Tag: Academic research

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Brain Glucose Levels Act as a Metabolic Switch for Myelin Formation

Scientists have long known that myelin doesn’t appear everywhere in the brain at once. Some regions myelinate early, others much later, and the timing shapes everything from motor development to cognitive maturation. What has remained elusive is why these regional differences emerge in the first place. A new study in Nature Neuroscience, titled “Glucose-dependent spatial and temporal modulation of oligodendrocyte progenitor cell proliferation via ACLY-regulated histone acetylation,” points to an unexpected driver: shifting glucose levels that act as a metabolic switch, telling progenitor cells when to divide and when to mature into myelin‑forming oligodendrocytes.

The work, led by researchers at the Advanced Science Research Center at the CUNY Graduate Center (CUNY ASRC), maps glucose distribution across the developing mouse brain and reveals that these spatial and temporal fluctuations are not just metabolic background noise. They are instructive signals. “Regions with high glucose levels exhibited greater OPC proliferation and histone acetylation than regions with low glucose,” the authors wrote in the paper’s abstract, suggesting glucose as a key regulator of oligodendrocyte progenitor cell (OPC) population dynamics.

Using MALDI imaging at the CUNY ASRC MALDI Imaging Core Facility, the team visualized glucose concentrations across brain regions during early development in mice. Areas rich in glucose contained actively dividing OPCs, while regions with lower glucose levels harbored cells beginning to differentiate into oligodendrocytes. This pattern suggested that glucose availability helps determine whether OPCs expand their numbers or transition toward myelin production.

“Our findings show that glucose is not just fuel for the brain, it’s also a signal for the cells to divide,” said lead author Sami Sauma, PhD, a postdoctoral researcher with the CUNY ASRC Neuroscience Initiative. “When glucose levels are high in a particular brain region, progenitors use it to drive proliferation. As glucose levels shift, the same cells switch gears and begin maturing.”

An enzyme, ATP‑citrate lyase (ACLY), which converts glucose‑derived citrate into acetyl‑CoA in the nucleus, is central to this process. This acetyl‑CoA fuels histone acetylation, activating genes required for cell proliferation. When the researchers deleted Acly in OPCs, the cells could no longer proliferate efficiently, leading to a temporary reduction in myelin due to decreased OPC numbers. Yet differentiation still occurred, thanks to a compensatory pathway: mature oligodendrocytes can generate acetyl‑CoA outside the nucleus from alternative fuels such as ketone bodies.

This metabolic flexibility proved more than a biochemical curiosity. When mice lacking ACLY in OPCs were placed on a ketogenic diet, their myelin deficits improved. “The same cell lineage interprets different metabolic signals at distinct stages of development,” said senior author Patrizia Casaccia, MD, PhD, founding director of the CUNY ASRC Neuroscience Initiative. “By understanding how glucose and alternative energy sources regulate proliferation and myelin formation, we are uncovering new metabolic strategies that could be harnessed to protect myelin in the developing brain.”

The developmental window examined in mice corresponds to roughly 32 to 40 weeks of human gestation—a period when premature infants are particularly vulnerable to white‑matter injury. The findings raise the possibility that metabolic support during this stage could help preserve the progenitor cells responsible for building myelin. They may also inform future approaches to repairing myelin in disorders such as multiple sclerosis.

The post Brain Glucose Levels Act as a Metabolic Switch for Myelin Formation appeared first on GEN – Genetic Engineering and Biotechnology News.

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STAT+: Katherine Szarama named acting director of FDA’s vaccines and biologics center

WASHINGTON — The Food and Drug Administration has named Katherine Szarama as the acting director of the Center for Biologics Evaluation and Research, which regulates vaccines, gene therapies, and the blood supply. 

A Health and Human Services official confirmed the move, which was first reported by Politico, to STAT. 

She is replacing Vinay Prasad, who left the agency on Thursday after a tumultuous tenure during which he issued a series of controversial decisions on rare disease drugs and vaccines. FDA Commissioner Marty Makary said in March that Prasad would return to the University of California San Francisco. 

Continue to STAT+ to read the full story…

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Korean Medical Consultation With Open-Weight Large Language Models: Pilot Comparative Evaluation of Retrieval-Augmented Generation With Metadata Filtering

Background: This study develops an open-source large language model–based chatbot tailored for Korean health consultations. The chatbot was implemented using the retrieval-augmented generation (RAG) technique alongside metadata filtering to enhance its performance. Objective: This study aims to analyze and compare the performance of a RAG-based chatbot with other leading language models in the context of Korean health consultations. Methods: A 10.4 GB Korean medical document corpus (487,277 segments) was constructed from official websites of major Korean hospitals, public health sources, and medical textbooks. This study quantitatively compared 5 open-source large language models (Qwen3:4B, Mistral:7B, Llama-3.1:8B, Gpt-Oss:20B, and Gemma3:27B) in 3 configurations: baseline (model only), RAG-only, and RAG with metadata filtering. The RAG system used a specialized Korean embedding model (upskyy/bge-m3-korean) and an Elasticsearch store. Performance was assessed by an emergency medicine specialist using a validation set of 226 questions across 7 common diseases and scoring responses based on accuracy, safety, and helpfulness. Results: The application of RAG alone failed to yield statistically significant performance improvements and, in some cases (Llama 3.1: 8B and Gemma 3: 27B), resulted in decreased scores. However, the combination of RAG with metadata filtering yielded statistically significant (<.05) performance increases in most models. Notably, the average score for Mistral:7B increased from 3.79, SD 0.08, to 4.10, SD 0.10, and Gpt-Oss:20B increased from 4.43, SD 0.05, to 4.51, SD 0.04, with the latter achieving the highest safety score (4.61, SD 0.03). The Gemma3:27B model, which possessed a high baseline performance (4.42, SD 0.03), was an exception, exhibiting no significant improvement (=.14) even with filtering. Conclusions: The effectiveness of RAG for specialized domains such as Korean medical consultation is highly dependent on a metadata filtering process that controls the quality of retrieved information; simple information augmentation is insufficient. Furthermore, the benefit of RAG is limited when a model’s intrinsic knowledge (eg, Gemma3:27B) already meets or exceeds the quality of the external knowledge base. This finding indicates that performance enhancement strategies must account for both the retrieval mechanism’s quality and the model’s preexisting capabilities.
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Gamification of Cognitive Behavioral Therapy Homework: Therapist Concept Mapping Approach

Background: Greater homework adherence in cognitive behavioral therapy (CBT) is associated with positive treatment outcomes. However, the problems emerging from CBT homework use are common and affect adherence. In recent years, gamification has been explored to increase intervention adherence, but not yet in relation specifically to homework assignments. Objective: In this study, the aim was to gain a better understanding of obstacles to CBT homework and the use of gamification to overcome these. Methods: Concept mapping, a method to organize related information visually, was used in this study. For the 1-day face-to-face concept mapping session, 7 therapists (32 to 55 y, 6 females) participated and generated items based on 2 focal questions of interest. The generated items were grouped on perceived similarity, and each individual item was rated on (1) severity and difficulty (focal question 1) and (2) importance, acceptance by therapist, and acceptance by patient (focal question 2). The item groups on perceived similarity were inserted into computer software. Based on multidimensional scaling and hierarchical cluster analyses, item clusters were generated by the computer software and were presented to the therapists. The therapists were asked for their preference for the number of items a cluster should contain. Results: Through brainstorming, the therapists collectively generated a list of 29 possible reasons for not doing homework by patients. In the same manner, a list of 38 game design elements that could help patients make CBT homework was generated. External factors (eg, no time due to crisis situations) and lack of motivation (eg, not aspiring to a therapy goal) were perceived as the most important reasons for patients not to do homework. External and symptoms-unrelated internal factors were considered by therapists as the most difficult for patients to change for improved homework adherence. The game design elements, facilitation, and rewards were rated as most important to help patients do homework. These elements were also seen as most accepted by therapists. Conclusions: Facilitation of doing homework and rewards seem to have the potential to tackle some of the external factors and lack of motivation to make CBT homework that patients could have. Conclusions were limited by the small number of participating therapists. Future research is needed on the effects of specific game design elements, the number of these elements, their combinations, and patients’ preferences.
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Exploring Benefits of and Barriers to Patient Involvement Through Digital Tools in Psycho-Oncology: Qualitative Study Within the Reduct Trial

Background: Patient and public involvement is essential for developing patient-centered and acceptable eHealth interventions, yet little is known about how digital collaboration with patient representatives can best be implemented in psycho-oncological research. Objective: This study aimed to identify the benefits and barriers of digital collaboration in the development of an e-mental health application and provide recommendations to optimize digital collaboration with patient representatives in psycho-oncology research. Methods: Conducted from July to September 2023, this study involved digital semistructured interviews with 5 patient representatives from the Reduct trial, a multicenter randomized controlled trial to evaluate the efficacy of the web-based psycho-oncological training Make It. The interviews were analyzed using qualitative content analysis. Results: The findings highlighted multiple advantages of digital collaboration. These included significant reductions in travel costs and effort, personal acceptance and preference for digital methods, enhanced flexibility and accessibility, a reduced health burden, increased efficiency, and scalability. Conversely, several challenges were identified: social impacts or impediments due to less face-to-face interaction, technical difficulties, compromised effectiveness and quality of communication, diverse personal preferences and acceptance levels, organizational issues, cognitive demands, socioeconomic barriers, and safety concerns. The following recommendations to optimize digital collaboration were identified: maintaining regular communication and information exchange, valuing and committing to the collaboration, using diverse communication channels, ensuring comprehensible communication, integrating feedback, fostering openness and understanding, diligent documentation and recordkeeping, and providing targeted training and support for patient representatives. Conclusions: These findings confirm and specify previously known opportunities and challenges of digital collaboration, adding crucial insights for its implementation in psycho-oncological research. This research contributes to enhancing patient-centered approaches in psycho-oncology. Trial Registration: German Clinical Trials Register DRKS00025213; https://drks.de/search/en/trial/DRKS00025213
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STAT+: In her own words: Surgeon general nominee Nicole Saphier expresses enthusiasm and caution for MAHA

Now that Casey Means is no longer the Trump administration’s choice for surgeon general, attention is turning to the new nominee for the position. 

Nicole Saphier, whose candidacy was announced Thursday, is a licensed physician — unlike Means, whose license lapsed. A radiologist at the Memorial Sloan Kettering Cancer Center, Saphier (pronounced SAA-fire) is director of breast imaging at MSK Monmouth in New Jersey. She may be more widely known as a regular contributor to Fox Business, where she has said that the overwhelming majority of “good research” disputes the notion that vaccines are linked to autism, but has expressed an openness to alternative childhood vaccine schedules. 

Saphier has weighed in on many other concerns shared by the Make America Healthy Again movement promoted by health secretary Robert F. Kennedy Jr., agreeing with Kennedy on some positions but also clearly questioning others. In her own words, here are her views on vaccines, peptides, Tylenol in pregnancy, dietary guidelines, breast cancer, and also, Casey Means.

Continue to STAT+ to read the full story…

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Advanced Neural Probes Reveal Predictable Patterns in Epileptic Brain Activity

In addition to suffering seizures, many people with epilepsy also experience bursts of abnormal brain activity called interictal epileptiform discharges (IEDs). These can happen thousands of times a day and interfere with attention, memory, language, and sleep. New data from a study led by scientists at University of California, San Francisco (UCSF) shows that these brain blips are not random events as once thought. The data shows that they unfold in a predictable pattern that can be detected before they occur, suggesting it may be possible to prevent them. 

Details of their work are published in Nature Neuroscience in a paper titled “Laminar organization of cellular microcircuits modulating human interictal epileptiform discharges.” In it, the scientists explain that they used a high-resolution technology recently adapted for humans that records individual neuron activity to track more than 1000 neurons in four patients undergoing surgery for epilepsy. The so-called Neuropixel probes provide “a view into new ways we might address a debilitating aspect of epilepsy that we haven’t been able to tackle,” said Jon Kleen, MD, PhD, an associate professor of neurology at UCSF and co-senior author of the study. 

Preventing brain blips would be a boon for patients’ quality of life because over time, the effects of these mental disruptions can be significant and may account for some of the cognitive impairment experienced by about half of people with epilepsy. 

Neuropixels probes, which are thin devices lined with hundreds of sensors, are designed to record activity throughout the human cortex. This means that unlike current sensors which are limited to brain signals on the surface of the brain, Neuropixels can provide a three-dimensional view of brain activity. For the study, the scientists implanted the probes seven millimeters deep into the part of the brain where patients’ seizures originate—this is the tissue that surgeons typically remove to reduce epilepsy symptoms. 

Inserting the probes here made it possible to observe what happened in the neurons before, during, and after each IED. While seizures appear as a burst of neurons firing in synchrony, when IEDs occur, they unfold sequentially. Specifically, one set of neurons was active about a second before the IED started followed by another set that generated the sharp electrical spike at its peak, and then a third set became active as the IED faded. “We could see individual neurons that were just microns apart from each other playing different roles in the process,” said Alex Silva, the study’s first author and a medical student and doctoral candidate in the UCSF-UC Berkeley Joint PhD program in bioengineering. “It was really striking.”

Previous studies have demonstrated that most neurons involved in IEDs are used in normal cognitive processing. According to this study, nearly 80% of the neurons involved in IEDs were also involved in language and perception. Current implantable devices for epilepsy may be able to help. They include closed loop neurostimulators that can detect abnormal brain activity and deliver electrical pulses that interrupt it. So in the case of IEDs, devices that monitor single neurons could use the activity of the first set of neurons announcing the arrival of the abnormal pattern as a warning signal. “That would be a major step forward, changing treatment from reactively responding to abnormal brain bursts to proactively preventing them in the first place,” Kleen said.

The post Advanced Neural Probes Reveal Predictable Patterns in Epileptic Brain Activity appeared first on GEN – Genetic Engineering and Biotechnology News.

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CAR T-Cell Therapy Failure Linked to Senescent CD8+ T Cells

Researchers at Rutgers University have identified a factor that may help explain why chimeric antigen receptor (CAR) T-cell therapy fails in a majority cancer patients. In a study published in Cell Reports, the investigators found that the poor initial quality of a patient’s harvested CD8+ T cells that are used to manufacture CAR T-cells lack the ability to mount a robust immune response.

“Many of their T-cells are in a defective state called senescence, which means they can’t proliferate in the lab, they can’t migrate to tissue effectively, and they can’t kill very well,” said senor author Ricardo Iván Martínez-Zamudio, PhD, an assistant professor at Rutgers Robert Wood Johnson Medical School.

Building a CAR T-cell therapy depends on successfully harvesting a patient’s own T cells, then modifying to target tumor cells, growing a robust population of these engineered cells in the lab, then reinfusing them into the patient. But, as the new research shows, the efficacy of this process depends on the inherent capacity of the harvested cells to both proliferate in the lab and to retain their immune function.

The Rutgers study showed that in patients where CAR T therapy is ineffective, a large proportion of a patient’s harvested cells are senescent. Their research demonstrated that CD8+ T cells from donors with higher levels of senescence expanded less under standard CAR T culture conditions than cells from donors with lower senescence levels.

Further, a retrospective look at clinical outcomes of published datasets from lymphoma patients treated with CAR T-cell therapy found that patients whose starting cells and final CAR T-cell products had strong senescence signatures were more likely to fail treatment, while those with lower senescent profiles were more likely to respond. This indicated that the state of CD8+ T cells prior to engineering could be influencing the efficacy of CAR T treatments.

To better understand the molecular basis of CD8+ T cell senescence, the researchers collected blood from both younger and older donors, isolated CD8+ T cells, and used a fluorescent marker to identify senescent cells. They then performed multi-omics profiling, including gene expression and chromatin analysis, to map the regulatory networks controlling senescence.

The resulting data showed that T cell senescence, rather than chronological age of the donor, drives most of the molecular differences in CD8+ T cells. “The senescence program is essentially precoded,” Martínez-Zamudio said. “It’s not that older people develop some new dysfunctional program. The capacity is there from the beginning.”

The study identified a number of transcription factors, including AP1, KLF5, and RUNX2, that regulate this dysfunctional program. When the research altered these to effect gene expression patterns in senescent cells, they were able to partially restore aspects of T cell responsiveness. Their ability to proliferate, however, remained limited.

The implications of this research extend beyond cancer therapy. While it is known that senescent CD8+ T cells accumulate with age and contribute to declines in immune function and chronic inflammation, the study also found that senescence gene signatures were enriched in patients with lupus, suggesting this may also play a role to autoimmune diseases.

“Our study defines the gene-regulatory mechanisms underlying human CD8+ T cell senescence, highlights [transcription factor] network perturbation as a viable strategy to manipulate the senescence state, and identifies senescent CD8+ T cell gene signatures as prognostic tools for immunotherapy outcome,” the researchers wrote.

Based on this, the investigators think that T cell senescence profiling could be used to help determine which patients would benefit from CAR T therapy and those that wouldn’t and could help guide alternative treatments. Because the current findings were a retrospective analysis of patient data, the Rutgers team now plan to test this approach in prospective clinical studies through collaborations with Rutgers Cancer Institute.

The study also indicates the potential to improve CAR T-cell therapy by target the senescence program, by altering transcription factor activity to modify gene expression. But restoring the proliferative capability of these cells using this approach will require more research. Another route for improvement suggested by the research is to develop method to reprogram, or selectively eliminate, senescent cells during the CAR T-cell manufacturing process.

The post CAR T-Cell Therapy Failure Linked to Senescent CD8+ T Cells appeared first on Inside Precision Medicine.

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Evaluating Biomedical Feature Fusion on Machine Learning’s Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation

Background: Accurately differentiating severe from nonsevere COVID-19 clinical types is critical for the health care system to optimize workflow. Current techniques lack the ability to accurately classify COVID-19 clinical types in patients, especially as SARS-CoV-2 continues to mutate. Objective: We explore the predictability and interpretability of multiple state-of-the-art machine learning (ML) techniques trained and tested under different biomedical data types and SARS-CoV-2 variants. Methods: Comprehensive patient-level data were collected from 362 patients (severe COVID-19: n=148; nonsevere COVID-19: n=214) infected with the original SARS-CoV-2 strain in 2020 and 1000 patients (severe COVID-19: n=500; nonsevere COVID-19: n=500) infected with the Omicron variant in 2022‐2023. The data included 26 biochemical features from blood testing and 26 clinical features from patients’ clinical characteristics and medical history. Different ML techniques, including penalized logistic regression, random forest, -nearest neighbors, and support vector machines, were applied to build predictive classification models based on each data modality separately and together for each variant. Fifty randomized train-test splits were conducted per scenario, and performance results were recorded. Results: The fusion (hybrid) characteristic modality yielded the highest mean area under the curve (AUC) in this study, achieving 0.915, while the biochemical and clinical modalities had AUCs of 0.862 and 0.818, respectively. All ML models performed similarly under different testing scenarios and were consistent when cross-tested with data of patients infected with the original strain and those infected with the Omicron variant. Our models ranked elevated d-dimer (biochemical), elevated high sensitivity troponin I (biochemical), and age greater than 55 years (clinical) as the most positively predictive features of severe COVID-19. Conclusions: These results are compatible with the hypothesis that ML is a useful tool for predicting severe COVID-19 based on comprehensive individual patient–level data. Further, ML models trained on the biochemical and clinical modalities together show patterns consistent with enhanced predictive performance. The improved performance observed with Omicron variant data agrees with the hypothesis that ML approaches may retain utility across variants in this study setting, although further validation is required before clinical application. Future work using larger datasets with more ethnic variation and investigating unbiased ML interpretation methods may be able to provide further validation.
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Use of a Large Language Model to Reveal Narrative Architectures of Veteran Transition Stress: Development and Validation Study

Background: The stress caused by multiple aspects of veterans’ transitions from military to civilian, termed transition stress, represents a unique source of psychological impact that is underresearched due to its qualitative nature. The assessment of this complex psychological phenomena has thus relied on laborious interviews designed to extract quantitative information from qualitative narratives of the transition to civilian life. We sought to determine if large language models (LLMs) could be used as valid measurement tools to extract relevant information from open-ended narratives. Objective: This study sought to develop and validate a generative artificial intelligence (AI) approach to automate the quantification and subsequent thematic analysis of veteran transition stress. Methods: Utilizing transcripts from interviews of a sample of US military veterans, we developed an LLM to rate transition stress severity and examined the model’s reliability in relation to human coders and validity in relation to a set of related questionnaire measures. Next, we used the LLM scores to quantitatively define high and low transition stress groups, enabling a targeted, automated analysis of themes related to narrative identity and life transition themes that might differentiate the two groups. Results: LLM ratings of transition stress correlated highly with the human expert ratings and showed significant, theoretically congruent correlations with measures of clinical symptoms, reintegration difficulties, and veterans’ self-ratings of transition difficulty. Critically, the AI-derived thematic analyses of the narratives from high and low transition stress veterans revealed clearly distinct and informative patterns. Conclusions: These findings suggest that generative AI offers a robust, scalable, and reliable method for multidimensional analysis of complex, narrative-based psychological constructs.