<![CDATA[Auvelity is now approved for the treatment of Alzheimer disease agitation. Rajesh R. Tampi, MD, MS, DFAPA, DFAAGP, weighs in. ]]>
<![CDATA[Check out the pipeline updates from April!]]>
Genomics Pioneer and Life Sciences Entrepreneur J. Craig Venter Dies at 79
J. Craig Venter, PhD, the founder, board chair, and CEO of the J. Craig Venter Institute (JCVI) has died in San Diego following a brief hospitalization for unexpected side effects that arose from the treatment of a recently diagnosed cancer, noted the JCVI in a press statement.
Venter helped define modern genomics and launch the field of synthetic biology. He was skillful in building interdisciplinary teams, pushing for new ideas and faster methods, and insisting that discovery should translate into real-world impact. He was also a major advocate for strong federal science funding and for partnerships that accelerate progress across government, academia, and industry.
“Craig believed that science moves forward when people are willing to think differently, move decisively, and build what doesn’t yet exist,” said Anders Dale, PhD, president of JCVI. “His leadership and vision reshaped genomics and helped ignite synthetic biology. We will honor his legacy by continuing the mission he built—advancing genomic science, championing the public investments that make discovery possible, and partnering broadly to turn knowledge into impact.”
“Venter has been recognized as an essential force in the impetus to evolve genomics from a slow, academic discipline into a fast-moving, data-driven, and commercially relevant enterprise, leaving a lasting imprint on biotechnology, medicine, and synthetic biology,” says John Sterling, GEN’s Editor in Chief, who has known and worked editorially with Venter over the past 35 years.
“Venter was controversial and often challenged the scientific orthodoxy, with critics accusing him of hype and going overboard on privatization. To many, he was a visionary focusing on technological acceleration and blending academic science with the zeal of an entrepreneur. Supporters saw him as a pioneer who sped up genomics by years.”
At the NIH, he played a key role in driving gene discovery using expressed sequence tags (ESTs), enabling rapid identification of large numbers of human genes and accelerating genome mapping efforts. He went on to lead efforts that, along with the NIH, produced the first draft sequences of the human genome, a milestone that helped usher biology into the digital age. He and colleagues later published the first high-quality diploid human genome, demonstrating the importance of capturing genetic variation inherited from both parents.
In synthetic biology, Venter and his teams constructed the first self-replicating bacterial cell controlled by a chemically synthesized genome—proof that genomes could be designed digitally, built from chemical components, and “booted up” to run a living cell. He also pursued scientific discovery at global scale.
Through the Sorcerer II Global Ocean Sampling Expedition, Venter and his teams used metagenomics to reveal amazing microbial diversity, reporting the discovery of millions of new genes and expanding the known universe of protein families—work that deepened understanding of the ocean microbiome and its impact on planetary systems.
Beyond his scientific achievements, and in addition to founding the JCVI, he also co-founded Synthetic Genomics, Human Longevity, and most recently Diploid Genomics, advancing efforts to translate genomics and synthetic biology into tools for the benefits of human health and environmental sustainability.
The post Genomics Pioneer and Life Sciences Entrepreneur J. Craig Venter Dies at 79 appeared first on GEN – Genetic Engineering and Biotechnology News.
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by Antonio Regalado March 20, 2026
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<![CDATA[The FDA has approved Auvelity, an oral NMDA/sigma-1 therapy, offering a new option to curb Alzheimer disease agitation and ease caregiver burden.]]>
<![CDATA[FDA approves Axsome’s AXS-05 for Alzheimer’s agitation, backed by multiple trials.]]>
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.
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.
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.