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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:

• Cardiovascular Expertise – Dr. Justin D. Pearlman, MD, PhD, FACC
• Pharmacology and Oncology Expertise – Dr. Stephen J. Williams, PhD
• Principal Investigator, initiator of the project – Aviva Lev-Ari, PhD, RN

 

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

https://www.google.com/search?q=Michael+Levin+%40Tufts&oq=Michael+Levin+%40Tufts&aqs=chrome..69i57j69i58.894128314j0j15&sourceid=chrome&ie=UTF-8

Most recent 20 Publications by Michael Levin, PhD

SOURCE

https://facultyprofiles.tufts.edu/michael-levin-1/publications

SCHOLARLY ARTICLE

The nonlinearity of regulation in biological networks

1 Dec 2023npj Systems Biology and Applications9(1)

Co-authorsManicka S, Johnson K, Levin M…1 more

VIEW PDF10.1038/S41540-023-00273-W

3

SCHOLARLY ARTICLE

Toward an ethics of autopoietic technology: Stress, care, and intelligence

1 Sep 2023BioSystems231

Co-authorsWitkowski O, Doctor T, Solomonova E…2 more

VIEW PDF10.1016/J.BIOSYSTEMS.2023.104964

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

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

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

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…3 more

VIEW PDF10.3390/IJMS241311121

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…4 more

10.1109/TMBMC.2023.3272150

2

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…4 more

10.1109/TMBMC.2023.3272158

2

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

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

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…1 more

VIEW PDF10.1016/J.PATTER.2023.100737

3

SCHOLARLY ARTICLE

Making and breaking symmetries in mind and life

14 Apr 2023Interface Focus13(3)

Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z…3 more

VIEW PDF10.1098/RSFS.2023.0015

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…1 more

VIEW PDF10.1098/RSFS.2022.0072

1

SCHOLARLY ARTICLE

The collective intelligence of evolution and development

Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications

Co-authorsWatson R, Levin M

VIEW PDF10.1177/26339137231168355

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

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

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…1 more

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

SCHOLARLY ARTICLE

Biological Robots: Perspectives on an Emerging Interdisciplinary Field

1 Jan 2023Soft Robotics

Co-authorsBlackiston D, Kriegman S, Bongard J…1 more

10.1089/SORO.2022.0142

1

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

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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Al is on the way to lead critical ED decisions on CT

Posted in AI Models in Healthcare, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Cancer Screening, Crohn's disease, CT, Gastroenterology, Liver & Digestive Diseases Research, Machine Learning, tagged Artificial intelligence, Crohn's disease, CT scans, Emergency department, Gastroenterology, Machine Learning (ML), Tel Aviv University on July 31, 2021| Leave a Comment »

Al is on the way to lead critical ED decisions on CT

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Artificial intelligence (AI) has infiltrated many organizational processes, raising concerns that robotic systems will eventually replace many humans in decision-making. The advent of AI as a tool for improving health care provides new prospects to improve patient and clinical team’s performance, reduce costs, and impact public health. Examples include, but are not limited to, automation; information synthesis for patients, “fRamily” (friends and family unpaid caregivers), and health care professionals; and suggestions and visualization of information for collaborative decision making.

In the emergency department (ED), patients with Crohn’s disease (CD) are routinely subjected to Abdomino-Pelvic Computed Tomography (APCT). It is necessary to diagnose clinically actionable findings (CAF) since they may require immediate intervention, which is typically surgical. Repeated APCTs, on the other hand, results in higher ionizing radiation exposure. The majority of APCT performance guidance is clinical and empiric. Emergency surgeons struggle to identify Crohn’s disease patients who actually require a CT scan to determine the source of acute abdominal distress.

Aid seems to be on the way. Researchers employed machine learning to accurately distinguish these sufferers from Crohn’s patients who appear with the same complaint but may safely avoid the recurrent exposure to contrast materials and ionizing radiation that CT would otherwise wreak on them.

The study entitled “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department” was published on July 9 in Digestive and Liver Disease by gastroenterologists and radiologists at Tel Aviv University in Israel.

Retrospectively, Jacob Ollech and his fellow researcher have analyzed 101 emergency treatments of patients with Crohn’s who underwent abdominopelvic CT.

They were looking for examples where a scan revealed clinically actionable results. These were classified as intestinal blockage, perforation, intra-abdominal abscess, or complex fistula by the researchers.

On CT, 44 (43.5 %) of the 101 cases reviewed had such findings.

Ollech and colleagues utilized a machine-learning technique to design a decision-support tool that required only four basic clinical factors to test an AI approach for making the call.

The approach was successful in categorizing patients into low- and high-risk groupings. The researchers were able to risk-stratify patients based on the likelihood of clinically actionable findings on abdominopelvic CT as a result of their success.

Ollech and co-authors admit that their limited sample size, retrospective strategy, and lack of external validation are shortcomings.

Moreover, several patients fell into an intermediate risk category, implying that a standard workup would have been required to guide CT decision-making in a real-world situation anyhow.

Consequently, they generate the following conclusion:

We believe this study shows that a machine learning-based tool is a sound approach for better-selecting patients with Crohn’s disease admitted to the ED with acute gastrointestinal complaints about abdominopelvic CT: reducing the number of CTs performed while ensuring that patients with high risk for clinically actionable findings undergo abdominopelvic CT appropriately.

Main Source:

Konikoff, Tom, Idan Goren, Marianna Yalon, Shlomit Tamir, Irit Avni-Biron, Henit Yanai, Iris Dotan, and Jacob E. Ollech. “Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department.” Digestive and Liver Disease (2021). https://www.sciencedirect.com/science/article/abs/pii/S1590865821003340

Other Related Articles published in this Open Access Online Scientific Journal include the following:

Al App for People with Digestive Disorders

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/24/ai-app-for-people-with-digestive-disorders/

Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/05/04/machine-learning-ml-in-cancer-prognosis-prediction-helps-the-researcher-to-identify-multiple-known-as-well-as-candidate-cancer-diver-genes/

Al System Used to Detect Lung Cancer

Reporter: Irina Robu, Ph.D.

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

Artificial Intelligence: Genomics & Cancer

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

Yet another Success Story: Machine Learning to predict immunotherapy response

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

https://pharmaceuticalintelligence.com/2021/07/06/yet-another-success-story-machine-learning-to-predict-immunotherapy-response/

Systemic Inflammatory Diseases as Crohn’s disease, Rheumatoid Arthritis and Longer Psoriasis Duration May Mean Higher CVD Risk

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/10/09/systemic-inflammatory-diseases-as-crohns-disease-rheumatoid-arthritis-and-longer-psoriasis-duration-may-mean-higher-cvd-risk/

Autoimmune Inflammatory Bowel Diseases: Crohn’s Disease & Ulcerative Colitis: Potential Roles for Modulation of Interleukins 17 and 23 Signaling for Therapeutics

Curators: Larry H Bernstein, MD FCAP and Aviva Lev-Ari, PhD, RN https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/

Inflammatory Disorders: Inflammatory Bowel Diseases (IBD) – Crohn’s and Ulcerative Colitis (UC) and Others

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/gama-delta-epsilon-gde-is-a-global-holding-company-absorbing-lpbi/subsidiary-5-joint-ventures-for-ip-development-jvip/drug-discovery-with-3d-bioprinting/ibd-inflammatory-bowl-diseases-crohns-and-ulcerative-colitis/

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