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The Significance of Sensory Disorders in Autism

2 Replies

DOI: 10.31038/PSYJ.2023572

Abstract

Background: Autism is said to be a Neurodevelopmental Disorder which has a whole range of different strands. One of the most overlooked and misunderstood is the sensory differences – often referred to as Sensory Processing Disorder (SPD) or Sensory Integration Disorder (SID).

Aim: The aim of this study is to improve the understanding of the sensory differences and their implications in autism.

Methodology: Qualitative methods that include observation, personal accounts and ongoing research.

Current situation: Today unusual responses to sensory stimuli are recognized by many individuals with autism, their families and many professionals working in the field of Occupational therapy who work with them, although their significance is often overlooked by professionals working in other disciplines.

Summary: This study will show that both the sensory differences and their significance has been known for centuries. It will also propose that they are of great significance in autism for they affect every aspect ofindividual’s daily life.

Keywords

Autism, Neurodevelopmental delay, Sensory differences, Sensory processing disorder (SPD), Sensory integration disorder (SID), Soft neurological signs, Aberrant reflexes, Visual differences, Auditory differences, Tactile differences

The significance of our senses in development has been known for centuries, as was summed up by the German philosopher Immanual Kant in the 18th century when he said, “All our knowledge begins with the senses, proceeds to understanding, and ends with reason.” During the 19th century great doctors like Drs. John Langdon Down and the French physician Édouard Séguin worked with a range of children with learning disabilities of various kinds, while some of their other contemporaries worked with children who were blind, deaf or deaf-blind. As a group they were all keenly observant as can be seem from their books and papers, all having a real understanding of the sensory differences and their implications. During the time that Dr J.L. Down ran the Earlswood Asylum he noted that some of the children there fitted into different groups, some having savant skills and others having the “mannerisms and behaviour” that we would connect with autism today.

In a paper written in 1907 Dr Séguin made a comment worth repeating and, although his terminology is unpleasant to our ears, its importance is undeniable. As he noted, “Deafness and blindness from birth have the same effects as paralysis on ungifted children, by depriving them of the cognizance of a whole series of phenomena. But it is a fact curious enough to be noted, that partial obliteration of one of these channels of knowledge will produce the symptoms of superficial idiocy surer than its complete destruction.” Moving into the 20th century we find that the importance of the sensory differences and their possible link to autism was highlighted by Bergman and Escalona in their [1] paper “Unusual Sensitivities in Very Young Children.” As they wrote “Colors, bright lights, noises, unusual sounds, qualities of material, experiences of equilibrium, of taste, of smell, of temperature, seemed to have an extraordinarily intensive impact upon these children at a very early age.”

In 1964 the seminal book Infantile Autism by Dr Bernard Rimland noted that many such children had unusual sensitivities in several, if not in all, their senses; an idea he supported by quoting from several studies that described peculiar reactions such as “ill focused eyes,” “functionally blind,” “blind while seeing, and deaf while hearing.”

Interest in this area has fluctuated over the decades. It gained attention in the 1960s and 1970s due to the work of several experts including the late Dr’s. Ornitz and Ritvo. In their 1968 paper they noted how common, and extremely important, those perceptual differences and their consequences were and postulating that the sensory differences could be the basis of autism [2-4].

In his book The Ultimate Stranger: The Autistic Child, Dr Carl Delcato detailed his research. While it was dismissed by many in the scientific community, his findings are importantbecause he found thatthere were three categories of sensory difference each with its own specific effects. Those three categories included children who were hypersensitive, others who were hyposensitive and a third category that he called White Noise in which those sensitivities were mixed. He also found that their mannerisms were directly linked to the sensory differences. He explained that “One or more of their intake channels (sight, sound, taste, smell, or feel) was deficient in some way. Their strange repetitive behavior was their attempt, through much repetitive stimulation, to normalize that channel or channels [5].

Delacato concluded that the sensory differences were “the most unique feature of autism.” Ornitz took that idea a stage further by suggesting that the seemingly unusual responses to sensory stimuli could be “used to identify autism in young children.”

In their book Deaf-Blind Children and Infants [6] Treffry and McInnes told us about the children they worked with, all of whom had a sensory impairment. The connection being that, as Treffry and McInnes told us, the result of those impairments was “… not a reflection of the child’s ability to process information and draw logical conclusions, but rather a measure of his ability to gather the information in the first place.”

By the late1970s personal accounts had begun to creep into the literature. One came from Jerry, a former patient of Leo Kanner who told Dr. Jules Bemporad about his childhood world which he said had consisted of confusion and terror and was “frightening” because it was full of “painful stimuli that could not be mastered.” Then there was Tony W. who recalled his childhood experiences telling us that “I was afraid of everything! I was terrified to go in the water swimming, [and of] loud noises; in the dark I had severe repetitive nightmares and occasionally hearing electronic noises with nightmares. I would wake up so terrified and disorientated” [7].

In “An Inside View of Autism” Temple Grandin noted that “My senses were oversensitive to loud noise and touch. Loud noise hurt my ears and I withdrew from touch to avoid over-whelming sensation.” She talked about her tactile problems saying that “When people hugged me, I stiffened and pulled away to avoid the all-engulfing tidal wave of stimulation. The stiffening up and flinching was like a wild animal pulling away.” She follows that by noting that “The nerve endings on my skin were supersensitive. Stimuli that were insignificant to most people were like Chinese water torture” [8,9].

Since the 1970’s some professionals (both inside and outside the world of autism) have been researching both neurodevelopment delay and the individual senses. That has led to a strong body of research that links neurodevelopment delay (and what are termed “soft neurological signs” which include aberrant reflexes) to the sensory differences. There is also a great deal of evidence that hearing can have a major impact on behavior from peoplelike Dr. G. Bérard, whose groundbreaking work continues to help many people worldwide, some with autism or other neurodevelopmental conditions [10].

Others work in the field of vision (an area in which the sensitivities are most complex). They include mild to severe Visual Impairment (VI) and what is often termed Visual Stress or Meares-Irlen syndrome. *Today research indicates that most children on the autism spectrum have severe visual stress which cause them to see the world around them as if everything is fragmented or distorted, with some even seeing faces as if they are totally blank. Since those early years there have been an increasing number of accounts (from individuals and families) about the sensory differences and the difficulties they cause. Many people are now taking those account seriously and today research is proving them to be true, as in “The pattern of sensory processing abnormalities in autism” by Janet Kern and her colleagues [11].

In 2007 the neuroscientists Henry and Kamila Markram came up with the Intense World Syndrome, based around hypersensitivity. Their initial interest in this topic was triggered by their son, who has autism. They and their colleague postulated that sensory overload interferes with social communication and language and that those obsessive and repetitive behaviors are the child’s attempt to bring order and predictability into a bewildering world. That led them to suggest that their hypothesis offers a unifying theory of autism. It is certainly a very positive theory and one that (in part) confirms previous research [12].

Comments

  • Research into NDD has shown that while the sensory differences and their effects are common among the neurodivergent community they are most severe in autism.
  • The Intense World Syndrome. The idea that sensory overload interferes with social communication and language and that those obsessive and repetitive behaviors are the child’s attempt to bring order and predictability into a bewildering world is certainly correct. The flaw being that it focusses solely on hypersensitivity whereas the sensory issues which are far more complex than that.

Conclusion

The link between sensory differences and autism is far clearer now that it has ever been. That is partially because of accounts by a range of different people from different countries across the world who are living with autism and because there is now a great body of evidence from reputable scholars working in a range of disciplines who confirm that neurodevelopmental delay can cause a range of sensory differences that have a major impact on people’s lives. In Lucy Blackman’s article “Reflections on Language” in [13-22] she asked “So, if one doesn’t have depth perception, what does that mean in terms of facial expression? If one hears the subtle sounds of speech out of order, which I do, how does one process language? If affection in the form of cuddles and kisses cause pain and discomfort in one’s infancy, how on earth does one develop interaction which might compensate for not interacting to speech and glance?” How indeed?

Conflict of Interest

The author has no conflict of interest to declare.

Funding

None

References

  1. Bergman P, Escalona SK (1947) Unusual Sensitivities in Very Young Children. The Psychoanalytic Study of the Child 3: 1
  2. Rimland B (1964) Infantile Autism. New York: Appleton-Century-Crofts.
  3. Ornitz EM, Ritvo ER (1968) Perceptual Inconstancy in Early Infantile Autism: The Syndrome of Early Infant Autism and Its Variants Including Certain Cases of Childhood Schizophrenia. Arch Gen Psychiatry 18: 76-98.
  4. Ornitz EM (1989) Autism at the interface between sensory and information processing. In G. Dawson (Ed.), Autism: Nature, diagnosis, and treatment .The Guilford Press.
  5. Delacato C (1974) The Ultimate Stranger. New York: Doubleday.
  6. McInnes JA, Treffry JM (1982) Deaf-Blind Infants and Children. Buckingham: Open University Press.
  7. Bemporad JR (1979) .Adult recollections of a formerly autistic child.Journal of Autism and Developmental Disorders 9: 179-198.
  8. Grandin T. An Inside View of Autism.
  9. Grandin T., Margaret M. Scariano (1986) Emergence: Labelled Autistic Arena Press.
  10. Berard G (1993) Hearing equals behaviour. New Canaan: Keats Publishing. (Original published 1982).
  11. Kern J. et al. The pattern of sensory processing abnormalities in autism.
  12. Markram K, Markram H (2010) The intense world theory–a unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience 224.
  13. Blackman L (2005) Reflections on Language” pp: 146-167 in Autism and the Myth of the Person Alone ed. by Douglas Biklen. New York University Press.
  14. Mayoral M (2010) Neurological soft signs in juvenile patients with Asperger syndrome, early-onset psychosis, and healthy controls. Early Intervention in Psychiatry.
  15. Parmar KR. et al. Visual Sensory Experiences From the Viewpoint of Autistic Adults, Front Psychol. [crossref]
  16. Volkmar FR, Cohen DJ (1985) ‘The experience of infantile autism: A first person account by Tony W.’ Journal of Autism and Developmental Disorders 15: 45-54.
  17. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington: American Psychiatric Publishing, 2013.
  18. https://www.disabilitymuseum.org/dhm/lib/detail.html?id=1531&page=11
  19. http://auditoryintegrationtraining.co.uk/auditory-integration-training-ait-for-hearing-autism-adhd-add-dyslexia-and-other-special-needs-2/clinical-studies-for-auditory-integration-training/
  20. https://blog.donnawilliams.net/2014/09/20/visual-perceptual-disorders-as-a-cause-of-autism/
  21. https://www.brainbalancecenters.com/blog/retained-primitive-reflexes-sign-brain-imbalance
  22. Neurodevelopmental delay – https://www.moveplaythrive.com/research/178-research-summaries-goddard

Pilot Results from the Ambulatory Electronic Health Record (EHR) Evaluation Tool: Lessons Learned

4 Replies

DOI: 10.31038/JCRM.2023633

Commentary

Most studies focused on electronic health record (EHR) safety, specifically the ability of these systems to detect and prevent adverse drug events (ADEs), has been performed in the inpatient setting. To address this gap, the Ambulatory EHR Evaluation Tool was developed and piloted with seven clinics in 2019. Each of these clinics used one of the leading outpatient EHR systems, as identified by the Office of the National Coordinator (ONC) [1]. The tool consists of a medication safety test and a medication reconciliation module. For the medication safety test, it simulates a physician prescribing medications to their patients. The testing methodology closely follows the inpatient version of the tool, which is administered by the Leapfrog Group. For the medication reconciliation module, clinics were asked to electronically reconcile two medication lists.

To take the medication safety test, clinics downloaded a set of test patients and associated medication test orders to enter into their operational EHR using Computerized Physician Order Entry (CPOE). Included with these test patients were basic demographic details (e.g., age and weight), allergies, and relevant laboratory values. While entering these test orders, licensed prescribers recorded any advice or information they received (if any). The tool assesses basic and advanced decision support features [2]. Once finished with the test, clinics received an overall percentage score of unsafe orders detected, as well as individual order category scores. The test also included two subcategories: nuisance orders and fatal orders. Nuisance orders are low-priority medication combinations (i.e., drug-drug interactions and therapeutic duplications) that should be delivered un-interruptively, as they can contribute to alert fatigue [3]. These orders were reverse scored, in that clinics which alerted on these test orders were scored as “incorrect”. For fatal orders, these were high-priority medication interactions that if prescribed, can lead to serious injury and even death. Lastly, for the medication reconciliation module, we provided clinics with a test patient that was recently discharged from the hospital and is returning to their outpatient clinic for a follow-up visit. Clinics were provided with two medication lists: one from the recent hospitalization, and the most recent ambulatory medication list. These medication lists had the following discrepancies: removal and addition of a medication, and a change in the dose of a medication.

The mean overall score for the medication safety test was 54.6% (Table 1). The range was 42.5%, the minimum score was 37.5%, and the maximum score was 80%. Generally, clinics performed well in areas of basic decision support such as drug allergy (100%), drug-drug interaction (89.3%), drug dose (daily) (78.6%), and drug pregnancy (75%). In contrary, clinics performed poorly in areas of advanced decision support areas like drug age (39.3%). Most alarmingly, none of the clinics in the study had drug laboratory or drug monitoring alerts implemented. In terms of fatal order performance, the mean score was 67.9%. Only one clinic alerted on all the fatal orders in their test. For the nuisance orders, the mean score was 64.3%.

Table 1: Mean percentage scores for each order category

TAB 1

For the medication reconciliation module, three clinics (43%) had an EHR-based medication reconciliation functionality. However, only one clinic (14%) could demonstrate it during the pilot. In addition, none of the clinics’ EHR systems provided CDS during this process. Instead of electronic processes for medication reconciliation, most clinics compared medication lists manually, which was usually performed by a nurse or medical assistant.

The results from the initial pilot of the Ambulatory EHR Evaluation Tool revealed that while basic CDS features like drug allergy and drug interaction checking were widely implemented, areas of more advanced decision support were not implemented. A major commonality between all the clinics was that certain types of alerts were turned off completely. This occurred mostly in advanced decision support areas like drug laboratory, drug monitoring, and drug age; all of which are critical areas for patient safety. In addition, the mean fatal order score was only 68%, which is considered low given the severity of these medication orders. We expected all clinics to score a 100% in this subcategory. In terms of the results of the medication reconciliation module, only one clinic could demonstrate this functionality even though all the clinics were certified through Meaningful Use. Moreover, although most of the clinics understood the importance of medication reconciliation, the electronic processes at their individual facilities were poorly understood and thus unused.

In a broader context, the results from this pilot reveal significant gaps in the implementation of advanced CDS features in the outpatient setting. This is further magnified by the fact that commercial outpatient pharmacies are no longer routinely checking prescriptions for common medication errors, thus leaving the only effective medication safety net at the ambulatory clinic level. This leaves an enormous medication safety gap in the outpatient setting, where most medications are prescribed in the healthcare system. Hopefully, as this tool becomes more widely used, outpatient clinics will use it as a quality improvement tool to assess and identify gaps in the implementation of their medication related CDS, which as of now, is the only critical safety net for outpatient medication use.

References

  1. Office of the National Coordinator for Health Information Technology. Office-based Physician Electronic Health Record Adoption. Published 2016. Accessed January 11, 2019. https://dashboard.healthit.gov/quickstats/pages/physician-ehr-adoption-trends.php
  2. Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, et al. (2007) Medication-related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review. J Am Med Informatics Assoc 14: 29-40. [crossref]
  3. Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, et al. (2013) Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Informatics Assoc 20: 489-493. [crossref]

Solving Epidemics of Lyme and Other Vector-Borne Infections through the Immune System

5 Replies

DOI: 10.31038/IDT.2023415

 
 

“Le microbe n’est rien, le terrain est tout.” “(The microbe is nothing, the terrain is everything).” -Louis Pasteur

Abstract

Vector-borne infections such as Lyme disease and co-infections, rickettsiosis, dengue, West Nile fever, and malaria that are known for their high morbidity, and even mortality, have been globally on the rise due to several key factors. Among these are the increased population of and exposure to ticks and mosquitoes that are caused by climate change and deforestation, as well as a generally more health-compromised population as evidenced by skyrocketing prevalence of chronic diseases. The implicated factors in chronic sickness are continual rise in environmental pollution, unhealthy diet and substance abuse, overuse of antibiotics leading to microbial evolvement into more aggressive forms, disturbed biome, secondary fungal infections and antimicrobial resistance (AMR). Some of the most common vector-borne infections lack effective treatments, including chronic post-treatment Lyme disease with diagnostic tests not always being reliable. Additionally, Lyme’s Borrelia Burgdorferi, that is the most common vector-borne disease in North America and Europe, presents a particular challenge to its treatments by existing in many different genotypes with some being particularly aggressive. This article presents a different approach to vector-borne, and infections in general, based on several principal factors. These concern the increased focus on the host innate immunity to defeat infectious agents using a novel diagnosis to ascertain reasons for suboptimal immunity, treatment to address this and elicit proper immune stimulation by vaccine-like action. Both diagnosis and treatment concern energy medicine, which is formally recognized by the NIH, and based on the physics of the living, water, and all matter in nature.

Keywords

Lyme disease and co-infections, Vector-borne infections, West Nile encephalitis, Antimicrobial resistance, Bioresonance testing, Energy medicine, Homeopathy, Digital medicine

Introduction

According to the US CDC, the incidence of one of the most common vector-borne, Lyme disease has risen by 44% from 1999 to 2019 and has been responsible for almost a half-million new cases in the US annually, at a cost of some $1.3 billion a year. Studies have found that over 14% of the population worldwide in 80 countries, have contracted Lyme disease [1]. Yet, due to the presence of different strains of the bacteria, its ability to mutate, and the likely compromised immune response of the host, laboratory tests are not always reliable or drug therapies efficacious [2-6]. Consequently, as many as up to 20% of these patients remain chronically or post-treatment permanently ill with many seeking alternative treatments. Unfortunately, their shared experience and lack of published convincing results to the contrary, indicate that despite these treatments using prolonged antibiotic courses, antimalarial drugs, herbs, oxygen, ultraviolet light, electrocutions, and many supplements for immune support at the cost of up to six-digit out-of-pocket figures, their failure and harm prevail.

Many patient testimonials reveal unfortunate experiences with “progressive Lyme clinics” in the US or Europe, ending up feeling sicker and even being hospitalized. These treatments had in common the prevailing errors: excessive focus on “killing” the microbe and blinded attempts to stimulate immunity without knowing the fundamental causes of its malfunction. In light of the encountered failures with both conventional and integrative approaches, concerned Lyme specialists have called for seeking a different approach to the problem [7]. However, a true different approach, instead of usual “stronger” pharmaceuticals, must foremost involve the state of the terrain, as noted by the great Pasteur, or immunity that allows the evolvement of infection in the first place. Voluminous toxicological literature documents immunosuppressive effects of environmental pollutants which ae abundant in the modern environments, some 100,000 agents with hardly any safety testing [8-10]. According to Harvard University EPA Working Group 2007 Report, traces of hundreds of environmental pollutants have been found in the bodies of 100% tested Americans that roughly encompasses all industrialized nations.

Some of these, heavy and other metals, affect genetic and epigenetic mechanisms yet cannot be completely excreted or metabolized [11]. The exposure and pathologies commence since pre and postnatal periods and continue to accrue through food, water, and air from modern kitchens to Greenland [12-24]. Among metals, mercury that is the most toxic nonradioactive element exerts multisystemic, including immune invasion and harms through massive use of fossil fuels, and silver amalgam fillings [25-28]. Dental restorations, including silver amalgams, and ubiquitous toxic metals in the bodies of modern populations, also act as conducting receiving antennas for just as ever-present electromagnetic fields, resulting in their enhanced combined pathogenicity, further adding to the immune burden. Electromagnetic fields alone cause immune suppression, leading to chronic infections, cancer and numerous other diseases [29-36].

Also, just as prevalent and steadily rising are opportunistic fungal infections, due to a massive use of antibiotics and the high consumption of simple sugars, which cause increased immune burden, immunosuppression and immune invasion [37,38].

Additionally, antibiotics further impair the host resistance by altering immune response, damaging immune cells, the microbiome, and possibly the human genome that contains numerous bacterial remnants, thus exacerbating the vicious cycle and compromising overall health [39,40]. Consequently, they have been associated with many chronic diseases, including malignancies, with antibiotics produced AMR causing 700,000 global annual mortality that is expected to reach 10 million by 2050 [41,42].

Besides the known serious limitations of laboratory tests to diagnose toxicological agents where it counts the most, inside the internal organs in the living, infectious agents, including Lyme and co-infections bacteria, may elude these tests too [43-45]. Physics-based alternative medicine bioresonance test, applied kinesiology, has been used to address this diagnostic gap. Likewise, physics-based homeopathic or homeopathic-like copies of toxicological, infectious or any pathogen, isodes, have been employed for their established, based on hormetic effect, detoxification of toxicants, and antimicrobial vaccine-like immune stimulation [46-57]. The offered advantage of such immune enhancement therapeutics is the replacement of a direct microbicidal effect to avoid triggering mutations and antimicrobial resistance, with vaccine-like specific immune stimulation against infectious agents. Recently published studies and case reports presented the efficacy of this approach for COVID-19, and resolving pneumonia, and H Pylori infection without antibiotics [58,59].

In the long experience of this author, homeopathic remedies prepared from organs, sarcodes, indicated additional detoxifying and restorative effects likely due to enhancement of homeostatic function to expel xenobiotics. Remedies prepared from bodily fluids in order to dispel contained infectious or toxicological agents represent autoisodes. Isodes, sarcodes, and autoisodes have been registered with the American FDA and its many counterparts worldwide. Since homeopathic remedies largely represent energetic signals, not chemical substances, materials scientists and physicists deem that the prevailing portrayal of these remedies as overdiluted placebos, constitutes “distortion of and ignorance in science,” and “unnecessary confusion.” [60-72]. Considering that physics deems the living to be fundamentally electromagnetic systems, their response to meaningful energetic signals is obligatory [63-75].

Capitalizing on this premise, we can use the immune system, instead of drugs, herbs and other “killing” means, to dispense with infectious agents and also cultivate, unlike antimicrobial agents, future resistance against it, through vaccine-like energetic signals. Well-known matter-energy duality, natural resonance frequency phenomena in physics, and water science support imprint of energy fields of microbes in water, thus creating agent specific energetic vaccines. This approach utilizes the same immune stimulation principle as the pharmaceutical vaccines – delivery of a weakened microbe – but only in its energetic, instead of material molecular form (Figure 1).

FIG 1

Figure 1: Immune stimulation principle

Materials and Methods

The applied sarcodes and isodes were obtained from homeopathic pharmacies. Most of the time, sarcodes and isodes were used after adjusting their potencies by an automated water programming device* in order to better match the patient’s individual disease state, according to bioresonance testing.

The same water programming platform was used to prepare autoisodes. Scientific literature referred to energetically modified high-dilution homeopathic remedies as homeopathic-like [76]. Medical Nobelist Montagnier, among other researchers, produced positive biological responses with electronically modified high dilutions and immunologist Benveniste named this paradigm digital biology [77-81]. An automated platform can be particularly helpful in acute and life-threatening emergencies when effective antimicrobial drugs do not exist, or are available, and shortens the production of vaccines from years, for pharmaceutical ones, to minutes, for energetic ones.

Bioresonance testing, applied kinesiology, is based on phenomena of resonance, matter-energy duality, and natural resonant frequency of all matter in nature, including the living. The diagnostic tissue resonance interaction method has been used as a highly specific and sensitive technique for cancer detection [82]. The test is performed with a subject in a supine position on an examining table, holding a metal rod that is connected to a metal platform through a cable forming a conductive circuit between a testee and the platform. When glass vials with energetic imprints of body organs, toxicological, infectious, or other pathogens are separately placed on the platform, a person responds to their corresponding fields with an involuntary muscle stress response, if a tested substance is related to their pathology [83,84]. Muscle response displays a change of tone and a slight movement of the right leg. This reaction can be likened to self-awareness of meaningful information, as in a lie detector test through stressful brain wave pattern. Due to multiple intertwined connections between skin and internal organs, more than a single mechanism of response might be involved. The conduction circuit would encompass the brain and spinal cord with sensory and motor nerves, autonomic nervous system, widespread connective tissue, and biological water that possesses permittivity and connection with DNA [85-93]. A tester detects the muscle response by holding his/her hands on the subject’s ankles. In the event of a tested substance not being part of the pathology, muscle response is absent. The test can also determine the potential benefit, absence, or iatrogenicity of a tested therapeutic substance. On the whole, the test utilizes the same fundamental properties of the living, electromagnetism and electronics, as all biophysical tests such as ECG, EEG, and MRI.

Treatment of all of the cases was guided by bioresonance testing that also suggested otherwise, undiagnosed Lyme disease. Testing and treatment of the cases primarily focused on detection of toxicological agents, opportunistic fungal, parasitic, and viral infections, residues of antibiotics, endocrine impairment of excessive electromagnetic radiation, all of which have known immunocompromising effects. Basic healthy lifestyle guidance was provided in eliminating simple sugars, and reducing exposure to environmental pollutants and electromagnetic radiation that was not always optimally followed.

___________________________________________________

*Therapeutic Frequency Imprinting Device, US Patent #10941061

Patient Cases

Case 1

Man in his forties with a recent diagnosis of Lyme disease, but suspecting having had it since his 20s. Complaints: poor memory, anxiety, neurological symptoms, decreased vision, poor energy, sex drive, with head and back pains. He tried many alternative treatments without success. After a fairly short series of remedies, he reported feeling the best he had in years and virtually free of symptoms.

Case 2

An alternative practitioner in her 40s with Lyme disease and head-to-toe problems for several years: decreased memory, burning mouth, fears, knee and back pain, headaches, hypoglycemic spells with sugar cravings, thyroid malfunction, poor energy, and an inability to lose excessive weight. After a few treatments, she reported feeling ‘the best ever in my life.’

Case 3

A woman in her 40s with massive body breakdown over the years, and a tentative Lyme diagnosis. Presented with periodic fevers, debilitating back pains, fatigue, headaches, photophobia, abnormal space perception, food allergies with cravings, multiple infections, parasitic, bacterial, viral, enlarged lymph nodes, and a neurological voice disorder. Many prior treatments did not help. By the end of her treatment course, she reported: “I feel so much better than when I started. I keep being amazed by it.”

Case 4

A woman in her thirties, with debilitating symptoms for years, was diagnosed with Lyme disease and Bartonella, two years prior to the visit. Neither prolonged multiple antibiotic treatments nor integrative treatments worked. She complained of intense pains with other neurological symptoms and fatigue. Other symptoms: excessive weight gain, food intolerances, respiratory and vaginal infections, severe mental impairment with brain fog, falling down after making even a few steps, auditory hallucinations of birds chirping, loose bowel movements, and depression. Her integrative MD was planning on implementing a special Alzheimer’s alternative and pharmaceutical drug program.

Besides Lyme, bioresonance testing detected other pathogens, especially affecting her brain: pesticides, herbicides, (she lived in a farm region), solvents, mercury, and flu virus. Following a single treatment, she reported that she stopped falling down, had a substantial increase in energy, disappearance of auditory hallucinations, and normalized bowel movements. Alzheimer-like mental state disappeared too, with an increase in mental clarity, word recall, and overall feeling much healthier and happier.

Case 5

A middle-aged woman complained of chronic facial pains and twitches, fatigue, anxiety, fears, and headaches. She was unsuccessfully treated by an osteopathic doctor and a pain specialist with a published book on the subject. Among other causes, bioresonance testing identified Lyme infection affecting her trigeminal nerve and TMJ. She responded to the treatment well, and regained a normal life. Her pain specialist doctor supported this approach.

Case 6

A nine-year-old girl was referred to a psychiatrist for psychotropic medications by a surrendered child psychologist, because of the therapist’s inability to remedy her progressing restlessness, OCD, aggressiveness, moodiness, and overall unpredictable behavior over the years. Bioresonance testing suggested mercury, Lyme, and strep infections in the brain. She was 90% cured after a single treatment and completely after the second one. “She is just normal. Huge change, huge, huge, since we came here,” said the mother.

Case 7

A woman in her sixties with generalized joint pains, chronic anemia, and fatigue. She was treated for years by her rheumatologist with two anti-inflammatory drugs for rheumatoid arthritis. Bioresonance testing detected Lyme infection and mercury in her joints and bone marrow. Within a year, she came off both drugs, had her anemia resolved, and quality of life restored to normal.

Case 8

A man in his fifties frequented emergency rooms with typical heart angina pains. All heart tests, including coronary artery catheterization performed at a prestigious university heart center, turned normal and his cardiologists offered no diagnosis. Bio-resonance testing suggested Lyme carditis and following brief treatment his chest pains ceased.

Case 9

A middle-aged woman suffered from debilitating migraine headaches for many years. The major cause seemed to be a missed Lyme infection in the brain. The outcome: resolved migraines.

Case 10

A woman in her sixties underwent a complete personality change with severe depression, anxiety, panic attacks with crying, compromised cognition, blurred vision, sensation of inflamed brain, arthritic pains, fatigue, dizziness, and inability to read due to poor focus, or retain information. After two years of unproductive treatments by conventional specialists, she was diagnosed with Lyme and co-infections by an integrative MD. Weeks of several antibiotic treatments along with supplements hardly helped. I advised her to stop all of her antibiotics and supplements, due to the detected side effects by bioresonance testing. Despite her fear of stopping the antibiotics, she had to discontinue one of these anyway, admitting to suffering its side effects. Following her first treatment she reported that her arthritis, panic attacks with crying, and inflamed brain were resolved; her energy and focus considerably progressed, with brain fog and dizziness being hardly present. She stated: “I can tell you that on your drops, especially the Lyme ones, I felt much more Herxheimer reaction than on my antibiotics.” Following the discontinuation of her second antibiotic and receipt of another treatment, she reported that her problems were gone.

Comment: The more intense Herxheimer reaction implies more intense apoptosis of bacteria, due to a potent immune response.

Case 11

A young woman with dozens of mental, emotional and physical symptoms combined, reluctantly followed my advice to discontinue her antibiotics for Lyme and co-infections. Following her first treatment, she reported no longer looking and feeling like a corpse, and even the return of her five-year absent sex drive since the onset of Lyme disease. She also reported much improvement in her pains leading to decreasing her opiate pain regimen, and internal body vibrations with muscle twitches that she had suffered after using some “Lyme electrocuting machine”. She too, noted stronger Herxheimer reaction to homeopathic-like Lyme drops, compared to all of her prior antibiotics.

Case 12

Man in his early sixties with multiple medical problems for years: sinusitis since infancy, fatigue in the afternoon for decades, brain fog, arthritic pains, and chocolate cravings. All these were resolved in eight-nine visits.

Case 13

Fifteen-year-old boy treated with several courses of antibiotics for Lyme infection. However, his complaints persisted: fatigue, headaches, arthritic pains, shortness of breath on walking, low appetite, difficulty with schoolwork. All of these have been resolved after a few treatments.

Case 14

Athletic man in his twenties, with fatigue, depression, panic attacks, body heaviness, brain fog, compromised short-term memory, motor speech problems, and a sense of body detachment for years. He received over a half dozen psychotropic drugs throughout the ordeal and was still consuming a few. After eight months of the treatment, reported being off psychotropic drugs for months for the first time in twelve years. Most of the problems were resolved, with others better or much better. He noted, “My workplace is so loaded with computers and fluorescent lights which I believe drain me and slow my complete recovery.”

Case 15

A woman in her thirties on a continuous 15-year antibiotics treatment. The latest regimen consisted of four antibiotics for chronic Lyme, Bartonella, sinus, and urinary tract infections. She was receiving other drugs for years, eleven in total, also for chronic Babesiosis, herpes and candida infections, a peptic ulcer, and countless other ailments. Virtually bedridden for many years, she was also receiving intravenous mineral and fluid infusions for severe fluid-mineral imbalance with dehydration and generalized edema. She was managed by prominent Lyme disease specialists who used multiple drug regimens, but her response was so poor that some of them even advised her to seek alternative treatments. The latter harmed her.

The initial bio-resonance testing indicated an even greater number of present chronic bacterial and viral infections, as well as systemic candidiasis. Mercury toxicity was also prominently present, likely due to silver amalgam fillings and a flu shot containing thimerosal received in the past that made her bedridden. Further follow-up testing indicated one of her cats as the Bartonellosis carrier which a blood test confirmed. The total iatrogenic damage in this case was so substantial that there were concerns about it taking years to mitigate. However, within the first two months of weekly treatments, she was able to discontinue all of the drugs and intravenous infusions that she was unable to do for years, since a cessation led to an increase in pain and all of her ailments. Nine months into the treatment, she reported having more energy, stamina, a positive outlook, and a far better quality of life.

Case 16

“My journey began working as an RN in a small rural hospital. During this time, I became a mother of six children and I started having reactions to nearly every medication I tried to put into my body, from lidocaine to antibiotics, to a Mantoux test. I would begin to shake and have involuntary body movements. Following a trip to the Mayo clinic, I was told it was all in my head. In 2009, I began to feel very fatigued and began to experience episodes of involuntary movements that lasted longer and were more dramatic. The naturopath found that I had elevated mercury in my system and then, during the process of removing my mercury fillings, I was introduced to FCT*. I was barely able to work most days, some days I had no ability to figure out how to prepare a meal, and I had no awareness where my limbs were in space. My first FCT testing found that not only did I have mercury in my system, but I also had Lyme and 2 or 3 other co-infections from the tick. This was later confirmed through laboratory blood work. The medical doctor recommended two antibiotics to be taken for a year. I chose to be treated with FCT which involved taking 1 drop of energized water, under my tongue, of each of the different causative agents that were making me ill, and also, ones that would support the tissues that were stressed. I made a complete recovery until I was bitten by a tick again in 2011 and once again chose to treat Lyme with FCT.

I am familiar with many other patients who have chosen the antibiotic route and they still have symptoms, or if they try going off them, their symptoms return. I know one gentleman who has been on antibiotics for 3 years with no resolution to his Lyme symptoms if he tries to go off them. I live a full and wonderful life, hiking, fishing, skating, skiing, and caring for my grandchildren. I am truly fortunate to have found FCT and have witnessed the benefits that many people can have as a result of using it.

As an RN, I appreciate all the science that is behind FCT, so if God would like me to serve others by offering FCT, I will be happy to do so.

Have a wonderful day!”

Annette Roiko, RN 1/17/14

*FCT–Field Control Therapy® expresses the concept that health, or disease, originates from corresponding cellular fields, as per retired Stanford University Professor of Materials Science, William A. Tiller, Ph.D.

Case of a Recovered Dying Dog from Apparently West Nile Encephalitis

Although the incidence of West Nile Virus infection is relatively low, it occurs worldwide, can be lethal due to CNS involvement, and has been on the rise lately [94,95]. According to the CDC, about 1 out of 10 people with severe CNS infection die and no known effective treatment is available. The disease also affects a number of animals, including dogs. In light of these facts, this presented case might be edifying.

An elderly dog of a patient of mine, Duke, was developing limb paralysis and lethargy. As the “control” group, several animals at the same farm treated by a veterinarian for the same pathology had to be euthanized months before. Duke’s evaluation suggested West Nile encephalitis virus infection, which is typical for that climate, as well as toxic metals and other pollutants in his body. My patient stated that the area was frequently exposed to chemtrails and their neighbor often burned some odorous materials outside. The veterinarian suspected the same infection and Duke was given its isode that resulted in a 50% improvement. Soon after, two immune organ sarcodes and two environmental isodes added further progress however, soon after the dog suddenly became completely paralyzed and comatose, with generalized body trembling. To add to the family’s misfortune, another dog suddenly died following a major seizure that was consistent with West Nile encephalitis. My patient notified me of the both events, without mentioning that a veterinarian with a lethal injection for Duke had arrived. I insisted on continuing the treatment but realized that the West Nile virus strain isode at my disposal was not matching his infection strain, as these mosquitoes and viruses vary greatly. My patient also reported another recent exposure to chemtrails. Under the circumstances of unavailable matching isodes, autoisodes can be very helpful. My patient was able to prepare an autoisode in a recommended potency using an automated water imprinting device, after drawing blood with an insulin syringe needle from Duke’s paw and placing a drop in his mouth. The next day the dog showed good progress and completely recovered several days later. Nine months since, he enjoys his daily chores.

A recent study indicated that autoisodes could be successfully used in the COVID-19 pandemic [58].

Conclusion

Since infectious diseases have and will continue their rise, parallel to environmental pollution and climate change, a different approach is compulsory due to the aforementioned limitations of the pharmaceutical paradigm in the treatment of all categories of infections, and to avoid further increases of antimicrobial resistance. As conventional medicine takes pride in being scientific, its neglect of the full body of science that, among others, strongly supports the interface of biology and physics, challenges this assertion [63,96].

Based on the physics of organisms and water, contemporary research demonstrated the effective application of energetic counterparts of environmental and microbial pathogens, as well as immune responses, and its potential to finally address Pasteur’s call for addressing a terrain–human petri dish [97,98]. The presented approach needs to be validated through proper clinical trials.

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A Short Comment on “1 Case Clinical Report of Cranium Aggressive Fibromatosis and Literature Review”

1 Reply

DOI: 10.31038/JCRM.2023632

Short Commentary

Aggressive fibromatosis (AF) [1], known as a benign fibrous neoplasm arising from fascia, periosteum and musculoaponeurotic structures of the body, are rarely occurred in the head and neck region and tends to be locally aggressive, with nature to invade and erode skull and soft tissues, making excision much difficult. The presence of vital neurovascular structures in the head and neck makes it even more complicated. Owing to the features above mentioned, it is wise not to compromise on the vital structures considering the benign nature of the disease.

This clinical one case report revealed the clinical characteristics and appropriate therapeutic methods of aggressive fibromatosis (AF) in skull. In the meantime, it recommended the reasonable strategies for prevention and relatively favorable prognosis of AF, especially occurs in skull, which is very rare in neurosurgical clinic.

Findings and Significance of the Characteristics in Skull AF from this Work

It was indicated that the main manifestation of skull AF was headache and skull tumor. There was prominent osteolytic destruction found in X-ray plain film for skull AF. And CT scanning showed that skull sclerotin was disorganized and inhomogeneous, with widen diploe. The skull fibromatosis constituted by fibroblasts and myofibroblas, which were mainly spindle-shaped without heteromorphism. Immunohistochemistry showed positive expression of β-catenin and Vim in these cells. The enlarged incision was adopted for the strategy of operation in this patient of skull AF. After follow-up, there was no recurrence of AF discovered.

Taken together, it has been demonstrated from this study that skull AF is very rare in neurosurgical clinic. The clinical manifestation and iconography of AF were lack of specificity. Therefore, skull AF is hard to diagnose preoperatively. The effective diagnose is mainly dependent on histopathologic examination. As for treatment, operation is the most optimal method so far, which has a good therapeutic effect.

This work gave deeper insights into the distinctive elevation of the effects of AF clinical administration in a sooner future. From this case report, it therefore clearly can be seen that skull AF ought to be diagnosed by neuroimaging, such as CT and MRI. As for treatment, complete surgical excision of aggressive fibromatosis has been considered the only effective treatment. Chemotherapy may have a significant role, considering the possible hormonal etiopathogenesis of the condition. Radiotherapy, Other methods, expatiated as follows, are partially effective, already confirmed clinically previously.

Current Therapeutic Strategies and Future Prospect of Head and Neck AF

Head and neck fibromatosis is a rare condition with heterogeneity in presentation, proximity to vital structures, and locally aggressive nature. For the AF occurs in the skull, it is much rarer than any locations in the head and neck. These features make its treatment extremely challenging. Because of its rarity, variability in behavior, and the characteristics of these tumors, a standard treatment protocol has not yet been established. Although retrospective in nature, this study sheds new lights insight on various aspects of management of this rare entity. It has inherent limitations as it was retrospective, with a limited number of patients. However, as per our experience, we could conclude that surgery followed by multimodality management offers the best control, if not cure, for fibromatosis of head and neck region, may represent a superior strategy in AF administration clinically until now. Importantly, for differently special cases, unique therapeutic methods would be taken according to different types and locations of AF.

Apart from existing therapeutic strategies [2-4], such as complete surgical excision, chemotherapy post operations and antioestrogen therapy [5], etc., recently, therapies by using [6-8] non-steroidal anti-inflammatory drugs (NSAIDs) and interferon (IFN) α and tyrosine kinase inhibitor Imatinib have come to the fore. Some other novel and more effective treating methods are under studying.

References

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Not from Concentrate – An Exploration of the Minds of Consumers by Combining Experimental Design of Ideas (Mind Genomics) with Artificial Intelligence (AI)

3 Replies

DOI: 10.31038/PSYJ.2023571

Abstract

AI (artificial intelligence) was used in the Mind Genomics platform (BimiLeap) to generate sets of messages about orange juice, not from concentrate. These messages, called ‘elements’ were edited slightly, and combined into vignettes comprising 2-4 elements, the combinations dictated by an underlying experimental design. Each of 100 respondents evaluated totally unique sets of 24 vignettes, with the vignettes created to allow statistical analysis by OLS regression and then clustering. Respondents rated each vignette on a two-dimensional scale; desire to drink, and believe the information, respectively. The key equation was the relation between the presence/absence of the 16 elements and the desire to drink. Two clear mind-sets emerged, MS 1=Stress better ingredients:  MS2=Stress better functionality. AI was then used to summarize the results emerging from the two mind-sets, providing the summary based on themes, points of view, and opportunities for new products and services. The paper demonstrates synergies, viz.,  speed, simplicity, learning, and commercial opportunities currently available when one merges an information generating tool (AI) with rapid, hard-to-game evaluations by ‘real people.’

Introduction

The topic of NFC, not from concentrate, is an interesting issue in the change of the desirability of a product description over time. Where NFC was once the hallmark of quality for orange juice, the change in the world of citrus, especially in Florida, has eroded the value of NFC. Typical studies on these types of topics focus on limited aspects, such as the change in the dollar value of the slogan (viz., what are people willing to pay), or perhaps a historical retrospective of the business literature dealing with aspects of NFC.

The study reported here moves beyond a focused investigation of the topic into what might be more appropriately called an AI-enhanced exploration of the topic, coupled with the response of people. The approach used here, Mind Genomics, allows the researcher to explore how a person responds to various aspects of a topic, doing so in a way which moves towards the world of induction and so-called grounded-theory al. research [1]. Rather than developing a hypothesis emerging from a thorough understanding of the past, through published literature the emerging science of Mind Genomics encourages the exploration of a topic in a structured, templated fashion. The exploration is fast, inexpensive, disciplined, and almost always generates powerful new insights as it encourages research to explore and discover, rather than to confirm or falsify a hypothesis.

The origin of this specific study can be traced to the International Citrus & Beverage Conference, held in September 2023, in Clearwater, Florida. The conference brings together the various individuals involved in the world of citrus and allied products and services. The specific origin of the study was the conference presentation given by author Moskowitz to demonstrate the Mind Genomics method as a new technology. Discussions with authors Plotto and Sims about the best way to talk about Mind Genomics devolved into the notion that one could best explain the method by a live demonstration, from start to (almost) finish, in the allotted time of 50 minutes. Author Sims suggested the topic of ‘NFC’, and acted as the technical expert, to introduce the problem. Author Schneider, in turn, ran the computer, typed in the ideas or selected them when the ideas were presented as a group, from which one was to select messages (elements, ideas) to be tested. The output of the demonstration comprised both a book of information about NFC from different points of view (Idea book, produced by the embedded AI, called Idea Coach), as well as the results from the Mind Genomics ‘experiment’, after the 100 respondents participated.

Background to NFC

In the mid 1940s, in order to get a natural source of vitamin C to those in war-torn Europe, frozen concentrated orange juice was developed and became a leading seller once it hit the retail market. Evolving processing and storage techniques later allowed for the use of orange juice concentrate to develop refrigerated ‘Ready to Serve’ orange juice, creating a new segment in the market. Soon, this reconstituted ready to serve orange juice became the preferred choice of consumers over the frozen concentrate [2].

The reconstituted refrigerated 100% orange juice led the market until the introduction of not from concentrate, or NFC, orange juice to the market in the 1980s. Not from concentrate 100% orange juice is essentially made by extracting the juice, pasteurizing, and packaging. Though this juice was more costly to produce, store, and distribute, it was marketed as a ‘premium’ experience with superior flavor as it is not subject to the heat of evaporation.

NFC orange juice grew in popularity as consumers shifted from juice from concentrate to NFC, looking for more natural and healthier products [3]. Today, over 90% of Florida’s oranges are processed for NFC juice. However, due to challenges such as the state-wide devastation of citrus greening disease, juice production is declining [4]. Citrus greening has led to a decrease in Florida’s juice quality as infected oranges are described as bitter and sour and are lacking in sugar and orange flavor [5]. The lower sugar content and off attributes associated with infected oranges is making it more challenging for Florida orange juice producers to make 100% NFC orange juice with only Florida oranges. For example, companies such as Florida’s Natural, are now adding Mexican Valencia orange juice concentrate to their NFC juice to increase the sugar content of the juice and meet consumer demand. No studies have been done to assess what consumers think about not from concentrate orange juice versus from concentrate orange juice.

Background to Mind Genomics

Mind Genomics emerged from the confluence of three disciplines, psychophysics, statistics, and consumer research, respectively.

Psychophysics – provided Mind Genomics with the goal of measuring the strength of ideas. The origins of psychophysics lie in the pioneering work of scientists such as G.T. Fechner and S.S. Stevens, both focused on measuring the strength of sensations [6,7]. Their pioneering work, often called ‘Outer Psychophysics’ by Harvard’s S.S. Stevens, focused on the relation between the physical measurement of stimulus magnitude and the perceived magnitude. Stevens’ ‘Inner Psychophysics’ was to measure the strength of the percept.  Mind Genomics used Stevens’ notion of the magnitude of an idea as the basis for the effort to measure the strength of our perception.

Statistics – provided Mind Genomics with a way to organize the ‘test stimuli’ into combinations, so that these combinations or vignettes could somewhat approximate the nature of information coming to respondents in the form of text information from which ideas would be generated in the mind of the person. The specific approach contributed by statistics is known as ‘experimental design.’ The contribution comprises the precise combinations needed to test, so that one can deconstruct the response to the combination (called vignettes henceforth in this paper) to the presence/absence of specific phrases. In this way it would be possible to create known combinations of test stimuli, present them to people, get the reactions, and finally use statistics to estimate the contribution of each component in the vignette to the response.

Consumer research – provided Mind Genomics with the recognition of the importance of the everyday. Rather than putting the respondent into an unusual situation, and then doing the experiment with the respondent now considered a ‘test subject’, consumer research focused on the quotidian, the ordinary. The goal was no longer to prove or disprove a hypothesis by experimentation, but rather to focus on the normal world, albeit from the eyes of someone who wants to know that world, in a quantitative fashion. Could numbers be put on the features of the ordinary world, to express the magnitude of different features of this world as they are perceived by people.

During the past three decades, the ‘emerging’ science of Mind Genomics has evolved to the point where it has become a DIY, do-it-yourself, research system, almost fully templated. The approach has evolved from the user creating one set of test vignettes (combinations of elements, viz., messages) to small, automatically created sets of vignettes, different for each respondent (study participant).  In the most current version of Mind Genomics, each respondent evaluates sets of 24 vignettes, each vignette comprising a minimum of two and a maximum of four elements (messages). The underlying experimental design works with four topics, viz., questions, and with each topic generating four different elements, viz., answers. The experimental design puts together the answers into small, easy to read vignettes, the aforementioned combinations. The respondent reads each of 24 vignettes, and for each vignette assigns a response from a rating scale.

The objective of the Mind Genomics study is to make the effort easier, so that anyone can become a researcher. Indeed, elementary school students ages eight and above have found this templated approach to be fun, investigating topics such as the nature of third grade mathematics in ten years [8].

The Mind Genomics study continues to be enhanced. Current efforts, presented in this paper, include the use of AI (artificial intelligence) to help the researcher come up with the elements by suggesting questions and then answers to those questions, once the researcher describes the issue in the ‘Idea Coach’. Thus, it becomes far easier to investigate new topics, even with virtually no knowledge, because the embedded AI provides a true coach.  Additional enhancements using AI include the summarization of results by AI using a number of queries to bring together the strong performing results in a user-friendly way.

Running a Mind Genomics Study on the Topic of NFC, not from Concentrate

The Mind Genomics study begins at the website (www.BimiLeap.com). After the researcher has created an account, the researcher begins a study by naming the study, selecting the language (currently only a few languages are implemented beyond English), and then agreeing to respondent privacy.

The next step requires the researcher to develop four questions, and for each question develop four answers. Figure 1 Panel A shows the request for the four questions. Figure 1 Panel B shows the request for four answers to one question, the question having been developed already by the researcher. Both Panel A and Panel B are shown ‘filled in’. For the researcher beginning the study, these screens are empty, requesting the researcher to fill them in, the researcher first creating the four questions and then afterwards filling four Panel B’s, one for each question.

FIG 1

Figure 1: Panels showing the input of the researcher. Panel A shows the request for four questions. Panel B shows the request for four answers to one of the four questions. Panel C shows the instructions to create a self-profiling classification question. Panel D shows the anchored five-point rating scale.

It is at this point that many researchers feel nervous. Idea Coach, embodying AI, was developed to decrease the nervous response, and push the study towards creation and then completion. Table 1 shows the first iteration of the Idea Coach, to provide questions. The researcher need only press the red oval in the formatted BimiLeap program to be taken to a screen which instructs the researcher to describe the topic in a box provided. The researcher then presses the request and receives a set of 15 questions. The researcher can select one or more questions, paste those questions into the appropriate screen (Figure 1 Panel A), edit the question if desired, add one’s own question, or run the request again for a mostly new set of questions. This process of requesting questions, selecting, and pasting, can go on for a while, but usually by four or five requests, and by thus 60-75 mostly different questions, the researcher will have selected the best questions and edited them. The same process happens for the request for four answers for each question.

While the researcher is using Idea Coach, both for questions and for answers, in the background the program is storing the guiding ‘squib’ for the development of questions, and the guiding question for the development of answers. When the researcher asks Idea Coach four times for questions, and three times for answers to each of four questions, the Idea Coach will produce 3 + 3 + 3 + 3 + 3 ‘pages’ of questions or answers. These are recorded for the researcher, along with a detailed analysis of the patterns uncovered on the particular page whether question or answer. The material is returned to the researcher in the form of an Excel book, the Idea Book, each page or tab corresponding to one of the different requests. Table 1 shows an example of what is returned after the first request to Idea Coach for questions.

Table 1: Results from the Idea Book, showing the 15 questions and AI summarization of those questions. Table 1 shows the results from the third time the Idea Coach was requested to provide 15 questions to address the topic.

TAB 1(1)

TAB 1(2)

TAB 1(3)

The depth of information in Table 1 deserves a comment. One of the benefits of current AI is that the AI technology can be queried, as it was to develop the 15 questions, using the short paragraph, here really a sentence: Topic: Why would from concentrate orange juice be any less acceptable then NFC orange juice, please expand on this because I’m less than 12 years old. This single statement became a query, which generated the 15 Topic Questions listed. The AI then stored these 15 questions, while the Mind Genomics program, BimiLeap, continued to interact with the researcher, in order to select the four questions. After the four questions and four answers to each question were selected, BimiLeap used AI to ‘interrogate’ each set of 15 questions (and later each set of 15 answers). The results of the interrogation, viz., the summarization by AI, appears in Table 1, which shows the results from the third iteration, viz., the third time the researcher asked for the 15 questions to address the topic.

Once the researcher has selected the elements (the four sets of four answers), the next step is to add self-profiling classification questions. The now-standard version of BimiLeap automatically asks the respondent for gender and age, allowing the researcher to generate an additional eight questions, each with up to eight possible answers. Figure 1 Panel C shows the self-profiling classification question asked in this study. With up to eight classification questions, it is possible to use the first portion of the study, self-profiling, as a complete study in itself.

When the researcher has completed the setup of questions and answers (topics and elements), and then completed the self-profiling classification, it is time to create the rating scale. The rating scale for this study is unique in that it contains two parts, wanting to drink orange juice versus not wanting to drink orange juice, as well as believing versus not believing the material. Figure 1 Panel D shows the rating scale and the five answers.

How do feel about orange juice when you read this?
1=I don’t want to drink orange juice and i don’t believe what i just read
2=I don’t want to drink orange juice but i do believe what i just read
3=I can’t answer
4=I do want drink orange juice but i don’t believe what I just read
5=I do want drink orange juice and i do believe what I just read

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Marine-Biocement Promotes the Sustainable Eelgrass Meadows Protection at the Japanese Coastal Ocean

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DOI: 10.31038/GEMS.2023575

Abstract

In this study, we isolated five urease producing bacterial strains from coastal environment and create marine-biocement using Microbial Induced Calcium Carbonate Precipitation (MICP) procedure. It was shown that the wide range of urease producing bacteria could be isolated from coastal environment. The reinforcement effect of S1-1 strain showes the highest CaCO3 amount and Unconfined Compressive Strength (UCS) level. The SEM-EDX results showed that S1-1 definitely precipitated calcium carbonate and bound the sea sand together. Therefore, urease producing bacteria S1-1 that can be newly used for biomineralization. Germination test showed that the marine-biocement retained its form in seawater for about one month, and then collapsed along with the eelgrass grew. This indicates that the marine-biocement has the strength suitable for the germination of eelgrass. After disintegrating, the marine-biocement returned to the sea environment as sea sand, suggesting that marine-biocement may be used as an innovative seeding medium for Zostera marina bioenvironmentally friendly.

Keywords

Marine-biocement, MICP, Eelgrass meadow, Blue carbon

Introduction

Japanese coastal area has typical biodiversity in the Asian ocean cause of two big ocean currents such as Japan current from southeast asia and Oyashio current from Arctic ocean. Moreover, one of the eelgrass “Zostera marina” habits around the Japanese coastal area and provide the life of marine biodiversity. This huge ecosystem services based on the feeding place, spawning ground and habitats for shells, squid and small fishes each other. Unfortunately, eelgrass meadows in Japan were significantly damaged by development of the coastal area and factory effluent in the 1960s, a period of high economic growth, and are expected to continue declining in the future [1]. These phenomena cause the coastal areas to lose their habitat for seaweed and to reduce important fishery resources that have been deprived of habitats. Because, Zostera marina support marine life, including epiphytic organisms as well as coastal fisheries resources and contribute to marine environments by stabilizing bottom sediment and maintaining coastal water quality [2]. In addition to this, it recently has been reported that Zostera marina absorbs not only CO2 from the sea, but also about 17% from atmospheric CO2 when exposed at low [3]. Thus, Zostera marina habitats are considered one of the most valuable marine ecosystems because they has both a supportive place for marine life and a very efficient storehouse of atmosphere-derived CO2 as blue carbon [4,5]. In this study, we suggest the novel eelgrass protection technique applied with MICP technology. MICP has developed for the geotechnical applications such as liquefaction after earthquake, exchange heavy metal ions and self-repairing concrete for coastal erosion [6-8]. We modified the following MICP reaction mechanisms.

for 1, 2

Marine bacterial urease catalyzes hydrolysis urea to ammonium and carbonate ion same as Sporosarcina pasteurii (eq1). After that, calcium carbonate precipitates from carbonate ion and calcium ion (eq2) between coastal sand particles. Then, coastal sand combines by calcium carbonate crystals and produces solidified marine-biocement (Kusube et al. 2020) [9]. The marine-biocement is harmless for several marine organisms cause of produced from marine bacteria and coastal sand. Therefore, we propose sustainable and convenience protection of eelgrass meadows with this marine-biocement.

Materials and Methods

Isolation and Identification of Urease Producing Bacteria

Urease producing bacteria were isolated from coastal sand collected from Shirahama-cho, Wakayama, Japan (33.692241°N, 135.336596°E) with Urea agar base (UAB) plate (Thermo Scientific Co. Waltham, Massachusetts, US) for screening culture media. The colonies with pinkish halo were pure cultured on a fresh UAB plates at least 5 times and were cultured at 30.0°C. After single colony culture, bacterial genomic DNA was extracted by 95.0°C boiling for 15 minutes. The 16SrDNA gene was amplified with universal primer 27F and [10] on the extracted each bacterial DNA. PCR program showed as denaturation at 94.0°C for 10 minutes and 25 cycles of denaturation (95.0°C, 30 seconds), annealing (55.0°C, 15 seconds) and extension (72.0°C, 30 seconds). And finally, gene extended at 72.0°C for 3 minutes. The elongated 16SrDNA sequences were determined by macrogen.co (Tokyo, Japan) and isolated marine bacteria was identified with Basic Local Alignment Search Tool BLASTN program [11]. A Phylogenetic tree was constructed by the neighbor-joining (NJ) method using MEGA X (ver.10.2.4) software with alimental sequenced data. An interior branch test was carried out (heuristic option and 1000 replications) to check the tree topology for robustness [12].

MICP Mechanism Applied to Marine-Biocement

Coastal sand 40.0 g was mixed for the making marine-biocement with isolated marine bacterial pellet from 100 mL culturing media and ionic solution 10 mL containing 0.75 M calcium chloride and 1.5 M urea [13-15] in the plastic tube (27 mm diameter and 50 mm height) with 2 mm mesh holes on the side of tubes to easy penetrate ionic solutions. The soaking materials placed at 25°C for 1 day to fully enzymatic reaction on the surface of the sand particles. To increase hardness of the marine-biocement, this treatment was repeatedly 2 more times. After this enzymatic reaction, marine-biocement was washed out excess ionic solution in distilled water and completely dried up at 110°C for 3 days to prevent hardening by quenching.

Measurement of Unconfined Compressive Strength of Marine-Biocement

In this study, Unconfined Compressive Strength (UCS) measurement was adopted to assess the strength for the purpose of determining the physical strength properties imparted on MICP. The UCS measurement have been used in most of the experimental programs reported in the literature in order to evaluate the effectiveness of the stabilization of marine-biocement [16] (Cheng, Shahin and Ruwisch 2014). UCS measurement is a compression test of a cylindrical rock specimens under confining pressure where the loading path is followed by a computer. After MICP curing, specimens were extruded from the mold, and it was sized 27 mm in diameter and 40 mm in height. And the strength properties of each marine-biocement were measured by UCS measurement, and shear strength were determined. Shear strength is a measure of how much stress (force/area) can be applied before the material undergoes shear failure.

Scanning Electron Microscopic (SEM) Observation and Energy Dispersible X-ray Spectrometry (EDX) Measurement of Marine-Biocement

The binding precipitate morphology and chemical component was analyzed with SEM-EDX analysis. Marine-biocement was dewatering treated with 50% acetone solution stepwise to 100% acetone in order to reduce the water molecules in the marine-biocement and completely vacuum drying with evaporator. After drying, marine-biocement for SEM-EDX specimens were stored in desiccator at room temperature. The binding precipitate morphology was observed by SEM (FlexSEM1000Ⅱ, HITACHI, Tokyo, Japan). The SEM specimens were coated 17 nm thickness platinum with AUTO FINE COATER (JFC-1600H) and SEM analysis was carried out at 15 kV and 62 mA. The chemical components of binding precipitates were analyzed by EDX system (AZtecOne, HITACHI, Tokyo, Japan). Kα spectra was used to analyze the surface precipitates.

Quantitative analysis of CaCO3 in marine-biocement

To dissolve calcium carbonate, a calcite-acid reactor chamber was used [17]. The device consists of a reactor chamber, pressure meter and valve for the exhaust gas.

The ratio of binding calcium carbonate C is defined as:

for 3

The 1.0 g of marine-biocement was placed into reactor chamber, and binding calcium carbonate include the marine-biocement was measured under standard conditions (25.0°C, 1 atm). The 10 mL of 1.0 M hydrochloric acid added to generate the CO2 gas from the calcium carbonate. The calibration curve was made with standard pure CaCO3 reagent (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan). The reactor chamber was sealed and gently shake to facilitate dissolve CaCO3 in the reactor chamber. The binding calcium carbonate in marine-biocement was calculated from the calibration curve and the blank as non-treated coastal sand.

Germination test from Marine-Biocement with Zostera marina

In the creation process, a hole of φ 3 mm H 15 mm was dented on the upper part of the marine-biocement and three seeds of Zostera marina from Eiga jima, Hyogo prefecture provided by the NPO corporate seed of Zostera marina bank were embedded in marine-biocement of S1-1 which was most hardest of all specimens in UCS test. Only the seeds that showed high density in saturated saline were selected. Selected three seeds were planted each marine-biocement hole and covered with sea-sand and placed in a water tank (35 L) as germination condition irradiated for 10 hours for a day with LED light to accelerate germination, circulation of natural seawater controlled at 10°C constant [18]. As a control, seeds were sowed at 10 mm deep under the seasand and germinated spontaneously and compared with the germination rate from marine-biocement.

Results and Discussion

Phylogenetical Relation of Isolated Urease Producing Bacteria

Five isolated strains were identified from approximately 1,200 bp of 16SrDNA sequences. The results shown that they were closely related as well as to bacteria genus belonging to the Cupriavidus, Bacillus and Pseudomonas. These 16SrDNA sequences submitted on DDBJ and BLAST results suggested that the closest relatives of Cupriavidus basirensis S1-1 (Accession No. LC760339), Bacillus cereus S1-2 (Accession No. LC760340), Pseudomonas ceruminis S1-4 (Accession No. LC760341), Pseudomonas nitroreducens S1-5 (Accession No. LC760342) and Priestia megaterium S1-8 (Accession No. LC760343) cause of E value 0.0, 0.0,0.0 and 0.0, identified value 99.24%, 99.50%, 98.24% and 98.86% respectively. These sequences have been deposited in DDBJ are available under accession numbers LC760339-43). These strains included the urease producing bacteria isolated from marine environment previously reports (Figure 1). The relationship between isolates and previously reported marine urease producing bacteria showed in Figure 1. Phylogenetic analysis shows that the no specific species need for producing marine-biocement, it means that is able to isolate in every marine environment. Moreover, these bacterial species were already applied for industrial bio-remediation such as oil degradation, clean up for soil and water contaminated with heavy metals and/or chlorinated organic compounds [19,20].

fig 1

Figure 1: Neighbor-joining tree based on bacterial 16S rRNA gene sequence data from different isolates of the current study along with sequences available in the GenBank database. Bootstrap values calculated from 1000 resamplings using neighbor-joining are shown at the respective nodes when the calculated values were 50% or greater. The phyla to which the strains belong are presented on the right.

Characterization of Marine-Biocement

The relationship between bacterial CaCO3 (weight%) and UCS level (kPa) of the marine-biocement was shown at Figure 2. Isolated bacteria produced 1.7%-3.9% of CaCO3 crystalline and then hardness showed 36-449 kPa in UCS level. Strain S1-1 showed the highest CaCO3 amount and UCS level were 3.9% and 449 kPa, respectively. On the other hand, bacterial-free blank included 1.8% of CaCO3 content and showed 106 kPa hardness. In addition, marine-biocement with strain S1-1 shows CaCO3 content is 2.1 times higher than blank, and 4.2 times higher UCS level than that of blank. This result shows that was good related CaCO3 content and UCS levels. Because CaCO3 crystalline could be binding with each sea-sand particles to be strength enhancement (Figure 2). Moreover, CaCO3 crystalline grew up on the surface of sand particles by urease producing bacteria. The important point is the urease producing bacteria was adsorbed on the surface of the sea-sand particles through the bacterial membrane electrostatic interactions. Previous studies have shown that bacterial cell surfaces are negatively charged and adsorb onto the particle surface [21,22]. The marine-biocement S1-1 induced ideal MICP system and was ocean-friendly materials cause of natural sea-sand and isolated bacterial species from the ocean. Biomineralization with S1-1 never reported. Therefore, it was suggested that S1-1 may be a urease producing bacteria that can be newly used for biomineralization.

fig 2

Figure 2: Relationship between CaCO3 content of the marine-biocement and UCS. (S1-1: Cupriavidus basirensis, S1-2: Bacillus cereus, S1-4: Pseudomonas putida, S1-5: Psedomomas nitroreducens. S1-8: Bacillus megaterium).

Observation of Surface of Marine-Biocement Using SEM-EDX

SEM allows a direct and closer look at the CaCO3 bonds developed at the interparticle soil particles and the EDX analyzer records the counts of representative elements from the elements’ spectrum for provides insights the MICP mechanism with coastal sand with local marine-bacteria [23]. The hardest marine-biocement with isolated strain S1-1 surface characteristics was shown in Figure 3. Figure 3(a) shows a SEM image of specimen of marine-biocement S1-1, and b, c, e and f show calcium and silica elemental mapping results by EDX analysis. EDX analysis was used to observe the elements included in marine-biocement. Figure 3(d) shows precipitated short rod like crystals on surroundings of the sand particle surfaces. The average of crystalline size was 10-20 μm in length and 5 mm in diameter, and this rod like shape agree with aragonite crystals [24]. The crystal phase (Figure 3(a)) and calcium phase (b) were overlapped in these SEM-EDX images, but silica phase was detected at the other region in Figure 3. Because of calcium from bacterial CaCO3 and sand main silica compounded chemicals such as SiO2 and Al2O3 for about 70% of the total in Japanese coastal sand. Furthermore, this is the evidence of CaCO3 crystal layer could be bind another sand particle on the same frame of the sand particle surface. SEM image of bacterial free specimen was shown in Figure 3(g) and 3(h).

fig 3

Figure 3: SEM images of marine-biocement with strain S1-1. CaCO3 crystals on the surface of sand particle (a) and Enlargement of crystal region (d). Chemical composition analysis on the sand surface by Energy Dispersive X-ray (EDX) system (b, c, e) and (f). Blank as Marine-biocement surface without urease producing bacteria surface SEM image (a). And chemical composition by EDX analysis of blank.

There were observed flat phase on all surfaces and never confirmed rod like crystals as aragonite. Quantitative EDX results were provided to supplemental Figure 1 and shows that the composition ratio of marine-biocement that is mainly contained carbon (48%), oxygen (34%), silicon (7%) and calcium (4.3%) in bacterial one. In contrast that, no calcium detected in bacterial free specimen. Therefore, isolated urease producing bacteria begin to the MICP process in marine-biocement same as the other applied MICP materials (Supplemental Figure).

Zostera marina Germination Test with Marine-Biocement for the Blue Carbon Systems

Figure 4(a) shows the germinated two white coleoptiles from marine-biocement after 23 days after seed sowing. Green leaves started photosynthetic for some bubbles come from leaves surface in 30 days. After 2 months, green leaves growing up to 4.7 cm length and leaf vein formation was confirmed. This final germination rate was 26.7% of marine-biocement S1-1 and 23.3% from sea-sand. The maximum leaf length from the marine-biocement and sea-sand were 4.7 and 4.9 cm, respectively. The lack of difference in germination results between marine-biocement and sea sand conditions indicate that marine-biocement can be used to germinate Zostera marina. From these results, it means that this marine-biocement can be applied to the marine environment and ecosystem protection.

fig 4

Figure 4: Germination image of Z. marina from seeds. (a)Two white coleoptiles from marine-biocement after 23 days after the germination test (b) 2 months after the test, they were grown green leaves up to 4.7 cm. The formation of parallel veins was confirmed.

Acknowledgment

This paper is a summary of work that under the Marine Challenge Program 2017 and Marine Tech grand prix 2018 andsupported by JSPS KAKENHI (JP18K05695), “Innovation inspired by Nature” Research Support Program from Sekisui chemical Co., Ltd, 20th ESPEC for Global Environment Research and Technology and Mitsumasa Ito Memorial Research Grant. We would like to thank Mitsui Chemicals, Inc. for providing the technology for the mass cultivation of marine bacteria.

Data Availability

The data underlying this article are available in the GenBank Nucleotide Database at https://www.ncbi.nlm.nih.gov/nucleotide/ and can be accessed with following accession number as LC760339-760343.

Author’s Contribution

Yuki Nakashima, Momoka Miyasaka and Masataka Kusube were involved in study design and data interpretation. Koki Kusumoto, Keizo Kashihara, Kazuyuki Hayashi and Shinya Maki were involved in the data analysis. All authors critically revised the report, commented on drafts of the manuscript, and approved the final report.

Funding Statement

This work was supported by the Marine Challenge Program 2017 Leave a Nest Co., Ltd, JSPS KAKENHI under Grant JP18K05695 and 20th ESPEC for Global Environment Research and Technology (Charitable Trust) and Mitsumasa Ito Memorial Research Grant.

Disclosure Statement

No potential conflict of interest was reported by the authors.

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The Evolution of Type 2 Diabetes Mellitus and Insulin Resistance

6 Replies

DOI: 10.31038/EDMJ.2023732

Abstract

Over the past 25 years of studying type 2 diabetes mellitus, a working hypothesis has emerged to move the development of precision medicine for type 2 diabetes mellitus forward. Earlier studies using amplified genomic DNAs for genomic-wide searches of human genes have led many investigators astray. However, a recent study has taken a different approach, using next-generation RNA sequencing, revealing an essential down-regulation of two genes, TPD52L3 and NKX2-1. The current compendium focuses on describing all of the important priciples to clarify the hypothesis from the beginning: insulin sensitivity and glucose effectiveness, genetics, free fatty acids, cell membranes, atomistic glucose and glucose transport, β-cell functions, membrane flexibility and (pre-) diabetes type 2.

Furthermore, this study sheds light on the importance of considering membrane flexibility in the context of type 2 diabetes and questions the potential risk associated with using the term ‘insulin resistance’.

Keywords

Type 2 diabetes mellitus; Insulin sensitivity; Glucose effectiveness; Genetics; Free fatty acid; Cell membrane; Glucose transport; β-cell function; membrane flexibility

Introduction

While Falta and Boller introduced the concept of “insulin resistance” in their seminal work published in 1931, it was not until 1933, when MacBryde noted that scholars had not reached a consensus on its definition, leading to gaps in research and clinical care. Despite remarkable advancements in medicine, these gaps continue to exist, even after nine long decades [1,2]. Over the years, accumulating data on insulin resistance have been published, enabling reconsideration of its meaning. Another unresolved problem relates to the diabetes susceptibility loci in and around the CDKAL1, CDKN2A/CDKN2B, HHEX, KCNJ11, SLC30A8, and TCF7L2 genes, suggesting that the single-nucleotide polymorphisms within or near these genes most likely do not alter their expression or function [3]. A recent study reported that common variant studies of type 2 diabetes mellitus have identified more than 700 risk loci for type 2 diabetes, half of which have been discovered in the past three years [4]. The question is: Are we on the right track? Indeed, genes are normally copied exactly during chromosome duplication. Rarely, however, mutations occur in genes to give rise to altered forms, most –but not all– of which function less well than the wild-type alleles. One study, based on next-generation RNA sequencing, found that an inherited mitochondrial defect, reducing the mitochondrial respiratory chain complex activity, as well as a defect associated with alterations in lipid storage played a critical role in the onset of type 2 diabetes mellitus. Against the background of the above considerations, this type 2 diabetes mellitus compendium furnishes an overview of recent advances in biochemistry and molecular biology in the context of type 2 diabetes mellitus. The compendium will help physicians and/or students of medicine gain an in-depth understanding of the molecular mechanisms of this disease.

Insulin Sensitivity and Glucose Effectiveness

Computational modeling of glucose and insulin kinetics following intravenous glucose challenge has demonstrated that individuals with type 2 diabetes mellitus show significant reductions in insulin sensitivity (SI) and insulin-independent glucose removal rate (SG) compared with normoglycemic individuals (Table 1) [5]. Insulin sensitivity essentially reflects the ability of insulin to enhance the effect of glucose to normalize its own concentration, and glucose effectiveness refers to the ability of glucose, independent of a dynamic insulin response, to enhance net glucose disappearance. Thus, these individuals exhibit reduced responsiveness to circulating insulin as well as reduced glucose effectiveness.

Table 1: Measures of glucose effectiveness and insulin sensitivity for a two- compartment minimal model

Units

Control subjects

Type 2 diabetes individuals

P value

Δ (%)

Tracer
SG
h-1 0.41 ± 0.04 0.33 ± 0.02

< 0.001

19.5

13C
h-1 0.52 ± 0.05 0.37 ± 0.02

< 0.001

28.8

2H
average

24.1
SI
pmol x L-1 x h-1 0.0082 ± 0.0012 0.0036 ± 0.0006

< 0.001

56.1

13C
pmol x L-1 x h-1 0.0098 ± 0.0013 0.0042 ± 0.0008

< 0.001

57.1

2H
Average

56.6

Data are based on the reference data listed by Weijers [5]. SG: glucose effectiveness; SI: insulin sensitivity

In the latter condition, glucose ⎼ independent of changes in the insulin concentration ⎼ is less able to facilitate its own uptake through a mass action effect and suppress its own release. A prospective study investigated the development of type 2 diabetes in normoglycemic offspring of parents who had type 2 diabetes. The study revealed that the offspring exhibited significant defects in both glucose effectiveness and insulin sensitivity more than a decade before disease development [6]. Moreover, a key feature in type 2 diabetes is an essentially larger defect in insulin sensitivity (56.6%) compared with glucose effectiveness (24.1%). What do these findings imply?

Genetics

In the ongoing research on the genetic basis of type 2 diabetes, earlier studies using amplified genomic DNAs for genome-wide searches of human genes have led many investigators astray. However, a recent study has taken a different approach, using next-generation RNA sequencing to examine genome-wide changes in gene expression in the skin of patients with type 2 diabetes, compared to non-diabetic patients [7]. This new study identified two previously unknown genes significantly downregulated in indivividuals.

The previous findings revealed that tumor protein D52-like3 (TPD52L3), a gene in the gene metabolism category, exhibited the most significant downregulation with a value of 3.7 × 10-9 in the studied group, which consisted of individuals with type 2 diabetes. There is no established link between the gene TPD52L3 and type 2 diabetes or wound healing. However, a study involving exogenous expression of human TPD52 in cultured cells demonstrated a notable increase in lipid droplets [8]. Lipid droplets serve as storage organelles for excess fatty acids within adipocytes, which are fat cells. Tumor protein D52 is the founding member of the TP52-like protein family representing four paralogous mammalian genes, i.e. TPD52, TPD52L1, TPD52L2, and TPD52L3 [8,9]. When analyzing TPD52 and TPD52L3 genes, researchers found that the two sequencers shared 63 identical positions and 42 similar positions, resulting in an overall homology of 67.9% (Figure 1) [10]. Indeed, based on the high sequence homology with TPD52L3, it appears plausible that the primary function of TPD52L3 is lipid storage in adipose cells. A reduction in TPD52L3 expression could increase the release of free fatty acids into the bloodstream. The difference in the unsaturation index (number of cis carbon-carbon double bonds per 100 fatty acyl-chains) between the released free fatty acids from human white fat cells and the serum-free fatty acids in the healthy controls (85.5 versus 191.9, respectively) is significant [5]. The release of these saturated free fatty acids leads to a considerable decrease in the unsaturation index of erythrocytes and vascular membranes. As a result, the membrane flexibility of these cells is reduced. The decrease in membrane flexibility, in turn, negatively impacts the rate of glucose transport across the cell membrane, initiating the onset of type 2 diabetes.

FIG 1

Figure 1: Alignment of the human TPD52 (upper row) and the human TPD52L3 (lower row) protein sequences. Amino acid residues are indicated by single letters. Vertical lines indicate identical residues and colons/dots indicate highly/weakly conserved residues.

In the second scenario, the most downregulated gene in the gene regulation category was NKX2-1, and it exhibited a down regulation value of 3.7 × 10-9 [7]. NKX2-1, a transcription factor, is associated with reduced mitochondrial respiratory chain complex activity, resulting in decreased ATP production, among other cellular functions [11]. This idea supports a study’s data proposing that the dysregulation of intramyocellular fatty acid metabolism in the offspring of individuals with type 2 diabetes is linked to an inherited defect in mitochondrial oxidative phosphorylation [12]. The β-oxidation of fatty acids plays a crucial role in compensating and restoring ATP production by increasing plasma-free fatty acids through hydrolysis. However, this increase in free fatty acids results in significant decrease in the unsaturation index of serum free fatty acids. Consequently, the reduction in the unsaturation index leads to decreased membrane flexibility and lowers the rate of glucose transport across the cell membrane, ultimately initiating the onset of type 2 diabetes. Notably, reduced mitochondrial activity is recognized as one of the key characteristics of type 2 diabetes [13].

Free Fatty Acids

After four billion years of evolution, the earliest protocells evolved to ‘modern’ cells enclosed by membranes consisting of phospholipids, the chief constituents of biological membranes. Glycerol-based phospholipids are the major class of naturally occurring phospholipids. Typically, a phospholipid consists of glycerol-3-phosphate, with a saturated fatty acid at position 1 and an unsaturated fatty acid at position 2 of the glycerol. Saturated fatty acids possess essentially linear alkyl chains with no double bonds. On the other hand, double bonds in unsaturated fatty acids are nearly in the cis configuration, which creates a bend in the fatty-acid chain. Molecules such as palmitoleic acid (C16:1) and oleic acid (C18:1) are bent at the cis double bond, and the two chain parts form an angle of 133 degrees [14,15]. This bend has important consequences for structure and functionality of biological membranes because, while saturated fatty acids are able to pack closely together, unsaturated fatty acids prevent such close packing.

Type 2 diabetes mellitus, gestational diabetes mellitus, and impaired glucose tolerance are characterized by elevated plasma free fatty acid levels [16,17]. This is confirmed by findings that the percentages of docosahexaenoic acid (C22:6 n-3) and arachidonic acid (C20:4 n-6), released from white adipocytes, are decreased by approximately 110-fold and 9-fold, respectively, compared with the human serum pool, and the unsaturation index of released free fatty acids from human white adipocytes is markedly lower than the unsaturation index of serum free fatty acids in healthy controls (85.5 and 191.9, respectively) [5]. Therefore, an increased release of free fatty acids from adipose tissue into the blood circulation elevates the plasma concentration of saturated fatty acids. Hence, a shift from unsaturated to saturated fatty-acyl chains in phospholipids of erythrocyte membrane and vascular endothelium is a hallmark of type 2 diabetes mellitus [18]. Borkman et al. in 1993 suggested that decreased insulin sensitivity is associated with decreased concentration of polyunsaturated fatty acids in skeletal-muscle phospholipids, raising the possibility that changes in the fatty-acid composition of muscle modulate the action of insulin [19].

Cell Membranes

Phospholipid bilayers form spontaneously and rapidly, when phospholipids are added to water. As evident in Figure 2, two acyl chains (the hydrocarbon chain region) yield a roughly cylindrical molecule with an area (A) that can pack in parallel arrays to form extended sheets of membranes composed of a mosaic of proteins and phospholipids in a fluid phospholipid matrix [20].

FIG 2

Figure 2: The most basic structural result obtained from x-ray scattering from oriented bilayers in model phospholipid membrane systems is the area (A) per lipid molecule (the cross-sectional area of the cylindrical part of the phospholipid). DHH represents the membrane bilayer thickness.

The driving force behind this aggregation phenomenon is the weak, noncovalent bond (van der Waals force) between a pair of carbon atoms, which can be calculated with the Lennard-Jones potential [21]. The interaction energy (U) is related to the distance (r) between two carbon atoms, as illustrated graphically in Figure 3. This graph suggests that the minimum energy principle favors a carbon-carbon distance of about 4 Å, which is the most stable distance between the centers of two carbon atoms, with a minimum interaction energy of -0.77 kJ/mol. Furthermore, when the carbon atoms in two acyl chains of a phospholipid diverge, their interaction energy decreases as a function of distance r approximately with the sixth power, and when they approach each other, their interaction energy increases as a function of distance r approximately with the sixth power. Thus, the flexibility of a lipid bilayer is largely determined by the amount of weak noncovalent forces of carbon–carbon interactions, i.e., the number of -C=C- double bonds along the phospholipid unsaturated acyl chains.

FIG 3

Figure 3: The van der Waals interaction energy profile as a function of the distance (r) between the centers of two carbon atoms. The energy is calculated using the empirical equation U=B/r12 – A/r6. Values for the parameters B=11.5 ⨯ 10-6 kJnm12/mol and A=5.96 ⨯ 10-3 kJnm6/mol for the interaction between two carbon atoms.

The unsaturation index is widely recognized as a useful parameter for describing the flexibility of a biological membrane. It is calculated by multiplying the mean number of cis double bonds per lipid acyl chain by 100 [22]. Therefore, an increase in saturated fatty acids of membrane phospholipids, as observed in erythrocytes, results in a decrease in membrane flexibility and is marked by a decrease in the unsaturation index. A number of studies of fully hydrated, fluid phase, model phosphatidylcholine bilayers have shown that introducing one or more carbon-carbon cis double bonds into the saturated acyl chains will increase the cross-sectional area A by about 18% and decrease the attraction energy by about 34% (Table 2) [23].

Table 2: Experimental data of fully hydrated fluid phase phosphatidylcholine lipid bilayers

 

DLPC

DMPC

DPPC

  

DOPC

PDPC
Fatty acid structure

[C12:0)]2

[C14:0]2

[C16:0]2

[C18:1]2

C16:0,C22:6
Temperature (C°)

30

30

50

30

30
Area A per lipid molecule (Å)2

63.2

60.6

64.0

72.5

74.8
Mean Area A sat. unsat. (Å)2

62.6

73.6
Mean -C=C- interchain dist.(Å)

4.48

4.84
Mean interact. energy U (kJ/mol)

-0.59

-0.39
UI

0

0

0

100

300

Data are based on the original data listed by Weijers [40].
DLPC: Dilauroylphosphatidylcholine; DMPC: Dimyristoylphosphatidylcholine; DPPC: Dipalmitoylphosphatidylcholine; DOPC: Dioleoylphosphatidylcholine; PDPC: Palmitoyl-Docosahexaaenoic-Phosphatidylcholine; –C=C⎼: Carbon-Carbon double bound; UI: Unsaturation Index.

An important different method, compared to the number of studies of fully hydrated, fluid phase, model phosphatidylcholine bilayers, for quantifying the mechanical properties of a single cell, has recently developed, in which a spherical cell is aspirated into a micropipette aspiration channel with a controlled suction pressure [24]. The micropipette pressurization of giant diacylphosphatidylcholine bilayers demonstrated that poly-cis unsaturated chain bilayers are thinner and more flexible than saturated/monounsaturated chain bilayers. However, the most striking result was the major increase in bending flexibility, which occurred when two or more cis double bonds were present in one or both chains of the lipid [25].

Atomistic Glucose and Glucose Transport

Glucose tissue uptake is performed by different specific glucose transporters. Glucose transporter proteins are integral membrane proteins containing 12 membrane-spanning helices. The glucose channel of a glucose transporter comprises eight helices that are immersed in a box formed by the remaining four helices [26]. The cross-section of this box has a mean surface area of 1,100 Å2, which covers an area of about 17 molecules of a phosphatidylcholine bilayer with saturated fatty acyl chains. Thus, the insertion of a glucose transporter molecule across a phospholipid cell membrane requires flexibility of the bilayer membrane.

β-Cell Functions

Variations in the lipid composition of cell membranes can profoundly impact the function of proteins embedded with them. Even small changes in the lateral pressure of a bilayer membrane can lead to significant alterations in the conformational distribution of the embedded proteins [27]. In type 2 diabetes mellitus, the redistribution process in membrane phospholipids can be triggered by a deficiency in cis carbon-carbon double bonds, compared to healthy individuals. A reduction in the area A of lipid molecules in the cell membranes leads to decreased flexibility. This reduction in flexibility can hinder the movement and conformational changes of proteins embedded in the membrane glucose transporters.

The collected information enables an exploration of ‘insulin resistance’ in the pathophysiology of type 2 diabetes. During the prediabetic phase, a crucial aspect of type 2 diabetes etiology is a decrease in unsaturation index of membrane phospholipids observed in erythrocytes compared to healthy controls. This reduction in the unsaturation index lowers flexibility of β-cell membranes, leading to a slower transmembrane glucose transport via GLUT2 in the β-cells. Consequently, the insulin granules inside the β-cells contain reduced insulin. Thus, instead of attributing the condition solely to ‘insulin resistance’, the primary factor behind decreased glucose levels within the β-cells and subsequent lower insulin production is the reduction in membrane flexibility.

Following the synthesis of monomer insulin within the β-cell, six monomer insulin molecules come together to form stable hexamers with a molecular weight of 36,000 [28]. These hexamers are then enclosed within mature intracellular vesicles and transported at any time to the β-cellʼs plasma membrane. A fusion pore is created upon the fusion of the intracellular vesicle membrane with the β-cell plasma membrane, releasing monomer insulin molecules into the bloodstream. Given the relative large size of the monomer insulin molecule (30Å wide and 35Å high), compared to the glucose molecule (overall size 10Å), both the vesicle membrane and the β-cell plasma membrane require significantly flexibility [29,30]. In individuals with type 2 diabetes, a reduction in the unsaturation index results in decreased membrane flexibility, leading to a slower rate of transmembrane insulin transport in the bloodstream. This phenomenon is in accordance with the data of individuals with type 2 diabetes, showing a reduction in insulin sensitivity by 57.9% and a much lower reduction in glucose effectiveness by 26.2% (Table 1). Thus, instead of using the term ‘insulin resistance’, the key issue lies in reducing flexibility in vesicle and β-cell plasma membranes, which impacts insulin release.

Membrane Flexibility and (Pre)diabetes Type 2

Table 3 records the biochemical outcomes of control individuals and individuals with prediabetes or type 2 diabetes mellitus. A study demonstrated that compared with healthy controls, individuals with gestational diabetes mellitus showed reductions in total polyunsaturated fatty acids (31.9 vs. 37.5; Δ=-14.9%), arachidonic acid (11.0 vs. 12.8; Δ=-14.1%), and unsaturation index (137 vs. 163; Δ=-15.8%) [31]. Another study revealed that compared with healthy controls, individuals with impaired glucose tolerance showed reductions, in total polyunsaturated fatty acids (25.8 vs. 30.7; Δ= -16.0%), arachidonic acid (11.1 vs. 12.5; Δ= – 11.2%), and unsaturation index (113 vs. 130; Δ= – 12.6%) [32]. Furthermore, a study found that compared with healthy controls, individuals with type 2 diabetes without retinopathy showed reductions in total polyunsaturated fatty acids (31.9 vs. 38.0; Δ= – 16.1%), arachidonic acid (11.3 vs. 13.0; Δ= -13.1%), and unsaturation index (134 vs. 155; Δ= – 13.6%) [33], whereas individuals with type 2 diabetes with retinopathy showed even lower values, compared to healthy controls, in total polyunsaturated fatty acids (29.5 vs. 38.0; Δ= – 22.4%), arachidonic acid (9.7 vs. 13.0; Δ= -25.4%), and unsaturation index (128 vs. 155; Δ= – 17.4%) [33].

Table 3: Erythrocyte acyl composition in phospholipids of control individuals, individuals with gestational diabetes, with impaired glucose tolerance, with type 2 diabetes without and with retinopathy, and type 2 diabetic men (study group).

Total SFAs (%)

Total MUFAs (%)

Total PUFAs (%)

C20:4 n-6 (%)

UI
GDM
 Controls (n=61)

33.6

15.9

37.5

12.8

163
 Patients (n=53)

37.7

18.0

31.9

11.0

137
 Δ (%)

+12.2

+13.2

-14.9

-14.1

-15.8
IGT
 Controls (n=42)

42.4

22.6

30.7

12.5

130
 Patients (n=28)

44.8

24.6

25.8

11.1

113
 Δ (%)

+5.6

+8.8

-16.0

-11.2

-13.1
Type 2 diabetes ret.(-)
 Controls (n=18)

42.1

18.8

38.0

13.0

155
 Patients (n=14)

44.2

21.7

31.9

11.3

134
 Δ (%)

+5.0

+15.4

-16.1

-13.1

-13.6
Type 2 diabetes ret.(+)
 Controls (n=18)

42.1

18.8

38.0

13.0

155
 Patients (n=46)

46.9

21.3

29.5

9.7

128
 Δ (%)

+11.4

+13.3

-22.4

-25.4

-17.4
Type 2 diabetes (study group)
 Controls (n=14)

44.0

20.2

28.8

11.1

126
 Patients (n=21)

42.9

20.6

31.6

14.3

141
 Δ (%)

-2.5

+2.0

+9.7

+28.8

+11.9

Ex-posed calculations performed by the author are based on the original data listed by Min et al. [31,32], Koehrer et al. [33], and Pelikánová et al. [40]. SFA: saturated fatty acid; MUFA: monounsaturated fatty acid; PUFA: poly-unsaturated fatty acid; UI: unsaturated index; C20:4 n-6: arachidonic acid. The values of gestational diabetes mellitus and impaired glucose tolerance are the means of phosphatidylcholine and phosphatidyletanolamine values. The values of type 2 diabetes mellitus with retinopathy are the means of mild, moderate, and severe diabetic retinopathy values [33].

These results imply that a decrease in the unsaturation index in individuals with prediabetes or type 2 diabetes mellitus has the potential to translate into an increase in attractive forces between the mutual membrane phospholipid acyl chains, which redistributes the lateral pressure profile of the cell membrane [27]. This redistribution induces alterations in mechanical and biochemical properties of the glucose transport channel proteins, i.e., it reduces the pore diameter, which in turn, reduces the rate of glucose transport across the cell membrane, and therefore causes the onset of type 2 diabetes mellitus [10].

These findings are substantiated by two surprising facts. First, Shulman et al. studied muscle glycogen synthesis in subjects with type 2 diabetes mellitus and matched controls using in vivo carbon-13 nuclear magnetic resonance spectroscopy [34-36]. They noted that the muscle glycogen synthesis rates in subjects with type 2 diabetes were about 50% lower of the rates observed in controls. The same group of researchers investigated, under hyperglycemic-hyperinsulinemic conditions, the pathway: transmembrane glucose transport into the muscle cell, conversion of intracellular glucose into glucose-6 phosphate, and then, after two more intermediates, the addition of the latter through glycogen synthase to the glycogen polymer. They concluded that their experimental results are consistent with the hypothesis that the transmembrane glucose transport into the muscle cell is the rate-controlling step in insulin-stimulated muscle glycogen synthesis in patents with type 2 diabetes, and the delivery of insulin is not responsible for the insulin resistance. This idea is in agreement with a constriction of the glucose channel within the three-dimensional structure of glucose transporter-4, which reduces the rate of transmembrane glucose-transport in type 2 diabetes [37]. Second, epidemiological evidence suggests that human plasma free fatty acid levels are generally elevated during the course of a pregnancy, probably with vital functions on fetal energy metabolism [38,39]. It is not unlikely that this increase also leads to an additional reduction in membrane flexibility and increases the amount of maternal glucose as an additional source of energy for the fetus.

The problem with the hypothesis is the lack of evidence of the healing power of an increase in unsaturation index in relation to type 2 diabetes. There is, however, an overlooked study with a surprising result. In 1991, Pelikánová et al. described, among others, a study group of 21 men with mild-to-moderate type 2 diabetes and 14 control men matched for age, sex, bodyweight, and dietary intake [40]. In this study, type 2 diabetes was defined by the criteria of the National Diabetes Data Group. The individuals were enrolled within 1 year after diagnosis, treated only with a diet, were less than 45 years of age and free of signs of atherosclerotic complications, and had a BMI < 30 kg/m2 [41]. The amount of saturated fatty acids in the diet was lower for the type 2 diabetes individuals than for the healthy controls (35.4 ± 12.2 g/d vs. 47.7 ± 10.8 g/d). Table 3 presents the ex-post calculations based on the original data of erythrocyte phospholipids, described by Pelikánová et al. [40]. The salient points in the data of the individuals in the study group show essential increases, compared with healthy controls, in the total polyunsaturated fatty acids (31.6 vs. 28.8; Δ=+9.7% ), arachidonic acid (14.3 vs. 11.1; Δ=+28.8%), and unsaturation index (141 vs. 126, Δ=+11.9%).

For the first time in the type 2 diabetes literature, individuals with mild-to-moderate type 2 diabetes have been treated successfully with diet alone in combination with a rise in the arachidonic acid level. The authors of the Pelikánová-study suggested that the increase in arachidonic acid level could be diet-induced or represent an increased incidence of atherosclerotic complications. A third option, however, arises in the use of arachidonic acid as a supplement in training adaptations among resistance-trained males [42]. However, the crucial biochemical outcome of this study concerns, independent of the provenance of arachidonic acid, the experimental result of an increase, compared with healthy controls, in the number of carbon-carbon double bonds in the cell membranes of the 21 men of the study group. This increased the unsaturation index, which is intricately linked to a rise of membrane flexibility and therefore led to an additional increase in the rate of glucose transport across the cell membrane. In addition, the increase in membrane flexibility resulted in an improved transmembrane insulin transport from pancreatic β-cells into the blood circulation. The influence of arachidonic acid on membrane flexibility is particularly important because arachidonic acid with four carbon-carbon double bonds is an important molecule in the field of membrane flexibility.

The most important steps of the hypothesis are diagrammatically illustrated in Figure 4 and predict that using RNA sequencing, the most essentially downregulated regulation-related gene, NKX2-1, and the most essentially downregulated metabolism-related gene, TPD52L3, cause an increase in plasma saturated free fatty acid levels, which leads to a decrease in the membrane unsaturation index and thereby reduces the rate of transmembrane glucose transport, resulting in type 2 diabetes mellitus. The reliability of the ideas is supported by the understanding of the observations that, first, the reduced glycogen synthesis rate in subjects with type 2 diabetes mellitus was about 50% of the rate observed in healthy controls; second, the normoglycemic offspring of parents, who had type 2 diabetes, exhibited significant defects in both insulin sensitivity and insulin-independent glucose removal rate; and third, the key feature of type 2 mellitus is an essentially larger defect in insulin sensitivity compared with glucose effectiveness.

FIG 4

Figure 4: Hypothetical pathway of the development of type 2 diabetes

Attention to this hypothesis was very tastefully articulated by late Denis McGarry in his article “What if Minkowski had been ageusic…” [43]. He argued that hyperglycemia and insulin resistance might be better explained when viewed in the context of underlying abnormalities of lipid metabolism.

Obesity and Type 2 Diabetes

Obesity is a chronic metabolic disease that has become the main risk factor for various non-communicable diseases, in particular, type 2 diabetes. Obesity has been reported to account for 80-85% of the risk of developing type 2 diabetes, while recent research suggests that obese people are up to 80 times more likely to develop type 2 diabetes than those with a BMI of less than 22 [44]. The concentration of free fatty acids is elevated in obese individuals [45]. The increased release of free fatty acids from adipose tissue into the blood circulation elevates the plasma concentration of saturated fatty acids because the unsaturation index of released free fatty acids from human white adipocytes is markedly lower than the unsaturation index of serum free fatty acids in healthy controls (85.5 versus 191.9, respectively). This phenomenon causes a shift from unsaturated to saturated fatty-acyl chains in phospholipids of erythrocyte membrane and vascular endothelium with all the associated consequences (Figure 4). There is no question of suggesting that free fatty acids cause insulin resistance, as suggested by Boden, but a decrease in the unsaturation index reduces the pore diameter of the glucose transporter protein, which in turn, reduces the rate of glucose transport across the cell membrane [45].

Insulin Resistance

The findings presented above propose a potential solution for the importance of ‘insulin resistance’ in the pathogenesis of type 2 diabetes. Specifically, the significant downregulation of NKX2-1 and TPD52L3 appears to be the main cause of the reduction in cis carbon-carbon double bonds of phospholipid membranes. This reduction decreases the cross-sectional area A of the cylindrical part of the phospholipid molecule. As a result, there are increased attractive forces between the phospholipid acyl chains, causing a redistribution of lateral pressure in cell membranes. This, in turn, induces a cross-sectional contraction of all Class1 GLUT proteins, ultimately leading to a lower rate of transmembrane glucose transport. The idea aligns with observations from biophysical and structural studies, highlighting the critical role of interactions between membrane proteins and lipid molecules in their folding and stability [46-48]. Clinically, the results of a study by the Diabetes Prevention Program Research group, are quite exciting. They indicate that among high-risk individuals, lifestyle intervention resulted in a 50% reduction in the incidence of type 2 diabetes development, while metformin led to a 31% reduction, compared to placebo [49]. This valuable insight into treating type 2 diabetes according the Diabetes Prevention Program Research group deserves wider recognition and attention.

The consistent findings from various studies strongly support the conclusion that lifestyle change treatment can effectively compensate for the loss of membrane flexibility by inducing an increase in the membrane unsaturation index. Therefore, it is advisable to incorporate the assessment of the unsaturation index into the treatment protocol. By doing so, we can better address the needs of individuals with type 2 diabetes and work towards normalizing membrane flexibility. Furthermore, it is essential to reconsider the notion of ‘insulin resistance’. The original interpretation that cells do not respond to insulin is inaccurate. Instead, the correct understanding is that the amount of carbon-carbon double bonds of cell membrane regulates the rate of glucose transport. Thus, the concept of ‘insulin resistance’ loses relevance, especially considering the significant role of membrane flexibility in glucose transport.

Conclusions and Future Recommendations

The proposals of the Diabetes Prevention Program Research group remain the method of choice for the treatment of type 2 diabetes. The key to lifestyle modification consists of increasing the patient’s unsaturation index level, which would promote a phospholipid shift from saturation to unsaturation, and thus reduce the incidence of type 2 diabetes. Clinical evidence has demonstrated that all of the 21 men in the study group managed their disease by increasing the level of the unsaturation index without taking metformin. Based on the considerations described above, one could conclude that considerable clinical benefit would accrue by the essential concept: saturated fatty acids makes the human cell membrane more rigid, while unsaturated fatty acid increases its flexibility, which ultimately postpones the onset of type 2 diabetes mellitus.

Two different techniques quantifying the mechanical properties of cell membranes came to the same conclusions: an increase in carbon-carbon double bonds by poly-cis unsaturated chain bilayers creates more membrane flexibility than saturated/monounsaturated chain bilayers.

A substantial difference can be made in extending the quality of type 2 diabetes mellitus treatment by introducing the unsaturation index assessment, because the regulation of membrane flexibility represents a great step forward in the development of precision medicine for type 2 diabetes mellitus.

The investigation of the genetic origin of type 2 diabetes mellitus must be moved from chemistry of DNA to chemistry of RNA.

Physical activity, as a part of standard lifestyle intervention, needs to be accompanied by the following official instructions throughout the disease period:

– Increase the rate of glucose transport and insulin transport across cell membranes through caloric restriction and walking 3 to 3.5 miles per hour, for at least 150 minutes per week.

– Replace “insulin resistance” by “reduction in membrane flexibility”.

Conflict-of-Interest

The author declares that the research was conducted in the absence of any commercial of financial relationships that could be constructed as a potential conflict of interest.

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Initial Processes on Replication of DNA by Interactions of Helical Structured Molecules – Origin of Life in the Water of the Earth (II)

2 Replies

DOI: 10.31038/GEMS.2023572

Abstract

This paper described a hypothesis about phenomenon those may have occurred early in the evolution of life or pre-life to reveal how every creature of the Earth has strong preference for specific chirality. Thread of helical molecule cannot be replicated by using a casting mold at once. Replication of creature has been carried out by DNA. The interactions among molecules of DNA are able to estimate from stereo structure of DNA. There are a D-typed chiral strand and a L-typed chiral strand linked by bases in a DNA. Those strands are generated simultaneously in the vicinity, but two will not coalesce due to the different typed chirality of the helix. The paired strands of DNA are able to become a representative of a series of amino acids for a protein i.e., DNA is able to memorize the information of replication of a protein. If amino acid molecules will be bonded with other amino acid at random timing, the alignment of amino acids is randomized in the protein. In such case, the replication is not accurate, and it will be eliminated by natural selection. Current amino acids alignment is stored by using genetic code in DNA, and replication of protein is constructed by using genetic codes sequences sequentially. In the double helix of DNA, there are leading strands and lagging strands, and the two strands proceed in rotationally opposite directions to find matched base pairs, the protein is formed when two genetic codes are in a key-keyhole relationship. The processing is carried out by amino acid units. The nucleobase ciphers of codon and anticodon represent amino acids at matching processing. The chirality of biomolecules takes one of the most important roles in the activities of creature in the Earth.

Keywords

Protein, Chirality, Helix, DNA, Leading strand, Lagging strand, mRNA, tRNA

Introduction

Although we have much knowledge in the field of molecular biology, the reason for biology’s strong preference for specific chirality of amino acids, sugars, and other molecules remains unanswered question in the field of life research [1]. S. Karasawa described that origin of life can be explained by intermolecular interactions among molecules via a helical structure of water [2]. After that, the study was advanced by focusing helical of DNA. This paper is the results of such studies. C. R. Cantor and P. R. Schimmel presented from mathematical requirements that asymmetric molecules can only have a helical structure as a polymer structure [3]. L. E. MacKenzie and P. Stachelek reported that the chirality twisted molecular structures will interlock and bind only if their chirality allows them. The chirality of a molecule interacts selectively with other molecules [4]. A. Shimada proposed that thing that separates life from non-life is enantioselectivity which escape from racemization [5]. N. Nemoto described that the chirality of biopolymers might depend on thesynthetic process of the biopolymer units [6]. The formation of biochemical molecules depends on the surrounding environment, and the homochirality of creature was established though natural selection that allowed to survive the creature fit for to live. K. Tamura pointed that biological homochirality could have been determined through the process of coevolution between nucleotides and amino acids. And he reported that aminoacylation of tRNA could be the key step in the origin of amino acid homochirality and once L-amino acids had been selected, the elongation of L-amino acids by the ribosome would have synthesized proteins composed of L-amino acids [7,8]. Current building blocks of amino acids are left-handed (L-type) chiral molecules, and ribose is right-handed chiralty (D-type). L-type amino acids never mingle with nucleic acids made of D-type sugars in a cell. K. Tamura and P. Schimmel showed that an RNA minihelix was aminoacylate by an aminoacyl phosphate D-oligonucleotide with a clear preference for L- as opposed to D-amino acids [9]. As the result of evolution, current ragging processing of D-type lagging strand in replication of DNA includes stops, turns, and synthesizes intermittently as shown in Okazaki fragment [10]. The double helix of DNA cannot form without selectivity of the chirality. The chirality of molecules supports self-assembly of molecules and formation of protein by using complex enzymes those are made by proteins. However, the replication of DNA has not fully discussed from the viewpoint of interaction among helical structures up to now. S. Karasawa described that if amino acid molecules are related to mRNA physically and amino acids bind at random timing to mRNA, accurate replication is impossible [2]. Current alignment of amino acids for formation of protein is realized by the genetic codes as merely representatives of amino acids. When codon and anticodon are in a key-keyhole relationship in the matching process, the amino acid is settled correct position, and it makes possible to reproduce the accurate protein.

The Environment for Formation of Cell Membrane

Movements of Liquid Water Molecules

The biochemical reactions are carried out in environment of liquid water. Even when ice melts into liquid water, there exist many of hydrogen bonds. The enthalpy of hydrogen bond of water is about 21 kJ/mol, and the change of the thermal energy from ice to water is 6.0 kJ/mol. There exist more than 90 % of hydrogen bonds among molecules in liquid water. The hydrogen bonds will generate many clusters in the liquid water. Since the tetrahedral molecule of water has an electric polarization, and it assists to form a cluster of helical structure similar to α-quartz that is minimum size with minimum energy in stereo structure made of asymmetric tetrahedrons [11]. The structure of liquid water is constantly being generated and extinguished, and its average lifespan is about 10-12 seconds. Helical structure of liquid water is similar to lattice structure of α-quartz, and it is minimum size with minimum energy in lattice structure of tetrahedrons The phase transition of quartz from β to αcan be explain as the shrink of size by rotation of unit SiO4 alternately around the electrical axis to change the symmetry from hexagonal to trigonal, and the tetrahedral unit projected to the X-Y plane from square to trapezoidal. The unit structure of helix is aligned with the central shafts stand on a planar boundary vertically and it has three-rotational symmetrical electrical axes. When the pair of hydrogen atoms located at the ends of the long and short sides vibrates around electrical axis, the up and down movements of the molecular pair of the inner vertices of tetrahedron and vertices of the outer side moves in the opposite direction. The movements of asymmetrical tetrahedrons make possible to form helical structure of DNA. Since helix structure is minimum size with minimum energy, it has tendency to expand more wider area. So, the clusters of helixes are formed. Even though the lifetime of the cluster is very short, the coupling among the helical area it yields rapid biochemical interactions. As evidence of connection by the helical state of water, linked bubbles are observed at thawing ice of carbonated water.

Catalytic Effects due to Helical Structure of Water Molecules

Proteins are made up of amino acids. On the other hand, nucleic acids are made up of nucleotides. Proteins and nucleic acids are different types of biomolecules those have different functions in the body. The control function is realized by different the chirality of molecules should be controlled and that of controller. Waves on the sea surface are happening all the time. Fluctuations of pressure in water causes the helical structure of water repeatedly expands and contracts via rotation of tetrahedron around each electric arises. Such repeated expansion and contraction of helical molecules will contribute to activities of a life. When a filamentous series of L-type of amino acids invades into the holes of helical structure, L-type structured of water will be long lifetime owing to the same chirality. The cluster of helix makes couplings with neighboring similar helical structures. The linkage takes role of a catalyst in the wet process. There are two types of helical molecular movements of liquid water as shown in Figure 1.

fig 1

Figure 1: Helical structure of liquid water. Here, electric polarization in a water molecule is expressed by arrows. An oxygen atom is expressed by large circle, and a hydrogen atom is expressed by small circle.

Largely Linked Helical Movements of Molecules in a Membrane

Tamura reported that a clear preference for L-amino acids as opposed to D-amino acids was noted in the efficient nonenzymatic aminoacylation of an RNA minihelix (progenitor of the modern tRNA) by an aminoacyl phosphate oligonucleotide [7]. There exists linkage of L-type of helical movement caused by aligned L-type molecules. Interlinks among helical movements will induce large helical movements. The exist of large systematic helical movements will support to form large helical arrangement of molecules such as DNA. L-type of helical movements in the vicinity of L-type amino acid able to assist to synthesize L-typed helical filamentous molecules of protein. Current DNA is consisted of enormous number of molecules. When DNA had been first created, every element that makes up the mechanism of DNA must be incorporated at the same time. The first DNA had formed through interactions among molecules in the vicinity of plural organized molecules. Since asymmetric tetrahedral molecules can only have a helical structure as a polymer structure [3] and D-type of sugars exist in the cell, we can assume that D-type of helical movements on the surface of cell membrane able to assist to synthesize D-typed helical filamentous molecules of sugars. The rotational thermal oscillations of helical structures of asymmetric molecules will induce surrounding thermal motions. It is considered that when two kinds of chiral molecules rotate, the opposite directional rotation may occur by different chiral molecules by a gear mechanism as shown in Figure 2.

fig 2

Figure 2: The illustration shows an image of only the mechanism that D-type of helical movements are induced by interactions among L-type of helical molecules. There exist large systematic helical movements.

Evolution of Cell Membrane

Origin of Main Component in Cell Membrane

The first life forms were born depending on the environment around them. The current cell membrane contains hydrophobic long chain of C16H34 or C18H38 as a main component. These molecules would have been in liquid state in the early Earth because these molecules are in liquid state between about from 20°C to 300°C. Since those molecules are hydrophobic long chains and the specific gravity are lighter than water, those molecules stay for long time as an oil film on the surface of water. Such hydrocarbon molecules had been synthesized from CO2 in the early atmosphere of the Earth due to the collision of the H+ of the solar wind. The smaller hydrocarbon molecules produced in the upper sky remain in the atmosphere and undergo repeated synthesis reactions [12]. In helical structure of liquid water, there are vacant shafts surrounded by a tetrahedron of water molecules as shown in Figure 1. The vacant shafts would have up-taken a C16H34 or C18H38, and those molecules aligned vertically on the water surface.

The processes of evolution of cell membrane are as follows.

  1. Fatty acids produced by oxidation of end terminal of hydrocarbons stayed in the hydrophobic layer.
  2. The membrane with linked head of fatty acids by glycerol laterally became robust.
  3. Long-chains polymeric carbohydrates hydrates (Cx(H2O)y) such as sugars tended to adhere on the hydrophobic layer.

    4. Adhered Sugars became a Component of Nucleic Acids

    The Structure of Phospholipid Membrane

    The current cell membrane has a hydrophilic “head” containing a phosphate group and two hydrophobic “tails” derived from fatty acids, joined by a glycerol molecule as shown in Figure 3.

    fig 3

    Figure 3: The structure of phospholipid membrane. Phospholipid arrangement in cell membranes contains a chiral center at the C2 position of glyceryl moiety.

    A phospholipid contains a chiral center at the C2 position of glyceryl moiety. The twisted phospholipids will interlock, and the interlocked molecules induce systematic motions of the same kind chiral molecules due to systematic thermal vibrations of atoms in the molecule.

    Screw Movement in a Phospholipid Bilayer

    In case that the bilayer of which one of the layers is rotated by 180°, the progress of helix is changed from output side to input side at the center of the bilayer as illustrated in Figure 4. Despite this bilayer having two chirality centers, the bilayer provides a one directional screw movement by the one directional rotation.

    fig 4

    Figure 4: The bilayer of which one of the layers is rotated by 180° where a progress of screw movement continues from output side to input side at the center of the bilayer.

    Here, there are two kinds of helical structures concerned with rotational direction of screw movements. When L-type of amino acids inserted in the phospholipid’s bilayer, L-type of bilayer will be formed, and D-type of sugar molecules proceeds in emitting direction along the induced L-type bilayer. The chirality of molecule that is synthesized by insertion and the molecule that is emitted by synthesize are different.

    How DNA Structure was Formed

    How mRNA and tRNA had Formed

    The evolution of repetitive productions of useful proteins has added the process of regenerating proteins using RNA precursors. The decompose shortly RNA of a tool for replication is favorable for life. So, DNA suitable for memory needs to be synthesized. The tool for replication of protein was replicated from DNA. The evolved process of replication of protein is carried out by mRNA and tRNA with support of plural of complicated enzymes, where amino acid is carried by tRNA, and those amino acids are arranged to a protein by the information of mRNA. Each step of matching between codon and anticodon was checked by as if a contact point of gear mechanism. The lagging strand that adhered with amino acid makes link to corresponding portion of mRNA by using genetic codes. Since the chirality of amino acid adhered to leading strand is L-type, the new leading strand fits with L-typed surrounding molecules and it will be a long strand. On the other hand, the chirality of new lagging strand is D-type, and it is easily leaved from surrounding molecules, and it will become short strands.

    Structure of Double Helix of DNA

    DNA is made of chemical building blocks of nucleotides. Each block is made of three parts: a phosphate group, a sugar group and nitrogen bases. The phosphate and sugar groups are linked into chains alternatingly, and a pair of strands is coupled by nitrogen bases [13,14]. There are rotational thermal vibrations of atoms in the helical structure of molecules. Sugar molecules are possible to adhere on the surface of the membrane, and those will be linked by phosphate. Strands of DNA will be formed along the rotating helical structures of the surface as shown in Figure 2. The synthesis of strand is facilitated by nucleophilic action of 3′ hydroxy group (-OH) of the terminal residue to phosphate group (PO4). Then, carbon 3′ will be adhered to carbon 5′ by phosphate-mediated ester bonds. The growing of leading strand proceeds continuously repeatedly from the 5′ to 3′ end. L-type of helical structure of strand in a cell are induced in the vicinity of the protein.

    How Genetic Codes had Formed

    Two of strands in a DNA are linked by bases. The strands of DNA are coupled by nitrogen bases by hydrogen bond at each synthesis steps, naturally generated nitrogen base pairs will be mixture of various combinations. As the result of natural selection, the linkage of strands favorable for survival was remained. Bases of current DNA are adenine (A), cytosine (C), guanine (G) and thymine (T). There are two kinds of pairing bases those have complementary stable pairs. One of the pair is A (adenine) and T (thymine), and the other is C (cytosine) and G (guanine). The one amino acid is assigned by three set of bases among 4 set of bases. Since tRNA must link with anticodon and amino acid, structure of tRNA becomes complicated. “Aminoacyl tRNA synthetase” binds the amino acid to specific tRNA that identifies their corresponding codons and “ribosome” ensures that tRNA correctly identifies mRNA. Although the mechanism of tRNA is complicated, the resultant role of tRNA is simple. An illustration on the stepping proceedings of activated portions of leading strand and lagging strand are shown in Figure 5.

    fig 5

    Figure 5: Proceeding of activated paired bases in a double helix DNA

    Enzymes of “Helicase” and “Polymerase” for Replication of DNA

    Current replication of DNA is processed by using existing DNA. Depending on the twisting direction, the joint of nitrogen base of DNA can be separated, or that can be brought closer. The motion of twist cannot achieve without any change of the surrounding molecules. Since the system of molecules to drive the twist motion includes amino acids and the amino acids make protein, enzyme for the function of twist is reproduced by the protein. The current DNA system makes use of helicase to separate two strands of a DNA to join and polymerase to join two stands. Helicase continues to move toward the unelicited double-stranded region, and leaved two single-stranded DNA templates that induce the formation of double-stranded daughter DNA duplexes. Since the separated strand can only be synthesized in the direction of extending the 3′ end, only the leading strand can continuously replicate as helicase moves.

    The processes of current replication of DNA are as follows.

    1. Helicase unwinds DNA, and two strands are used for replication of DNA.
    2. New bases are added to the complementary parental strands.
    3. Leading strand is made continuously, while lagging strands is made by pieces by piece.
    4. When nucleotides (bases) are matched, two of double helices are synthesized.

      5. The representation on replication of DNA is shown in Figure 6. The proceeding of active portion in lagging strand rotates in the opposite for that of leading strand, and the synthesizing DNA waits for some parts of strand to be exposed and continues until it meets the 5′ end of the previously synthesized lagging strand. A short DNA fragment formed by the lagging strand is called “Okazaki fragment” [10]. The Okazaki fragment is connected to the next Okazaki fragment shortly after synthesis to form a continuous new DNA. The different chirality of strands is necessary because reproduced two DNA’s must be divided into two equal parts in the beginning processing of a cell division.

      fig 6

      Figure 6: Replication of DNA by using original DNA

      Conclusion

      This paper described a hypothesis about how DNA may have evolved in the water environment of the Earth. The interactions among molecules in DNA are able to get hints from stereo structure of DNA. There are leading strand and lagging strand in a DNA. The proceeding of lagging strand is rotationally opposite direction to that of leading strand. The strands with different chirality in a DNA are coupled by nitrogen bases through hydrogen bond at each synthesis steps. When DNA is first created, every element that makes up the mechanism must be incorporated at the same time. It is known that L-type chirality of amino acid had been selected, and L-type of helical movements of molecules are able to assist to synthesize L-typed helical filamentous molecules. It can be considered that D-type of helical movements will be induced through interactions among L-type of helix via gear mechanism. Based on such estimations, it is possible to explain the generation of D-type of sugar of DNA on the surface of cell membrane. The first formation must be achieved tremendous number of try and errors. Current process of replicating DNA uses existing DNA. After separation of paired strands of DNA, the proceeding of leading strand is rotationally opposite to that of lagging strand, it makes possible to check of key-lock relationship on codon and anticodon. Replication of a protein involves multiple reactions. As further prospect, an enzyme is made by protein. However, our understanding is causal law, we cannot express precise progress of concurrent different reactions by using logical expression. Overlapping multiple causal laws can be expressed by using a pattern. Although there exists unknown mechanisms, to reveal various aspects of surroundings contributes to improve the understanding of the complicated mechanism of protein replication. It can be concluded that the chirality of biomolecule is able to take a role of bridge between origin of life and the molecular biology.

      Acknowledgement

      The author expresses his sincere thanks to Professor Koji Tamura from Tokyo University of Science for helpful discussions and comments on the manuscript.

      References

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Extreme Enrichment of Cs during the Crystallization of the Ehrenfriedersdorf Pegmatite Melt Related to the Variscan Tin Mineralization

2 Replies

DOI: 10.31038/GEMS.2023571

Abstract

This short paper shows the excessive enrichment of Cs in a volatile-rich evolved pegmatitic silicate melt. This water-rich melt (plus B and F) with about 30% bulk water was, with high probability, created by an input of a supercritical fluid from the earth’s mantle. The water content of a granitic melt is at ~900°C, and crustal pressures (<5 kbar) are too low to create such a volatile-rich evolved melt. Furthermore, the quartz contains nanodiamonds and graphite. These are strong hints to the participation of supercritical fluids.

Keywords

H2O-B2O3-F-rich melt, Cs-rich melt inclusions, Multistage liquid-liquid immiscibility, Supercritical fluids

Introduction

During the study of melt inclusions in quartz of a pegmatite body related to the Variscan Ehrenfriedersdorf tin deposit in the Central Erzgebirge/Germany, we often found high concentrations of Cs in different inclusion types: water-poor melt inclusions rich in Cs, water-rich melt inclusions with a moderate Cs content, and extreme Cs-pentaborate rich inclusions trapped near the crest of the main solvus. The primary solvus curve (B vs. H2O) results from the simultaneous enrichment of water, boron, and fluorine and forms a pseudo-binary solvus (H2O + B2O3 + F + Silicate melt) versus temperature (Figure 1a and 1b).

FIG 1A

Figure 1a: Boron versus water concentration in conjugate type-A (blue) and type-B melt inclusions (red) in the Ehrenfriedersdorf pegmatite quartz. Both compounds portray a solvus curve (melt-water). Included are the corresponding isotherms (500, 600, and 650°C) – see Thomas et al. (2003) [1].

From these curves (Figures 1a and 1b), we see that at the critical point (solvus crest), a high concentration of H2O, B2O3, and F are present: about 27.5, 4.2, and 9.0%, respectively. The data are from published and unpublished data from Veksler and Thomas (2002) [1-4].

FIG 1B

Figure 1b: Fluorine versus water (H2O) concentration obtained from melt inclusions from the same Ehrenfriedersdorf pegmatite quartz.

From unpublished hydrothermal diamond anvil cell (HDAC) experiments performed in 2002 together with Ilya Veksler and Christian Schmidt, we know that using synthetic pegmatite melts similar to the Ehrenfriedersdorf pegmatite with about 50 [% (vol/vol)] water in the temperature range from 840 down to 300°C multistage liquid-liquid immiscibility processes happens. Each main phase ever formed tends to liquid immiscibility. Such compartments are very contrasting and show extreme enrichment of rare elements like boron, fluorine, cesium, beryl, tin, and others (e.g., Thomas et al. 2019 and 2022) [5,6]. This specific experiment, unfortunately, ended short before the total homogenization at about 900°C by the crash of a diamond of the HDAC. This experiment shows, however, clearly that the formation of such water-rich melt is only possible at very high temperatures, and consistently, with cooling, multi-phase separation happens steadily down to low temperatures around 160°C (Figures 2 and 3). If we look at Figure D at Schröcke’s contribution (1954) [7], it follows that primary forming this pegmatite type is impossible in situ. We need high water and energy to develop a mass of pegmatite bodies shown there (see also Johannes and Holtz, 1996) [8]. In a couple of publications, Thomas [9-14] and Thomas et al. [15,16] have shown that by the finding of typical mantle minerals in granites and pegmatites, the supplier of the necessary water (energy and other components) can be supercritical fluids coming directly from the mantle region. From the unfinished HDAC experiment (see above), we have learned that during heating and cooling, many phase changes happen, primarily by liquid-liquid immiscibility (see exemplary Figures 2 and 3).

FIG 2

Figure 2: Look through the microscope at the sample chamber formed by an Ir-gasket with a hole of 300 µm between two diamonds of the HDAC at two different temperatures. Conspicuous are the different phases formed by liquid-liquid immiscibility.

FIG 3

Figure 3: The same HDAC experiment at lower temperatures (390 and 160°C). V: Vapor, XXX: late-formed crystal. The “crystals” marked area stands for the melt 1+2, now wholly solidified.

Sample Material

The sample material (Qu8) comes from a miarolitic pegmatite body in the Sauberg tin mine near Ehrenfriederesdorf, Germany. A description of the locality is in Webster et al., 1997 [4]. The quartz sample was as big as your fist. Many 500 µm thick on both sides polished slices are produced from this sample. The vapor phase of some melt inclusions in this quartz contains high hydrogen, methane, and CO2 concentrations: XH2=0.58, XCH4=0.26, XCO2=0.16 (Thomas and Webster, 2000) [17]. In a quartz crystal (Qu8-45) from the same pegmatite, here not studied, we have found large aggregates of graphite and nanodiamond.

Methodology

The general used methods are described in Webster et al. 1997 [4] and Thomas [13] and references in there). For the microprobe study (main and trace elements), we mainly used the CAMECA SX50 microprobe. We used Raman spectroscopy (see Thomas 2023e and references) [13] to determine the water as the basis component for constructing the pseudo-binary solvus curves (see also Thomas and Davidson, 2016) [18].

Results

This contribution is only restricted to cesium (Cs) results. Cesium, with a Clarke value of about 5.0 ppm in granitic rocks (Rösler and Lange, 1975) [19], is enriched to extremely high values of about 160000 ppm. That corresponds to an enrichment of 32000 fold, an incredible value. Further, we see relationships between Cs and H2O, Cs and B2O3, and B2O3 with H2O. Figure 4a demonstrates the enrichment of Cs (as Cs2O) with water. The Cs shows here a good Lorentzian distribution. The data come from typical melt inclusion in the pegmatite quartz from the Sauberg mine. The same distribution type results for Cs and B in Figure 4b. This figure shows strong enrichment of Cs at a more or less constant B2O3 concentration of about 2.3% B2O3. According to He et al. (2020) [20], B2O3 reduces pollucite’s [(Cs, Na)(AlSi2)O6 • nH2O] crystallization temperature (maybe under 700-600°C) and improves immobilization through an encapsulation effect here by Al-silicates. The melt inclusions are relatively water-poor (~5%) and represent the heavy residue. Figure 4c shows the simplified solvus curve for the system melt-H2O versus B2O3 (Figure 1a). Figure 4d displays the extreme Cs enrichment near the solvus crest of the Melt-H2O – B2O3 system as Cs-pentaborate (Ramanite-(Cs). At first, this Cs-pentaborate was found in pegmatite material from the Isle of Elba (see Thomas et al., 2008) [21] – later also in Malkhan (Thomas et al., 2012) [22] and Ehrenfriedersdorf (Sauberg mine). The Lorentzian distribution of Cs vs. B is untypical, and Cs vs. H2O is typical for some elements (Be, Sn, and others [5,6]. Table 1 gives the fit data for the three Cs distributions.

Table 1: Lorentzian fit data

Distribution

Area

Center (%)

Width (%)

Offset (%)

Height (%)

R2
Cs2O-B2O3

5.226

2.264

0.553

1.621

6.012

0.90983
Cs2O-H2O

26.592

25.991

5.631

0.134

3.006

0.96752
Ramanite-(Cs)

13.309

29.992

0.900

5.676

9.419

0.99436

In comparison to Figure 4a, the distribution of Cs2O vs. H2O shown in Figure 4d is a little bit shifted to higher water concentrations (maybe the real critical point of the system) or by the crystal water in the formula of Ramanite-(Cs) [CsB5O8 • 4H2O] – see Thomas et al. 2008) [21].

FIG 4A

Figure 4: (a) Distribution of  Cs with H2O, (b) Cs with B, (c) H2O and B, (d) Cs as Ramanit-(Cs) with H2O. The microprobe data for (b) are from Thomas et al. 2019 [5].

Discussion

The here-shown enrichment of Cs during the crystallization of a pegmatite-forming melt related to the Variscan tin deposit Ehrenfriedersdorf is unexpectedly high. The origin of such distributions is, at the moment, not clear. More research is indispensable. Are such Lorentzian-type distributions of elements with water a characteristic feature of the participation of supercritical fluids at the tin mineralization here? Some elements (Be) are Gaussian distributed (see Thomas 2023d) [12]. What are the reasons for the different distribution types? Which physicochemical processes are the determining steps? Figure 4 shows that the behavior of Cs in water-rich high-temperature melt-water systems is very complicated. Therefore, is storing the radioactive 137Cs as boron-stabilized pollucite doubtful (see Yokomori et al., 2014) [23,24]. Because each element in the supercritical state can enriched to high values, we assume, in principle, that universal cooperative interactions between the particles under these conditions will work. The opposite is, up to now, not proved.

Acknowledgment

The first author thanked I-Ming Chou and William (Bill) A. Bassett for their professional introduction to the HDAC technique during my visit to the Argonne National Laboratory in the summer of 2001. Iliya Veksler and Christion Schmidt are thanked for the joint HDAC experiments on synthetic pegmatite melts.

References

  1. Thomas R, Förster HJ, Heinrich W (2003) Behaviour of Boron in a peraluminous granite-pegmatite system and associated hydrothermal solutions; a melt and fluid-inclusion study. Contribution to Mineralogy and Petrology 144: 457-472.
  2. Veksler IV, Thomas R (2002) An experimental study of B-, P- and F.rich synthetic granite pegmatite at 0.1 and 0.2 GPa. Contribution to Mineralogy and Petrology 143: 673-683.
  3. Thomas R, Schmidt C, Veksler I, Davidson P, Beurlen H (2006) The formation of peralkaline pegmatitic melt fractions: evidence from melt and fluid inclusion studies. Extended abstract. XLIII. Congresso Brasileiro de Geologia, 2006, published 2008, 757-761.
  4. Webster JD, Thomas R, Rhede D, Förster HJ, Seltmann R (1997) Melt inclusion in quartz from an evolved peraluminous pegmatite: Geochemical evidence for strong tin enrichment in fluorine-rich and phosphorus-rich residual liquids. Geochimica et Cosmochimica Acta 61: 2589-2604.
  5. Thomas R, Davidson P, Appel K (2019) The enhanced element enrichment in the supercritical states of granite-pegmatite systems. Acta Geochim 38: 335-349.
  6. Thomas R, Davidson P, Rericha A, Voznyak DK (2022) Water-rich melt inclusions as “frozen” samples of the supercritical state in granites and pegmatites reveal extreme element enrichment resulting under non-equilibrium conditions. Mineralogical Journal (Ukraine) 44: 3-15.
  7. Schröcke H (1954) Zur Paragenese erzgebirgischer Zinnlagerstätten. Neues Jahrbuch Mineral. 87: 33-109.
  8. Johannes W, Holtz F (1996) Petrogenesis and experimental petrology of granitic rocks. Springer.
  9. Thomas R (2023a) Unusual cassiterite mineralization, related to the Variscan tin-mineralization of the Ehrenfriedersdorf deposit, Germany. Aspects in Mining & Mineral Sciences 11: 1233-1236.
  10. Thomas R (2023b) Ultrahigh-pressure and temperature mineral inclusions in more crustal mineralizations: The role of supercritical fluids. Geology, Earth and Marine Sciences 5: 1-2.
  11. Thomas R (2023c) A new fluid inclusion type in hydrothermal-grown beryl. Geology, Earth and Marine Sciences 5: 1-3.
  12. Thomas R (2023d) To the geochemistry of beryllium: The other side of the coin. Geology, Earth and Marine Sciences 5: 1-4.
  13. Thomas R (2023e) Raman spectroscopic determination of water in glasses and melt inclusions: 25 years after the beginning. Geology, Earth and Marine Sciences 5: 1-2.
  14. Thomas R (2023f) The Königshain granite: Diamond inclusions in Zircon. Geology, Earth and Marine Sciences 5: 1-4.
  15. Thomas R, Davidson P, Rericha A, Recknagel U (2023a) Ultrahigh-pressure mineral inclusions in a crustal granite: Evidence for a novel transcrustal transport mechanism. Geosciences 13: 94: 1-13.
  16. Thomas R, Recknagel U, Rericha A (2023b) A moissanite-diamond-graphite paragenesis in a small beryl-quartz vein related to the Variscan tin-mineralization of the Ehrenfriedersdirf deposit, Germany. Aspects in Mining & Mineral Sciences 11: 1310-1319.
  17. Thomas R, Webster JD (2000) Strong tin enrichment in a pegmatite-forming melt. Mineralium Deposita 35: 570-582.
  18. Thomas R, Davidson P (2016) Revisiting complete miscibility between silicate melts and hydrous fluids, and the extreme enrichment of some elements in the supercritical state – Consequences for the formation of pegmatites and ore deposits. Ore Geology Reviews 72: 1088-1101.
  19. Rösler HJ, Lange H (1975) Geochemische Tabellen. VEB Deutscher Verlag für Grundstoffundustrie Leipzig. 675 p.
  20. He P, Fu S, Wang M, Duan X, Wang O, et al. (2020) B2O3-assisted low-temperature crystallization of pollucite structures and their potential applications in Cs+ immobilization. Journal of Nuclear Materials 540.
  21. Thomas R, Davidson P, Hahn A (2008) Ramanite-(Cs) and ramanite-(Rb): New cesium and rubidium pentaborate tetrahydrate minerals identified with Raman spectroscopy. American Mineralogist 93: 1034-1042.
  22. Thomas R, Davidson P, Badanina E (2012) Water- and boron-rich melt inclusions in quartz from the Malkhan pegmatite, Transbaikalia, Russia. Minerals 2: 435-458.
  23. Thomas R, Davidson P (2007) The formation of granitic pegmatites from the viewpoint of melt and fluid inclusions and new experimental work. Granitic Pegmatites: the state of the art – International Symposium, Porto, Portugal, 13-16.
  24. Yokomori Y, Asazuki K, Kamiya N, Yano Y, Akamatsu K, et al. (2014) Final storage of radioactive cesium by pollucite hydrothermal synthesis, Scientific Reports 4, 4195: 1-4.

Prostate Cancer Prediction and Detection Using Hybrid CNN-Deep Learning Techniques

2 Replies

DOI: 10.31038/CST.2023833

Abstract

Prostate cancer is a widely recognized form of cancer characterized by the proliferation of malignant cells in the prostate gland, a small organ responsible for producing seminal fluid in men. Typically, the progression of prostate cancer is slow and initially confined to the prostate gland, often causing minimal harm. Common symptoms include frequent urination, weak urine flow, blood in the urine or seminal fluid, and pain during urination. However, the current method for detecting prostate cancer, known as Driving While Intoxicated (DWI), suffers from several limitations such as low accuracy, complexity, high computational requirements, and the need for extensive training data. To address these challenges, researchers in the medical imaging field are exploring different Convolutional Neural Networks (CNNs) models and techniques for object detection and segmentation. In this study, a modified CNN system is proposed to develop an automated algorithm capable of detecting clinically significant prostate cancer using DWI images of patients. The study employed a clinical database consisting of 970 DWI images from individuals, with 17 cases diagnosed with prostate cancer (PCa) and 14 cases considered healthy. The performance of the proposed system was evaluated using a training database containing 940 patients, while the remaining 20 patients were reserved for testing. The results demonstrated that the proposed system exhibited improved sensitivity, reduced computational requirements, high performance, and lower time complexity compared to the current prototype system.

Keywords

Detection, Prediction, Neural networks, CNN, Prostate cancer, Deep learning, DWI, CAD, Image processing and classification.

Introduction

In the year 2020, prostate cancer ranked as the third highest cause of cancer-related fatalities in men in the United States. During this period, there were an estimated 161,461 new cases of prostate [1,2] cancer, constituting 19% of all newly diagnosed cancer cases. Additionally, prostate cancer was responsible for 26,831 deaths, accounting for 8% of all cancer-related deaths. While prostate cancer is the most frequently occurring cancer among men, the prognosis for treatment is considerably favorable when the disease is detected in its early stages. Consequently, the implementation of effective monitoring and early detection methods plays a crucial role in enhancing the survival rates of patients. Machine Learning (ML) is an integral component of Artificial Intelligence (AI) that employs statistical, probabilistic, and historical tools to make informed decisions or predictions based on new data. Within the realm of clinical imaging, the fusion of imaging and ML techniques has given rise to computer-assisted detection and diagnosis. This innovative approach has demonstrated substantial potential in assisting radiologists with precise diagnoses, reducing diagnosis time, and enhancing diagnostic accuracy [3,4]. The conventional method for constructing ML models entails extracting quantitative imaging features like shape, volume, intensity, and other data attributes from the image data. These features are subsequently utilized in conjunction with ML classifiers such as Support Vector Machine (SVM) or Decision Trees (DT). Deep learning techniques have demonstrated their efficacy in various computer vision tasks, encompassing segmentation, detection, and classification. These techniques leverage convolutional layers to extract distinctive features, progressing from low-level local patterns to high-level global patterns within input images. The incorporation of a fully connected layer at the end of the convolutional layers enables the conversion of intricate patterns into probabilities assigned to specific labels [5,6]. The performance of deep learning-based methods can be further enhanced by employing different types of layers, such as batch normalization layers, which normalize layer inputs to possess a zero mean and unit variance, and dropout layers, which randomly exclude selected nodes. Nevertheless, achieving optimal performance necessitates identifying the ideal combination and configuration of these layers, as well as precise tuning of hyperparameters. This challenge persists as one of the primary obstacles in the application of deep learning techniques across diverse domains, including medical imaging. In the context of clinical imagery, each MRI slice contains valuable information pertaining to the location and size of prostate cancer. Ishioka et al. conducted a cut-level analysis involving 318 patients, utilizing U-Net and Res Net models. Remarkably, they achieved an impressive AUC (Area Under the Curve) value of 0.78 on the test set, utilizing only 16 separate slices, without any additional training or validation steps [7,8].

The research paper focuses on the following areas: Our study aims to predict the recurrence of prostate cancer using H&E stained tissue images without relying on manually designed features. Instead, we utilize deep learning techniques to automatically learn a hierarchical representation of features, enabling the differentiation between recurring and non-recurring morphological patterns [9,10]. We propose a two-stage deep learning-based approach to classify H&E stained tissue images and estimate the probability of prostate cancer recurrence. This approach can also be extended to predict other specific tissue classes, such as cancer grades, types, and molecular subtypes, across different organs to facilitate precise treatment planning. In the first stage, we employ a convolutional neural network (CNN) to accurately identify the locations of nuclear centers within a given tissue image. Our core location algorithm is trained to detect both epithelial and stromal cores within tumor areas as well as non-tumor regions of the tissue images. For the second stage, we utilize another modified CNN that takes patches centered on the identified nuclear centers as input, producing patch-wise predictions of cancer recurrence likelihood. This stage can be easily adapted and retrained to estimate the likelihood of different cancer subtypes in diverse types of tissue images [11,12]. The final estimation of subtype likelihood for a given patient (indicating the likelihood of recurrence) is determined by aggregating the patch-wise probabilities obtained from the modified CNN. To address unwanted variations in tissue images caused by staining and scanning discrepancies across medical clinics, we incorporate a color standardization step as a preprocessing technique. This helps correct the effects of such variations. Additionally, we conducted a literature survey where Jemal [1] proposed a cancer journal targeting clinicians, providing information on the number of reported deaths in 2012 categorized by age for the top 10 leading causes of death, including the 5 leading causes of cancer-related deaths [13,14].

Literature Survey

Jemal [1] introduced a publication called the “cancer journal for Clinicians,” which presents data on the actual number of deaths in 2012 categorized by age for the top 10 leading causes of death, including the top 5 leading causes specifically related to cancer. The NAACCR (North American Association of Central Cancer Registries) indicated that all cancer cases were classified using the International Classification of Diseases for Oncology, with the exception of cancers occurring in children and adolescents. Causes of death were documented based on the International Classification of Diseases [15,16]. To account for potential delays in data collection, the disease recurrence rate provided in the current study was adjusted whenever possible. These delays might arise due to the flexibility in data capture or the process of reviewing and updating the data. The likelihood of receiving a diagnosis of invasive cancer is higher in men (42%) compared to women (38%). In 2016, it was estimated that approximately 10,380 children (from birth to 14 years) would be diagnosed with cancer, excluding benign/marginal brain tumors. Among those diagnosed, 1,250 children were projected to succumb to the disease. The calculation method used for 2016 does not include case estimates for benign and marginal brain tumors, as reporting data on these tumors was not mandatory until 2004 [17,18]. From 1975 to 2012, the incidence rates of cancer in children and adolescents exhibited an annual increase of 0.6%. A publication called Cancer Inst. Monogr [2] proposed that “Overdiagnosis of prostate cancer” can be attributed to two main factors. Firstly, there is a relatively large, asymptomatic population affected by this disease, as indicated by postmortem studies and histologic examination of prostates removed for other reasons. Secondly, the widespread use of screening methods such as prostate-specific antigen (PSA) tests and digital rectal examinations, which have been implemented among various groups of men in the United States over the past two decades, contributes to the over-diagnosis of prostate cancer. Cancer overdiagnosis refers to the detection of cancers that would not have become clinically evident during a patient’s lifetime or would not have led to cancer-related death. Importantly, these studies identified instances of cancer even in men in their twenties, with a prevalence ranging from 8% to 11%, highlighting the long period of latency between the development of prostate cancer and the appearance of symptoms in some individuals [19,20]. The incidence of prostate cancer observed in the group undergoing screening showed a 58% higher rate compared to what would be expected in a contemporary US population, as determined using Surveillance, Epidemiology, and End Results (SEER) data. In addition, there was a substantial 200% increase in incidence when compared to men from the pre-PSA era. These findings suggest the potential for significant overdiagnosis when annual screening is conducted over many years, as practiced in the PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial). Furthermore, assessments of overdiagnosis can be influenced by racial disparities. Etzioni et al., using SEER registry data from 1988 to 1998, demonstrated how racial differences impact the over-diagnosis of prostate cancer [21,22]. The majority of prostate tumors detected through screening are asymptomatic and localized within the clinical setting. According to SEER data from 2004 to 2005, approximately 94% of newly diagnosed men exhibit T1 or T2 disease classification. While the existence of overdiagnosis poses challenges, an even greater concern is the subsequent overtreatment of tumors detected through screening. In the United States, a significant number of men with such diseases receive aggressive treatment. Efforts have been made to improve the identification of aggressive prostate cancer by utilizing prostate-specific antigen (PSA) testing. One approach to mitigate over diagnosis involves selectively implementing screening and biopsy procedures solely for individuals at a higher risk of prostate cancer-related mortality. Various research groups have assessed the effectiveness of serum PSA as a predictor of future aggressive disease. Recently, Williams et al. developed a prediction model based on the Early Detection Research Network (EDRN) and validated it using the placebo group of the Prostate Cancer Prevention Trial. This model incorporates clinical factors to selectively identify only those cases exhibiting aggressive cancer [23,24]. As per the article titled “Prostate Magnetic Resonance Imaging Interpretation Varies Substantially Across Radiation Oncologists,” the proposed system underwent evaluation using diffusion-weighted imaging (DWI) datasets from 40 patients. Among these patients, 20 cases were benign while the other 20 were malignant, and the datasets were collected at seven different b-values. The evaluation revealed that the proposed system achieved an impressive area under the curve (AUC) of 0.99 after the second stage of classification. This indicates that the performance of the proposed system surpasses that of other systems that do not involve segmentation [25,26]. In recent statistics, it was reported that approximately 164,690 new cases of prostate cancer were diagnosed in 2018, resulting in about 29,430 deaths. The utilization of prostate-specific antigen (PSA) screening has played a role in reducing the mortality rate associated with prostate cancer by more than 20%. Magnetic resonance imaging (MRI) techniques have demonstrated the potential for detecting and localizing prostate cancer without the drawbacks associated with invasive procedures. Computer-aided detection (CAD) systems have been developed utilizing various features, such as dynamic, wavelet, and co-occurrence, derived from T2-weighted MRI, dynamic contrast-enhanced MRI (DCE-MRI), and apparent diffusion coefficient (ADC) maps. One particular CAD system was assessed using datasets obtained from three different institutions, and the highest AUC achieved was 0.71, with an accuracy of 88%. A novel Computer-Aided Detection (CAD) system is proposed for the non-invasive identification of prostate cancer through the utilization of Diffusion-Weighted Imaging (DWI). The system employs a two-stage classification approach, where refined Apparent Diffusion Coefficient (ADC) volumes at different b-values are utilized for training. What sets this proposed system apart is its ability to detect prostate cancer using DWI without the requirement for precise prostate segmentation. The accuracy of the system is assessed by evaluating DWI datasets acquired from two distinct scanners with different magnetic field strengths. In the paper titled “Prostate Imaging Reporting and Data System Version 2 (PIRADS v3): A Graphic Appraisal” authored by Hassan Zadeh et al., a suggestion is made to employ kernel methods in image analysis. These methods leverage Mercer kernel functions, which enable the mapping of input vectors into a higher-dimensional feature space. This mapping facilitates the efficient computation of inner products without the need to explicitly calculate the feature vectors. This technique, commonly known as the “kernel trick,” has found widespread application in various domains, including sequence analysis and regression analysis [27]. The recursive least-squares (RLS) algorithm is widely employed in the fields of signal processing, communications, and control. It serves as an efficient online method for finding the least-squares straight Predictor. In the work by Rosencrantz, A. B. et al. [5], titled “Inter observer reproducibility of the pi-rads form 2 dictionaries: a multi-center education of six knowledgeable prostate radiotherapists,” a method is proposed to enhance the reliability of the pi-rads form 2 dictionaries among multiple observers. The proposed approach aims to eliminate the dictionary entry with the least impact on the overall system when a new sample is added to the dictionary. The objective of the paper is to develop a pruning technique for Kernel Least Mean Square (KLMS) that is online, computationally simple, and capable of addressing three key issues mentioned earlier. Although the computational complexity of the critical condition remains high, scaling as O(K^2), the use of a recursive approach is motivated. The proposed pruning method, in conjunction with QKLMS (Quantized Kernel Least Mean Square), solves a fixed-budget KLMS algorithm and is referred to as QKLMS-FB [1]. The QKLMS algorithm incorporates a basic vector quantization (VQ) technique to quantize the feature space and control the size of the adaptive filter within the system. However, many existing VQ algorithms are unsuitable for online bit learning due to offline training of the codebook and heavy computational burden. In contrast, the proposed method distinguishes itself from other approaches by updating the coefficients with each new data sample, enabling real-time monitoring of dictionary growth. The computational complexity of Ek(i) is substantial as it involves all the learned observations. In the case of uniform input data distribution and fixed input statistics, the critical value is directly proportional to the magnitude of the coefficient data. Hence, our pruning rules focus on eliminating points with the smallest magnitudes. The Gaussian function is well-suited for piece methods because of its general, differentiable, and continuous characteristics. It offers the advantage that when two Gaussian functions are convolved, the resulting function is another Gaussian function with modified mean and variance. This property simplifies the integration process. By employing recursive exploitation, the computational complexity of the feature space can be significantly reduced from O(K^2) to O(K), which is typically sufficient for most piece methods [2].

Related Work

Existing System

In recent times, there have been multiple endeavors to create techniques utilizing quantitative image processing and artificial intelligence to achieve precise predictions of prostate cancer recurrence. The ability to accurately predict recurrence during the initial diagnosis stage assists healthcare professionals in determining suitable treatment options, including radical prostatectomy, radiation therapy, chemotherapy, hormone therapy, and active surveillance for less aggressive tumors. By accurately forecasting the tumor’s response to treatment, long-term treatment outcomes can be improved, and the risks associated with unnecessary treatment can be minimized [3]. There is a growing demand for recurrence prediction models based on image processing techniques. Previous attempts using clinical, pathological, and demographic factors have not been successful in accurately stratifying the majority of patients with intermediate cancer grades, such as Gleason scores of 3+4 or 4+3. Additionally, these models tend to overestimate the probability of recurrence for low-risk patients [5]. It is worth mentioning that in many image analysis-based approaches, the available image data from multiple patients is divided into case-control pairs. The cases consist of patients who experienced biochemical recurrence within a specified follow-up period (typically five years), while the controls are patients who did not experience recurrence. The case-control pairs are usually matched based on clinical and demographic factors such as age, Gleason score, pathological stage, and race, which help in the development of new biomarkers. In this paper, we will outline some of the previous methods used to predict prostate cancer recurrence solely based on H&E-stained images of the prostate tissue [6]. In recent times, various endeavors have been made to develop methods based on quantitative image processing and artificial intelligence (AI) that can effectively predict the recurrence of prostate cancer. Precisely predicting recurrence during the initial diagnosis stage enables healthcare professionals to make informed decisions regarding treatment options. Additionally, accurate prediction of tumor response to treatment contributes to improved long-term treatment outcomes and minimizes the risks associated with unnecessary treatment. Previous attempts utilizing clinical, pathological, and demographic factors have struggled to accurately stratify a significant portion of patients with intermediate cancer grades, such as Gleason scores of 3+4 or 4+3. Furthermore, these models generally tend to overestimate the likelihood of recurrence for patients at low risk. In previous studies, several approaches have been proposed to predict prostate cancer recurrence using H&E-stained tissue images. Jafari-Khouzani et al. were among the early contributors in this field, introducing a method that utilized second-order image intensity surface features extracted from co-occurrence matrices of H&E stained tissue images. Teverovskiy et al. conducted a comparative analysis to assess the predictive power of image-based features, such as nuclear shape and texture, along with clinical features like Gleason score, for prostate cancer recurrence prediction. Lee et al. presented a data integration scheme that incorporated both imaging and non-imaging data, such as proteomics, to enhance prediction accuracy [4]. Expanding on this data integration approach, Golugula et al. proposed an extension that included histological images in combination with proteomics data. Additionally, methods utilizing graph-based features, including co-occurring organ tensors, co-occurring organ angularity, and cell cluster graphs, have been suggested for distinguishing between recurrent and non-recurrent cases. Texture-based features derived from image analysis, such as first-order statistical intensity and steerable orientation channels (e.g., Gabor channels), have also been explored for prostate cancer recurrence prediction. In a recent development, Lee et al. introduced a novel data integration approach known as supervised multi-view canonical correlation analysis (SMVCCA). This method combines data from histology images and proteomic tests to predict prostate cancer recurrence more accurately [7]. While many of the aforementioned methods employ supervised learning using H&E stained tissue images to assess the probability of prostate cancer recurrence in a patient, they heavily rely on hand-crafted features. These features often necessitate extensive human effort for selection and are typically dependent on expert knowledge. Moreover, these features often struggle to generalize effectively across diverse patient populations, and some may require manual parameter tuning (e.g., edge orientations in surface-based features) before application to new images. Consequently, it is crucial to develop methods for automatic feature extraction to enhance the predictive capabilities of prostate cancer recurrence algorithms [8]. With this motivation, we propose a deep learning-based approach that exclusively utilizes tissue images, without relying on any additional data sources, to distinguish between recurrent and non-recurrent patients who have undergone radical prostatectomy. We believe that through thorough training, the proposed model will demonstrate robust generalization capabilities on independent external validation datasets. Before introducing our proposed approach for predicting prostate cancer recurrence, we provide a concise overview of the compelling field of deep learning [9]. Convolutional neural networks (CNNs) have demonstrated outstanding performance in image classification tasks, particularly when a large amount of labeled data is available for training. Traditional machine learning approaches often required substantial effort to preprocess raw data, such as image pixels, into suitable feature vectors, often involving manual feature engineering. In contrast, deep learning architectures, such as CNNs, learn data representations automatically, capturing multiple levels of abstraction within hierarchical layers of artificial neurons. These advancements, combined with improved training algorithms and hardware capabilities, have significantly enhanced accuracy across various domains, including speech recognition, visual object recognition, drug discovery, and economics [10]. To harness the capabilities of deep CNNs in extracting meaningful representations from raw image data, the proposed approach aims to predict prostate cancer (PCa) recurrence using tissue images from patients who have undergone radical prostatectomy. By leveraging the power of deep CNNs, the algorithm automatically learns and extracts relevant features from the provided tissue images. The subsequent sections will provide a detailed explanation of the proposed algorithm [11].

Disadvantages of the Existing System:

  • It has less accuracy.
  • It has high time complexity.
  • The Existing System has less performance.
  • The Existing System has High Computational Cost.
  • The Existing System uses a lot of training data.

Applied Prototype (System)

The applied system is a hybrid CNN-Deep Learning technique (CNN-RNN) having the following extremely efficient advantages:

Pros of Applied Approach (System): Below are furnished Pros of the applied system:

  • The Proposed System has high accuracy.
  • The Proposed System has low time complexity.
  • The Proposed System has high performance.
  • The Proposed System reduces Computational costs.
  • The Proposed System applies extremely well with less training database (Figure 1).

fig 1

Figure 1: Architecture of modified CNN

Implementation Prototype

The term “System Design” refers to the process of specifying the strategy, components, interfaces, artifacts, and documents of a system in order to meet certain requirements. It can be thought of as the application of systems theory to component development and often involves collaboration between the fields of systems analysis, systems design, and systems engineering. The success of a system is measured by its performance or effectiveness in providing the desired output. Requirement analysis is a critical step in the development of a system, as accurate and complete requirement information is necessary to design a system that will function appropriately in the desired environment. It is the responsibility of the users of the system to provide this information, as they are the individuals who will ultimately use the system.

Project Implementation Details

System Modules

There are three modules

  • Collecting Data Sources
  • Processing data sets
  • Feature Learning

Collecting Data Sources: Here 2 types of data sets are used to perform risk assessment.

♦ Structured Data. It refers to records with an excessive degree of association, to such a quantity that incorporation in a social database is steady and right away seek with the aid of using basic, direct net crawler calculations or different hunt activities. This is normally visible in .csv format.

Here data set related to hypertension shown in the following table refers to attributes used to predict either high or low risk (Table 1).

Table 1: Structured data

Name

Data type
Age int
Sex int
cp(chest pain) int
Trestbps (threshold blood pressure) int
chold int
smoke int
Physical inactivity int
Thalach (max heart rate) int
Ckd (chronic kidney disease) int
Pt (potassium levels in mg/mol) int
Stress levels int
Ca (blood vessels) int
Thal (sugar levels) int
target int

♦ Unstructured Data. The text is typically composed of words and sentences, but it may also include information such as dates, numbers, and facts. As a result, it often contains inconsistencies and ambiguities, which make it difficult to analyze using standard methods compared to data stored in structured form in databases or semantically labeled documents.

This looks as in the following manner:

Preprocessing data sets:

  • Properties i.e., qualities thru sufferers are exaggerated and removed from the (fact) datasets.
  • Preferring perhaps getting rid of replica values and including lacking values
  • Each characteristic significance in affecting the affected person may be determined by the usage of correlation evaluation or among max combining phases.
  • Unstructured facts to established facts with designated goals.

Feature learning:

  • The extracted features are passed into modified CNN layers to train the neural network structure.
  • Then extract the high-level features from the modified CNN.
  • Modified CNN consists of the following layers in this model:
  1. Contribution values
  2. Hybrid Hidden values
  3. Production values
  • This neural network has parameters (w, b) = {h1, h2, h3, b1, b2, b3}
  • The accuracy rate is predicted.
  • The results demonstrate that our method has the advantage to infer the prediction of such fatal diseases over the other three methods (KNN, Decision Tree, and Gaussian Naive Bayes).

Here Implementation goes on with structured data and unstructured data and then the obtained results are compared with performance metrics. Through them can know the best method for predicting the risk of hypertension.

Implementing Using Structured Data

This is done using three MLA (Machine Learning Algorithms) [8] like DT i.e., Decision

Tree, KNN i.e., K-Nearest Neighbor, and NB i.e., Naive Bayes. For implementation can use Python and Implementation starts with,

  • Load the data set in .csv format.
  • Then pre-process the data to remove any noisy data.
  • Need to do some correlation analysis so that how attributes are related to the target class using heat map in correlation matrix with the diverging palette.
  • Then need to divide the data set for training purposes to build the model.
  • Like here the cardinality of the data set is 303*14. So, need to split the data by giving test size and random state. Here test size is mentioned as 33% so 203 rows for training the model and the rest 100 rows to test the model and this could be randomly divided as the random state is given.
  • Here while doing this splitting the target class must not be included as the model is developed using only attributes.
  • Then after splitting always scale the features after splitting the data set because we want to ensure that the validation data is isolated. This is because the validation data acts as new, unseen data. Any transformation on it will reduce its validity.
  • Now we are finally ready to create a model and train it. Remember that this is a two-class classification [15] problem. We need to select a classifier, not a regression.
  • Let us analyze three models, Decision Tree Classifier, Gaussian Naive Bayes Classifier, and KNN Classifier.

Decision Tree

Decision Tree builds a hybrid prototype that forecasts the output features based on characteristic splitting criterion here GINI index is used based on the calculation with respect to values tree is constructed using the following algorithm:

Input – Preparing related records and their associated group names; qualities, list, and competitor arrangement qualities; Attribute choice technique, a system to decide the parting rule comprises of parting trait and, conceivably, but fragmented-point or parting subsection.

1

Output: A DT (decision tree).

Approach:

KNN-Nearest Neighbor

The K-Nearest Neighbor (KNN) specification [15] is provided by the preparation dataset, & Nearest K-case in preparation informational collection is originated. KNN is essential to decide the estimation of separation and the optimal K-value. The information is standardized at first. At that point, we utilize the Euclidean separation to gauge the separation. With respect to the choice of attributes K, it has been found that the prototype is most efficient if the k=5. Hence, pick k=5. The algorithm is as follows:

Data Intake

Consider, D (dataset), is a dataset of experimental-related records and groups;

K-assumption value;

Data Result: KNN approach;

Approach:

  1. Begin
  2. K-value and simu() function initiation;
  3. Experiment with the provided dataset (D);
  4. Di is experimenting set and y is evaluating the dataset.
  5. Calculate simu(Di,y);
  6. The biggest K-scores of simu(Di,y);
  7. Calculate average of simu() for KNN (K-Nearest Neighbors);
  8. Assume average of simu() is larger than edge
  9. The cancer patient output is ‘y’;
  10. Else, not a cancer patient;
  11. Finished.

Naive Bayes

Naive Bayes classification [15] is a basic probabilistic classifier. It requires ascertaining the likelihood of highlighting traits. This algorithm is as follows:

Input: D: a set of tuples Output: Calculates probability with respect to every member of the target class.

2

Method. These models are now capable of ‘predicting’ whether a patient is at high risk or low risk of hypertension.

Implementing Using Unstructured Data

Implementation using unstructured data can be trained using Convolutional [20] Neural Network techniques.

Neural Networks

  • NN (Neural Networks) are mathematical computational components known as AN (Artificial Neurons).
  • Inspired by the Neural Activity of the brain, they try to replicate similar behavior mathematically. (Practically far simpler than neurons in the brain).
  • Neural Networks are better than traditional machine learning algorithms [8] and perform well with huge data.
  • Because the machine learning algorithm’s performance saturates when the data set size grows.
  • The input layer is where data is provided.
  • Each neuron in the input layer corresponds to the value of a feature.
  • The Hidden Layer is where computation takes place.
  • Simple Neural Networks generally contain 1 or 2 hidden layers

The output Layer generates the result of the neural network (Figure 2).

fig 2

Figure 2: Neural Network Showing Layers

Neuron

There are weights associated with each neuron. Consider the figure to below. There are three features namely X1, X2, X3 (Figure 3).

fig 3

Figure 3: Neuron Computation with Weights

Why not TRADITIONAL machine learning?

  • Data from medical [14] data centers is usually a huge data set.
  • As shown in the figure traditional machine learning algorithm’s performance saturates when the data set size grows.
  • There is an under-utilization of data in decision-making. Deep Learning [19] systems are built just for that purpose (Figure 4).

fig 4

Figure 4: Performance Comparison

Procedure

Implementing the modified CNN is through using unstructured data because this predicts with high accuracy. This entire computation could be seen in hidden layers. This could be done in five stages.

  • At first, unstructured data which consists of patient data is taken to create vector values.
  • Then the data is organized into 2 columns such that one with text and the other with target class to experiment on NN (Neural Network) model & test predictions using the vector values.

Step 1: Representation of Text Data

  1. Each word can be spoken to as a vector of numerical qualities (A section grid).
  2. All words spoken to RD-characteristics vector, if d = 50 i.e., we speak to each word as a section vector (segment framework) containing 50 lines.
  3. Presently the content can be spoken to by attaching the segment vectors meaning we stack up the section vectors one next to the other to make of network of measurement d x n. (Just words are stacked next to each other in a sentence).

Step 2: Convolution Layer of Text MCNN

  1. Start a pointer at position 1. (1st word)
  2. Expecting the pointer is at the position I, take words locations I-1, I-2, I, i+2, i+1.
  3. Transpose every one of them to shape push grids of 50 segments and annex them next to each other changing over them into a solitary column vector of size 960×960.
  4. Addition the pointer and change to a new line.
  5. For first, second, and n-1 and nth words we have openings i.e., for the essential word we don’t have two past words. In such a case fill them with zero vectors.
  6. Toward the finish of the above procedure, we get an nx250 network which is our convoluted grid.
  7. The weight network W1∈R100×250 is of size 100×250. This means we are expecting the neural system to separate 100 highlights for us.
  8. Presently we complete the accompanying computation.
    h1i,j =f(W1[i]·sj+b1)
  9. This is the speck result of lattices. b1 is a segmented network of 100 lines. The inclination is utilized to use to move the learning procedure.
  10. Without including it, it is the straightforward weighted whole of highlights and there is no learning procedure.
  11. We get a 100xn element chart h1.f is an actuation work that is utilized to get non-linearity. We utilized Tanh’s actuation work.
    h1 =(h1i,j)100×n

Step 3: POOL Layer of Text Modified CNN

  1. From the element diagram h1 which is 100xn dimensional, we pick the most extreme component in each line of the lattice acquiring 100 greatest qualities from each line.
  2. From these 100 qualities we develop a 100×1 lattice h2 (segment vector).
  3. The explanation of picking max pooling activity is that the job of each word in the content isn’t totally equivalent, by most extreme pooling we can pick the components which assume key jobs in the content.
  4. Before the finish of Step 3 we have separated 100 highlights from unstructured information.step 3

Step 4: Full Connection Layer of Text Modified CNN

  1. At that point give this grid as a contribution to a neural system that conveys the accompanying calculation which is like that of in sync 2. (dot result of networks).
  2. W3 is the weighted network of the full association layer and b3 is inclination.
    h3 =W3h2+b3

Step 5: Modified CNN Classifier

  1. Delicate max classifier as yield classifier which predicts the danger of the disease (high or low).
  2. This calculation goes with the sigmoid formula and calculates the probabilistic value to predict (Figure 5).

fig 5

Figure 5: Modified CNN Step-wise Implementation

The algorithm or step-by-step procedure is as follows:

# At first unstructured data is taken and sent to the word2Vec algorithm to create vector values.

  1. train    pd.read_csv(fname)
  2. train    train[“TEXT”]
  3. train    str.lower()
  4. corpus    str.split()
  5. patient corpus    Word2Vec(corpus, size=50,min_count=3)
  6. patientcorpus.save(‘patientwordvec’)

# Then data is organized so that one contains text and other with target class.

  1. X and Y  placeholder_value(n_W0,n_C0, n_y, n_H0,)
  2. Initialize_attributes()
  3. Propagation_forward(parameters, X)
  4. Calculate_cost(Y,Z2)
  5. FineTuner tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)
  6. inittf.Initializer_global_variables()
  7. tf.Session() of session:
  8. 9.1 session_run(initialization)
    9.2 epoch in range (epochs_num):
    9.2.1epcost 0.
    9.2.2 for j in range (0, m):
    9.2.2.1d j+1
    9.2.2.2 cost_tempsession_run([cost,optimizer],dictionary_feed={X:X_training[j:d][:][:][:],Y:Y_training[j:d][:]}
    9.2.2.3 epcost += temp_cost
  9. if print_cost == True
  10. 1print (“Cost after epoch%i:%f”% (epochs, epcost))
  11. if epochs% 1 == 0 and print_cost == True:
    12.1costs.append (epcost)
  12. correct_prediction = tf.equal(Z2, Y)
  13. predict_op           Z2
  14. Calculate accuracy using experimental dataset and evaluating dataset.

NN (Neural Network) Logical flow:

In this, can observe at first input value is sent to the neural network then based on weights convoluted matrix is built. Then predictions are made and adjusted by calculating the squared mean error means loss function. This loss score is done by using a gradient descent optimizer and then weights are updated again and prediction goes on (Figure 6).

fig 6

Figure 6: Logical Flow of Neural Network

Results

Analysis of Structured Data

The structured hypertension data set used for analysis is in the following Figures 7 and 8.

fig 7

Figure 7: Hypertension structured data

fig 8

Figure 8: Correlation Analysis

Here is the view of correlation analysis of attributes with target by knowing significant attributes with help of diverging palette. Before predicting scaling need to be done for the train data set and test data set. This could be seen in following Figure 9.

fig 9

Figure 9: Scaled Matrices for Training and Test data sets

Then the results that means predictions obtained by three algorithms [8] are shown in the following Figure 10:

fig 10

Figure 10: Decision Tree Results

Results of Decision Tree: Figure 10

Results of KNN: Figure 11

fig 11

Figure 11: KNN Results

Results of Naive Bayes: Figure 12.

fig 12

Figure 12: Naive Bayes Results

Analysis of Unstructured Data

The unstructured hypertension data set used for analysis to get numerical values for vector values is in following Figure 13. Then data need to be organized into the following way to experiment NN (Neural Network) dataset, test, and to predict (Figure 14). Then after training the neural network when the model is built sent to test set for predictions (Figure 15).

fig 13

Figure 13: Unstructured Data Sent to Word2Vec for Create Numerical Values

fig 14

Figure 14: Unstructured Data Processed to Structured Format

fig 15

Figure 15: Modified CNN Results

Validation Metrics

For the exhibition evaluation withinside the test. Initially, we suggest to calculate precision, accuracy, and recall.

  • Accuracy of Decision Tree: 71.0%
  • The precision of Decision Tree: 67.24137931034483%
  • The recall of Decision Tree: 79.59183673469387%
  • The F1-score of Decision Tree: 72.89719626168225%
  • The accuracy of KNN: 68.0%
  • The precision of KNN: 68.08510638297872%
  • The recall of KNN: 65.3061224489796%
  • The F1-score of KNN: 66.66666666666666%
  • The accuracy of NaiveBayes: 84.0%
  • The precision of NaiveBayes: 83.6734693877551%
  • The recall of NaiveBayes: 83.6734693877551%
  • The F1-score of NaiveBayes: 83.6734693877551%.

In organized information or structured data, the NB classification [15] is the best in test. In any case, it is likewise seen that we cannot precisely anticipate whether the patient is in a high hazard as indicated by the patient’s age, sex, clinical lab, and other organized information. In other words, on the grounds that cerebral dead tissue is an ailment with complex side effects, we can’t foresee where the patient is in a high-hazard gathering just in the light of these straightforward highlights.

  • The Train Accuracy of modified CNN: 1.0
  • The Test Accuracy of modified CNN: 90.90909090909091%
  • The precision of Modified CNN: 83.6734693877551%
  • The recall of Modified CNN: 83.6734693877551%
  • The F1-score of Modified CNN: 83.6734693877551% (Figure 16).

fig 16

Figure 16: Iteration vs. Cost

Taking everything into account, for illness chance demonstrating, the precision of hazard expectation relies Upon the various range spotlight of the medical [14] medical institution information, i.e., the higher is the detail portrayal of the ailment, the better the exactness will be. To find the precision rate can arrive at 90.00% to all the more likely assess the hazard. The following bar plots show the comparison: Figure 17.

fig 17

Figure 17: Accuracy Analysis of Different Algorithms

Research Implementation Time-Period

It takes few seconds for the execution. The mission execution additionally relies upon at the gadget overall performance. System overall performance is primarily based totally at the gadget software, gadget hardware and area to be had with inside the gadget (Figures 18-21 and Table 2).

fig 18

Figure 18: Prostate Cancer Data set

fig 19

Figure 19: Patients with and without prostate Cancer

fig 20

Figure 20: Roc Curve for accuracy

fig 21

Figure 21: Prostate Accuracy vs. Data Size

Table 2: Comparison of different models vs different sizes of the dataset

Model

Size

Top-1/Top-5 error

Layers

Model Description
Decision Tree

218

 53/90

8

 5 Conv + 3fc layer
Naive Bayes

440

 38.66/7.2454

13

 13 Conv + 3 fc layer
KNN

562

 27.3312/11.3214

16

 16 Conv + 3fc layer
CNN

668

 19.456/10.767

19

 19 Conv + 3fc layer
MCNN

740

 12.7878/8.357

21

 21 Conv + 3fc layer

Conclusion

The current research in the area of clinical big data analysis has not focused on both structured and unstructured types of information. The proposed framework outperforms several typical prediction algorithms in terms of accuracy, performance, and conjunction speed. This is achieved through a hybrid Convolutional Neural Networks approach that utilizes both structured and unstructured data from hospitals. Data mining and deep learning play critical roles in various fields including machine learning, artificial intelligence, and database systems, with the main aim of improving the performance of prediction models. The performance of the proposed algorithm was statistically compared with various approaches, including linear regression and SVM principals, and was found to be superior. Furthermore, there are several advanced strategies that can be used to further improve the accuracy of the model. Compared to other common prediction algorithms, our proposed algorithm achieved an accuracy of 91% with a faster assembly speed.

Conflicts of Interest

All authors involved in this research journal affirm that this article has no conflict of interest and has collectively contributed towards its goal and objectives.

Data Availability

The data used to support the findings of this study is available from the corresponding author upon request (head.research@bluecrest.edu.lr).

Funding

This research work was done independently by the authors who had not received any funds for it from Liberia Government because this country is in crisis and poor, and from the University.

Authors Contribution

Asadi Srinivasulu contributed to the conceptualization, references collection, data curation, formal analysis, methodology, software, data curation, investigation, resources, software selection, writing the original draft, methodology, supervision, writing review, and editing, project administration, visualization, and investigation. Participated in the formal analysis. Contributed to proofreading of the research article in data collection and environment, and participated in the plagiarism check and correction process. Saad Ali Alahmari contributed formal analysis, methodology, software, data curation, investigation, methodology, software, data curation, and investigation, and contributed analysis, visualization and data collection, plagiarism, paraphrasing, and diagrams.

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