 percent increase in risk for CAD; the total genetic risk burden is proportional to the number of risk variants inherited, rather than any single variant. A single number for genetic risk of CAD can be obtained by knowing the number of genetic risk variants inherited by each individual, and taking into account the risk transmitted by each variant for CAD. The Genome-Wide Association Studies (GWAS), have previously identified the increased risk for each variant. Utilizing blood or saliva, one can genotype the extracted DNA for the number of genetic variants inherited by any one individual. The range for each single genetic risk variant is zero to two. It is zero (neither of the parent’s has the genetic variant), 1 (if only one of the parents transmits the genetic variant), and 2 (if both of the parents transmit the genetic variant). In accounting for the risk, it is standard practice to determine the weighted risk by multiplying the number of copies of each genetic risk variant times the natural log of the odds ratio [25]. The number resulting from summation of these products is the numeric Genetic Risk Score (GRS).

Clinical Trials Indicate CAD is a Preventable Disease

Several risk factors have been recognized since the 1960s that predispose to CAD. A central culprit in the pathogenesis of coronary atherosclerosis is that of cholesterol primarily due to plasma LDL (Low-Density Lipoproteins) cholesterol. Multiple trials over the past three decades have documented 30 to 40 percent decrease in cardiac events associated with decreasing plasma LDL-C [26, 27]. Other risk factors for CAD include hypertension, diabetes, smoking, age, family history, and sedentary way of life. Reduction in risk of these factors by lifestyle changes along with certain drugs has consistently shown a marked reduction in cardiac events due to CAD. Most of the studies have been performed in individuals who have already experienced a cardiac event, thus consist of secondary prevention of recurring events. Nevertheless, studies of primary prevention, primarily decreasing plasma LDL-C and controlling hypertension consistently show decreased cardiac events [28].

Evaluation of the Genetic Risk Score to Risk Stratify for CAD in the Implementation of Primary Prevention

One of the ultimate aims for pursing genetic risk for CAD was to develop a more sensitive and appropriate technique to select individuals who would benefit most from primary prevention. It is obvious that patient selection for secondary prevention simply requires proof the individuals has had a cardiac event or has significant coronary atherosclerosis. Selecting asymptomatic individuals without a cardiac history is a much greater challenge. One might champion treating everyone with a statin drug who has increased plasma LDL-C. However, the level of plasma LDL-C currently used as a target for effective control is ≤ 70 mg/dl [29–31]. This is confounded by the observation that the average plasma LDL-C in an American male in his 40s is 147 mg/dl and in a female is 121 mg/dl [29–31]. Thus, do you want to treat everyone with a statin, knowing only 50 percent will benefit? Nevertheless, Mendelian randomization studies show it is crucially important to lower the plasma LDL-C earlier in life rather than at mid-age or later in males. Ference et al. in a recent Mendelian randomization study showed a 55 percent reduction in cardiac risk for each mmol/L (38.7 mg/dl) reduction in plasma LDL-C [32]. In contrast to clinical trials in which exposure is usually for 5 years, the observed average is 20 percent reduction of risk for CAD for each mmol/L reduction in plasma LDL-C [33]. The reciprocal of this observation was also determined from longitudinal meta-analysis showing the risk for CAD doubles for each additional 10 years of exposure to increased plasma LDL-C [34].

The conventional risk factors, such as cholesterol, that we currently use for primary prevention are unfortunately age dependent and become more accurate and sensitive as one gets older. This is illustrated for the number one risk factor for CAD, namely plasma LDL-C (see table 2) The genetic risk factors if shown to be effective predictors of genetic risk for CAD would have the advantage of being independent of age, since one’s DNA does not change in a lifetime.

Table 2. Lifetime Changes in Plasma LDL-C.

The plasma LDL-C of neonate	21–39 mg/dl
Second decade of life	90 mg/dl
Fourth decade of life	130–150 mg/dl

The first study to evaluate the GRS as a means of stratifying for CAD risk was that of Mega [35]. This study performed in 2015 utilized only 27 genetic risk variants for CAD to genotype a sample size of 48,421 individuals. The study population consisted of four clinical trials, including Jupiter, which evaluated the role of decreasing plasma LDL-C, with a statin. Two of the trials involved secondary prevention and the other two involved primarily prevention. The GRS stratified the individuals into high, intermediate, and low risk. The high risk group identified by GRS was also the same group that had the most effect from statin therapy. The GRS was equally effective in identifying individuals for primary or secondary prevention. Furthermore, individuals with a high GRS required treatment of only 25 individuals with statin to prevent one cardiac event. This is in contrast to over 100 that would require treatment if stratified on the basis of known conventional risk factors. Similar results were obtained upon genotyping individuals in the West of Scotland Coronary Prevention Study (WOSCOPS). Individuals in the high genetic risk group exhibited a risk reduction of 44 percent versus a relative risk reduction of only 24 percent in others [36]. Moreover, in order to prevent one coronary event with statin therapy, those in the high genetic risk group required treatment of 13 versus 38 in the low risk group. The results confirmed that GRS has higher efficacy and is more discriminatory in risk stratification of CAD than conventional risk factors. The GRS was better at discriminating who is at a higher risk for CAD and identified individuals who would benefit the most from statins.

Risk Stratification for CAD Utilizing a Polygenic Score

The current genetic risk variants account for only about 30–40 percent of inherited risk for CAD. The inclusion of more genetic risk variants would be expected to further enhance the power of prediction and risk stratification of CAD. One technique by Inouye [37] was to include less stringent statistics, such as a false discovery rate of only 5 percent. This gave rise to a microarray containing 1.7 million genetic risk variants. The other approach by [38] included a technique that predicts association with CAD followed by further pruning to ensure exclusion of linkage disequilibrium; this microarray contains 6.6 million genetic risk variants.

Risk stratification was performed in a test set with a large sample size of 288,978 from the UK biobank utilizing the 6.6 million microarrays. Analysis showed, 8 percent of the population inherited a threefold increased risk for CAD, and 0.5 inherited a fivefold increased risk for CAD. Conventional risk factors would not have identified the 8 percent with the threefold increased risk. For example, hypercholesterolemia was present in only 20 percent of the individuals with only threefold risk in CAD, hypertension was present in only 28 percent of the group with threefold increased risk, and family history was present in only 35 percent of the threefold increased risk group.

The 1.7 microarray was used to genotype a UK biobank sample size of nearly 500,000. The top 20 percent risk group had a fourfold increased risk for CAD. These results and those of Khera et al., [38] confirm increased predictive power over that of the previous microarrays utilizing far less genetic risk variants.

Genetic Risk for CAD is Markedly Reduced by Lifestyle Changes and Drug Therapy

Multiple studies show increased discriminatory power for risk stratification of CAD utilizing microarrays containing genetic risk variants. However, this will all be for naught, if one cannot modify and reduce genetic risk. There is often the myth circulating among the populous that, if it is in your gene, you cannot do anything about it. This, of course, is a myth that has been proven by multiple therapies. Nevertheless, it is important to show that the genetic risk predicted for CAD can indeed be reduced by appropriate therapies. The first study to comprehensively assess the effect of lifestyle changes on genetic risk for CAD was published by [39]. The sample size consisted of 55,685 participants. The endpoint was favorable lifestyle, versus unfavorable lifestyle, with the former defined as no current smoking, no obesity, a healthy diet, and frequent exercise, versus a lifestyle with at least two of these bad components [39]. Risk stratification using the GRS showed, those with a high GRS for CAD (20 percent) had a 91 percent higher risk of cardiac events, than those with low GRS. Individuals with a healthy lifestyle and a high GRS had a 46 percent lower risk of cardiac events than an unfavorable lifestyle

Tikkanen et al [40] assessed the effect of physical activity on the genetic risk for CAD as determined by GRS. The sample size was that of 468,095 individuals obtained from the UK biobank. Physical activity consisted of hand grip for 3 seconds and cardiorespiratory fitness determined by oxygen consumption during cycle ergometer on a stationary bike. The higher level of physical activity was associated with less CAD in each of the low, intermediate, and high risk categories. The highest GRS had the most benefit from cardiorespiratory exercise, with 49 percent lower risk for CAD.

Genetic risk stratification for CAD has been assessed by several different investigators involving over 1 million participants and found to be highly discriminatory. Perhaps more important, genetic risk for CAD is significantly reduced by lifestyle changes and drugs. These studies refute the myth, that genetic risk cannot be attenuated or eliminated.

Genetic Risk Score, a Paradigm Shift to Halt the Epidemic of CAD

The current methods of assessing the risk for CAD by the ACA/AHA, specifically the Framingham risk score, the Randall score, and the pooled cohort equations, are not independent of other confounding factors such as age and conventional risk factors. However, GRS is completely independent of an individual’s age, based on the fact that one’s germline DNA does not change over one’s lifespan. Additionally, the GRS is relatively independent of conventional risk factors for CAD since only one third of them act through known risk mechanisms. GRS is required for a comprehensive risk assessment given that 40–60 percent of the risk of CAD is due to genetic factors. Furthermore, GRS correlates closely with sub-clinical CAD, in contrast to the lack of correlation between conventional risk factors and sub-clinical CAD [36]. Lastly, GRS is comparatively less expensive and minimally invasive; and testing can be obtained through blood or saliva samples (figure 1).

JMG-2020-309_Roberts R_F1

Figure 1. The sample can consist of saliva or blood. The DNA will be genotyped for the genetic risk variants predisposing to CAD. The genetic risk score (GRS) is calculated as a single number to provide the three categories high, intermediate, and low risk.

Documentation that genetic screening is effective for risk stratification of CAD is complemented by the observation that changes in lifestyles and drug therapy are extremely effective in reducing the risk. Given we have a CAD pandemic that in many countries is still increasing, beckons the need for progressive prevention based on comprehensive risk stratification using both the GRS and conventional risk factors for CAD. Risk stratification followed by primary prevention in premenopausal women has the potential to markedly attenuate the development of CAD. A similar approach adopted at an earlier age in males would be expected to have an analogous outcome.

Acknowledgment

The author has received research funding from the Canadian Institutes of Health Research, CIHR no. MOP P82810, the Canada Foundation for Innovation, the CFI, no. 11966, and the Dignity Health Foundation.

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Corruption of in the Misuse of Public Resources: A Mind Genomics Cartography

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DOI: 10.31038/ASMHS.2020412

Abstract

We present a cartography of how people respond to statements about corruption in the use of public funds, using the procedures of Mind Genomics. Respondents read and rated short vignettes about corruption, systematically varied in four aspects, specifically who is IN CHARGE, FOR WHAT is the public money used, WHAT HAPPENED, and what were the EFFECTS, respectively. The data suggest that different elements drive ‘Makes me Angry,’ versus ‘Drives me to do Something’ versus ‘Engages my Attention.’ Mind Genomics further reveals mind-sets, showing different criteria that respondents used to evaluate the individual messages. The paper then shows how to assess the interactions between who is IN CHARGE and the other attributes, and how to measure the degree to which the ‘right person’ can reduce the seriousness of the corrupt act.

Introduction

As we continue apace into the 21^st century, now in 2020, we can recognize that the brave new world of tomorrow looks a lot like the brave new world of yesterday. Technology may have given more capabilities to more people, but we remain ever aware that the human condition is filled with behaviors that we would call unethical. Over the centuries the sins that we commit stay the same, whether sins of lust, avarice, theft, murder, and so forth [1]. The situation changes, but people do not. Indeed, the French proverb has never been as true as it is now ‘The more things change the more they remain the same.’ We deal here with the response to the notion of corruption. There is an extensive legal literature, an extensive sociological literature, an extensively moral and ethics literature, as well as literatures in specific field, all deal with aspects of corruption of one or another sort. References abound. There is no need to quote the literature; it is simply gargantuan. One need only look at the number of different papers dealing with corruption, as indexed by Google Scholar®. Table 1 gives a sense of the enormity of the literature.

Table 1: ‘Hits’ in Google Scholar® as of February 2020

Corruption in	Number of Hits
Education	1,503,000
Public Works	1,220,000
Public Funds	1,070,000
Taxation	498,000
Corporate Governance	468,000
Medicine (Funds)	180,000
Military Procurement	58,600

A review of the literature through Google Scholar reveals many different aspects to the investigation of corruption, as well as a long and venerable history. Corruption is not new, but rather as old as society. One might think, of course, that in the state of nature as posited by Swiss philosopher Jean Jacques Rousseau [2] there might not have been corruption, but putting philosophy aside, all philosophers and students of society recognize the inimical nature of misusing public resources for private and person gains. Some, however, go so far as to investigate whether in the end the ‘invisible hand’ so promoted by Adam Smith, might work to make good the evil that corruption produces [3, 4]. The focus of this paper and its contribution is on the evaluation of the seriousness of corruption from the point of view of the average citizen. We focus here specifically on the misuse of public resources by of different kinds, by different types of people. Can we understand what is perceived to be important by different people? Can we understand how the perception of corruption can be reduced by identifying different people doing corrupt act? And finally, can we find out the types of corruption to which people ‘pay attention’, i.e., raise eyebrows, but really manifest themselves by holding the respondent’s interest. As we will see, not all corruption is equally ‘interesting;’

There is little, however, in the way of a psychological analysis of people who are confronted with the facts of corruption. There are, of course, no lack of information about reactions to corruption. One need only listen to the vox populi, the voice of the people, on a daily basis to hear allegations of corruption, emotional reactions to such allegations, and hypotheses about who is doing ‘what,’ and why, leading to the corruption. Despite the voluminous detail about corruption as a legal and societal topic, and corruption as a topic for the news and gossip columns, there appears to be a paucity of experiments on the person, subjective reaction to corruption. The focus of this paper is on corruption with the psychology of the everyday, those quotidian reports of corruption in different situations. If we were to put this paper into the context of history and academic literature, the paper falls into the group of papers dealing with the psychology of corruption, rather than the sociology of corruption. The paper complements and augments efforts to understand corruption, papers that we would call ‘methodological’ rather than substantive. Examples of these papers abound, with the topics united by a search for method, but diverse in the specific areas, such as governmental corruption [5], organizational corruption [6] criminal corruption [7], or political corruption [8]. Zaloznaya has put it best in the title of a recent paper ‘The social psychology of corruption: Why it does not exist and why it should [9].”

The Mind Genomics ‘cartographic’ process to explore and map a topic

Mind Genomics is an emerging science, a branch of experimental psychology dealing with the psychology of the ordinary, of the everyday [10, 11]. Mind Genomics has roots in experimental psychology itself, especially when studying consideration time (response time), as well as roots in statistical experimental design [12], and in consumer research. The objective of the research is to understand how people make decisions in the ordinary events of their lives. As such, the topics are not specific, often artificial, non-representative situations designed to reveal a phenomenon. Rather, the study is designed as a step to create of a library of understanding of behavior, metaphorically mapping the mind of people, and the alternative ways that make their decisions in the same topic area (here corruption). The easiest way to understand Mind Genomics is through a worked research example, with real data, dealing with a meaningful problem. Our topic in this paper is the misallocation, or really misappropriation of public funds by various individuals. The question was posed by senior author CHL as one of the topics of her personal ongoing study of today’s society and the change of values. Mind Genomics proceeds by a series of steps, in a systematic manner to map out an area of human thinking or human behavior. We call the approach a ‘cartography’ because there is no underlying hypothesis of ‘what exists,’ and/or what should happen. Rather, Mind Genomics presents the information in a systematized form, and uses mathematical modeling to relate the information given (acts by people, and their results) to judgments. The structure which emerges give a sense of how people ‘think’ about relevant problems, in this case issues involved in the misallocation of community funds designated for certain projects of a societal nature.

Step 1 – Select the topic, the four questions, and the four answers to each question: The topic here is the misallocation of public funds by individuals in power. The four questions are shown in Table 2, each followed by the four answers to that question. In the actual experiment the respondent does not see the full question, although as Table 2 shows, part of the question is embedded in the answer to make the experiment easier. Since the Mind Genomics method is inexpensive, easy, and fast, it lends itself to iterative experiments, each taking no more than a few hours. Consequently, the questions and the answers need not be the correct ones. The researcher can repeat the experiment for several iterations, until the questions and the answers make sense, and are exactly on target, at least from the point of view of experts in the topic area.

Table 2: The four questions, and the four answers to each question

	Question A: Who is person in charge of making the decision?
A1	In charge: wealthy businessperson
A2	In charge: member of traditional ruling class
A3	In charge: woman director from a local school
A4	In charge: well-known & successful actor
	Question B: What is money used for?
B1	Purpose: money to fund student education programs
B2	Purpose: money to fund environment safety & sustainability
B3	Purpose: money for hospitals, schools, roads
B4	Purpose: money to maintain citizen health
	Question C: What happened?
C1	Event: money simply disappears.
C2	Event: money redirected to ‘new uses’ by controlling ‘inner circle’
C3	Event: call vote to redirect money to new use
C4	Event: land sold, money directed to payoff ‘newly discovered ‘ government obligations
	Question D: What was the effect?
D1	Effect: people demonstrate to demand politicians and government should act with morals
D2	Effect: people are what’s happening — rush to grab their portion … Me First Mentality
D3	Effect: economy stagnates, young people leave in droves
D4	Effect: demoralized angry people who are angry at growing gap between rich & poor

Step 2 – Create the response rating: For this study we considered two different responses, become angry, and prompted to do something. The 5-point rating scale comprised two statements in a single rating, either be angry (no, yes), do something (no, yes). The lowest point on the scale was ‘irrelevant.’

The actual instruction was simple: Please read the whole screen & type the number which shows how you feel

1=No idea what to do…

2=Not angry … Will do nothing

3=Angry …Will do nothing…

4=Not angry … Will work actively to make change.

5=Angry … Will work actively to make change

Step 3 – Send out an invitation to respondents to participate : The participants were part of the Luc.id panel, comprising 29+ million respondents in the US and many more world-wide. These respondents are incentivized to participate.

Step 4 – Present 24 vignettes as the experiment: When a respondent opts in to participate, the respondent receives 24 vignettes, one vignette at a time, the vignettes created according to a recipe book, or experimental design [13]. Each vignette comprises at most one answer from a question, but many vignettes are lacking answers from one or two questions, so the vignettes range from two elements (answers) to four elements. The respondent reads each of the 24 vignettes, and for each vignette assigns a rating on the above-shown 5-point scale. The task takes no more than 5–7 second for each vignette. Each respondent evaluates a totally unique set of combinations. It is rare in a sample of 50 respondents, each respondent evaluating 24 vignettes, (total of 1200 vignettes) to encounter more than one-two vignettes which are repeated. The combinations are selected to be permutations of each bother, but also to share no vignettes in common. The scientific rationale is to cover as many combinations as possible, even with ‘noise’, a sharp contrast to the standard way of testing a limited number of combinations but testing them with many respondents in order to reduce ‘noise.’ To distinguish Mind Genomics from conventional research we can say that Mind Genomics looks for the for the general pattern, which emerges clearly from the noise. Conventional research cannot find underlying general patterns because the strategy for conventional research is to measure with precision, and not with scope.

Step 5 – Create four new variables in place of the single 5-point rating scale

Not Angry: Ratings 1, 2, and 4 become 100, ratings 3 and 5 become 100

Angry: Ratings 3 and 5 become 100, ratings 1,2 and 4 become 0

Would not take action: Ratings 1 and 2 and 3 become 100, ratings 4 and 5 become 0

Would take action: Ratings 4 and 5 become 100, ratings 1,2 and3 become 0

Each respondent thus generates a vector of four new numbers, one number for each of the four new variables. A person can either be Not Angry or Angry, but not both. A person can be promoted now to take no action or to take action, but not both. The value of each of these four new variables is completely determined by the one rating assigned by the respondent, using the 5-point scale. As a standard practice, we want to avoid the situation where an individual’s ratings for all 24 vignettes are either 100, or 0, based upon the transformation of the original 1–5 rating scale. In that unfortunate case, viz., when all 24 vignettes have the same value, the analysis program (OLS, ordinary least-squares regression) will ‘crash’, returning the disheartening but appropriate statement ‘your dependent variable has no variance.’ We avoid this statistical problem by adding a very small positive number to each newly created value. We add a very low random number
(< 10^–5) to each newly created binary answer, 0 or 100. Now, each respondent has four numbers, corresponding to the four newly created variables, all numbers around 0 or 100, but not exactly 0 or 100. This prophylactic action prevents the regression program from crashing.

Step 6: Invite respondents to participate: The panelists came from a pool of respondents who had previously agreed to participate in these studies. The respondents were provided by Luc.id, Inc., the on-line panel provider which has provided panels for numerous previous Mind Genomics studies. Luc.id takes care of the invitation, the remuneration for participation, and complies with the privacy issues, shielding the respondent from being identified, except with respect to gender and age.

Step 7: Orient the respondent: Each respondent who agreed to participate was led to a website, shown an orientation page, and instructed to read the entire vignette, and to select a rating from the 5-point scale to reflect the respondent’s feeling about that specific vignette just read. The Mind Genomics program recorded the rating, transformed it, and measured the Consideration Time (response time) from the moment the vignette appeared on the screen to the moment that the vignette was rated. Consideration Times lasting 9 seconds or longer were assumed to reflect other activities going on at the same time and were transformed to 9.

Step 8: Create the individual level models for each respondent for each of the three major responses (feel anger, feel prompted to do something, Consideration Time, respectively) : The model or equation, created by the aforementioned OLS regression, relates the presence/absence of the 16 elements (see Table 1) to the specific binary response. For each person and for each of the three dependent variables, the equation is written as: Dependent variable = k₁(A1) + k₂(A2) … k₁₆(D4).

The equation shows the contribution of each element to the rating. There is no additive constant here because it is difficult to interpret what the constant ‘means’ in this particular study. ratings. The powerful is each element coefficients represent the number of points on the binary 0/100 scale contributed by the specific answer or the number of seconds taken to read the element when it is present in the vignette. The coefficients give a sense of how the elements ‘drive’ the ratings.

Step 9: Create new Mind-sets based upon the pattern of coefficients : Using the individual level models created in Step 8, cluster the 50 respondents into two groups, doing so three times, one for each of the three dependent variables (anger, act, Consideration Time.) The clustering is based upon well-accepted procedures to divide groups of ‘things’ based upon the patterns exhibit by these things [14] The coefficients emerging from the 50 individual equations for a single dependent variable (e.g., Consideration Time) are used to divide the respondents into two groups. For this Mind Genomics cartography, we used k-means, with the Pearson correlation as the measure of distance. The clustering program creates the clusters or segments strictly using mathematical considerations, without interpreting what the clusters ‘mean.’ Finally, the meaning of the clusters is assigned by the researchers. The clusters are then relabeled ‘mind-sets’ to reflect the fact that they show how people’s mind consider the same evidence but arrive at different conclusions.

Step 10: Classify the respondent regarding membership in key subgroups : Defined each respondent by age, gender, concern with corruption, and by mind-set for each of the three dependent variables. The first three variables, gender, age, concern with corruption, come from a simple classification questionnaire administered at the start of the Mind Genomics experiment. The latter, membership in Mind-sets, comes from the statistical analysis in Step 9. Our focus will be on Total Panel and on Mind-sets, although one can do a complete analysis looking at the general attitudes about corruption, as well as gender, and age.

Step 11: Compute average ratings by the total panel, and the key self-defined subgroups: Each group comprised individuals who were ‘homogeneous’ with respect to one variable of classification, that variable being self-defined (e.g., age). Table 3 shows the average ratings for all respondents in a key group (row) by all of the vignettes that the respondent evaluated. The experimental design ensured that the respondents tested different vignettes, so the averages just give a sense of the differences among the groups, but averages computed on comparable, not identical vignettes.

Table 3: Average ratings by total panel key groups, across all the relevant vignettes for the subgroup.

	Base size	Anger – No	Action – No	Anger – Yes	Action – Yes	Consideration Time
Total	50	54	52	46	48	3.3
Gender – Male	27	50	47	50	53	3.4
Gender – Female	23	58	58	43	42	3.2
Age < 29	15	58	66	42	34	1.6
Age 30+	32	54	44	46	56	4.4
Age Not given	3
Q3=1 not interested at all regarding corruption in the world of government & public issues	8	46	62	54	38	1.9
Q3=2 would like to change something but don’t know / feel powerless	16	57	53	43	48	3.4
Q3=3 angry at government corruption & voice my protest	6	49	49	51	51	4.0
Q3=4 feel personally endangered by corruption in government & try to become a role model for change	2	56	39	44	61	4.0
Q3=5 Option decline to answer	8	56	64	44	37	2.9

For example, look at consideration time, No differences by gender, Old respondents take much longer to respond than do younger respondents (4.4 seconds vs 1.6 seconds).

Those respondents not interested in the topic of corruption (Question 3, answer 1) or simply decline to answer (Question 3 answer 5) show much shorter consideration times. Even ahead of the experiment, they announce their disinterest, and give a cursory response, a very short one, suggesting that they are totally uninterested. Future researchers might use this question or the pattern of response times to screen out respondents who clearly do not wish to pay attention, or who behaviorally appear to rush through the evaluation. At a more granular level consider the two age groups, respondents 29 and younger versus 30 and older. The younger respondents will be angrier, but also more likely to take no action. The older respondents will feel less angry but say that they feel that they are more likely to take action. The foregoing analysis is the type of analysis one does with survey day, looking at the mean rating, and drawing conclusions from the patterns of the ratings. The data points themselves, averages, are not ‘cognitively rich.’ The analysis by averages (along with inferential statistics) tells us about more versus less of an attribute. It will be the job of Mind Genomics to embed cognitive richness into the results, the analysis, and in turn, the conclusions and the next steps beyond the research.

Step 12 – Build models (equations) relating elements to binary ratings : The underlying experimental design combining the 16 elements into 24 vignettes allows the researcher to estimate the contribution of each element to the rating or to consideration time. In Step 10 we combine the data from respondents who belong to a specific group (e.g., males, e.g., mind-set 1 based on consideration time). The 16 answers or elements remain uncorrelated with each other, because we are simply combining different experimental designs. In practice, we create one ‘grand’ equation for each of the three main dependent variables, Angry-YES, Do Something-Yes and Consideration Time. The form of the equation is the same for the three different variables: Dependent variable = k₁(A1) + k₂(A2) … k₁₆(D4). The only difference is the source of the data, which this time is the data from the larger group, rather than from the individual respondent as we did in Step 8 above.

Step 13 – What drives key responses by total panel and emergent mind-sets? : The focus now turns to the interpretation of the results. We begin with the total panels, shown in Table 3. The table presents the coefficients. For the contribution of elements to ‘angry’ and to ‘do something’ we have shaded every element which has a coefficient of 16 or higher, or a consideration time of 1.1 seconds or longer. These are elements which are statistically ‘significant’ in terms of inferential statistics (absolute value of the t-statistic > 2). One way to ‘make sense’ of the vast amount of metricized data is to sort the table, first by those elements driving ‘angry’ and by those elements driving ‘Do something.’ Table 3 suggests that anger is ignited by EVENTS which occur, or the PURPOSE for which the money was allocated. Being prompted to do something is ignited by the EFFECT that the action has, and by WHO is in charge, and presumably doing the stealing. The consideration time differs by element, but for total panel there is no clear pattern.

When we plot the coefficients for Do Something vs Angry
(Figure 1) we see that they are independent of each other. Knowing that an act makes one angry does not predict whether the person will do anything. There appears to be only one anomalous element, where there is little anger, but people want to do something. This is D1, Effect: people demonstrate to demand politicians and government should act with morals.

Mind Genomics-041_ASMHS_f1

Figure 1: Scatterplot of relation between coefficients for ‘Do something’ versus for ‘Angry’ Data from the total panel.

Moving beyond the total panel to mind groups defined by the pattern of responses

A continuing outcome of Mind Genomics experiments is the discovery of mind-sets, groups of individuals who are similar to each other in the criteria that they adopt to make a decision. As noted in Steps 8 and 9 above, we created the individual-level models. We then clustered the respondents three times, each time based on the pattern of their individual models but using different data sets. The data sets were, respectively, the 50 individual equations created for ‘makes me angry’, the 50 individual equations created for ‘prompts me to want to do something,’ and finally the 50 individual equations created for Consideration Time

Table 4 shows the key results for clustering based on ‘makes me angry.’

Table 4: What drives corruption: How the 16 elements drive ratings of angry, ‘do something,’ and the consideration time to read and process the element

	Total Panel	Feel Angry	Promoted to Do something	Consideration Time
	Strongly drives ‘Angry’
C4	Event: land sold, money directed to payoff ‘newly discovered ‘ government obligations	19	10	1.2
D3	Effect: economy stagnates, young people leave in droves	19	16	0.9
C2	Event: money redirected to ‘new uses’ by controlling ‘inner circle’	18	8	1.2
C1	Event: money simply disappears.	18	8	0.9
B4	Purpose: money to maintain citizen health	17	15	1.1
	Strongly drives ‘Prompted to do something’
D1	Effect: people demonstrate to demand politicians and government should act with morals	7	19	0.5
D4	Effect: demoralized angry people who are angry at growing gap between rich & poor	13	18	0.7
B3	Purpose: money for hospitals, schools, roads	12	18	1.1
B1	Purpose: money to fund student education programs	15	17	0.7
A1	In charge: wealthy businessperson	10	16	1.0
	Not a strong driver
C3	Event: call vote to redirect money to new use	13	13	1.4
A2	In charge: member of traditional ruling class	10	15	1.1
B2	Purpose: money to fund environment safety & sustainability	13	14	1.0
A4	In charge: well-known & successful actor	11	14	1.0
A3	In charge: woman director from a local school	9	13	0.8
D2	Effect: people are what’s happening — rush to grab their portion … Me First Mentality	14	11	0.7

Mind-set 1 – Angry when reading about what happened, or who was in charge

Mind-set 2 – Angry when reading about the effects of corruption

Table 5 shows the key results for clustering based on ‘Prompts me to want to do something.’

Table 5: Two mind-sets based upon coefficients for ‘Makes me angry’

	Clusters based on coefficients for ‘Makes me angry’	MS1 Angry	MS 2 Angry
	Mind-set 1 – Angry when reading about what happened, or who was in charge (n=30)
C1	Event: money simply disappears.	38	4
C2	Event: money redirected to ‘new uses’ by controlling ‘inner circle’	34	-6
A3	In charge: woman director from a local school	29	-8
A1	In charge: wealthy businessperson	28	-8
A2	In charge: member of traditional ruling class	28	-1
C4	Event: land sold, money directed to payoff ‘newly discovered ‘ government obligations	26	10
A4	In charge: well-known & successful actor	25	-5
	Mind-set 2 – Angry when reading about the effects of corruption (n=20)
D3	Effect: economy stagnates, young people leave in droves	-13	48
D2	Effect: people are what’s happening — rush to grab their portion … Me First Mentality	-12	32
D4	Effect: demoralized angry people who are angry at growing gap between rich & poor	-10	31
D1	Effect: people demonstrate to demand politicians and government should act with morals	-19	29
	Does not Strongly Anger either mind-set
B4	Purpose: money to maintain citizen health	15	21
B3	Purpose: money for hospitals, schools, roads	7	19
B1	Purpose: money to fund student education programs	15	19
B2	Purpose: money to fund environment safety & sustainability	16	18
C3	Event: call vote to redirect money to new use	19	3

Mind-set 1 – Want to do something when learning who was in charge (n=25)

Mind-set 2 – Want to do something when learning about the effect of corruption (n=25)

Table 6 shows the key results for clustering based on Consideration Time’

Table 6: Two mind-sets based upon coefficients for ‘Prompts me to want to do something’

	Clusters based on coefficients for ‘Prompts me to want to do something’	MS1 Do	MS2 Do
	Mind-set 1 – Want to do something when learning who was in charge (n=25)	25	25
A1	In charge: wealthy businessperson	38	-3
A2	In charge: member of traditional ruling class	33	0
A3	In charge: woman director from a local school	33	-3
A4	In charge: well-known & successful actor	28	4
	Mind-set 2 – Want to do something when learning about the effect of corruption (n=25)
D4	Effect: demoralized angry people who are angry at growing gap between rich & poor	10	32
D3	Effect: economy stagnates, young people leave in droves	12	30
D2	Effect: people are what’s happening — rush to grab their portion … Me First Mentality	0	29
D1	Effect: people demonstrate to demand politicians and government should act with morals	16	28
	Does not strongly affect either mind-set
B3	Purpose: money for hospitals, schools, roads	6	22
B2	Purpose: money to fund environment safety & sustainability	11	14
C4	Event: land sold, money directed to payoff ‘newly discovered ‘ government obligations	7	13
B4	Purpose: money to maintain citizen health	16	13
C3	Event: call vote to redirect money to new use	9	12
B1	Purpose: money to fund student education programs	14	12
C2	Event: money redirected to ‘new uses’ by controlling ‘inner circle’	5	6
C1	Event: money simply disappears.	12	6

Mind-set 1 – Longest Consideration Time (most involved in reading) when information is about Who is in Charge (n=21)

Mind-set 2 – Longest Consideration Time when information is about the specific event which happened (n=29)

Can the response to corruption be modified by WHO is perceived as the corrupt person

Up to now the analysis has focused on each of the 16 elements as a contributor to the feeling of being angry, of wanting to do something, or of capturing the attention of the respondent (consideration time). What happens, however, when we do the analysis, but hold constant the person in charge. That is, when the corruption occurs, is the magnitude of the corruption the same when we have two radically different individuals in charge, such as a a wealthy businessperson versus a woman director from a local school? Or does being one of the individuals, e.g., the woman director from a local school, give the person a ‘pass’ on corruption?. Most people would probably say that WHO a person IS does not affect how the person is judged. That statement is reasonable, expected, and totally ‘politically correct.’ It may or may not be true. The interaction of WHO and ACTION as joint drivers of innocence versus guilt can quantified easily by Mind Genomics, following these analysis steps.

The permutation structure of the Mind-Genomics study creates many different combinations, in which the person in charge and the act of corruption appear together. It will be the fact that there are so many different combinations tested that will allow us to uncover the interactions, to measure the response to each element in the presence of each of the other elements in the study. We will thus uncover either no effect, or suppression, or synergism, respectively.
We explore the nature of the interaction by so-called ‘scenario analysis,’ a term coined specifically for this type of analysis and discussed in depth in the book ‘Selling Blue Elephants’ [10].
For our analysis, we sort the data file of 1200 records by the specific value of Answer or Element A, ‘who is in charge.’ There are four specific individuals in charge and some vignettes there is no individual stated to be in charge. The underlying permuted experimental design thus allows us to create five strata of vignettes, each stratum defined by the value of Question A, viz., the specific person in charge.
For each of the five strata, we have the remaining 12 elements acts as independent variables, which they can do because the underlying experimental designs, when combined, still keeps the 12 elements statistically independent of each other.
Once again, the OLS regression analysis is run, stratum by stratum, for each of the three dependent variables (Makes me angry; Prompts me to do something; Consideration Time).
Table 7 show the five strata across the top, beginning with A0 (those vignettes with no mention of who is in charge), and then the strata where the person in charge is, respectively, a wealthy businessperson, a member of the traditional ruling class, a woman director from a local school, or a well-known and successful actor. The structure of the table is the same for each of the three dependent variables. We will focus on the first dependent variable, ‘Makes me angry.’
To conserve space, and to illustrate the principles, we present only the strongest two elements and the weakest two elements, based upon the stratum which is lacking an element from question A (who is in charge).
We first look at the Maximum coefficient across all 12 elements. This is the highest coefficient of any element B1-D4, which are now the independent variables in the model. For ‘makes me angry’ the highest coefficient is 27 when there is no person in charge. This is element B4 (Purpose: money to maintain citizen health). The maximum coefficient varies by who is in charge. When a member of the traditional ruling class is mentioned, the maximum coefficient is much higher, 39.
The standard deviation of 12 coefficients is a measure of variation across the 12 coefficients. It is 7 for the stratum where there is no mention of a person in charge, but 16, much greater, when there the person in charge is a member of the traditional ruling class.
The Table then shows the highest coefficients for the strata lacking the person in charge. These are elements B4 (Purpose: money to maintain citizen health) and C4 (Event: land sold, money directly to pay off ‘newly discovered’ government obligation.) One can see how these two basically anger-producing can be affected by changing the specific person in charge. When element B4 is substituted (A well-known & successful actor), the anger from 27 to 9!.
The table then shows the lowest coefficient for the strata lacking the person in charge. This is D2: (Effect: people are what’s happening — rush to grab their portion …Me First Mentality.) This element starts out with a very low anger producing response, a coefficient of +7. Put in the same well-known and successful actor in charge, and the anger goes to 16.
The same insights can be obtained from the other dependent variables. The table is shortened to show the key numbers from the analysis, and to explicate some of the anomalies which can emerge when elements of a ‘corruption’ nature are paired with the individual in charge. Sometimes the corruption can be overlooked, sometimes it can be made more severe.

Table 7: Two mind-sets based upon coefficients for Consideration Time

	Clusters based on coefficients for Consideration Time	MS1 CT	MS2 CT
	Mind-set 1 – Longest Consideration Time (most involved in reading) when information is about Who is in Charge (n=21)
A2	In charge: member of traditional ruling class	2.3	0.0
A4	In charge: well-known & successful actor	2.2	-0.2
A1	In charge: wealthy businessperson	2.1	0.0
A3	In charge: woman director from a local school	2.0	-0.4
	Mind-set 2 – Longest Consideration Time when information is about the specific event which happened (n=29)
C3	Event: call vote to redirect money to new use	1.0	1.9
C2	Event: money redirected to ‘new uses’ by controlling ‘inner circle’	1.1	1.8
C4	Event: land sold, money directed to payoff ‘newly discovered ‘ government obligations	0.8	1.8
	Does not strongly engage either mind-set
C1	Event: money simply disappears.	1.3	1.2
B3	Purpose: money for hospitals, schools, roads	1.0	1.3
B4	Purpose: money to maintain citizen health	0.8	1.1
B2	Purpose: money to fund environment safety & sustainability	0.7	1.4
B1	Purpose: money to fund student education programs	0.1	1.3

Finding these individuals in the population

A continuing finding from Mind Genomics studies is that who a respondent IS does not easily predict how the respondent will think, in general, and cannot at all predict how a specific person will think when the topic is limited, and for which there is no available data. In other words, efforts to predict mind-set membership will not be particularly successful because people stay the same in terms of WHO they are, yet respond differently for different topics. We need another method to assign a new person to a newly created or identified mind-set, with the property that no matter who the person is, and no matter what the mind-sets are, the assignment is as effective as possible. The assumption is that there are NO DATA in the world available relevant to the newly discovered mind-sets, or at least no data exhibiting sufficient precision. Recent work by author Gere has emerged out with a PVI, a personal viewpoint identifier, based upon the ability of the degree of the elements to separate people and divide them into meaningful groups. The PVI involves Monte Carlo simulation of the mind-set models. The mind-set segments themselves can come from anywhere. It is the job of the researcher to identify the mind-sets, and their nature. The PVI simply finds the mind-sets in the population. The metaphor here is ‘mining ore that is 98% saturated with what is being sought’ (the PVI, created precisely for the topic) versus ‘mining ore that is ½% saturated with is being sought’ (using massively powerful analytics with Big Data, conveniently available, but often totally irrelevant). For this study we created three separate PVIs, based upon the pairs of patterns of the coefficients for the three dependent variables in this study, Anger, Do Something, and attention (Consideration Time.) These three PVI’s are incorporated into one test instrument, shown in Figure 1. The result is a set of 18 questions, which when answered together assigned the new person into the appropriate mind-set for each dependent variable (Figure 1).

Table 8: Scenario analysis, showing how the different people ‘in charge’ affects the response to the other elements.

		No one stated to be in charge	In charge: wealthy businessperson	In charge: member of traditional ruling class	In charge: woman director from a local school	In charge: well-known & successful actor
		A0	A1	A2	A3	A4
		Dependent variable = Makes me angry
	Maximum coefficient across B1-D4	27	34	39	27	26
	Standard deviation of 12 coefficients	7	9	16	5	5
B4	Purpose: money to maintain citizen health	27	23	24	22	9
C4	Event: land sold, money directed to pay off ‘newly discovered ‘ government obligations	26	23	22	14	19
D1	Effect: people demonstrate to demand politicians and government should act with morals	10	1	13	14	12
D2	Effect: people are what’s happening — rush to grab their portion… Me First Mentality	7	22	26	18	16

		Dependent variable = Prompts me to want to do something
	Maximum coefficient across B1-D4	56	32	34	32	26
	Standard deviation of 12 coefficients	16	9	12	8	7
B3	Purpose: money for hospitals, schools, roads	56	3	24	25	18
B4	Purpose: money to maintain citizen health	25	23	20	5	21
D2	Effect: people are what’s happening — rush to grab their portion …Me First Mentality	2	26	23	13	1
D3	Effect: economy stagnates, young people leave in droves	2	29	30	29	12

		Dependent variable = Consideration Time
	Maximum coefficient across all B1-D4	2.0	3.6	2.8	2.4	2.3
	Standard deviation of 12 coefficients	0.4	0.7	0.7	0.5	0.6
D3	Effect: economy stagnates, young people leave in droves	2.0	-1.1	2.0	1.3	1.2
C4	Event: land sold, money directed to payoff ‘newly discovered ‘ government obligations	1.8	3.6	1.0	0.5	1.0
B1	Purpose: money to fund student education programs	0.7	1.0	0.2	1.3	1.4
D2	Effect: people are what’s happening — rush to grab their portion … Me First Mentality	0.6	-0.8	2.8	1.1	0.9

Mind Genomics-041_ASMHS_f2

Figure 2: The PVI (personal viewpoint identifier) which assigns a new respondent into one of two complementary mind-sets for each variable (drives me to do something, makes me angry, interests me).

Discussion and conclusion

At the most fundamental level, the project of Mind Genomics is to understand the way people think by presenting to them combinations of messages relevant to a topic, and then measuring their cognitive response (rating), or now their subconscious response (Consideration Time.) The traditional way of such approaches is to isolate the variables, and test these variables, perhaps with many replicates, so that one can ‘suppress the noise.’ The ingoing belief is that one-at-a-time research, with low noise, will eventually reveal how people make decisions. There is another aspect to the traditional approach. That is to create unusual situations, situations which by happenstance allow the researcher to decide between or among different ingoing hypotheses. This is the so-called experimentum crucis, the crucial experiment whose outcome decides for one hypothesis, and falsifies another. Science thus advances, one observation at a time, producing perhaps a cadre of observations about nature that must be woven together by one or another enterprising researcher looking for the ‘grand pattern.’ How then could conventional research address the topic of corruption using traditional methods. The answer is not clear. The respondent might be asked directly to rate the seriousness of the corruption, but as we see the seriousness, as measured by the coefficient, can be influenced by the other elements in the vignette, such as ‘Who is in charge.’ Mind Genomics provides an alternative way to explore human decision making, this alternative using the type of stimuli to which a person is exposed daily, viz., combinations of messages. The benefits are that the research can cover a wider array of topics, rolled up into one in the vignettes, can create a within subjects design leading to the discovery of mind-sets, can do the work quickly, affordably, and allow for iterations. Perhaps the most important benefit is that the topic can be explored, and the different Mind Genomics studies combined to create an integrated library of knowledge about a topic in a very short time. The knowledge about corruption, the experiment taking two hours, advances our knowledge considerably. One can only wonder to what extent our knowledge and understanding might advance regarding ethics and their violation if our ‘project’ were to do these Mind Genomics cartographies in a concerted, systematic, deliberate, and expansive way.

Acknowledgement

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

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Believability and Feelings in Fake News: A Mind Genomics Cartography

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DOI: 10.31038/PSYJ.2020222

Abstract

Respondents evaluated vignettes combining varieties of messages, with the vignettes defined to be aspects of so-called ‘fake news.’ Each respondent rated a unique set of 24 unique vignettes, systematically varied by an experimental design with the vignettes comprising 2–4 elements. The respondent rated each vignette on a 5-point anchored scale, measuring two factors, feeling (angry vs happy; do not believe vs believe). The data suggest dramatically differences among elements in the degree to which the elements drive both emotion and believability, respectively. Various pairs of mind-sets or different ways of thinking about the information emerged from the clustering of patterns of linkages of elements to emotion, to believability, and to consideration time (response time.) The emergent mind-sets differ on the primary axis of topic (what, how) versus motivation (why.)

Introduction

We depend upon the daily news for a lot of our information, ranging from the weather and what to wear on to the state of our economy, and of course what actions we should take. The common view is that to a great degree the news that we consume, whether from papers or from electronic modes of presentation are ‘objective.’ That is, we recognize that people may slant the news, but we accept their slanting as ‘part of the news itself,’ recognizing that people have a confirmation bias [1], believing that which agrees with their feeling. When we say that we accept the ‘bias,’ we mean that we accept bias which is not conscious, but rather part of the ‘earnest seekers after truth,’ albeit a seeker who must by the human condition have some bias.

The great gift of reporters is that they have the luxury to describe the news after it has happened. It is understood that the reporter will change the story a bit, polishing it to make it attractive for the news consumer to consumer. Polish may be simple, such as better organization of the raw information, out to better, more felicitous but not necessarily ‘faithful’ reportage of the happened. And, of course, we accept the fact that the news may be presented in a new context. What we think may be a virtue, such as the warm pictures of dictators receiving flowers from children, may actually be horrible in its true context (e.g., agitprop, agitation propaganda [2].

The literature of the news, the reporter, and the emerging world of ‘fake news’ comes on top of this tradition of respecting the fundamental honesty of the reporter, perhaps at the same time taking into account some of the predilections of the reporter to present information which is not important, but which is perceived to help along a ‘story.’ The topic of Fake News is not new. Fake news, albeit of a strategic nature for war, is well known. One needs only look at the history of espionage, and the ‘fake news’ fed to the enemy by agents who have been turned. One need not even use a living person. Ewen Montagu’s riveting book, The Man Who Never Was provides a detailed account of the WWII effort by the Allies to fool the German High Command about the deployment of troops and material, by outfitting a soldier’s body with information, news and plans. The entire effort was an elaborate hoax to fool the German enemy [3].

Issues with fake news

When a historically so-called objective source of information is polluted by deceit, or perhaps even by mass access of people to create news on social media, one of the results is that the media is no longer believed [4,5] That bold statement may be cause for alarm, but the ‘numbers’ suggest that fake, or created news, is all around us. For example, according to Allcott & Gentzkow [6], studying the outcome of the 2016 US election (Trump vs Clinton), “ … the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; …. people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media network”. Furthermore, it Fake News appears almost impossible to stop. Tandoc et. al [5] described the situation in these dire words, focusing on what cannot be done anymore.” The nature of online news publication has changed, such that traditional fact checking and vetting from potential deception is impossible against the flood arising from content generators, as well as various formats and genres.’

Fake News may be impossible to stop because it is constructed to be inherently interesting, persuasive, and propagandistic. Tandoc et. al. [5] presented a typology of Fake News, using two dimensions of classification, level of factuality, and level of deception. These are not opposites, because within the compass of Fake News are news, satire, news parody, fabrication, manipulation, advertising, and finally propaganda, respectively.

A further aspect of Fake News is the nature of what people want to consume as news. People like their news in different ways. There may be a single definition of what it means to present “NEWS” in a manner consistent with the ethics and morality. Yet, an interview with 61 high school students suggested that the students may prefer opinionated rather than objective news. In Marchi’s [7] words ’This does not indicate that young people disregard the basic ideals of professional journalism but, rather, that they desire more authentic renderings of them.’

Fake News, Mind Genomics cartography and process specifics

The Mind Genomics studies are called cartographies because they ‘map’ the way a person thinks of a topic. The term ‘cartography’ is used metaphorically, analogous to mapping human genome. The fundament for Mind Genomics is that every topic relevant to a person in which opinions matter can be studied by a process which reveals the way the person values and responds to information about that topic. The Mind Genomics process cuts the topic into manageable pieces and explores those pieces through experiment. The experiment reveals the specific criteria and weights of the information about the topic, leading to a decision [8, 9].

The foregoing definition is general. It is in the specifics that Mind Genomics thinking comes alive. We deal here with aspects of the emerging topic of ‘fake news,’ Our goal is to identify what specific features that we wish to investigate drive a person to ‘believe’ the news, as well as to feel angry or happy about what is read. It should become immediately obvious that there are a great many cartographic explorations possible for any topic, and that there is no specific, limited, fundamental set of aspects of the topic. We are NOT exploring a limited topic like the set of genes on a chromosome whose number is fixed by biology and nature. Rather, we are using the metaphor of genomics to explore human decision making.

Step 1-Select a topic: The topic may be broad or narrow, naturally occurring or constructed, historic or modern. Our topic is the emerging world of so-called Fake News. We define fake news following one of the more recent classification [5] specifically:

… a typology of types of fake news: news satire, news parody, fabrication, manipulation, advertising, and propaganda. These definitions are based on two dimensions: levels of facticity and deception.

Step 2-Define a set of four questions telling a ‘story’ about the topic, and for each question provide exactly four alternative answers: The questions and the answers are left to the researcher. Table 1 shows those chosen here. It is important to accept the fact that these questions and answers represent just a sliver of the topic. [Table 1]

Table 1: The four questions and the four answers to each question developed for this first Mind Genomics cartography on Fake News

	Question A: What is the story about?
A1	reason: politician wants to gain new votes
A2	reason: politician wants to construct a new power base
A3	reason: public official wants to create approval of policies
A4	reason: public official wants to disguise problems
	Question B: How is the story presented?
B1	story: created with selective false facts
B2	story: interview constructed by writer
B3	story: expose written to be interesting & influence feelings
B4	story: breaking news taken from legitimate sources and “edited”
	Question C: What are specific topics?
C1	topic: public works and infrastructure
C2	topic: behavior of elected government officials
C3	topic: issues in educating young people to question and develop critical thinking
C4	topic: issues negatively affecting quality of life of citizens
	Question D: Deliberate distortions
D1	featured: government wrongdoing
D2	featured: putting positive spin on mistakes
D3	featured: explaining away and denying previous history and lessons
D4	featured: overplaying to distract

Step 3-Create combinations of answers (so-called vignettes) using experimental design: One of the scientific foundations and thus premised of Mind Genomics research is that the respondents must be presented with the type of information that they would ordinarily encounter, namely mixtures of messages. It is the nature of researchers to isolate variables and test single variables, reducing the other information in order to suppress any noise. The data observed is thus a function of the variable being tested, or in our case the ‘answer’ being evaluated. The world of Mind Genomics begins with a different premise, namely that in order to understand the mind of the person, it is important to present information in a way that is impossible to ‘game,’ and ‘fake.’ When the respondent sees a single element or answer, the respondent can guess about the ‘proper rating’ to be assigned to that element or answer. Thus, in Table 1, one can present each of the 16 answers or elements, and the respondent may adjust the criteria used in order to be politically correct.

In the world of Mind Genomics. the researcher uses a main effects experimental design, specially created for this type of study, designed to be efficient, and yet statistically robust. The design comprises 24 vignettes or combinations. Each of the vignettes comprises, by a priori design, a specific set of 2,3 or 4 answers, with only one or no answers from each question.

Another feature of Mind Genomics is the creation of a set of isomorphic or permutated designs, having the same mathematical structure, but comprising different combinations. The result is that each person sees different sets of messages. With such a design, called a ‘space-filling design,’ the researcher ends up testing a relatively large proportion of the possible combinations [9].

The purist might argue that with permuted designs the measurement of each design is ‘noisy,’ because there is no effort to suppress the noise through replication. The Mind Genomics counter to that argument is that conventional research makes inferences about all possible design combinations from a truly small number of tested combinations, albeit combinations measured accurately because of replication. The difference is one’s view of noise versus signal. Mind Genomics accepts more signal (measurement of more combinations), and at the same time, accepts more noise (one replicate judgment per combination.) The world view of Mind Genomics is to measure the space of combinations, accepting the noise, but investigating many regions of the space.

Step 4-Invite respondents to participate by email: During the past two decades consumer researchers have shifted their data collection to the Internet, for reasons of cost and speed. The Internet and the technology empower the researcher to accomplish in the matter of an hour or two what took weeks, perhaps event months. Furthermore, with the competition for people’s ‘attention’ fiercely increasing, it has become obvious that one needs to get ‘respondents’ from companies who specialize in securing respondents to participate in panels. Without these companies, it is virtually impossible to complete a study with a reasonable number of respondents, and with an interview longer than 30 seconds. The respondents for this study were ‘sourced’ from Luc.id, a company specializing in providing on-line consumer panels world-wide.

The respondents received an invitation by email. Most respondents had participated in previous studies hosted by Luc.id (by not necessarily Mind Genomics studies), and readily accepted the email invitation. The respondents were led to the Mind Genomics by means of a link embedded in the email invitation. The response rate was dramatic, with most of the respondents participating within the first 20 minutes, and the entire experiment lasting 90 minutes, in which respondents participated.

Step 5-Instruct the respondents about what they will see: The Mind Genomics program (BimiLeap) presented the respondents with the simple instruction below, and then the five point rating scale. The approach deliberately leaves the task vague, requiring the respondent simply to read the vignette, and assign a rating.

Here are some descriptions of fake news. Please read the description and select how you feel

1=just don’t know.

2=don’t believe & feel angry

3= don’t believe & feel happy

4=believe & feel angry

5=believe & feel happy

Step 6-Present the respondent with the test stimuli: Present each respondent with 24 different vignettes, comprising combinations of answers, with the combinations comprising two answers, three answers, or four answers, respectively. Each vignette contained at most one answer from each question. The incomplete nature of the vignettes, i.e., vignettes comprising two or three answers instead of four answers, ensured that the answers could be arranged in a so-called ‘experimental design,’ with the 16 answers or elements being statistically independent of each other. That statistical independence cannot be intuited, but can only be shown by a multivariate statistical analysis (factor analysis).

Each respondent evaluated a unique set of 24 vignettes. The uniqueness was created using a permutation scheme [8]. The ‘structure’ of the vignettes remained the same, but the particular combinations changed. This feature of individual experimental designs, permutations of one another, empowers the researcher to cover more of the ‘design space’ of potential combinations, as well as study pairwise and higher order interactions, viz., the effect of one element or answers on the response to another element or answer.

Step 7-Obtain the ratings, transform the ratings to binary using the scheme below, and then create models or equations relating the presence/absence of the elements to the transformed ratings: We write the equation as: Binary Rating or Consideration Time = k₁(A1) + k₂(A2) … k₁₆(D4). For this study we opted to force the model through the origin, by not incorporating an additive constant. The choice of using an additive constant or not using an additive constant depends upon the interpretation of the additive constant. The constant is useful for simple linear scales, such as 1=not interested … 5=interested. The additive constant is less clear when we combine rating scale points.

Here are the transformations

Consideration: The number of seconds (0 to 9) between the appearance of the vignette on the respondent’s computer or phone screen, and the rating. Time is collected in tenths of a second

Don’t believe: Recode 2 and 3 as 100, recode 1,4 and 5 as 0

Believe: Recode 4 and 5 as 100, recode 1, 2, 3 as 0

Angry: Recode 2 and 4 as 100, recode 1,3, and 5 as 0

Happy: Recode 3 and 4 as 100, recode 1,2, and 4 as 0

Step 8- List the coefficients of the models in a single table: Each column corresponds to one of the dependent variables, and each row corresponding to one of the 16 elements, viz., answers. Table 2 shows the coefficients for the total panel. In order to find patterns in the results, we sort the table five times, from high to low, once for each dependent variable. We pick up only the strongest elements, as show below. As we see in the lists below, there are a few strong performing elements for each dependent variable. The elements do not necessarily tell a coherent story.

Table 2: Coefficients for the equations relating the presence/absence of the 16 elements to each dependent variable

	Total Panel	Consideration Time	Angry	Happy	Do not believe	Do believe
A1	reason: politician wants to gain new votes	1.5	12	11	18	4
A2	reason: politician wants to construct a new power base	1.3	15	9	21	3
A3	reason: public official wants to create approval of policies	1.7	14	12	21	5
A4	reason: public official wants to disguise problems	1.4	20	9	22	6
B1	story: created with selective false facts	1.2	31	1	15	17
B2	story: interview constructed by writer	1.4	17	8	12	13
B3	story: expose … written to be interesting & influence feelings	1.9	16	13	15	14
B4	story: breaking news taken from legitimate sources and “edited”	1.7	19	7	14	12
C1	topic: public works and infrastructure	1.4	13	10	12	11
C2	topic: behavior of elected government officials	1.6	13	7	9	11
C3	topic: issues in educating young people to question and develop critical thinking	1.8	5	18	6	18
C4	topic: issues negatively affecting quality of life of citizens	1.5	16	8	10	14
D1	featured: government wrongdoing	1.2	23	4	9	17
D2	featured: putting positive spin on mistakes	1.4	16	7	16	7
D3	featured: explaining away and denying previous history and lessons	1.4	28	-3	16	9
D4	featured: overplaying to distract	1.7	20	4	13	10

Consideration Time

B3 story: expose … written to be interesting & influence feelings

Angry

B1 story: created with selective false facts

D3 featured: explaining away and denying previous history and lessons

B4 story: breaking news taken from legitimate sources and “edited”

D1 featured: government wrongdoing

D3 featured: explaining away and denying previous history and lessons

D4 featured: overplaying to distract

Happy

C3 topic: issues in educating young people to question and develop critical thinking

Don’t Believe

A4 reason: public official wants to disguise problems

A3 reason: public official wants to create approval of policies

Believe

C3 topic: issues in educating young people to question and develop critical thinking

B1 story: created with selective false facts

D1 featured: government wrongdoing

Step 9 – Cluster the respondents based upon the pattern of coefficients, doing so for each of the five key dependent variables, angry, sad, do not believe, belie consideration time, respectively: One of the key contributions of Mind Genomics is the systematized, algorithmic discovery of two, and often three or more ‘mind-sets’ for each topic. It is obvious that people differ from each other. The typical way to create groups is by WHO THE RESPONSE ARE. A more sophisticated way to divide people does so on the basis of the patterns of self-description, where the topics of self-description are attitudes and behaviors This second clustering is called psychographic clustering because it depends on how the respondent reacts to a fixed set of questions, usually GENERAL questions.

Clustering by who a person IS, or how the person THINKS, are methods that can be relegated to the class of blunt measuring instruments. Although we can do the clustering quite straightforwardly and rigorously either on patterns of classification or patterns of responses to general questions about attitudes and behaviors, , it is not clear that the clusters created by using this information (attitudes, behavior) will be of any use to predict how the emergent clusters will react to topic-specific ‘micro-questions’. In fact, the reverse is often true. The clusters which emerge from conventional divisions by WHO or HOW ONE THINKS show similar patterns of reactions to a specific topic relevant to the researcher’s interest. For example, clusters of people made on the basis of their attitudes towards the general topic of “truth vs misrepresentation” may show a variety of response patterns to information about Fake News.

Mind Genomics clusters respondents based upon the pattern of their individual coefficients, when the coefficients emerge from an investigation of elements deal with a narrow, defined topic, and where the elements paint a ‘word picture.’ In other words, the clusters emerging from Mind Genomics come from the ‘bottom up,’ from the specifics of a situation. When doing a Mind Genomics study, and clustering the respondents in the foregoing manner, based upon the similarities of the coefficients, it becomes quite straightforward to define the meaning of the clusters, or mind-sets.

The mechanics of clustering are by now well known (Jain & Dubes,1988.) The fundamentals of clustering are to divide objects (viz., here people) by the differences in certain patterns, with ‘difference’ defined in various ways. In Mind Genomics one straightforward way, indeed the most frequently used, is to define a ‘distance’ between two people, based upon the simple index (1 – Pearson R = Pearson Correlation Coefficient.) When two people show a perfectly positive covariation, R=, +1, it means that their patterns are identical. Going one step further in the calculation, we define the distance between these two people as 0 (1–1=0), which makes sense. When respondents show opposite patterns, the most ‘opposite’ is a perfectly negative correlation of -1. The distance is highest, 2 (1 –1 =2).

We will do this clustering separately for each of the five dependent variables. The tables will show the differences in the patterns.

Mind-Sets based upon angry (see Table 3)

MS1 Story: Angry because the story is a fake, information ‘spun’ and story made-up

B1 story: created with selective false facts

B2 story: interview constructed by writer

B3 story: expose … written to be interesting & influence feelings

A2 reason: politician wants to construct a new power base

B4 story: breaking news taken from legitimate sources and “edited”

MS2 Features: Angry because the story has features which make one angry

D3 featured: explaining away and denying previous history and lessons

D1 featured: government wrongdoing

D2 featured: putting positive spin on mistakes

D4 featured: overplaying to distract

Table 3: How the different elements drive the rating of ‘angry’

		Angry MS1	Angry MS2
	Base size	23	32
B1	story: created with selective false facts	38	18
B2	story: interview constructed by writer	33	-3
B3	story: expose … written to be interesting & influence feelings	32	-1
A2	reason: politician wants to construct a new power base	31	8
B4	story: breaking news taken from legitimate sources and “edited”	30	7
A4	reason: public official wants to disguise problems	27	22
C4	topic: issues negatively affecting quality of life of citizens	24	9
C2	topic: behavior of elected government officials	21	8
A1	reason: politician wants to gain new votes	18	13
D3	featured: explaining away and denying previous history and lessons	8	51
D1	featured: government wrongdoing	-1	42
D2	featured: putting positive spin on mistakes	-14	42
D4	featured: overplaying to distract	1	39
A3	reason: public official wants to create approval of policies	14	14
C1	topic: public works and infrastructure	15	7
C3	topic: issues in educating young people to question and develop critical thinking	14	-2

Mind-Sets based upon happy (Table 4)

MS1-Story topic is interesting and well written, as well as responds positively to the positive efforts by a politician to spin a story:

B3 story: expose … written to be interesting & influence feelings

B2 story: interview constructed by writer

B4 story: breaking news taken from legitimate sources and “edited”

A3 reason: public official wants to create approval of policies

A2 reason: politician wants to construct a new power base

MS2-Story exaggerated to challenge a young person to think, as well as putting a positive spin on mistakes to distract:

D2 featured: putting positive spin on mistakes

C3 topic: issues in educating young people to question and develop critical thinking

D4 featured: overplaying to distract

Table 4: How the different elements drive the rating of ‘happy’

		Happy MS1	Happy MS2
	Base size	27	28
B3	story: expose … written to be interesting & influence feelings	31	-3
B2	story: interview constructed by writer	29	-9
B4	story: breaking news taken from legitimate sources and “edited”	24	-4
A3	reason: public official wants to create approval of policies	24	2
A2	reason: politician wants to construct a new power base	20	-6
A4	reason: public official wants to disguise problems	17	-7
A1	reason: politician wants to gain new votes	16	-2
D2	featured: putting positive spin on mistakes	-10	26
C3	topic: issues in educating young people to question and develop critical thinking	16	23
D4	featured: overplaying to distract	-14	19
C1	topic: public works and infrastructure	7	17
D1	featured: government wrongdoing	-8	15
D3	featured: explaining away and denying previous history and lessons	-19	13
C4	topic: issues negatively affecting quality of life of citizens	6	11
C2	topic: behavior of elected government officials	7	7
B1	story: created with selective false facts	11	-8

Mind-Sets based on ‘Do not believe’ (Table 5)

MS1 -Mistrusts reason for the news, offended by public officials and politicians

A4 reason: public official wants to disguise problems

A2 reason: politician wants to construct a new power base

A3 reason: public official wants to create approval of policies

A1 reason: politician wants to gain new votes

M2 – Mistrusts stories about government

D3 featured: explaining away and denying previous history and lessons

D2 featured: putting positive spin on mistakes

D1 featured: government wrongdoing

C3 topic: issues in educating young people to question and develop critical thinking

D4 featured: overplaying to distract

C1 topic: public works and infrastructure

Table 5: How the different elements drive the rating of ‘do not believe’

		BelNoS1	BelNoS2
	Base size	32	23
A4	reason: public official wants to disguise problems	39	2
A2	reason: politician wants to construct a new power base	38	1
A3	reason: public official wants to create approval of policies	37	11
A1	reason: politician wants to gain new votes	34	-2
D3	featured: explaining away and denying previous history and lessons	2	42
D2	featured: putting positive spin on mistakes	7	36
D1	featured: government wrongdoing	3	24
C3	topic: issues in educating young people to question and develop critical thinking	-2	22
D4	featured: overplaying to distract	7	21
C1	topic: public works and infrastructure	3	20
C2	topic: behavior of elected government officials	0	16
B4	story: breaking news taken from legitimate sources and “edited”	13	15
B3	story: expose … written to be interesting & influence feelings	12	15
B1	story: created with selective false facts	12	14
C4	topic: issues negatively affecting quality of life of citizens	4	10
B2	story: interview constructed by writer	10	9

Mind-Sets based on ‘Believe’ (Table 6)

What people ‘believe’ is a bit more complex, suggesting one group (MS1) believes stories having certain topics, where the other group (MS) believes stories crafted by politician. It may be that the groups are pointing to the types of fake news to which they have been exposed, or are suggesting that these are type of fake news that they believe are the most salient, within the confines of this Mind Genomics experiment.

MS1-Believe stories talking government wrongdoing

C3 topic: issues in educating young people to question and develop critical thinking

D1 featured: government wrongdoing

C2 topic: behavior of elected government officials

D4 featured: overplaying to distract

C1 topic: public works and infrastructure

MS2-Believe stories crafted by politicians

A4 reason: public official wants to disguise problems

A3 reason: public official wants to create approval of policies

Table 6: How the different elements drive the rating of ‘believe’

		BeYesS1	BelYesS2
	Base size	32	23
C3	topic: issues in educating young people to question and develop critical thinking	25	2
D1	featured: government wrongdoing	23	8
C2	topic: behavior of elected government officials	22	0
D4	featured: overplaying to distract	21	1
C1	topic: public works and infrastructure	20	2
D3	featured: explaining away and denying previous history and lessons	20	1
B1	story: created with selective false facts	20	7
C4	topic: issues negatively affecting quality of life of citizens	19	16
B3	story: expose … written to be interesting & influence feelings	17	9
D2	featured: putting positive spin on mistakes	16	-3
B2	story: interview constructed by writer	16	5
A4	reason: public official wants to disguise problems	-9	25
A3	reason: public official wants to create approval of policies	-16	23
A1	reason: politician wants to gain new votes	-9	18
A2	reason: politician wants to construct a new power base	-9	18
B4	story: breaking news taken from legitimate sources and “edited”	15	10

Mind-Sets based on ‘Consideration Time (Table 7)

Consideration Time (also known as response time or reaction time), may produce new, deeper, and perhaps more fundamental division of respondents The Consideration Time emerges from the ‘measurement’ of non-conscious processes, specifically the degree to which parts of a message engage., since the clustering is based on upon the pattern of what holds the respondent’s attention. Table 7 shows a very strong, and very clear segmentation, based upon the specifics, reason vs what is featured.

Table 7: How the different elements drive the magnitude of ‘Consideration Time’

		ConTS1	ConTS2
		24	31
A3	reason: public official wants to create approval of policies	2.8	0.9
A4	reason: public official wants to disguise problems	2.6	0.7
A2	reason: politician wants to construct a new power base	2.5	0.4
A1	reason: politician wants to gain new votes	2.3	0.9
B3	story: expose … written to be interesting & influence feelings	2.1	1.7
D4	featured: overplaying to distract	0.3	2.8
D3	featured: explaining away and denying previous history and lessons	0.2	2.4
D1	featured: government wrongdoing	0.0	2.4
D2	featured: putting positive spin on mistakes	0.3	2.3
C3	topic: issues in educating young people to question and develop critical thinking	1.7	2.0
B4	story: breaking news taken from legitimate sources and “edited”	1.5	1.7
C2	topic: behavior of elected government officials	1.8	1.6
C1	topic: public works and infrastructure	1.7	1.5
C4	topic: issues negatively affecting quality of life of citizens	1.7	1.5
B2	story: interview constructed by writer	1.1	1.3
B1	story: created with selective false facts	1.5	0.9

MS1 – Clearly engaged by the reason for the news

reason: public official wants to create approval of policies

reason: public official wants to disguise problems

reason: politician wants to construct a new power base

reason: politician wants to gain new votes

story: expose … written to be interesting & influence feelings

MS2 – Clearly engaged by the topic

featured: overplaying to distract

featured: explaining away and denying previous history and lessons

featured: government wrongdoing

featured: putting positive spin on mistakes

topic: issues in educating young people to question and develop critical thinking

Discussion

This paper maps out how people might response to information that they know might the substance of Fake News. Mind Genomics makes it impossible for the respondent to ‘game the system,’ and provide the researcher with appropriate answers. First, no one knows what the appropriate answer is, because of the five responses one can use to rate the vignette, four of the five responses comprise a pair of judgments combined. It is very difficult to game one’s response with this structure of responses. Second, the elements incorporated into a single vignette comprise 2–4 different messages. This structure of the stimulus encourages the respondent to give top of the mind answers, rather than trying to be consistent. During the evaluation of 24 vignettes, the memory of one’s answer quickly fades, as the respondent copes with each newly created combination. Participating in the experiment is easy when one assigns a ‘gut answer’ but extremely difficult when one tries to be consistent.

The key findings from this study suggest that Fake News can be deconstructed into different aspects. Beyond the deconstruction into components one requires studies with the person to discover how each aspect functions. The 16 elements studied in this cartography do not, by themselves, show the nature of what type of information drives emotional responses (angry, happy), as well as the type of information that is believed versus not believed. The Mind Genomics effort provides that next layer of data and insight about Fake News.

Acknowledgment

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

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