Leaders in Pharmaceutical Business Intelligence (LPBI) Group

The Current Impact and Future of Technology within Cardiovascular Surgery

September 23, 2023 by Arav Gandhi

The Current Impact and Future of Technology within Cardiovascular Surgery

Reporter: Arav Gandhi, Research Assistant 2, Domain Content: Cardiovascular Diseases, Series A

Medical professionals have been able to explore new methods and strategies to tackle complex medical conditions, especially with the limitations of other pre-existing conditions. For instance, through recent cardiology advancements, if the patient requires a heart transplant due to heart failure disease and is unable to undergo a human donor heart transplant as a result of pre-existing disease conditions or existing internal bleeding complications, there is a greater alternative to leaving it untreated. Medical professionals developed alternatives to humman donor transplants. One such a solution is transplanting a genetically modified pig heart, a new advanced experimental procedure that has been used over recent cases. Researchers continue to develop solutions that not only presents an alternative to current methods but also continue to maximize the potential of medical devices technology and of our understanding of medicine.

Recently, cardiologists at Henry Ford Health Hospital found themselves as the first physicians in the United States to employ an investigational device to treat a patient with severe tricuspid regurgitation. Having never been experimented upon prior to the situation, the K-Clip Transvascular Tricuspid Repair System utilizes a corkscrew anchor, which then clips the ring-shaped region of the valve. Similar to most dire situations where new technology is used, the patient, an 85-year-old male, continued to experience worsening symptoms for an entire year. His tricuspid valve, key in ensuring blood flow to the right ventricle and then to the pulmonary valve, was enlarged from his condition, resulting in the mass of his heart tripling in size. Cardiologists were then prompted to either utilize the new procedure or go untreated. With optimism, the cardiologists selected the procedure and applied a unique approach of an incision through the neck to reduce further risks of opening the chest and placed the device using real-time 3D imaging and 4D modeling. The medical professionals followed a minimally invasive procedure through the neck in contrast to traditional open-heart surgery and effectively employed recent advancements in imaging and modeling to ensure precision when planting the device, a new artificial tricuspid valve. The patient was later reported to have experience improve in the valve condition and a significant decrease in leakage, along with an improvement in his overall quality of life.

As a result, researchers should continue to focus not only on understanding undiscovered diseases and complications but also on developing alternative solutions to resolve cases in which the best practice approach can not be applied.

With the advancements in technology, the true extent of its application can not be discovered without experimentation and the application of imaging and other devices to resolve certain conditions. Beyond the technology itself, the introduction of new methods allows for less costly treatment plans, aiding especially those who come from a low-income background and currently struggle to afford basic healthcare. In the united States they are covered by MedicAid at all ages and by Medicare at age 65 and beyond. This is not the case in many countries in the World excluding Europe. The overall development of the field of medicine through advancement of medical technologies can indirectly allow for a improvement to the overall Global health care delivery and ascertain an increased life expectancies. This is primarily true, chiefly, in developing countries where established surgeries to resolve complex medical conditions still have the ability to achieve life-changing quality of life and longevity.

To learn more about the topic, check out the article below.

SOURCE

Walter, Michael. “Cardiologists Use New Annular Clipping Device for First Time in Us to Treat Severe Tricuspid Regurgitation.” Cardiovascular Business, Innovate Healthcare, 15 Sept. 2023, cardiovascularbusiness.com/topics/clinical/interventional-cardiology/cardiologists-severe-tricuspid-regurgitation-valve-k-clip?utm_source=newsletter

13.5 Tricuspid Valve

13.5.1 First-in-Man Mitral Valve Repairs Device used for Tricuspid Valve Repair: Cardioband used by University Hospital Zurich Heart Team

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/10/13/first-in-man-mitral-valve-repairs-device-used-for-tricuspid-valve-repair-cardioband-used-by-university-hospital-zurich-heart-team/

13.5.2 Advances and Future Directions for Transcatheter Valves – Mitral and Tricuspid valve repair technologies now in development

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/06/advances-and-future-directions-for-transcatheter-valves-mitral-and-tricuspid-valve-repair-technologies-now-in-development/

SOURCE

Volume Six: Interventional Cardiology for Disease Diagnosis and Cardiac Surgery for Condition Treatment

Posted in Cardiac and Cardiovascular Surgical Procedures, Cardiovascular and Vascular Systems, Cardiovascular Research, Frontiers in Cardiology and Cardiovascular Disorders, Tricuspid Valve Repair | Tagged Breakthrough Devices, Carbohydrate metabolism in cardiovascular disease, Cardiology, cardiovascular, tricuspid regurgitation | Leave a Comment »

The Continued Impact and Possibilities of AI in Medical and Pharmaceutical Industry Practices

September 18, 2023 by aptubman

The Continued Impact and Possibilities of AI in Medical and Pharmaceutical Industry Practices

Reporter: Adam P. Tubman, MSc Biotechnology, Research Associate 3, Computer Graphics and AI in Drug Discovery

Researchers have been able to discover many ways to incorporate AI into the practices of healthcare, both in terms of medical healthcare and also in pharmaceutical drug development. For example, given the situation where a doctor provides an inaccurate diagnosis to a patient because the doctor had an incomplete or inaccurate medical record/history, AI presents a solution that has the potential to rapidly and correctly account for human error and predict the correct diagnosis based on the patterns identified in other patient’s medical history to disease diagnosis indication. In the pharmaceutical industry, companies are changing and expanding approaches to drug discovery and development given the possibilities that AI can offer. One company, Reverie Labs, located in Cambridge, MA, is a pharmaceutical company utilizing AI for application of machine learning and computational chemistry to discover new possible compounds to be used in the development of cancer treatments.

Today, AI uses have had many other applications in medicine including managing healthcare data and performing robotic surgery, both of which transform the in-person patient and doctor experience. AI has even been used to change in-person cancer patient experiences. For example, Freenome, a company in San Francisco, CA uses AI in initial screenings, blood tests and diagnostic tests when a patient is being initially tested for cancer. The hope is that this technology will aide in speeding up cancer diagnoses and lead to new treatment developments.

The future will continue to bring many possibilities of AI, provided an acceptable level of accuracy is still maintained by AI technologies and that the technology remains beneficial. If research continues to focus on diagnosing diseases at a faster rate given the potential human errors in having an inaccurate or incomplete medical record upon diagnosis, AI could provide an improved experience for patients given the quicker diagnosis and treatment combined with less time spent either treating the wrong underlying condition or not knowing what condition to treat when accounting for an incomplete medical record. If this technology is proven to be successful not just in theory, but in practice, technology would then be available and could be beneficially applied to all diagnoses and treatment plans, across the world.

However, the reality regarding AI development is that its evolution depends on how much human effort is involved in its development. Therefore, the world won’t know or see the full benefits of AI until it is developed and actively applied. Similarly, the impact that AI will have in medical and pharmaceutical practices won’t be known until scientists fully develop and apply the technologies. Many possibilities, including a possible drastic lowering of the cost for pharmaceutical drugs across the board once drugs are much more readily discovered and produced, may carry a profound benefit to patients who currently struggle to afford their own treatment plans. Additionally, unforeseen advances in the medicinal and pharmaceutical fields because of AI development will lead to unforeseen effects on the global economy and many other life changing variables for the entire world.

For more information on this topic, please check out the article below.

SOURCE

Daley, S. (2018). Artificial Intelligence in healthcare: 39 examples Improving the Future of Medicine. Built In. https://builtin.com/artificial-intelligence/artificial-intelligence-healthcare

Posted in AI Models in Healthcare, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Hospital-based Medical Innovations, Machine Learning | Tagged AI in drug discovery, AI in Healthcare diagnosis | Leave a Comment »

Regulatory T cells (Tregs) are important for sperm tolerance and male fertility

September 11, 2023 by Dr. Sudipta Saha

Regulatory T cells (Tregs) are important for sperm tolerance and male fertility

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Regulatory T cells (Tregs) are specialized immune cells that modulate tissue homeostasis. They are a specialized subset of T lymphocytes that function as suppressive immune cells and inhibit various elements of immune response in vitro and in vivo. While there are constraints on the number or function of Tregs which can be exploited to evoke an effective anti-tumor response, sufficient expansion of Tregs is essential for successful organ transplantation and for promoting tolerance of self and foreign antigens. Current studies have provided evidence that a defect in the number or function of Tregs contributes to the etiology of several reproductive diseases.

In the male reproductive tract, prevention of autoimmune responses against antigenic spermatozoa, while ensuring protection against stressors, is a key determinant of fertility. Using an autoimmunity-induced model, it was uncovered that the role of Tregs in maintaining the tolerogenic state of the testis and epididymis. The loss of tolerance induced an exacerbated immune cell infiltration and the development of anti-sperm antibodies, which caused severe male subfertility. By identifying immunoregulatory mechanisms in the testis and epididymis.

Tregs modulate tissue homeostatic processes and immune responses. Understanding tissue-Treg biology will contribute to developing precision-targeting treatment strategies. Here, it was reported that Tregs maintain the tolerogenic state of the testis and epididymis, where sperm are produced and mature. It was found that Treg depletion induces severe autoimmune orchitis and epididymitis, manifested by an exacerbated immune cell infiltration [CD4 T cells, monocytes, and mononuclear phagocytes (MPs)] and the development of anti-sperm antibodies (ASA).

In Treg-depleted mice, MPs increased projections toward the epididymal lumen as well as invading the lumen. ASA-bound sperm enhance sperm agglutination and might facilitate sperm phagocytosis. Tolerance breakdown impaired epididymal epithelial function and altered extracellular vesicle cargo, both of which play crucial roles in the acquisition of sperm fertilizing ability and subsequent embryo development. The affected mice had reduced sperm number and motility and severe fertility defects.

Deciphering these immunoregulatory mechanisms may lead to the development of therapies for infertility and identifying potential targets for immuno-contraception. Ultimately, such knowledge fills gaps related to reproductive mucosa, which is an understudied facet of human male health.

References:

https://www.pnas.org/doi/10.1073/pnas.2306797120

https://pubmed.ncbi.nlm.nih.gov/24048122/

https://pubmed.ncbi.nlm.nih.gov/34845322/

https://pubmed.ncbi.nlm.nih.gov/29648649/

Posted in Autoimmune Inflammatory DIseases, Developmental biology, Diagnostic Immunology, Human Antibody Response, Human Immune System in Health and in Disease, Immunodiagnostics, Immunology, Immunotherapy, Innovation in Immunology Diagnostics, Personal Health Applications: Tech Innovations serves HealhCare, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Preimplantation Genetic Diagnosis and Reproductive Genomics, Protection Against Autoimmune Disease, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, Tolerance-inducing Autoimmune Disease Therapeutics | Tagged autoimmune disease, autoimmunity, fertility, immune cells, infertility, Regulatory T cell, sperm tolerence, Tregs | Leave a Comment »

ChatGPT Chemistry Assistant for Text Mining and the Prediction of metal–organic framework (MOF) synthesis

August 27, 2023 by 2012pharmaceutical

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

Reporter: Aviva Lev-Ari, PhD, RN

ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis

Zhiling Zheng
Oufan Zhang
Christian Borgs
Jennifer T. Chayes
Omar M. Yaghi*

Cite this: J. Am. Chem. Soc. 2023, 145, 32, 18048–18062 Publication Date:August 7, 2023 https://doi.org/10.1021/jacs.3c05819 Copyright © 2022 American Chemical Society. This publication is licensed under these Terms of Use.https://pubs.acs.org/doi/10.1021/jacs.3c05819

Abstract

We use prompt engineering to guide ChatGPT in the automation of text mining of metal–organic framework (MOF) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT’s tendency to hallucinate information, an issue that previously made the use of large language models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different trade-offs among labor, speed, and accuracy. We deploy this system to extract 26 257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90–99%. Furthermore, with the data set built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions about chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines.

This publication is licensed for personal use by The American Chemical Society.

Concluding Remarks

Our research has successfully demonstrated the potential of LLMs, particularly GPT models, in the domain of chemistry research. We presented a ChatGPT Chemistry Assistant that includes three different but connected approaches to text mining with ChemPrompt Engineering: Process 3 is capable of conducting search and filtration, Processes 2 and 3 classify synthesis paragraphs, and Processes 1, 2, and 3 are capable of summarizing synthesis conditions into structured data sets. Enhanced by three fundamental principles of prompt engineering specific to chemistry text processing, coupled with the interactive prompt refinement strategy, the ChatGPT-based assistant has substantially advanced the extraction and analysis of the MOF synthesis literature, with precision, recall, and F1 scores exceeding 90%.

We elucidated two crucial insights from the data set of synthesis conditions. First, the data can be employed to construct predictive models for reaction outcomes, which shed light on the key experimental factors that influence the MOF crystallization process. Second, it is possible to create an MOF chatbot that can provide accurate answers based on text mining, thereby improving access to the synthesis data set and achieving a data-to-dialogue transition. This investigation illustrates the potential for rapid advancement inherent in ChatGPT and other LLMs as a proof of concept.

On a fundamental level, this study provides guidance on interacting with LLMs to serve as AI assistants for chemists, accelerating research with minimal prerequisite coding expertise and thus bridging the gap between chemistry and the realms of computational and data science more effectively. Through interaction and chatting, the code and design of experiments can be modified, democratizing data mining and enhancing the landscape of scientific research. Our work sets a foundation for further exploration and application of LLMs across various scientific domains, paving the way for a new era of AI-assisted chemistry research.

SOURCE

https://pubs.acs.org/doi/10.1021/jacs.3c05819

ChatGPT accelerates chemistry discovery for climate response, study shows

Yaghi said. “AI has transformed many other sectors of our society – commerce, banking, travel. Why not transform science?”

These datasets on the synergy of the highly-porous materials known as metal-organic frameworks (MOFs) will inform predictive models. The models will accelerate chemists’ ability to create or optimize MOFs, including ones that alleviate water scarcity and capture air pollution. All chemists – not just coders – can build these databases due to the use of AI-fueled chatbots.

To help them teach ChatGPT to generate accurate and relevant information, they modified an approach called “prompt engineering” into “ChemPrompt Engineering.” They developed prompts that avoided asking ChatGPT for made up or misleading content; laid out detailed directions that explained to the chatbot the context and format for the response; and provided the large language model a template or instructions for extracting data.

The chatbot’s literature review – and the experts’ approach – was successful. ChatGPT finished in a fraction of an hour what would have taken a student years to complete, said Borgs, BIDMaP’s director. It mined the synthetic conditions of MOFs with 95% accuracy, Yaghi said.

https://data.berkeley.edu/news/chatgpt-accelerates-chemistry-discovery-climate-response-study-shows

Posted in ChatGPT in Academic Education, ChatGPT, GPT-4, Deep Learning, Natural Language Processing (NLP) | Leave a Comment »

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

August 19, 2023 by 2012pharmaceutical

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

Glycation
Oxidative Stress
Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
Membrane instability
Inflammation in the gut [mucin layer and tight junctions]
Epigenetics/Methylation
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:

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)?
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:

Biological and Biomedical Electrical Engineering (B2E2) at Cornell University, School of Engineering https://www.engineering.cornell.edu/bio-electrical-engineering-0
Bioelectronics Group at MIT https://bioelectronics.mit.edu/
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…

VIEW PDF

10.1038/S41540-023-00273-W

SCHOLARLY ARTICLE

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

1 Sep 2023BioSystems231

Co-authorsWitkowski O, Doctor T, Solomonova E…

VIEW PDF

10.1016/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 PDF

10.3389/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

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10.1016/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

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…

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10.3390/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…

10.1109/TMBMC.2023.3272150

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

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

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10.1007/S00018-023-04790-Z

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

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10.1016/J.DRUDIS.2023.103585

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

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10.1016/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…

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10.1016/J.PATTER.2023.100737

SCHOLARLY ARTICLE

Making and breaking symmetries in mind and life

14 Apr 2023Interface Focus13(3)

Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z…

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10.1098/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…

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10.1098/RSFS.2022.0072

SCHOLARLY ARTICLE

The collective intelligence of evolution and development

Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications

Co-authorsWatson R, Levin M

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10.1177/26339137231168355

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

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

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10.3390/BIOMIMETICS8010110

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…

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10.1016/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 PDF

10.1007/S10071-023-01780-3

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

SCHOLARLY ARTICLE

Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model

1 Jan 2023Entropy25(1)

Co-authorsShreesha L, Levin M

VIEW PDF

10.3390/E25010131

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

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10.1093/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

25% of US children have fatty liver
Type II diabetes can be manifested from fatty live with 151 million people worldwide affected moving up to 568 million in 7 years
A common myth is diabetes due to overweight condition driving the metabolic disease
There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
Thirty percent of ‘obese’ people just have high subcutaneous fat. the visceral fat is more problematic
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
At any BMI some patients are insulin sensitive while some resistant
Visceral fat accumulation may be more due to chronic stress condition
Fructose can decrease liver mitochondrial function
A methionine and choline deficient diet can lead to rapid NASH development

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ChatGPT Searches and Advent of Meta Threads: What it Means for Social Media and Science 3.0

July 11, 2023 by sjwilliamspa

ChatGPT Searches and Advent of Meta Threads: What it Means for Social Media and Science 3.0

Curator: Stephen J. Williams, PhD

The following explains how popular ChatGPT has become and how the latest social media platforms, including Meta’s (FaceBook) new platform Threads, is becoming as popular or more popular than older social Platforms. In fact, since its short inception since last week (Threads launced 7/07/2023), Threads is threatening Twitter for dominance in that market.

The following is taken from an email from Charlie Downing Jones from journoreasearch.org and https://www.digital-adoption.com/ :

U.S. searches for ChatGPT overtake TikTok, Pinterest, and Zoom

Google searches for ChatGPT have overtaken TikTok in the U.S., jumping to 7.1 million monthly searches compared to 5.1 million
The term ‘ChatGPT’ is now one of the top 100 search terms in the U.S., ranking 92nd, according to Ahrefs data
ChatGPT is now searched more than most major social networks, including LinkedIn, Pinterest, TikTok, and Reddit

Analysis of Google search data reveals that online searches for ChatGPT, the popular AI chatbot, have overtaken most popular social networks in the U.S. This comes when search interest in artificial intelligence is at its highest point in history.

The findings by Digital-adoption.com reveal that US-based searches for ChatGPT have exploded and overtaken popular social networks, such as LinkedIn, Pinterest, and Tiktok, some by millions.

Ranking	Keyword	US Search Volume (Monthly)
1	Facebook	70,920,000
2	YouTube	69,260,000
3	Twitter	15,440,000
4	Instagram	12,240,000
5	ChatGPT	7,130,000
6	LinkedIn	6,990,000
7	Pinterest	5,790,000
8	TikTok	5,130,000
9	Reddit	4,060,000
10	Snapchat	1,280,000
11	WhatsApp	936,000

Since its release in November 2022, searches for ChatGPT have overtaken those of most major social networks. According to the latest June search figures by search tool Ahrefs, searches for ‘ChatGPT’ and ‘Chat GPT’ are made 7,130,000 times monthly in the U.S.

That’s more than the monthly search volume for most of the top ten social networks, including LinkedIn, Pinterest, and TikTok. TikTok is one of the largest growing social media apps, with 100 million users in just a year.

The term ‘ChatGPT’ is now one of the top 100 search terms in the U.S., ranking 92nd, according to Ahrefs data

Searches for ChatGPT have eclipsed other major networks in the U.S., such as Reddit, by millions.

Every day search terms such as ‘maps’ and ‘flights’ have even seen their search volumes pale compared to the rising popularity of ChatGPT. ‘Maps’ is currently searched 440,000 times less than the chatbot each month, and ‘Flights’ is now Googled 2.2 million times less.

2023 has been a breakout year for AI, as searches for the term have more than doubled from 17 million in January 2023 to 42 million in May. In comparison, there were 7.9 million searches in January 2022. There has been an 825% increase in searches for ‘AI’ in the US compared to the average over the last five years.

There is a correlation between the uptick and the public releases of accessible AI chatbots such as ChatGPT, released on November 30, 2022, and Bing AI and Google Bard, released in May 2023.

According to TikTok data, interest in artificial intelligence has soared tenfold since 2020, and virtual reality has more than tripled.

AI has been a big topic of conversation this year as accessible AI chatbots and new technologies were released and sparked rapid adoption, prompting tech leaders like Elon Musk to call for AI regulation.

A spokesperson from Digital-adoption.com commented on the findings: “There has been a massive surge in AI interest this year. Apple’s announcement of Vision Pro has captured audiences at the right time, when new AI technologies, like ChatGPT, have become accessible to almost anyone. The rapid adoption of ChatGPT is surprising, with it becoming one of the fastest-growing tools available”.

All data was gathered from Ahrefs and Google Trends.

If using this story, please include a link to https://www.digital-adoption.com/ who conducted this study. A linked credit allows us to keep supplying you with content that you may find useful in the future.

If you need anything else, please get in touch.

All the best,
Charlie Dowling-Jones

charlie.dowling-jones@journoresearch.org

Journo Research

Part of Search Intelligence Ltd. Company registered in England No. 09361526

Why LPBI Needs to consider the new Meta Threads Platform

From Barrons

Threads Hits 100 Million Users Faster Than ChatGPT. Now It Needs Them to Stay.

Adam ClarkFollow

Updated July 10, 2023 9:00 am ET / Original July 10, 2023 7:44 am ET

The launch of Meta Platforms’ Threads looks to have outpaced even the viral success of ChatGPT in terms of signing up users. The next challenge will be keeping them around.

Since its inception on Thursday 7/07/2023, Meta’s new Threads platform has been signing up new users at an alarming rate. On rollout date 5 million signed up, then 30 million by next morning and now as of today (7/1/2023) Threads has over 100 million signups. Compare that to Twitter’s 436 million users, of which are tweeting on average 25% less than a few years ago, and it is easy to see why many social media pundits are calling Threads the new Twitter killer app.

Here are a few notes from the New York Times podcast The Daily

The Daily

1 day ago

Will Threads Kill Twitter?

Play • 33 min



Last week, Meta, the parent company of Facebook and Instagram, released Threads, a social media platform to compete with Twitter. In just 16 hours, Threads was downloaded more than 30 million times.

Mike Isaac, who covers tech companies and Silicon Valley for The Times, explains how Twitter became so vulnerable and discusses the challenges Meta faces to create a less toxic alternative.

Guest: Mike Isaac, a technology correspondent for The New York Times.

Background reading:

Threads is on pace to exceed 100 million users within two months, a feat achieved only by ChatGPT.
Here’s what to know about Threads and how it differs from Twitter.

Here are a few notes from the podcast:

Mike Isaac lamented that Twitter has become user unfriendly for a host of reasons. These include:

The instant reply’guys’ – people who reply but don’t really follow you or your thread
Your followers or following are not pushed to top of thread
The auto bots – the automated Twitter bots
Spam feeds
The changes in service and all these new fees: Twitter push to monetize everything – like airlines

Elon Musk wanted to transform Twitter but his history is always cutting, not just trimming the excess but he is known to just eliminate departments just because he either doesn’t want to pay or CAN’T pay. With Twitter he gutted content moderation.

Twitter ad business is plumetting but Musk wants to make Twitter a subscription business (the Blue check mark)

Twitter only gets a couple of million $ per month from Twitter Blue but Musk has to pay billions to just pay the interest on Twitter loan for Twitter puchase years ago

It is known that Musk is not paying rent on some California offices (some are suggesting he defaulted on leases) and Musk is selling Tesla stock to pay for Twitter expenses (why TSLA stock has been falling … the consensus out there)

Twitter is largest compendium of natural language conversations and Musk wanted to limit bots from scraping Twitter data to do AI and NLP on Twitter threads. This is also a grievance from other companies… that these ‘scrapers’ are not paying enough for Twitter data. However as Mike asks why do the little Twitter user have to pay in either fees or cutbacks from service. (the reason why Elon is limiting viewing per day is to limit these bots from scraping Twitter for data)

Another problem is that Twitter does not have its own servers so pays a lot to Google and AWS for server space. It appears Elon and Twitter are running out of money.

META and THREADS

Zuckerberg has spent billions of infrastructure spending and created a massive advertising ecosystem. This is one of the thoughts behind his push and entry into this space. Zuckerberg actually wanted to but Twitter a decade ago.

Usage and growth: The launch of Threads was Thursday 7-07-23. There were 2 million initial signups and by next morning 30 million overnight. Today Monday 7-10-23 there are 100 million, rivaling Twitter’s 436 million accounts. And as Musk keeps canceling Twitter accounts, angering users over fees or usage restrictions, people are looking for a good platform. Mastedon in too technical and not having the adoption like Meta Threads is having. Mike Isaac hopes Threads will not go the way of Google Hangouts or Plus but Google strategy did not involve social media like Facebook.

Signup and issues: Signup on Threads is easy but you need to go through Instagram. Some people have concerns about having their instagram thread put on their Threads feed but Mike had talked to the people at Meta and they are working to allow users to keep the feeds separate, mainly because Meta understands that the Instgagram and Twitter social cultures are different and users may want to keep Threads more business-like.

Important issues for LPBI: Twitter had decided, by end of May 2023 to end their relationship with WordPress JetPack service, in which WordPress posts could automatically be posted to your Twitter account and feed. Twitter is making users like WordPress pay for this API and WordPress said it would be too expensive as Twitter is not making a flat fee but per usage fee. This is a major hindrance even though the Twitter social share button is still active on posts.

Initial conversations between META and WordPress have indicated META will keep this API service free for WordPress.

So a little background on Meta Threads and signup features from Meta (Facebook) website:

Takeaways

Threads is a new app, built by the Instagram team, for sharing text updates and joining public conversations.
You log in using your Instagram account and posts can be up to 500 characters long and include links, photos, and videos up to 5 minutes in length.
We’re working to soon make Threads compatible with the open, interoperable social networks that we believe can shape the future of the internet.

Mark Zuckerberg just announced the initial version of Threads, an app built by the Instagram team for sharing with text. Whether you’re a creator or a casual poster, Threads offers a new, separate space for real-time updates and public conversations. We are working toward making Threads compatible with the open, interoperable social networks that we believe can shape the future of the internet.

Instagram is where billions of people around the world connect over photos and videos. Our vision with Threads is to take what Instagram does best and expand that to text, creating a positive and creative space to express your ideas. Just like on Instagram, with Threads you can follow and connect with friends and creators who share your interests – including the people you follow on Instagram and beyond. And you can use our existing suite of safety and user controls.

Join the Conversation from Instagram

It’s easy to get started with Threads: simply use your Instagram account to log in. Your Instagram username and verification will carry over, with the option to customize your profile specifically for Threads.

Everyone who is under 16 (or under 18 in certain countries) will be defaulted into a private profile when they join Threads. You can choose to follow the same accounts you do on Instagram, and find more people who care about the same things you do. The core accessibility features available on Instagram today, such as screen reader support and AI-generated image descriptions, are also enabled on Threads.

Your feed on Threads includes threads posted by people you follow, and recommended content from new creators you haven’t discovered yet. Posts can be up to 500 characters long and include links, photos, and videos up to 5 minutes in length. You can easily share a Threads post to your Instagram story, or share your post as a link on any other platform you choose.

Tune Out the Noise

We built Threads with tools to enable positive, productive conversations. You can control who can mention you or reply to you within Threads. Like on Instagram, you can add hidden words to filter out replies to your threads that contain specific words. You can unfollow, block, restrict or report a profile on Threads by tapping the three-dot menu, and any accounts you’ve blocked on Instagram will automatically be blocked on Threads.

As with all our products, we’re taking safety seriously, and we’ll enforce Instagram’s Community Guidelines on content and interactions in the app. Since 2016 we’ve invested more than $16 billion in building up the teams and technologies needed to protect our users, and we remain focused on advancing our industry-leading integrity efforts and investments to protect our community.

Compatible with Interoperable Networks

Soon, we are planning to make Threads compatible with ActivityPub, the open social networking protocol established by the World Wide Web Consortium (W3C), the body responsible for the open standards that power the modern web. This would make Threads interoperable with other apps that also support the ActivityPub protocol, such as Mastodon and WordPress – allowing new types of connections that are simply not possible on most social apps today. Other platforms including Tumblr have shared plans to support the ActivityPub protocol in the future.

We’re committed to giving you more control over your audience on Threads – our plan is to work with ActivityPub to provide you the option to stop using Threads and transfer your content to another service. Our vision is that people using compatible apps will be able to follow and interact with people on Threads without having a Threads account, and vice versa, ushering in a new era of diverse and interconnected networks. If you have a public profile on Threads, this means your posts would be accessible from other apps, allowing you to reach new people with no added effort. If you have a private profile, you’d be able to approve users on Threads who want to follow you and interact with your content, similar to your experience on Instagram.

The benefits of open social networking protocols go well beyond the ways people can follow each other. Developers can build new types of features and user experiences that can easily plug into other open social networks, accelerating the pace of innovation and experimentation. Each compatible app can set its own community standards and content moderation policies, meaning people have the freedom to choose spaces that align with their values. We believe this decentralized approach, similar to the protocols governing email and the web itself, will play an important role in the future of online platforms.

Threads is Meta’s first app envisioned to be compatible with an open social networking protocol – we hope that by joining this fast-growing ecosystem of interoperable services, Threads will help people find their community, no matter what app they use.

What’s Next

We’re rolling out Threads today in more than 100 countries for iOS and Android, and people in those countries can download the app from the Apple App Store and Google Play Store.

In addition to working toward making Threads compatible with the ActivityPub protocol, soon we’ll be adding a number of new features to help you continue to discover threads and creators you’re interested in, including improved recommendations in feed and a more robust search function that makes it easier to follow topics and trends in real time.

Should Science Migrate over to Threads Instead of Twitter?

I have written multiple time of the impact of social media, Science and Web 2.0 and the new Science and Web 3.0 including

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Science Has A Systemic Problem, Not an Innovation Problem

It, as of this writing, appears it is not crucial that scientific institutions need to migrate over to Threads yet, although the impetus is certainly there. Many of the signups have of course been through Instagram (which is the only way to signup for now) and a search of @Threads does not show that large scientific organizations have signed up for now.

A search for NIH, NCBI, AACR, and Personalized Medicine Coalition or PMC which is the big MGH orgaization on personalized medicine appears to return nothing yet. Pfizer and most big pharma is on @Threads now but that is because they maintain a marketing thread on Instagram. How necessary is @Threads for communicating science over Science 3.0 platform remains to be seen. In addition, how will @Threads be used for real time scientific conference coverage? Will Meta be able to integrate with virtual reality?

Other articles of Note on this Open Access Scientific Journal Include:

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Science Has A Systemic Problem, Not an Innovation Problem

Relevance of Twitter.com forthcoming Payment System for Scientific Content Promotion and Monetization

Is It Time for the Virtual Scientific Conference?: Coronavirus, Travel Restrictions, Conferences Cancelled

Part One: The Process of Real Time Coverage using Social Media

Posted in Artificial Intelligence - General, ChatGPT at LPBI Group, ChatGPT, GPT-4, Curation, Natural Language Processing (NLP), Open Access Journals, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific & Biotech Conferences: Press Coverage | Tagged Artificial intelligence, ChatGPT, Facebook, Meta, real time conference coverage, science 3.0, science communication, standing in scientific community, Threads, Twitter, web 3.0 | Leave a Comment »

OpenAI and ChatGPT face unique legal challenges over CopyRight Laws

July 10, 2023 by sjwilliamspa

OpenAI and ChatGPT face unique legal challenges over CopyRight Laws

Reporter: Stephen J. Williams, PhD

In previous weeks on this page and on the sister page ChatGPT applied to Cancer & Oncology, a comparison between ChatGPT, OpenAI, and Google large language model based search reveals a major difference between the algorithms with repect to citation and author credit. In essence while Google returns a hyperlink to the information used to form an answer, ChatGPT and OpenAI are agnostic in crediting or citing the sources of information used to generate answers to queries. With ChatGPT the source data, or more specifically the training set used for the AI algorithm is never properly cited in the query results.

This, as outlined below, is making a big problem when it comes to copyright law and intelectual property. Last week a major lawsuit has been filed because of incorrect and citing, referencing, and attribution of ownership of intellectual property.

As Miles Klee reports in The Rolling Stone

“OpenAI faces allegations of privacy invasion and violating authors’ copyright — but this may be just the tip of the iceberg”

The burgeoning AI industry has just crossed another major milestone, with two new class-action lawsuits calling into question whether this technology violates privacy rights, scrapes intellectual property without consent and negatively affects the public at large. Experts believe they’re likely to be the first in a wave of legal challenges to companies working on such products. Both suits were filed on Wednesday and target OpenAI, a research lab consisting of both a nonprofit arm and a corporation, over ChatGPT software, a “large language model” capable of generating human-like responses to text input. One, filed by Clarkson, a public interest law firm, is wide-ranging and invokes the potentially “existential” threat of AI itself. The other, filed by the Joseph Saveri Law Firm and attorney Matthew Butterick, is focused on two established authors, Paul Tremblay and Mona Awad, who claim that their books were among those ChatGPT was trained on — a violation of copyright, according to the complaint. (Saveri and Butterick are separately pursuing legal action against OpenAI, GitHub and Microsoft over GitHub Copilot, an AI-based coding product that they argue “appears to profit from the work of open-source programmers by violating the conditions of their open-source licenses.”)

Saveri and Butterick’s latest suit goes after OpenAI for direct copyright infringement as well as violations of the Digital Millennium Copyright Act (DMCA). Tremblay (who wrote the novel The Cabin at the End of the World) and Awad (author of 13 Ways of Looking at a Fat Girl and Bunny) are the representatives of a proposed class of plaintiffs who would seek damages as well as injunctive relief in the form of changes to ChatGPT. The filing includes ChatGPT’s detailed responses to user questions about the plots of Tremblay’s and Awad’s books — evidence, the attorneys argue, that OpenAI is unduly profiting off of infringed materials, which were scraped by the chat bot. While the suits venture into uncharted legal territory, they were more or less inevitable, according to those who research AI tech and privacy or practice law around those issues.

“[AI companies] should have and likely did expect these types of challenges,” says Ben Winters, senior counsel at the Electronic Privacy Information Center and head of the organization’s AI and Human Rights Project. He points out that OpenAI CEO Sam Altman mentioned a few prior “frivolous” suits against the company during his congressional testimony on artificial intelligence in May. “Whenever you create a tool that implicates so much personal data and can be used so widely for such harmful and otherwise personal purposes, I would be shocked there is not anticipated legal fire,” Winters says. “Particularly since they allow this sort of unfettered access for third parties to integrate their systems, they end up getting more personal information and more live information that is less publicly available, like keystrokes and browser activity, in ways the consumer could not at all anticipate.”

Source: https://www.rollingstone.com/culture/culture-features/chatgtp-openai-lawsuits-copyright-artificial-intelligence-1234780855/

At the heart of the matter is ChatGPT and OpenAI use of ‘shadow libraries’ for AI training datasets, in which the lawsuit claims is illegal.

An article by Anne Bucher in topclassactions.com explains this:

Source: https://topclassactions.com/lawsuit-settlements/class-action-news/class-action-lawsuit-claims-chatgpt-uses-copyrighted-books-without-authors-consent/

They say that OpenAI defendants “profit richly” from the use of their copyrighted materials and yet the authors never consented to the use of their copyrighted materials without credit or compensation.

ChatGPT lawsuit says OpenAI has previously utilized illegal ‘shadow libraries’ for AI training datasets

Although many types of material are used to train large language models, “books offer the best examples of high-quality longform writing,” according to the ChatGPT lawsuit.

OpenAI has previously utilized books for its AI training datasets, including unpublished novels (the majority of which were under copyright) available on a website that provides the materials for free. The plaintiffs suggest that OpenAI may have utilized copyrighted materials from “flagrantly illegal shadow libraries.”

Tremblay and Awad note that OpenAI’s March 2023 paper introducing GPT-4 failed to include any information about the training dataset. However, they say that ChatGPT was able to generate highly accurate summaries of their books when prompted, suggesting that their copyrighted material was used in the training dataset without their consent.

They filed the ChatGPT class action lawsuit on behalf of themselves and a proposed class of U.S. residents and entities that own a U.S. copyright for any work used as training data for the OpenAI language models during the class period.

Earlier this year, a tech policy group urged federal regulators to block OpenAI’s GPT-4 AI product because it does not meet federal standards.

What is the general consensus among legal experts on generative AI and copyright?

From Bloomberg Law: https://www.bloomberglaw.com/external/document/XDDQ1PNK000000/copyrights-professional-perspective-copyright-chaos-legal-implic

Copyright Chaos: Legal Implications of Generative AI

Contributed by Shawn Helms and Jason Krieser, McDermott Will & Emery

Copyright Law Implications – The Ins and Outs

Given the hype around ChatGPT and the speculation that it could be widely used, it is important to understand the legal implications of the technology. First, do copyright owners of the text used to train ChatGPT have a copyright infringement claim against OpenAI? Second, can the output of ChatGPT be protected by copyright and, if so, who owns that copyright?

To answer these questions, we need to understand the application of US copyright law.

Copyright Law Basics

Based on rights in Article I, Section 8 of the Constitution, Congress passed the first copyright law in 1790. It has been amended several times. Today, US copyright law is governed by the Copyright Act of 1976. This law grants authors of original works exclusive rights to reproduce, distribute, and display their work. Copyright protection applies from the moment of creation, and, for most works, the copyright term is the life of the author plus 70 years after the author’s death. Under copyright law, the copyright holder has the exclusive right to make copies of the work, distribute it, display it publicly, and create derivative works based on it. Others who want to use the work must obtain permission from the copyright holder or use one of the exceptions to copyright law, such as fair use.

The purpose of copyright law is to incentivize authors to create novel and creative works. It does this by granting authors exclusive rights to control the use of their work, thus allowing them to financially benefit from their works. Copyright law also encourages the dissemination of knowledge by allowing others to use copyrighted works under certain conditions, such as through the fair use doctrine, which allows for limited use of copyrighted material for the purposes of criticism, commentary, news reporting, teaching, scholarship, or research. By protecting the rights of authors and creators while also allowing for the use of copyrighted works for the public benefit, copyright law aims to strike a balance between the interests of authors and the public.

Inputs – Training ChatGPT with Copyrighted Material

ChatGPT was trained on a large training dataset sourced from the internet, including a vast amount of text from websites, articles, books, social media posts, and academic papers. Importantly, ChatGPT was not aware of the source of the data, it only saw the text and learned the patterns and relationships between the words, phrases and sentences. The vast majority of the text used to train ChatGPT was certainly subject to copyright protection—except for text that is in the public domain like facts or discoveries or works whose term of copyright protection has ended.

Did the ChatGPT training process violate the copyright of the original authors? To answer that question we must determine: if during the training process OpenAI copied the underlying works—because copyright infringement requires an act of copying works that are protected by copyright; is the output of ChatGPT a “derivative work” under copyright law; and would such copying or production of a derivate work fall under the “fair use” exception of copyright law.

Copying During Training

We are not aware of publicly available information regarding whether, during the training process, the training text for ChatGPT was copied or simply referenced. Therefore, we asked ChatGPT and received this answer:

Prompt: During the ChatGPT training process, was the underlying training text copied into a OpenAI database?

Answer: During the training process, text from various sources is used to train the ChatGPT model. It is likely that the text was copied into an internal database at OpenAI for the purpose of training the model. However, the training text is typically preprocessed to remove any sensitive information such as personal identifiers, copyrighted text, etc. In addition, the text is also used to train the model and not stored in a database. The model uses the text to learn patterns and structure of the language and once the training is done, it doesn’t require the text anymore.

So, it seems that during the training process, text was copied. It is also interesting that ChatGPT stated that the training text was “preprocessed” to remove any copyrighted text. That seems highly unlikely since otherwise nearly all text would have been removed.

Is ChatGPT Output a Derivative Work?

Under US copyright law, the owner of a copyright has the exclusive right “to prepare derivative works based upon the copyrighted work.” A “derivative work” is “a work based upon one or more preexisting works.” ChatGPT is trained on preexisting works and generates output based on that training.

As Daniel Gervais, a professor at Vanderbilt Law School who specializes in intellectual property law, says, the definition of a derivative work under copyright law “could loosely be used as a definition of machine learning when applied to the creation of literary and artistic productions because AI machines can produce literary and artistic content (output) that is almost necessarily ‘based upon’ a dataset consisting of preexisting works.” Under this view, it seems that all ChatGPT output is a derivative work under copyright law.

On a related point, it is worth noting that in producing its output, ChatGPT is not “copying” anything. ChatGPT generates text based on the context of the input and the words and phrase patterns it was trained on. ChatGPT is not “copying” and then changing text.

What About Fair Use?

Let’s assume that the underlying text was copied in some way during the ChatGPT training process. Let’s further assume that outputs from Chatto are, at least sometimes, derivative works under copyright law. If that is the case, do copyright owners of the original works have a copyright infringement claim against OpenAI? Not if the copying and the output generation are covered by the doctrine of “fair use.” If a use qualifies as fair use, then actions that would otherwise be prohibited would not be deemed an infringement of copyright.

In determining whether the use made of a work in any particular case is a fair use, the factors include:

The purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes.
The nature of the copyrighted work.
The amount and substantiality of the portion used in relation to the copyrighted work as a whole.
The effect of the use upon the potential market for or value of the copyrighted work.

In this case, assuming OpenAI copied copyrighted text as part of the ChatGPT training process, such copying was not for a commercial purpose and had no economic impact on the copyright owner. Daniel Gervais says “it is much more likely than not” that training systems on copyrighted data will be covered by fair use.

In determining if a commercial use will be considered “fair use,” the courts will primarily look at the scope and purpose of the use and the economic impact of such use. Does the use in question change the nature of the underlying copyright material in some material way (described as a “transformative” use) and does it economically impact the original copyright holder?

Without a specific example, it is difficult to determine exactly if a resulting output from ChatGPT would be fair use. The fact that ChatGPT does not copy and has been trained on millions of underlying works, it seems likely most output would be fair use—without using significant portions of any one protected work. In addition, because of the vast corpus of text used to train ChatGPT, it seems unlikely that ChatGPT output will have a negative economic impact on any one copyright holder. But, given the capabilities of ChatGPT, that might not always be the case.

Imagine if you asked ChatGPT to “Write a long-form, coming of age, story in the style of J.K. Rowling, using the characters from Harry Potter and the Chamber of Secrets.” In that case, it would seem that the argument for fair use would be weak. This story could be sold to the public and could conceivably have a negative economic impact on J.K. Rowling. A person that wants to read a story about Harry Potter might buy this story instead of buying a book by J. K. Rowling.

Finally, it is worth noting that OpenAI is a non-profit entity that is a “AI research and deployment company.” It seems that OpenAI is the type of research company, and ChatGPT is the type of research project, that would have a strong argument for fair use. This practice has been criticized as “AI Data Laundering,” shielding commercial entities from liability by using a non-profit research institution to create the data set and train AI engines that might later be used in commercial applications.

Outputs – Can the Output of ChatGPT be Protected by Copyright

Is the output of ChatGPT protected by copyright law and, if so, who is the owner? As an initial matter, does the ChatGPT textual output fit within the definition of what is covered under copyright law: “original works of authorship fixed in any tangible medium of expression.”

The text generated by ChatGPT is the type of subject matter that, if created by a human, would be covered by copyright. However, most scholars have opined, and the US Copyright Office has ruled that the output of generative AI systems, like ChatGPT, are not protectable under US copyright law because the work must be an original, creative work of a human author.

In 2022, the US Copyright Office, ruling on whether a picture generated completely autonomously by AI could be registered as a valid copyright, stated “[b]because copyright law as codified in the 1976 Act requires human authorship, the [AI Generated] Work cannot be registered.” The U.S. Copyright Office has issued several similar statements, informing creators that it will not register copyright for works produced by a machine or computer program. The human authorship requirement of the US Copyright Office is set forth as follows:

The Human Authorship Requirement – The U.S. Copyright Office will register an original work of authorship, provided that the work was created by a human being. The copyright law only protects “the fruits of intellectual labor” that “are founded in the creative powers of the mind.” Trade-Mark Cases, 100 U.S. 82, 94 (1879).

While such policies are not binding on the courts, the stance by the US Copyright Office seems to be in line with the purpose of copyright law flowing from the Constitution: to incentivize humans to produce creative works by giving them a monopoly over their creations for a limited period of time. Machines, of course, need and have no such motivation. In fact, copyright law expressly allows a corporation or other legal entity to be the owner of a copyright under the “work made for hire” doctrine. However, to qualify as a work made for hire, the work must be either work prepared by an employee within the scope of his or her employment, or be prepared by a party who “expressly agrees in a written instrument signed by them that the work shall be considered a work made for hire.” Only humans can be employees and only humans or corporations can enter a legally binding contract—machines cannot.

Medicine with GPT-4 & ChatGPT

ChatGPT applied to Cancer & Oncology

ChatGPT applied to Medical Imaging & Radiology

ChatGPT applied to Cardiovascular diseases: Diagnosis and Management

The Use of ChatGPT in the World of BioInformatics and Cancer Research and Development of BioGPT by MIT

Posted in Artificial Intelligence - General, ChatGPT at LPBI Group, ChatGPT in Academic Education, ChatGPT, GPT-4, Disputes and Settlements, Intellectual Property, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, Law and Medicine Conflicts, Natural Language Processing (NLP) | Tagged AI, Artificial Intelligence ( AI), ChatGPT, ChatGPT-4, copyright law, Creative Commons, generative AI, Intellectual property, large language models, legal conflicts, Natural Language Processing (NLP), Open access, OpenAI | Leave a Comment »

World Medical Innovation Forum, June 12 – June 14, 2023 The Westin Boston Seaport District organized by Mass General Brigham (MGB)

June 25, 2023 by 2012pharmaceutical

World Medical Innovation Forum, June 12 – June 14, 2023 The Westin Boston Seaport District organized by Mass General Brigham (MGB)

Reporter: Aviva Lev-Ari, PhD, RN

FRONTIER OF MEDICINE, HEALTHCARE AND BIOTECH WAS PRESENTED AT World Medical Innovation Forum, June 12 – June 14, 2023 The Westin Boston Seaport District organized by Mass General Brigham (MGB) https://pharmaceuticalintelligence.com/2023/06/25/world-medical-innovation-forum-june-12-june-14-2023-the-westin-boston-seaport-district-organized-by-mass-general-brigham-mgb/ via @pharma_BI @AVIVA1950 #WMIF @MGBInnovation

Aviva Lev-Ari, PhD, RN

Aviva Lev-Ari, PhD, RN, Founder, LPBI Group: 1.0 & 2.0

Since 2015, I am attending the World Medical Innovation Forum (WMIF) on a granted Press Pass covering the event in Real Time.

In 2019, I was ranked #3 Influencer

Top 3 Ranked by Betweenness Centrality in Top 10 Influencers #WMIF19 @phsinnovation @evankirstel @aviva1950 – Twitter Analytics by NodeXL http://bit.ly/2KB6cPN for #WMIF19 by @PHSInnovation at World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 8-10, 2019

www.worldmedicalinnovation.org

https://pharmaceuticalintelligence.com/2019/04/11/top-3-ranked-by-betweenness-centrality-in-top-10-influencers-wmif19-phsinnovation-evankirstel-aviva1950-tweeter-analytics-by-nodexl-http-bit-ly-2kb6cpn-for-wmif19-by-phs/

NodeXL http://bit.ly/2KB6cPN

@symplur‘s #Healthcare Hashtags – Twitter Analytics published for http://bit.ly/WMIF19

In 2023, I was registered as Press for the event, World Medical Innovation Forum, June 12 – June 14, 2023

Due to an emergency I could not attend in-person.

Below, based on all the videos posted at

https://www.youtube.com/c/WorldMedicalInnovationForum

I am adding key concepts Of Note:

World Medical Innovation Forum

2023 FIRST LOOK

Mark Albers, MD, PhD

Frank Wilkens Jr and Family Endowed Scholar in AD Research, MGH; Assistant Professor of Neurology, HMS

TYK2 as a novel therapeutic target in a subset of Alzheimer’s Disease with neuroinflammation

Predictive Biomarkers for ALS & AD
Genetic validation: TYK2 – NADALS Basket Trial

Yakeel Quiroz, PhD

Director, Familial Dementia Neuroimaging Lab and Director, Multicultural Alzheimer’s Prevention Program, MGH; Paul B. and Sandra M. Edgerley MGH Research Scholar; Associate Professor, HMS

Untangling the role of rare genetic variant in protection against Alzheimer’s disease: From biomarkers to novel therapeutic targets

· Unmet needs of AD

· Familial AD research: sporadic late onset +65

· COLBOS Extreme – Protection against Dementia

· Regulation of amyloid formation: Amyloid Beta aggregation & uptake

· Neuronal Tau – mutation on Reelin gene

· Bind to heparin

· Reduce Tau phosphorylation

Tanya Mayadas, PhD

Senior Staff Scientist, BWH; Professor of Pathology, HMS Targeting neutrophils for T cell-mediated anti-tumor immunotherapy

Targeting neutrophils for T cell-mediated anti-tumor immunotherapy

Dendritic cells lack robust anti-tumor
Neutrophil CD16B with Antigen to generate APCs
AAC increases T cells and neutral killer (NK) cell infiltration
AAC enhances anti PD-1 increases activation and accumulation of memory TSCM and TRM cells
Translation – AAC combination therapy

Synho Do, PhD

Director, Lab of Medical Imaging and Computation, Mass General Brigham; Assistant Professor, HMS

SafeAI: Live Error-free or Die

AI Classifier
AI algorithms effective errors – accountability for AI mistakes
Error-free in normal cases vs in all cases
Scalability and large-scale data throughput
Continuous Learning AI: Auto-label open DBs
Repurposing AI: from traditional AI to SafeAI

Tatjana Jakobs, MD

Associate Scientist, Schepens Eye Research Institute of Mass Eye and Ear; Associate Professor of Ophthalmology, HMS

Astrocyte-derived SPP1 prevents age and glaucoma-related loss of vision

Age and glaucoma
Mining astrocytes for neuroprotective factors
Spp1 is expressed in response to optic nerve damage – is neuroprotective
Overexpression of Spp1 via AAV2
Visual acuity in glaucoma
Retinal ganglion cells- Spp1 as a noval target

Russell Jenkins, MD, PhD

Investigator, Center for Cancer Research, MGH; Assistant Professor of Medicine, HMS

Evaluating Novel Cancer Therapeutic Strategies Using Living Tumor Biopsies

Matastatic cancer (melanoma) cured with immunotherapy
PD-1 and PDL-1 pathways – Solid tumors
Clinical Trial landscape is very complex
Combination therapy Trials – Human tumor immunity: Check inhibitors -pus antiPD-1 – functional precision cancer medicine
Testing across cancer types & stages
Deprioritized ineffective strategies
ROCHE leads the way
PDOTS tumor-immune interactions

Clotilde Lagier-Tourenne, MD, PhD

Araminta Broch-Healey Endowed Chair in ALS, MGH; Associate Professor of Neurology, HMS

Targeting disruption of stathmin-2 in neurodegenerative diseases

ALS and FTD (Frontaltemporal Dementia): Overlapping neurodegenerative diseases
TDP-43: an RNA binding protein in abnormal slpicing of stathmin-2 (STMN2)
STMN2 – neuromuscular junctions and axonal regeneration
Statin-mediated increase in STMN2 RNA with neurite extension– compound that restore STMN2 in neurodegenerative disease

Kerry Ressler, MD, PhD

Chief Scientific Officer, McLean Hospital; Professor of Psychiatry, HMS

Preventing post-traumatic stress disorder: Novel pharmacological approaches based on the neuroscience of fear

Two drugs for PTSD – no biomarkers known
Neuroscience – fear: amygdala biology of trauma
Memory of fear – Trauma consolidation: car crush
AURORA study
Amygdala reactivity 3 month and 12 month later – efficacy of NKB-expressing cells – antagonismachieve fear expression reduced
Predictive analytics to prevent sequela of PTSD
Atomwise – new compounds

Fabrisia Ambrosio, PhD

Atlantic Charter Director of the Discovery Center for Musculoskeletal Recovery, Schoen Adams Research Institute, Spaulding Rehabilitation Hospital; Faculty, HMS

Extracellular vesicle engineering to counteract age-related cognitive declines

Age and exercise have circulating rejuvenating effects
Lifespan: decline physical and cognitive
Extracellular Vesicles (EV): information exchange reprogram distal cells
EV cross the BBB: functional memory, circulatory contribution
CNS application to benefit from research on EV – evaluate cognitive outcomes
Molecules: Klotin effect in the aging brain
Molecular cargo: optimize cargo engineering, dosing study and Kinetics

Li Chai, MD

Pathologist and Principal Investigator, BWH; Associate Professor of Pathology, HMS

Novel mechanism and compound targeting oncogenic transcription factor SALL4 in cancer

Transcription factors SALL4 – a “fetal-oncoprotein” in the liver – is present in wild variety of tumors
SALL4 knockout mice are resistant to tumor formation
Mechanism: MYC and PTEN known to drive cancer formation
Liver cancer HCC: high incidence globally, high mortality, no effective therapy
SALL4 target viability: CP1, a non-IMiD (IP protected) small molecule SALL4 degrader reduceing tumor growth in vivo — >> apoptosis

Angela Shen, MD / Marcela Maus, MD, PhD

Original Presenter: Marcela Maus, MD, PhD Director, Cellular Immunotherapy, MGH; Associate Professor, HMS Video Presenter: Angela Shen, MD Vice President, Strategic Innovation Leaders, MGB Innovation

Novel CAR-T cells engineered to overcome obstacles observed in the clinic

CAR-T next generation genetically-modified (CAR) T in patients with cancer and other diseases
New targets – CD37 and CD70 – B & T cell lymphoma, AML
TACI/BCMA – multiple myeloma
Target multiple targets – CD79b/CD19 – B cell lymphoma
CRISPR – solid tumors, novel proof of concept POC
Allo and auto rejection – Cloaking
CAR-TEAM [T cell Engaging Antibody Molecule]

Ralph Mazitschek, PhD

Ralph Mazitschek, PhD Principal Investigator, MGH; Assistant Professor, HMS

Unlocking aminoacyl-tRNA-synthetases as novel drug targets for first-in-class therapeutics

Aminoacyl tRNA-Syntherases (aaRSs) as grug target after the biology is known
Human aaRSspoised for small moledule inhibitors
No drug no progress no interest
Current aaRS inhibitor discovery & development: high throughput screening & medicinal chemistry campaign
Human prolyl-tRNA synthease: NCP26 targets cancer cells by Inhibiting multiple myeloma growth in vitro and in vivo
Many aaRS Isoforms lessons from Kinase Inhibitors

2023 WMIF | The Dr. Is In | Inflammation and Immunology: Balancing the Risks and Rewards

World Medical Innovation Forum

•

114 views • 12 days ago

Posted in Conference Coverage with Social Media, Scientific & Biotech Conferences: Press Coverage | Leave a Comment »

Revolutionizing Business with Apple Vision Pro: The Ultimate Tool for Enhanced Visual Communication

June 22, 2023 by FrasonKalapurackal

Revolutionizing Business with Apple Vision Pro: The Ultimate Tool for Enhanced Visual Communication

Reporter: Frason Francis Kalapurackal, BE

Image Source: https://www.trustedreviews.com/versus/apple-vision-pro-vs-meta-quest-3-4333959

In today’s fast-paced business world, effective visual communication is more important than ever. This is where Apple Vision Pro comes in, serving as the ultimate tool for enhancing visual communication in the workplace. With its innovative software solution, it revolutionizes the way businesses communicate by providing new and improved methods for creating, editing, and sharing visual content. Apple Vision Pro boasts a user-friendly interface and advanced features that have made it an indispensable asset for businesses seeking to streamline their visual communication processes and boost productivity. The software empowers businesses to generate professional-looking visuals that are easily shareable and foster collaboration, making it an invaluable tool for staying ahead in today’s competitive landscape.

Apple Vision Pro sets itself apart with cutting-edge features such as augmented reality and machine learning, enabling users to create immersive and informative content. Through this tool, businesses can effortlessly produce and distribute videos, presentations, and other visual materials, making it a must-have for business owners, marketers, and creative professionals alike.

The impact of Apple Vision Pro is transformative, revolutionizing how businesses communicate visually. With its advanced capabilities and intuitive interface, it empowers users to craft visually stunning content that not only captivates but also educates. The tool offers a diverse range of visual elements like charts, graphs, images, and videos, enabling the effective conveyance of complex information to audiences. Furthermore, Apple Vision Pro facilitates real-time collaboration, making it easier for teams to work together, generate content collectively, and share ideas seamlessly. These capabilities enable businesses to enhance their visual communication efforts and create more impactful content, ultimately driving them towards achieving their goals with greater efficiency and effectiveness.

Some of the potential applications of Apple Vision Pro:

Gaming: Vision Pro could be used for gaming by providing a more immersive experience.
Productivity: Vision Pro could be used for productivity applications by providing a more natural way to interact with computers.
Creative applications: Vision Pro could be used for creative applications by providing a more immersive way to create and edit content.
Education: Vision Pro could be used for education by providing a more immersive way to learn.
Training: Vision Pro could be used for training by providing a more immersive way to learn new skills.
Remote collaboration: Vision Pro could be used for remote collaboration by providing a more immersive way to work with others.

Source Link:

https://www.forbes.com/sites/traceyfollows/2023/06/15/apple-vision-pro-signals-another-move-into-digital-identity-for-apple/?sh=36a7cf9b63d8

https://hbr.org/2023/06/what-is-apples-vision-pro-really-for

https://www.zdnet.com/article/apples-vision-pro-a-concept-prototype-with-this-enormous-potential/

https://www.core77.com/posts/123826/What-are-the-Actual-Applications-for-Apples-Vision-Pro-Goggles

Optimism for Future Equality of Access to Healthcare in the Inaugural address as AMA President, Jesse M. Ehrenfeld, MD, MPH | AMA 2023 Annual Meeting of House of Delegates

June 14, 2023 by 2012pharmaceutical

Optimism for Future Equality of Access to Healthcare in the Inaugural address as AMA President, Jesse M. Ehrenfeld, MD, MPH | AMA 2023 Annual Meeting of House of Delegates

Reporter: Aviva Lev-Ari, PhD, RN

Jun 13, 2023 #physicianadvocacy #futureofhealthcare #medicine

In his inaugural address as AMA President, Jesse M. Ehrenfeld, MD, MPH, highlights the need for a more inclusive and equitable future in medicine. He shares personal experiences of discrimination and emphasizes the importance of advocacy, addressing health disparities, and fighting against disinformation to ensure equitable care for all patients.

Watch the video

https://www.youtube.com/watch?v=yXIF_lbg2FM

Learn more about the AMA Recovery Plan for America’s Physicians: https://bit.ly/AMA_RecoveryPlan Get exclusive updates on the AMA Annual meeting: https://bit.ly/AMA_Meeting2023 The AMA is your powerful ally in patient care. Join now. https://bit.ly/AMAJoinRenew — Follow the AMA on YouTube: @americanmedicalassociation 🎥 Watch AMA Update for the latest in health care news for physicians: https://bit.ly/AMA_Update ✉️ Subscribe to AMA Morning Rounds newsletter, with the news you need every morning delivered to your inbox: https://bit.ly/AMA-MorningRounds 🎧 Listen to our latest podcast episodes now: https://bit.ly/AMA_Podcasts 📱 Download AMA Connect for news, podcasts, video updates and learning in one place: https://apple.co/3URzNES #physicianadvocacy #futureofhealthcare #medicine #amarecoveryplan