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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Curators:

 

THE VOICE of Aviva Lev-Ari, PhD, RN

In this curation we wish to present two breaking through goals:

Goal 1:

Exposition of a new direction of research leading to a more comprehensive understanding of Metabolic Dysfunctional Diseases that are implicated in effecting the emergence of the two leading causes of human mortality in the World in 2023: (a) Cardiovascular Diseases, and (b) Cancer

Goal 2:

Development of Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics for these eight subcellular causes of chronic metabolic diseases. It is anticipated that it will have a potential impact on the future of Pharmaceuticals to be used, a change from the present time current treatment protocols for Metabolic Dysfunctional Diseases.

According to Dr. Robert Lustig, M.D, an American pediatric endocrinologist. He is Professor emeritus of Pediatrics in the Division of Endocrinology at the University of California, San Francisco, where he specialized in neuroendocrinology and childhood obesity, there are eight subcellular pathologies that drive chronic metabolic diseases.

These eight subcellular pathologies can’t be measured at present time.

In this curation we will attempt to explore methods of measurement for each of these eight pathologies by harnessing the promise of the emerging field known as Bioelectronics.

Unmeasurable eight subcellular pathologies that drive chronic metabolic diseases

  1. Glycation
  2. Oxidative Stress
  3. Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
  4. Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
  5. Membrane instability
  6. Inflammation in the gut [mucin layer and tight junctions]
  7. Epigenetics/Methylation
  8. Autophagy [AMPKbeta1 improvement in health span]

Diseases that are not Diseases: no drugs for them, only diet modification will help

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

Exercise will not undo Unhealthy Diet

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

These eight Subcellular Pathologies driving Chronic Metabolic Diseases are becoming our focus for exploration of the promise of Bioelectronics for two pursuits:

  1. Will Bioelectronics be deemed helpful in measurement of each of the eight pathological processes that underlie and that drive the chronic metabolic syndrome(s) and disease(s)?
  2. IF we will be able to suggest new measurements to currently unmeasurable health harming processes THEN we will attempt to conceptualize new therapeutic targets and new modalities for therapeutics delivery – WE ARE HOPEFUL

In the Bioelecronics domain we are inspired by the work of the following three research sources:

  1. Biological and Biomedical Electrical Engineering (B2E2) at Cornell University, School of Engineering https://www.engineering.cornell.edu/bio-electrical-engineering-0
  2. Bioelectronics Group at MIT https://bioelectronics.mit.edu/
  3. The work of Michael Levin @Tufts, The Levin Lab
Michael Levin is an American developmental and synthetic biologist at Tufts University, where he is the Vannevar Bush Distinguished Professor. Levin is a director of the Allen Discovery Center at Tufts University and Tufts Center for Regenerative and Developmental Biology. Wikipedia
Born: 1969 (age 54 years), Moscow, Russia
Education: Harvard University (1992–1996), Tufts University (1988–1992)
Affiliation: University of Cape Town
Research interests: Allergy, Immunology, Cross Cultural Communication
Awards: Cozzarelli prize (2020)
Doctoral advisor: Clifford Tabin
Fields: Developmental biology, synthetic biology
SOURCE
Most recent 20 Publications by Michael Levin, PhD
SOURCE
SCHOLARLY ARTICLE
The nonlinearity of regulation in biological networks
1 Dec 2023npj Systems Biology and Applications9(1)
Co-authorsManicka S, Johnson K, Levin M…
VIEW PDF10.1038/S41540-023-00273-W
3

3 total citations on Dimensions.

Article has an altmetric score of 20
SCHOLARLY ARTICLE
Toward an ethics of autopoietic technology: Stress, care, and intelligence
1 Sep 2023BioSystems231
Co-authorsWitkowski O, Doctor T, Solomonova E…
VIEW PDF10.1016/J.BIOSYSTEMS.2023.104964
Article has an altmetric score of 36
SCHOLARLY ARTICLE
Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion
14 Aug 2023Frontiers in Cell and Developmental Biology11:1087650
Co-authorsGrodstein J, McMillen P, Levin M
VIEW PDF10.3389/FCELL.2023.1087650
Article has an altmetric score of 22
SCHOLARLY ARTICLE
Correcting instructive electric potential patterns in multicellular systems: External actions and endogenous processes.
30 Jul 2023Biochim Biophys Acta Gen Subj1867(10):130440
Co-authorsCervera J, Levin M, Mafe S
VIEW MORE INFO10.1016/J.BBAGEN.2023.130440
Article has an altmetric score of 20
SCHOLARLY ARTICLE
Regulative development as a model for origin of life and artificial life studies
1 Jul 2023BioSystems229
Co-authorsFields C, Levin M
10.1016/J.BIOSYSTEMS.2023.104927
1

1 citation on Dimensions.

Article has an altmetric score of 19
SCHOLARLY ARTICLE
The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left–Right Functional Differences
1 Jul 2023International Journal of Molecular Sciences24(13)
Co-authorsMasuelli S, Real S, McMillen P…
VIEW PDF10.3390/IJMS241311121
Article has an altmetric score of 7
SCHOLARLY ARTICLE
Bioelectricidad en agregados multicelulares de células no excitables- modelos biofísicos
Jun 2023Revista Española de Física32(2)
Co-authorsCervera J, Levin M, Mafé S
SCHOLARLY ARTICLE
Bioelectricity: A Multifaceted Discipline, and a Multifaceted Issue!
1 Jun 2023Bioelectricity5(2):75
Co-authorsDjamgoz MBA, Levin M
10.1089/BIOE.2023.0022.EDITORIAL
SCHOLARLY ARTICLE
Control Flow in Active Inference Systems – Part I: Classical and Quantum Formulations of Active Inference
1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):235-245
Co-authorsFields C, Fabrocini F, Friston K…
10.1109/TMBMC.2023.3272150
2

2 total citations on Dimensions.

Article has an altmetric score of 1
SCHOLARLY ARTICLE
Control Flow in Active Inference Systems – Part II: Tensor Networks as General Models of Control Flow
1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):246-256
Co-authorsFields C, Fabrocini F, Friston K…
10.1109/TMBMC.2023.3272158
2

2 total citations on Dimensions.

Article has an altmetric score of 1
SCHOLARLY ARTICLE
Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology
1 Jun 2023Cellular and Molecular Life Sciences80(6)
Co-authorsLevin M
VIEW PDF10.1007/S00018-023-04790-Z
1

1 citation on Dimensions.

Article has an altmetric score of 61
SCHOLARLY ARTICLE
Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging
1 Jun 2023Drug Discovery Today28(6)
Co-authorsPio-Lopez L, Levin M
VIEW PDF10.1016/J.DRUDIS.2023.103585
1

1 citation on Dimensions.

Article has an altmetric score of 17
SCHOLARLY ARTICLE
Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging
Jun 2023DRUG DISCOVERY TODAY28(6):1-13
Co-authorsPio-Lopez L, Levin M
VIEW MORE INFO10.1016/J.DRUDIS.2023.103585THIS
SCHOLARLY ARTICLE
Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine
12 May 2023Patterns4(5)
Co-authorsMathews J, Chang A, Devlin L…
VIEW PDF10.1016/J.PATTER.2023.100737
3

3 total citations on Dimensions.

Article has an altmetric score of 18
SCHOLARLY ARTICLE
Making and breaking symmetries in mind and life
14 Apr 2023Interface Focus13(3)
Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z…
VIEW PDF10.1098/RSFS.2023.0015
Article has an altmetric score of 7
SCHOLARLY ARTICLE
The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis
14 Apr 2023Interface Focus13(3)
Co-authorsPio-Lopez L, Bischof J, LaPalme JV…
VIEW PDF10.1098/RSFS.2022.0072
1

1 citation on Dimensions.

Article has an altmetric score of 30
SCHOLARLY ARTICLE
The collective intelligence of evolution and development
Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications
Co-authorsWatson R, Levin M
VIEW PDF10.1177/26339137231168355
Article has an altmetric score of 65
SCHOLARLY ARTICLE
Bioelectricity of non-excitable cells and multicellular pattern memories: Biophysical modeling
13 Mar 2023Physics Reports1004:1-31
Co-authorsCervera J, Levin M, Mafe S
10.1016/J.PHYSREP.2022.12.003
2

2 total citations on Dimensions.

Article has an altmetric score of 25
SCHOLARLY ARTICLE
There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines
1 Mar 2023Biomimetics8(1)
Co-authorsBongard J, Levin M
VIEW PDF10.3390/BIOMIMETICS8010110
4

4 total citations on Dimensions.

Article has an altmetric score of 26
SCHOLARLY ARTICLE
Transplantation of fragments from different planaria: A bioelectrical model for head regeneration
7 Feb 2023Journal of Theoretical Biology558
Co-authorsCervera J, Manzanares JA, Levin M…
VIEW PDF10.1016/J.JTBI.2022.111356
SCHOLARLY ARTICLE
Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind
1 Jan 2023Animal Cognition
Co-authorsLevin M
VIEW PDF10.1007/S10071-023-01780-3
2

2 total citations on Dimensions.

Article has an altmetric score of 62
SCHOLARLY ARTICLE
Biological Robots: Perspectives on an Emerging Interdisciplinary Field
1 Jan 2023Soft Robotics
Co-authorsBlackiston D, Kriegman S, Bongard J…
10.1089/SORO.2022.0142
1

1 citation on Dimensions.

Article has an altmetric score of 56
SCHOLARLY ARTICLE
Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model
1 Jan 2023Entropy25(1)
Co-authorsShreesha L, Levin M
VIEW PDF10.3390/E25010131
5

5 total citations on Dimensions.

Article has an altmetric score of 16
SCHOLARLY ARTICLE
Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms (July, blac073, 2022)
1 Jan 2023BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY138(1):141
Co-authorsClawson WP, Levin M
VIEW PDFVIEW MORE INFO10.1093/BIOLINNEAN/BLAC116
SCHOLARLY ARTICLE
Future medicine: from molecular pathways to the collective intelligence of the body
1 Jan 2023Trends in Molecular Medicine
Co-authorsLagasse E, Levin M
10.1016/J.MOLMED.2023.06.007

THE VOICE of Dr. Justin D. Pearlman, MD, PhD, FACC

PENDING

THE VOICE of  Stephen J. Williams, PhD

Ten TakeAway Points of Dr. Lustig’s talk on role of diet on the incidence of Type II Diabetes

 

  1. 25% of US children have fatty liver
  2. Type II diabetes can be manifested from fatty live with 151 million  people worldwide affected moving up to 568 million in 7 years
  3. A common myth is diabetes due to overweight condition driving the metabolic disease
  4. There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
  5. Thirty percent of ‘obese’ people just have high subcutaneous fat.  the visceral fat is more problematic
  6. there are people who are ‘fat’ but insulin sensitive while have growth hormone receptor defects.  Points to other issues related to metabolic state other than insulin and potentially the insulin like growth factors
  7. At any BMI some patients are insulin sensitive while some resistant
  8. Visceral fat accumulation may be more due to chronic stress condition
  9. Fructose can decrease liver mitochondrial function
  10. A methionine and choline deficient diet can lead to rapid NASH development

 

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Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes

Posted in Academic Publishing, Advanced Computing Platform, AI Models in Healthcare, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, Big Data & Analytics, BioIT: BioInformatics, BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Blockchain Transactions System, Centers for Medicare & Medicaid Services, Computational Biology/Systems and Bioinformatics, Curation, Data Science, Health Care System by Country, HealthCare IT, Innovations, Intelligent Information Systems, Machine Learning, Machine learning in predicting type 2 diabetes, Scientific Publishing, Technology Advance Assessment of, tagged , , , , , , , , , on May 29, 2021| Leave a Comment »

Developing Machine Learning Models for Prediction of Onset of Type-2 Diabetes

Reporter: Amandeep Kaur, B.Sc., M.Sc.

A recent study reports the development of an advanced AI algorithm which predicts up to five years in advance the starting of type 2 diabetes by utilizing regularly collected medical data. Researchers described their AI model as notable and distinctive based on the specific design which perform assessments at the population level.

Machine Learning, big data and Artificial Intelligence in Healthcare System
Image source: https://healthinformatics.uic.edu/blog/machine-learning-in-healthcare/

The first author Mathieu Ravaut, M.Sc. of the University of Toronto and other team members stated that “The main purpose of our model was to inform population health planning and management for the prevention of diabetes that incorporates health equity. It was not our goal for this model to be applied in the context of individual patient care.”

Research group collected data from 2006 to 2016 of approximately 2.1 million patients treated at the same healthcare system in Ontario, Canada. Even though the patients were belonged to the same area, the authors highlighted that Ontario encompasses a diverse and large population.

The newly developed algorithm was instructed with data of approximately 1.6 million patients, validated with data of about 243,000 patients and evaluated with more than 236,000 patient’s data. The data used to improve the algorithm included the medical history of each patient from previous two years- prescriptions, medications, lab tests and demographic information.

When predicting the onset of type 2 diabetes within five years, the algorithm model reached a test area under the ROC curve of 80.26.

The authors reported that “Our model showed consistent calibration across sex, immigration status, racial/ethnic and material deprivation, and a low to moderate number of events in the health care history of the patient. The cohort was representative of the whole population of Ontario, which is itself among the most diverse in the world. The model was well calibrated, and its discrimination, although with a slightly different end goal, was competitive with results reported in the literature for other machine learning–based studies that used more granular clinical data from electronic medical records without any modifications to the original test set distribution.”

This model could potentially improve the healthcare system of countries equipped with thorough administrative databases and aim towards specific cohorts that may encounter the faulty outcomes.

Research group stated that “Because our machine learning model included social determinants of health that are known to contribute to diabetes risk, our population-wide approach to risk assessment may represent a tool for addressing health disparities.”

Sources:

https://www.cardiovascularbusiness.com/topics/prevention-risk-reduction/new-ai-model-healthcare-data-predict-type-2-diabetes?utm_source=newsletter

Reference:

Ravaut M, Harish V, Sadeghi H, et al. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. JAMA Netw Open. 2021;4(5):e2111315. doi:10.1001/jamanetworkopen.2021.11315 https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2780137

Other related articles were published in this Open Access Online Scientific Journal, including the following:

AI in Drug Discovery: Data Science and Core Biology @Merck &Co, Inc., @GNS Healthcare, @QuartzBio, @Benevolent AI and Nuritas

Reporters: Aviva Lev-Ari, PhD, RN and Irina Robu, PhD

https://pharmaceuticalintelligence.com/2020/08/27/ai-in-drug-discovery-data-science-and-core-biology-merck-co-inc-gns-healthcare-quartzbio-benevolent-ai-and-nuritas/

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

https://pharmaceuticalintelligence.com/2018/12/10/can-blockchain-technology-and-artificial-intelligence-cure-what-ails-biomedical-research-and-healthcare/

HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/01/18/healthcare-focused-ai-startups-from-the-100-companies-leading-the-way-in-a-i-globally/

AI in Psychiatric Treatment – Using Machine Learning to Increase Treatment Efficacy in Mental Health

Reporter: Aviva Lev- Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/06/04/ai-in-psychiatric-treatment-using-machine-learning-to-increase-treatment-efficacy-in-mental-health/

Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/05/10/vyasa-analytics-demos-deep-learning-software-for-life-sciences-at-bio-it-world-2018-vyasas-booth-632/

New Diabetes Treatment Using Smart Artificial Beta Cells

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2017/11/08/new-diabetes-treatment-using-smart-artificial-beta-cells/

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