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Ozempic and other GLP1 agonists are associated with a reduced risk of developing cirrhosis and liver cancer in people with type 2 diabetes and chronic liver disease, according to a nationwide study from Karolinska Institutet in Sweden published in the journal Gut.
GLP1 agonists like Ozempic reduce blood sugar levels and are mainly used to treat type 2 diabetes. However, as the drug also reduces appetite, it is now increasingly used to treat obesity and has become a popular weight-loss drug.
Reduced risk of liver damage
Results from early clinical trials also suggest that GLP1 agonists may reduce the risk of liver damage. Therefore, researchers at Karolinska Institutet included all people in Sweden with chronic liver disease and type 2 diabetes in a register-based study. They then compared the risk of severe liver damage in those who were treated with GLP1 agonists and those who were not. The results show that those who took the drug for a long period of time had a lower risk of later developing more severe forms of liver disease such as cirrhosis and liver cancer.
According to the researchers, this suggests that GLP1 agonists could be an effective treatment to avoid severe liver disease in people with concurrent type 2 diabetes.
“Fatty liver disease is estimated to affect up to one in five people in Sweden, many of whom have type 2 diabetes, and about one in twenty develop severe liver disease,” says first author Axel Wester, assistant professor at the Department of Medicine, Huddinge, Karolinska Institutet. “Our findings are interesting because there are currently no approved drugs to reduce this risk.”
Many of the people in the study stopped taking GLP1 agonists, resulting in a lack of protective effect. However, those who continued taking their medication over a ten-year period were half as likely to develop severe liver disease.

Need to be confirmed
“The results need to be confirmed in clinical trials, but it will take many years for these studies to be completed,” says Axel Wester. “Therefore, we use existing registry data to try to say something about the effect of the drugs before that.”
A limitation of the method is that it is not possible to control for factors for which there is no data, such as blood tests to describe the severity of liver disease in more detail. However, the researchers have recently built a new database called HERALD where they have access to blood samples from patients in Region Stockholm.
“As a next step, we will investigate the effect of GLP1 agonists in this database,” says the study’s last author Hannes Hagström, consultant in hepatology at the Karolinska University Hospital and adjunct professor at the Department of Medicine, Huddinge, Karolinska Institutet. “If we get similar results, it would further strengthen the hypothesis that GLP1 agonists can be used to reduce the risk of severe liver disease.”
The research was mainly funded by Region Stockholm (CIMED), the Swedish Research Council and the Swedish Cancer Society. Hannes Hagström’s research group has received funding from Astra Zeneca, EchoSens, Gilead, Intercept, MSD, Novo Nordisk and Pfizer, although no industry-supported funding was obtained for this specific study.

An interdisciplinary collaboration used a cutting-edge form of RNA tagging to map the gene expression that occurs during follicle maturation and ovulation in mice.
The resulting atlas reveals a slew of previously unknown cellular and molecular interactions that drive ovulation, which is crucial for female fertility. The findings could prove pivotal for developing therapeutic treatments for infertility.
The research, published Jan. 22 in Proceedings of the National Academy of Sciences, was led by Iwijn De Vlaminck, associate professor of biomedical engineering in Cornell Engineering, and Yi Ren, assistant professor of animal science in the College of Agriculture and Life Sciences. The paper’s lead author is Madhav Mantri, Ph.D. ’23, now a postdoctoral researcher at Stanford University.
De Vlaminck previously used the imaging method, high-resolution spatiotemporal transcriptomics, to survey the entire spectrum of RNA in mouse tissues, which showed the role of elusive RNA in skeletal muscle regeneration and viral myocarditis. Transcriptomics essentially converts RNA into DNA copies, which are tagged with barcodes that capture their spatial location — data that can then be sequenced into an image.
In 2022, De Vlaminck gave a presentation on the myocarditis findings at the 2nd Intercampus Immunology Symposium, which Ren attended. She was intrigued by De Vlaminck’s approach and wondered if it could be applied to one of her chief interests: unraveling the cellular and molecular mechanisms that regulate ovulation.
Ovulation requires accurate coordination between female germ cells, called oocytes, and their release via the rupture of ovarian follicles, which provide the environment for oocytes to grow and mature. In mice, this rupture occurs every four to five days; in women, it’s approximately every four weeks. Oocytes expire quickly once they depart the ovary, so the timing of their release is critical.
“Ovarian follicles are like launching pads, and the ovary is like the ground control. Together they prepare the eggs for fertilization at the right time and right location,” Ren said. “All the different cell types in the ovary must work together through an amazingly complex and dynamic ‘social network’ that involves intricated communication between all cells. That’s the power of Iwijn’s technology. It combines high resolution in both time and space. So those two really capture the essence of ovulation.”
In the years since De Vlaminck’s myocarditis study, the spatial resolution of transcriptomics has significantly improved, from 100 micrometers to 10 microns per pixel — a tenfold enhancement that has resulted in near single-cell resolution. The flip side to obtaining so much data, however, is that parsing it all is daunting.

“We had about 10 images and we spent a good 10 months making sense of them,” De Vlaminck said.
For each image, the researchers sequenced hundreds of millions of DNA molecules, then translated them into a matrix of gene expression. Every pixel contained the expression level of all 22,000 protein-coding genes in the mouse genome. Multiply that by approximately 100,000 pixels. And that was only the beginning.
“You have to turn that data into biological findings, look at temporal patterns, fish out specialized cell states and so on,” De Vlaminck said. “It’s not just like a normal microscopy image where you have the image, and that’s it, you see what you see.”
Among the findings, the atlas reveals that roughly one hour before an egg is released, the follicles undergo an additional layer of selection to determine which ones will ovulate. This acute process had never been identified before, and when it goes awry, it may lead to reduced ovulation rates and could hinder fertility. The researchers were also able to detect early differentiation markers that decide the different paths cells may take in the ovary. In effect, the atlas captures dynamic cellular and molecular control programs in both the very early and very late stages of ovulation.
“This type of atlas provides so much more detail about where and when all the molecular changes happen in the ovary, details that were difficult to capture using other methodologies,” De Vlaminck said. “So that may inspire new interventions that target specific molecules we identify, for example, specific genes that are important for fertility management.”
Now De Vlaminck and Ren, who are both faculty with the Cornell Reproductive Sciences Center, plan to extend their collaboration into exploring fertility and ovulation problems associated with obesity and reproductive aging.

In the U.S. alone, more than 10% of infertility cases are caused by ovulation failure, the researchers noted, a problem that is exacerbated by increasing obesity, and maternal age.
“There is a growing interest at Cornell in these types of problems, where we can use cutting-edge engineering principles for reproductive medicine — an area where those cutting-edge tools are not used as much, or as early, as in some other fields, like cancer biology, for instance,” De Vlaminck said.
Co-authors include doctoral student Hanxue Hannah Zhang and Emmanuel Spanos ’24.
The research was supported by the Cornell Center of Vertebrate Genomics and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Human pathologists are extensively trained to detect when tissue samples from one patient mistakenly end up on another patient’s microscope slides (a problem known as tissue contamination). But such contamination can easily confuse artificial intelligence (AI) models, which are often trained in pristine, simulated environments, reports a new Northwestern Medicine study.
“We train AIs to tell ‘A’ versus ‘B’ in a very clean, artificial environment, but, in real life, the AI will see a variety of materials that it hasn’t trained on. When it does, mistakes can happen,” said corresponding author Dr. Jeffery Goldstein, director of perinatal pathology and an assistant professor of perinatal pathology and autopsy at Northwestern University Feinberg School of Medicine.
“Our findings serve as a reminder that AI that works incredibly well in the lab may fall on its face in the real world. Patients should continue to expect that a human expert is the final decider on diagnoses made on biopsies and other tissue samples. Pathologists fear — and AI companies hope — that the computers are coming for our jobs. Not yet.”
In the new study, scientists trained three AI models to scan microscope slides of placenta tissue to (1) detect blood vessel damage; (2) estimate gestational age; and (3) classify macroscopic lesions. They trained a fourth AI model to detect prostate cancer in tissues collected from needle biopsies. When the models were ready, the scientists exposed each one to small portions of contaminant tissue (e.g. bladder, blood, etc.) that were randomly sampled from other slides. Finally, they tested the AIs’ reactions.
Each of the four AI models paid too much attention to the tissue contamination, which resulted in errors when diagnosing or detecting vessel damage, gestational age, lesions and prostate cancer, the study found.
The findings were published earlier this month in the journal Modern Pathology. It marks the first study to examine how tissue contamination affects machine-learning models.
‘For a human, we’d call it a distraction, like a bright, shiny object’
Tissue contamination is a well-known problem for pathologists, but it often comes as a surprise to non-pathologist researchers or doctors, the study points out. A pathologist examining 80 to 100 slides per day can expect to see two to three with contaminants, but they’ve been trained to ignore them.

When humans examine tissue on slides, they can only look at a limited field within the microscope, then move to a new field and so on. After examining the entire sample, they combine all the information they’ve gathered to make a diagnosis. An AI model performs in the same way, but the study found AI was easily misled by contaminants.
“The AI model has to decide which pieces to pay attention to and which ones not to, and that’s zero sum,” Goldstein said. “If it’s paying attention to tissue contaminants, then it’s paying less attention to the tissue from the patient that is being examined. For a human, we’d call it a distraction, like a bright, shiny object.”
The AI models gave a high level of attention to contaminants, indicating an inability to encode biological impurities. Practitioners should work to quantify and improve upon this problem, the study authors said.
Previous AI scientists in pathology have studied different kinds of image artifacts, such as blurriness, debris on the slide, folds or bubbles, but this is the first time they’ve examined tissue contamination.
‘Confident that AI for placenta is doable’
Perinatal pathologists, such as Goldstein, are incredibly rare. In fact, there are only 50 to 100 in the entire U.S., mostly located in big academic centers, Goldstein said. This means only 5% of placentas in the U.S. are examined by human experts. Worldwide, that number is even lower. Embedding this type of expertise into AI models can help pathologists across the country do their jobs better and faster, Goldstein said.
“I’m actually very excited about how well we were able to build the models and how well they performed before we deliberately broke them for the study,” Goldstein said. “Our results make me confident that AI evaluations of placenta are doable. We ran into a real-world problem, but hitting that speedbump means we’re on the road to better integrating the use of machine learning in pathology.”

Researchers from Cleveland Clinic and IBM have published a strategy for identifying new targets for immunotherapy through artificial intelligence (AI). This is the first peer-reviewed publication from the two organizations’ Discovery Accelerator partnership, designed to advance research in healthcare and life sciences.
The team worked together to develop supervised and unsupervised AI to reveal the molecular characteristics of peptide antigens, small pieces of protein molecules immune cells use to recognize threats. Project members came from diverse groups led by Cleveland Clinic’s Timothy Chan, M.D., Ph.D., as well as IBM’s Jeff Weber, Ph.D., Senior Research Scientist, and Wendy Cornell, Ph.D., Manager and Strategy Lead for Healthcare and Life Sciences Accelerated Discovery .
“In the past, all our data on cancer antigen targets came from trial and error,” says Dr. Chan, chair of Cleveland Clinic’s Center for Immunotherapy and Precision Immuno-Oncology and Sheikha Fatima Bint Mubarak Endowed Chair in Immunotherapy and Precision Immuno-Oncology. “Partnering with IBM allows us to push the boundaries of artificial intelligence and health sciences research to change the way we develop and evaluate targets for cancer therapy.”
For decades, scientists have been researching how to better identify antigens and use them to attack cancer cells or cells infected with viruses. This task has proved challenging because antigen peptides interact with immune cells based on specific features on the surface of the cells, a process which is still not well understood. Research has been limited by the sheer number of variables that affect how immune systems recognize these targets. Identifying these variables is difficult and time intensive with regular computing, so current models are limited and at times inaccurate.
Published inBriefings in Bioinformatics,the study found that AI models that account for changes in molecular shape over time can accurately depict how immune systems recognize a target antigen. Through these models, researchers could home in on what processes are critical to target with immunotherapy treatments such as vaccines and engineered immune cells.
Researchers can incorporate these insights into other AI models moving forward to identify more effective immunotherapy targets.
“These discoveries are an example of what makes this partnership successful — combining IBM’s cutting-edge computational resources with Cleveland Clinic’s medical expertise,” Dr. Weber says. “These findings resulted from a key collaboration between everyone from a world-class expert in cancer immunotherapy to our physics-based simulation and AI experts. Collaboration when combined with innovation has terrific potential.”

Combining testosterone-blocking drugs in patients with prostate cancer relapse prevents the spread of cancer better than treatment with a single drug, a multi-institution, Phase 3 clinical trial led by UC San Francisco researchers has found.
The approach can extend the time between debilitating drug treatments without prolonging the time it takes to recover from each treatment.
Prostate cancer affects 1 in 8 men and causes 34,000 deaths each year in the United States. It is usually treated with one of several testosterone-lowering drugs for a set period of time.
“This adds to a growing body of evidence in favor of more intensive testosterone-blocking therapy in patients with higher-risk prostate cancer,” said Rahul Aggarwal, MD, professor in the UCSF School of Medicine and lead author of the paper.
The researchers’ findings were published on Jan. 23, 2024, in the Journal of Clinical Oncology. They were first announced in September 2022 at the annual meeting of the European Society for Medical Oncology.
A case for intensifying prostate cancer treatment
The new study focused on patients who had surgery for prostate cancer, and yet the cancer relapsed and was detected through a sudden jump in the blood levels of a protein called prostate-specific antigen (PSA).

“We looked at patients who had a fast rise in their PSA — an indicator of a higher-risk form of relapsed prostate cancer,” Aggarwal said. “Our goal was to test several different hormone therapy strategies to find the best approach in terms of delaying the cancer’s progression.”
Between 2017 and 2022, 503 patients were randomly assigned to take a single testosterone-lowering therapy chosen by their oncologist, or to combine it with one or two other testosterone-lowering drugs. The additional drugs were already FDA-approved for other cancers but hadn’t been tested in this way with prostate cancer.
The patients stayed on the assigned therapy for a year. Whether given singly or in combination, the drugs caused their testosterone to plummet. That put the brakes on their cancer but also caused fatigue, hot flashes, decreased libido and other problems for patients, according to Aggarwal.
Compared to the prostate cancer patients who only received a single drug therapy during their year of treatment, patients who received either one or two additional drugs stayed cancer-free, with low PSA levels, for longer.
Once off the treatment, patients who took the combination therapies saw their testosterone levels recover just as fast as others who took the single drug.
The researchers are following up with a more detailed analysis of how patients fared on the different treatments — which side effects they experienced and for how long, and how they felt overall as they recovered.
“New cancer therapies must clear a high bar to make their way to patients,” Aggarwal said. “With the evidence in this study and others, combination hormone therapy should be considered a standard of care in prostate cancer patients with high-risk relapse after prior treatment.”

The agency has reviewed reports of cancer patients whose treatments resulted in the development of secondary blood cancers. Several companies will be required to carry the new warning.The Food and Drug Administration is requiring companies that make specialized cancer therapies known as CAR-T to add a boxed warning that the treatments themselves may cause cancers.The agency noted that the benefits still outweighed the risks of the therapy, which involves removing a type of white blood cells — T cells — and then genetically engineering them to create proteins called chimeric antigen receptors (CAR). Infused back into a patient’s blood, the engineered cells allow the T cells to attach to cancer cells and kill them.But the therapies, which mostly treat blood cancers, including multiple myeloma, had already carried a warning for dangerous immune responses and for neurological risks. And the new warning follows reports of about 20 cases of secondary cancers that federal health officials and others have suspected were caused by CAR-T treatments, although more investigation may be needed to establish a definite link. The therapy has been used by an estimated 25,000 to 30,000 patients since it was first approved by the F.D.A. in 2017.Cancer patients who receive CAR-T treatments tend to have few options left, and would be unlikely to alter course even with the new warning, said Dr. John DiPersio, an oncologist with Washington University in St. Louis.“The risk of not doing this therapy for most patients who get it is rapid progression of their disease or certain death,” he said.The F.D.A. raised concerns about the adverse effects of the treatments late last year.In letters dated Jan. 19, the agency outlined the warnings to be included by some of the companies making CAR-T therapies, which had also been ordered to monitor patients for secondary cancers and report any to the F.D.A. The secondary cancers can lead to hospitalizations or death, the agency noted, requiring the drug companies to provide warnings on drug labels that secondary cancers “may present as soon as weeks following infusion, and may include fatal outcomes.”We are having trouble retrieving the article content.Please enable JavaScript in your browser settings.Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times.Thank you for your patience while we verify access.Already a subscriber? 

Each year, at least 1.7 million adults in the United States develop sepsis, and approximately 350,000 will die from the serious blood infection that can trigger a life-threatening chain reaction throughout the entire body.
In a new study, published in the January 23, 2024 online edition of npj Digital Medicine, researchers at University of California San Diego School of Medicine utilized an artificial intelligence (AI) model in the emergency departments at UC San Diego Health in order to quickly identify patients at risk for sepsis infection.
The study found the AI algorithm, entitled COMPOSER, which was previously developed by the research team, resulted in a 17% reduction in mortality.
“Our COMPOSER model uses real-time data in order to predict sepsis before obvious clinical manifestations,” said study co-author Gabriel Wardi, MD, chief of the Division of Critical Care in the Department of Emergency Medicine at UC San Diego School of Medicine. “It works silently and safely behind the scenes, continuously surveilling every patient for signs of possible sepsis.”
Once a patient checks in at the emergency department, the algorithm begins to continuously monitor more than 150 different patient variables that could be linked to sepsis, such as lab results, vital signs, current medications, demographics and medical history.
Should a patient present with multiple variables, resulting in high risk for sepsis infection, the AI algorithm will notify nursing staff via the hospital’s electronic health record. The nursing team will then review with the physician and determine appropriate treatment plans.
“These advanced AI algorithms can detect patterns that are not initially obvious to the human eye,” said study co-author Shamim Nemati, PhD, associate professor of biomedical informatics and director of predictive analytics at UC San Diego School of Medicine. “The system can look at these risk factors and come up with a highly accurate prediction of sepsis. Conversely, if the risk patterns can be explained by other conditions with higher confidence, then no alerts will be sent.”
The study examined more than 6,000 patient admissions before and after COMPOSER was deployed in the emergency departments at UC San Diego Medical Center in Hillcrest and at Jacobs Medical Center in La Jolla.

It is the first study to report improvement in patient outcomes by utilizing an AI deep-learning model, which is a model that uses artificial neural networks as a check and balance in order to safely, and correctly, identify health concerns in patients. The model is able to identify complex and multiple risk factors, which are then reviewed by the health care team for confirmation.
“It is because of this AI model that our teams can provide life-saving therapy for patients quicker,” said Wardi, emergency medicine and critical care physician at UC San Diego Health.
COMPOSER was activated in December 2022 and is now also being utilized in many hospital in-patient units throughout UC San Diego Health. It will soon be activated at the health system’s newest location, UC San Diego Health East Campus.
UC San Diego Health, the region’s only academic medical system, is a pioneer in the field of AI health care, with a recent announcement of its inaugural chief health AI officer and opening of the Joan and Irwin Jacobs Center for Health Innovation at UC San Diego Health, which seeks to develop sophisticated and advanced solutions in health care.
Additionally, the health system recently launched a pilot in which Epic, a cloud-based electronic health record system, and Microsoft’s generative AI integration automatically drafts more compassionate message responses through ChatGPT, alleviating this additional step from doctors and caregivers so they can focus on patient care.
“Integration of AI technology in the electronic health record is helping to deliver on the promise of digital health, and UC San Diego Health has been a leader in this space to ensure AI-powered solutions support high reliability in patient safety and quality health care,” said study co-author Christopher Longhurst, MD, executive director of the Jacobs Center for Health Innovation, and chief medical officer and chief digital officer at UC San Diego Health.
Co-authors of this study include Aaron Boussina, Theodore Chan, Allison Donahue, Robert El-Kareh, Atul Malhotra, Robert Owens, Kimberly Quintero and Supreeth Shashikumar, all at UC San Diego.

A 10-year follow up study of nearly 2,500 U.S. men who received prostate cancer treatment will help inform decision making in terms of treatments and side effects for a diverse population.
The CEASAR (Comparative Effectiveness Analysis of Surgery and Radiation for Localized Prostate Cancer) study, coordinated by Vanderbilt University Medical Center (VUMC), is a multisite research study conducting long-term follow up on men who were diagnosed with localized prostate cancer between 2011 and 2012.
Researchers have now followed the same cohort of men for more than a decade, administering a series of questionnaires regarding urinary, bowel, sexual and hormone therapy-related side effects of treatment. The newly released JAMA study builds upon previous publications of three-year and five-year results.
“Many men with localized prostate cancer survive for 15 years or more, with minimal differences in survival among various treatment strategies,” said first author Bashir Al Hussein Al Awamlh, MD, a fellow in Urologic Oncology at VUMC. “Given this long-time horizon, and similar survival rates, the choice of treatment for patients may be influenced by the adverse effects of the treatments.”
The study includes nearly 2,500 patients: 1,797 non-Hispanic white men, 350 non-Hispanic Black men, 184 Hispanic men, 77 Asian men and 33 “other” races.
“Unlike previous studies, it focuses on contemporary treatment options,” Al Hussein Al Awamlh added. “It uses real-world data representative of the U.S. population, with diversity of disease severity, geographic diversity, and racial/ethnic diversity.”
Patients were classified based on cancer risk into two categories: favorable prognosis and unfavorable prognosis, which is important because unfavorable prognosis patients receive more intensive treatments.

The favorable-prognosis group chose either: Active surveillance, an observation strategy in which treatment is only used if the cancer gets worse over time. Nerve-sparing prostatectomy, surgical removal of the prostate with attempt to protect nerves that run alongside the prostate in hopes of minimizing the impact of surgery on erectile function. External beam radiation therapy (EBRT), a common therapy that uses daily doses of radiation to destroy cancer cells. Low-dose-rate brachytherapy, a type of radiation therapy involving implantation of radioactive “seeds.”The unfavorable-prognosis disease group chose either: Prostatectomy, which is surgery to remove the prostate. External beam radiation therapy with androgen deprivation therapy (ADT), which is radiation in combination with an anti-hormone therapy used to reduce levels of androgen hormones to enhance the effectiveness of radiation.Findings: Surgery with radical prostatectomy was associated with an increased risk of urinary incontinence over 10 years compared to other treatments, irrespective of cancer risk. Fourteen to 25% of men who underwent surgery to remove the prostate reported bothersome leakage 10 years after treatment, as compared with 4-11% of men who underwent EBRT. Patients with favorable cancer prognoses experienced worse sexual impairment for the first three to five years following surgery with radical prostatectomy compared to other treatment options. Sexual function scores were similar across treatments after five years, which may reflect age-related decline, gradual decline associated with radiation, and conversion from active surveillance to treatment. There were no significant differences in sexual function impairment between surgery with radical prostatectomy and radiation with ADT for patients with unfavorable prognosis cancer. Radiation therapy combined with hormone therapy (ADT) was associated with slightly worse bowel and hormone functions at 10 years for patients with unfavorable prognosis prostate cancer.”The findings underscore the importance of counseling men with unfavorable prognosis prostate cancer differently than favorable prognosis cancer regarding expected long-term functional outcomes and suggest that adverse effects of treatments on sexual function may be deemphasized in decision making for some men,” said senior author Daniel Barocas, MD, MPH, professor and executive vice chair of Urology at VUMC.
“They also highlight the benefits of active surveillance, when oncologically safe for patients with favorable-prognosis prostate cancer, by avoiding adverse effects associated with other treatment options,” he said.
The authors are developing a personalized, patient-facing prediction tool using the study data to offer functional estimates through 10 years based on different treatment strategies to aid in decision making.
The study was funded by the National Institutes of Health/National Cancer Institute (R01CA230352), Agency for Healthcare Research and Quality (1R01HS019356, 1R01HS022640) and the Patient-Centered Outcomes Research Institute (CE-12-11-4667). Data management was facilitated by Vanderbilt University’s Research Electronic Data Capture (REDCap) system, which is supported by the Vanderbilt Institute for Clinical and Translational Research grant (UL1TR000011 from NCATS/NIH).

For decades, scientists have studied the intricate activity patterns in human and animal brains by observing when different groups of brain cells turn on. Equally important to understanding the brain and related diseases, however, is knowing how long those neurons stay active and when they turn off again.
Now, scientists at Scripps Research have developed a new technology that lets them track when, after a burst of activity, brain cells shut off — a process known as inhibition. The technique, published in Neuron on January 23, 2024, provides a new way to study not only the normal functioning of the brain, but how the brain’s “off switches” may go awry in normal behaviors as well as in diseases and disorders, including depression, post-traumatic stress disorder and Alzheimer’s disease.
“It’s generally agreed that the inhibition, of neurons is really the major way the brain is regulating activity,” says senior author Li Ye, Ph.D., professor and Abide-Vividion Chair at Scripps Research. “Scientists have been looking for a way to look at inhibition on a more trackable way, and until now, few had found it.”
For pioneering the new approach, Ye teamed up with John Yates, a professor of Molecular Medicine at Scripps Research. They wanted to study how brain cells changed when they were actively firing — emitting an electrical charge to pass messages to their neighbors — compared to when they were done firing. The scientists used optogenetics, in which cells’ activity can be controlled using light, to repeatedly activate and inhibit the cells. Then, they measured levels and characteristics of different proteins and their modifications. They identified that one protein, pyruvate dehydrogenase (PDH), was very rapidly changed immediately after brain cells were inhibited.
“When neurons are firing, you need a lot of energy, and this PDH protein is involved in producing that energy,” explains Ye. “But the brain really wants to conserve energy, so when a cell is done firing, we found that the brain rapidly shuts off the PDH protein. This happened much faster than anything else we saw in gene expression.”
To shut off PDH, the researchers found, cells add molecular tags called phosphates to the protein. Ye and his colleagues found antibodies that only recognized this inactive, phosphorylated form of PDH (pPDH). To test whether levels of phosphorylated PDH (or pPDH) could be used as a proxy for brain cell inhibition, Ye’s team used these antibodies to measure pPDH in mice that had been given anesthesia. Nearly the entire brain lit up with high levels of pPDH, correctly showing how most of the brain is inactive during anesthesia.
The group also studied levels of pPDH when animals were exposed to bright light that was then shut off. Brain cells in the visual cortex, responsible for vision, had low levels of pPDH when being exposed to light (because the active form of PDH would be required to give these cells signaling energy), but high levels of phosphorylated protein immediately increased after the light was off.

Ye’s group also used the new technique to study a less understood process: how the brain turns off the feeling of hunger after a meal. They showed how brain cells related to appetite shut off when a mouse starts to eat. Those findings could have implications for better understanding appetite, obesity and some weight loss drugs. More broadly, the pPDH antibodies could be used to compare levels of brain cell inhibition in healthy people and those with a variety of brain and metabolic diseases.
“There are a lot of questions that this technology can help us answer,” says Ye. “If the brain can’t turn cells off, or if they’re turned off faster or slower than usual, what happens? How does the inhibition of neurons play a role in different diseases?”
Ye and his colleagues are continuing to fine-tune the use of pPDH, but they say that other researchers are already using the technology — the antibodies used to measure pPDH are commercially available.
In addition to Ye and Yates, authors of the study, “Phosphorylation of pyruvate dehydrogenase inversely associates with neuronal activity,” include Dong Yang, Yu Wang, Tianbo Qi, Xi Zhang, Leyao Shen, Jingrui Ma, Zhengyuan Pang, Neeraj K. Lal, Daniel B. McClatchy, Saba Heydari Seradj, Verina H. Leung, Kristina Wang, Yi Xie, Filip S. Polli, Anton Maximov, Hollis T. Cline and Vineet Augustine of Scripps Research; and Oscar Christian Gonzalez and Luis de Lecea of Stanford University.
This work was supported by funding from the National Institutes of Health (DP2DK128800), and BRAIN initiative/NIMH (MH132570and the Dorris Scholar Award.

IntelliGenes, a first of its kind software created at Rutgers Health, combines artificial intelligence (AI) and machine-learning approaches to measure the significance of specific genomic biomarkers to help predict diseases in individuals, according to its developers.
A study published in Bioinformatics explains how IntelliGenes can be utilized by a wide range of users to analyze multigenomic and clinical data.
Zeeshan Ahmed, lead author of the study and a faculty member at Rutgers Institute for Health, Health Care Policy and Aging Research (IFH), said there currently are no AI or machine-learning tools available to investigate and interpret the complete human genome, especially for nonexperts. Ahmed and members of his Rutgers lab designed IntelliGenes so anyone can use the platform, including students or those without strong knowledge of bioinformatics techniques or access to high-performing computers.
The software combines conventional statistical methods with cutting-edge machine learning algorithms to produce personalized patient predictions and a visual representation of the biomarkers significant to disease prediction.
In another study, published in Scientific Reports, the researchers applied IntelliGenes to discover novel biomarkers and predict cardiovascular disease with high accuracy.
“There is huge potential in the convergence of datasets and the staggering developments in artificial intelligence and machine learning,” said Ahmed, who also is an assistant professor of medicine at Robert Wood Johnson Medical School.
“IntelliGenes can support personalized early detection of common and rare diseases in individuals, as well as open avenues for broader research ultimately leading to new interventions and treatments.”
Researchers tested the software using Amarel, the high-performance computing cluster managed by the Rutgers Office of Advanced Research Computing. The office provides a research computing and data environment for Rutgers researchers engaged in complex computational and data-intensive projects.
Coauthors of the study include William DeGroat, Dinesh Mendhe, Atharva Bhusari and Habiba Abdelhalim of IFH and Saman Zeeshan of Rutgers Cancer Institute of New Jersey.