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Some healthcare workers have been able to limit the role of AI in their work after going on strike. Is this a useful tactic for doctors?

Regenerative dairy farming is showing early signs of boosting soil carbon, with new UK data revealing measurable gains of up to 8.9 tonnes p...

Emily Mullin / Wired: An interview with Galen Buckwalter, a BCI recipient in a Caltech brain implant study, on his recent ability to use the implant to produce musical tones  —  Galen Buckwalter says brain-computer interfaces will have to be enjoyable to use if the technology is going to be successful.

A new global initiative launched today aims to close a critical gap in ALS/MND drug discovery - current cell models used for testing treatments do not currently reflect the diverse nature of the disease - that affects both researchers developing therapies and the people urgently waiting for them.

arXiv:2603.29316v1 Announce Type: new Abstract: Clustering mixed-type data remains a major challenge in biomedical research to uncover clinically meaningful subgroups within heterogeneous patient populations. Most existing clustering methods impose restrictive assumptions like local independence, fail to accommodate censored biomarkers, or unable to quantify variable importance. We propose a Bayesian finite mixture model (BFMM) clustering framework that addresses these limitations. BFMM flexibly models both continuous and categorical variables, incorporates three covariance structures to capture cluster-specific dependencies among continuous features, and handles censored observations through likelihood-based imputation. To facilitate feature prioritization, BFMM uses spike-and-slab priors to estimate variable importance on a continuous 0-1 scale. Simulation studies demonstrate that BFMM outperforms existing methods in clustering accuracy, particularly given strong within-cluster

arXiv:2603.29529v1 Announce Type: cross Abstract: We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transformers in protein structure prediction. We find that, at variance with feedforward networks, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties. We show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Finally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning.

arXiv:2603.29529v1 Announce Type: cross Abstract: We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transformers in protein structure prediction. We find that, at variance with feedforward networks, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties. We show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Finally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning.

arXiv:2603.29950v1 Announce Type: new Abstract: Effective collaboration requires teams to manage complex cognitive and emotional states through Socially Shared Regulation of Learning (SSRL). Physiological synchrony (i.e., longitudinal alignment in physiological signals) can indicate these states, but is hard to interpret on its own. We investigate the physiological and conversational dynamics of four medical dyads diagnosing a virtual patient case using an intelligent tutoring system. Semantic shifts in dialogue were correlated with transient physiological synchrony peaks. We also coded utterance segments for SSRL and derived cosine similarity using sentence embeddings. The results showed that activating prior knowledge featured significantly lower semantic similarity than simpler task execution. High physiological synchrony was associated with lower semantic similarity, suggesting that such moments involve exploratory and varied language use. Qualitative analysis triangulated these

arXiv:2603.29235v1 Announce Type: new Abstract: Performance diagnosis in production-scale AI training is challenging because subtle OS-level issues can trigger cascading GPU delays and network slowdowns, degrading training efficiency across thousands of GPUs. Existing profiling tools are limited to single system layers, incur prohibitive overhead (10--30%), or lack continuous deployment capabilities, resulting in manual analyses spanning days. We argue that continuous, cross-layer observability enabled by OS-level instrumentation and layered differential diagnosis is necessary to address this gap. We introduce SysOM-AI, a production observability system that continuously integrates CPU stack profiling, GPU kernel tracing, and NCCL event instrumentation via adaptive hybrid stack unwinding and eBPF-based tracing, incurring less than 0.4% overhead. Deployed at Alibaba across over 80,000 GPUs for more than one year, SysOM-AI helped diagnose 94 confirmed production issues, reducing median

DNA robots are emerging as tiny programmable machines that could one day deliver drugs, hunt viruses, and build molecular-scale devices. By borrowing ideas from traditional robotics and combining them with DNA folding techniques, scientists are creating structures that can move and act with precision. These robots can be guided using chemical reactions or external signals like light and magnetic fields.

Looks like Google's Fitbit and Pixel smartwatches might be getting a new sibling.

Multiple myeloma is a cancer in which plasma cells, which normally produce antibodies, multiply uncontrollably in the bone marrow. There is currently no cure. However, various therapies can stabilize the disease and alleviate symptoms. One such therapy is to treat the patient with their own stem cells. This often involves weeks in hospital. Using machine learning methods, a research team has now assessed the conditions in which some of the therapy can be safely carried out as an outpatient.

Learn how the Amazon molly, an all-female fish that reproduces asexually, uses gene conversion to maintain healthy DNA and survive more than 100,000 years without males.

New twin research shows that innate IQ plays a major role in predicting your future socioeconomic status. The study, which follows twins during the crucial early adult years, reinforces the view that heredity and genes shape our life opportunities—and the people we become.

Scientists are developing a revolutionary new stem cell therapy to treat a rare and life-threatening disease that leaves newborn babies unable to function without invasive surgery, thanks to a collaboration between UCL, the University of Sheffield and Queen's University Belfast.

The International Conference on Targeting Longevity 2026, scheduled for April 8–9 in Berlin, is set to challenge and potentially revolutionize the prevailing paradigms in aging research. This highly anticipated gathering of leading scientists and industry innovators urges a paradigm shift from viewing aging purely as a sequence of isolated molecular defects to understanding it as […]

Astrocytes make up a sizable population of supporting and structural cells in the brain, with a broad portfolio of activities that are collectively necessary for the normal operation of brain metabolism and neural activity. Like all cell populations, astrocytes are negatively impacted by the accumulating damage and dysfunction of aging, both internal to cells and in the tissue microenvironment. An area of focus for the research community is how aging provokes an ever increasing number of astrocytes into (1) a reactive, inflammatory state that harms brain tissue, but also (2) into a senescent state, which is also a source of inflammatory signaling that becomes detrimental to tissue structure and function when sustained over the long term. Reactivity and senescence may overlap in their contribution to […]

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In a sign of the times for gene therapy, Astellas did not take up its option on AviadoBio’s frontotemporal dementia treatment, the UK biotech company disclosed on Tuesday. Instead, a clinical trial of the therapy ...

The authorship of this article by Attar and colleagues (BMJ 2025;391:e083382, doi:10.1136/bmj-2024-083382, published 29 October 2025)1 has been corrected to remove Anthony Mathur and Sheik Dowlut.What happened after publicationAfter publication of the original article, Anthony Mathur and Sheik Dowlut, who were listed as authors of the study, wrote to the journal to say that they were not authors. The editors published an expression of concern2 due to several concerns, including authorship.On investigation, it transpired that Mathur and Dowlut’s only contribution to the work was to provide feedback on the manuscript before its submission to The BMJ. Mathur and Dowlut asked Attar by email before submission to remove them from the list of authors, but this was not done. Although Mathur and Dowlut were subsequently included among the recipients of emails sent by the journal during the submission process to all the listed authors, which identified them as authors of the...

This article by Attar and colleagues (BMJ 2025;391:e083382, doi:10.1136/bmj-2024-083382, published 29 October 2025)1 is retracted by the journal.What happened after publicationThe journal was alerted to issues in this content regarding the design, conduct, or reporting of the work.2 The editors published an expression of concern3 due to concerns that the trial might have breached accepted trial practices and/or be unreliable. The authors cooperated with the journal and provided additional information; they have also responded on PubPeer. The journal reviewed the issues, including the following points below.Concerns regarding the reliability of the trial and the integrity of the reported dataThe issues included apparent recruitment outside of the inclusion criteria, including those over 65 years old; discrepancy in the number of participants enrolled; and data irregularities, such as unusual patterns in the data (including improbable values and/or repeated numbers) and mismatched and

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. There are more AI health tools than ever—but how well do they work?  In the last few months alone, Microsoft, Amazon, and OpenAI have all launched medical chatbots.  There’s a clear demand…

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Vaginal microbiota transplantation without antibiotics fails to restore microbiome balance, yet engraftment suggests avenues for enhancing treatment efficacy.

arXiv:2603.27875v1 Announce Type: new Abstract: This work follows from a previous study on the estimation of an initial distribution of telomere length from a senescence times distribution done in [10.1051/m2an/2026022, J. Olay{\'e}]. In this previous study, we have presented an estimation method based on the fact that our telomere shortening model can be approximated by a transport equation. This method has encouraging results, but fails to provide a good estimation when the variability of the initial telomere length distribution is too small. We improve here this method by approximating our model with an advection-diffusion equation, which allows us to better take into account the randomness of the shortening values. We show that under this approximation, there exists a simple link between the Laplace transform of the initial telomere length distribution and that of the senescence times distribution. Then, by using a numerical method for inverting Laplace transforms called

arXiv:2603.28589v1 Announce Type: new Abstract: Autonomous systems that generate scientific hypotheses, conduct experiments, and draft manuscripts have recently emerged as a promising paradigm for accelerating discovery. However, existing AI Scientists remain largely domain-agnostic, limiting their applicability to clinical medicine, where research is required to be grounded in medical evidence with specialized data modalities. In this work, we introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research. It enables clinically grounded ideation by transforming extensively surveyed literature into actionable evidence through clinician-engineer co-reasoning mechanism, which improves the traceability of generated research ideas. It further facilitates evidence-grounded manuscript drafting guided by structured medical compositional conventions and ethical policies. The framework operates under 3 research modes, namely paper-based

arXiv:2603.28540v1 Announce Type: new Abstract: Medical device regulators in the United States(FDA), China (NMPA), and Europe (EU MDR) all use the language of risk, but classify devices through structurally different mechanisms. Whether these apparently shared concepts carry transferable classificatory signal across jurisdictions remains unclear. We test this by reframing explainable AI as an empirical probe of cross-jurisdictional regulatory overlap. Using 141,942 device records, we derive seven EU MDR risk factors, including implantability, invasiveness, and duration of use, and evaluate their contribution across a three-by-three transfer matrix. Under a symmetric extraction pipeline designed to remove jurisdiction-specific advantages, factor contribution is negligible in all jurisdictions, indicating that clean cross-jurisdictional signal is at most marginal. Under jurisdiction specific pipelines, a modest gain appears only in the EU MDR-to-NMPA direction, but sensitivity analyses

arXiv:2603.28057v1 Announce Type: new Abstract: Deep learning (DL) models have achieved strong performance in an intelligence healthcare setting, yet most existing approaches operate as black boxes and ignore the physical processes that govern tumor growth, limiting interpretability, robustness, and clinical trust. To address this limitation, we propose PhysNet, a physics-embedded DL framework that integrates tumor growth dynamics directly into the feature learning process of a convolutional neural network (CNN). Unlike conventional physics-informed methods that impose physical constraints only at the output level, PhysNet embeds a reaction diffusion model of tumor growth within intermediate feature representations of a ResNet backbone. The architecture jointly performs multi-class tumor classification while learning a latent tumor density field, its temporal evolution, and biologically meaningful physical parameters, including tumor diffusion and growth rates, through end-to-end

arXiv:2603.27994v1 Announce Type: new Abstract: This study examined how behavioral, emotional, and contextual factors influence Filipino students' willingness to use artificial intelligence (AI) for mental health support. Results showed that habit had the strongest effect on willingness, followed by comfort, emotional benefit, facilitating conditions, and perceived usefulness. Students who used AI tools regularly felt more confident and open to relying on them for emotional support. Empathy, privacy, and accessibility also increased comfort and trust in AI systems. The findings highlight that emotional safety and routine use are essential in promoting willingness. The study recommends AI literacy programs, empathic design, and ethical policies that support responsible and culturally sensitive use of AI for student mental health care.

arXiv:2603.27176v1 Announce Type: new Abstract: Lesion detection, symptom tracking, and visual explainability are central to real-world medical image analysis, yet current medical Vision-Language Models (VLMs) still lack mechanisms that translate their broad knowledge into clinically actionable outputs. To bridge this gap, we present MEDIC-AD, a clinically oriented VLM that strengthens these three capabilities through a stage-wise framework. First, learnable anomaly-aware tokens ( ) encourage the model to focus on abnormal regions and build more discriminative lesion centered representations. Second, inter image difference tokens ( ) explicitly encode temporal changes between studies, allowing the model to distinguish worsening, improvement, and stability in disease burden. Finally, a dedicated explainability stage trains the model to generate heatmaps that highlight lesion-related regions, offering clear visual evidence that is consistent with the model's reasoning. Through

arXiv:2603.26814v1 Announce Type: new Abstract: Affordance reasoning provides a principled link between perception and action, yet remains underexplored in surgical robotics, where tissues are highly deformable, compliant, and dynamically coupled with tool motion. We present arg-VU, a physics-aware affordance reasoning framework that integrates temporally consistent geometry tracking with constraint-induced mechanical modeling for surgical visual understanding. Surgical scenes are reconstructed using 3D Gaussian Splatting (3DGS) and converted into a temporally tracked surface representation. Extended Position-Based Dynamics (XPBD) embeds local deformation constraints and produces representative geometry points (RGPs) whose constraint sensitivities define anisotropic stiffness metrics capturing the local constraint-manifold geometry. Robotic tool poses in SE(3) are incorporated to compute rigidly induced displacements at RGPs, from which we derive two complementary measures: a

arXiv:2603.26776v1 Announce Type: new Abstract: Reliable photovoltaic defect identification is essential for maintaining energy yield, ensuring warranty compliance, and enabling scalable inspection of rapidly expanding solar fleets. Although recent advances in computer vision have improved automated defect detection, most existing systems operate as opaque classifiers that provide limited diagnostic insight for high-stakes energy infrastructure. Here we introduce REVL-PV, a vision-language framework that embeds domain-specific diagnostic reasoning into multimodal learning across electroluminescence, thermal, and visible-light imagery. By requiring the model to link visual evidence to plausible defect mechanisms before classification, the framework produces structured diagnostic reports aligned with professional photovoltaic inspection practice. Evaluated on 1,927 real-world modules spanning eight defect categories, REVL-PV achieves 93\% classification accuracy while producing

Explore how AI is transforming health insurance in the US, raising legal and ethical questions about patient care and oversight.

Patients with severe alcohol-associated hepatitis living in deprived areas are less likely to be referred for a liver transplant when their disease severity is moderate.

Two Northeastern University researchers want to make life better for people who've experienced serious physical trauma, the kind caused by bad car accidents or from injuries sustained during wartime; such procedures are crucial. That process takes on an added layer of complication when damage occurs to the nervous system.

Centenarians often live to 100+ due to a combination of protective genetic factors, which account for up to 50%, and healthy lifestyles, such as plant-forward diets, regular, natural movement and strong social connections. While these "agers" often possess unique immune system signatures, understanding the metabolic signs of healthy aging is not yet fully understood.

Angelina Jolie's appearance at a Shanghai Tom Ford event triggered online frenzy over clone and body double claims, fuelling debate on her looks amid longstanding health disclosures.

In chemical processes for producing pharmaceuticals, catalysts are a core technology that determines production speed and cost. However, until now, there has been a trade-off between "precise but disposable catalysts" and "reusable catalysts." A KAIST research team has developed an eco-friendly catalytic technology that combines these two types, operating solely with light and air. This opens a pathway to producing pharmaceutical ingredients more cheaply and cleanly, with expected reductions in carbon emissions and environmental pollution. The study is published in the Journal of the American Chemical Society.

A newly developed AI tool can dramatically speed up the search for the genetic causes of rare diseases, a process that often takes years and frequently ends without answers. The tool analyzes how genes have evolved across many species to uncover hidden clues about which gene is responsible for a patient's symptoms.

The goal is to make good data practices the path of least resistance. Perhaps the most important requirement is software that experimental scientists can use naturally within their existing workflows. The post Why Data Infrastructure Determines AI Success in Drug Discovery appeared first on GEN - Genetic Engineering and Biotechnology News.

An study showed how impaired transport protein and buildup of ceramides at the endoplasmic reticulum can help lock cells into replicative senescence, potentially pointing to avenues for research on cell aging. The post Link Between Ceramide Transport and Cell Senescence Could Inform Aging Biology Research appeared first on GEN - Genetic Engineering and Biotechnology News.

In a groundbreaking advancement that could reshape the landscape of genetic medicine, researchers have announced the successful completion of the first-ever clinical trial utilizing nuclease-free, homologous recombination-dependent gene editing in pediatric patients. This pioneering study, led by a team including Bedoyan, Morgan, and Sun, has targeted methylmalonic acidemia (MMA), a severe metabolic disorder, marking a […]

In a phase 2 trial, OCU410, a modifier gene therapy for geographic atrophy secondary to dry age-related macular degeneration, reduced lesion growth and size while slowing ellipsoid zone loss, according to a press release from Ocugen.“This outcome represents meaningful clinical and scientific validation of OCU410 but also further validation of our broader modifier gene therapy platform, giving us strong conviction as we advance into phase 3 and potential registrational pathway,” Shankar Musunuri, PhD, MBA, chairman, CEO and cofounder of Ocugen, told Healio.In the ArMaDa clinical trial, 51

The company says using AI cuts years off of research time.

Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to members of its One Medical service, would…

Last week, CalCAN staff attended a regenerative tree nut field day at Burroughs Family Farms in Denair, CA. Ward and Rosie Burroughs along... The post Community and Connection at the Burroughs Regenerative Tree Nut Field Day appeared first on CalCAN - California Climate & Agriculture Network.

Scientists led by Botond Roska at the Institute of Molecular and Clinical Ophthalmology Basel (IOB) and collaborators have identified genetic pathways and compounds capable of protecting cone photoreceptors from the degeneration that underlies conditions like age-related macular degeneration.

Another key player in China's biotech ecosystem has landed a pharma deal. On Sunday, Eli Lilly and Insilico Medicine announced a drug discovery collaboration, with the Indianapolis drugmaker paying $115 million ...

arXiv:2603.26411v1 Announce Type: new Abstract: Predicting how protein mutations affect drug binding remains a major challenge, particularly when the mutations are distal from the binding site. In this study, we introduce a coupled simulation workflow that combines long-time-scale molecular dynamics (MD) with high-throughput quantum mechanical (QM) analysis to reveal the electronic structure signatures of mutation induced drug resistance in the HIV-1 protease. Our workflow leverages GPU-accelerated MD to generate conformational ensembles, and performs in-operando linear-scaling density functional theory (DFT) calculations on selected frames parallelized on a coupled partition of CPU nodes. This design enables efficient, massively parallel quantum analysis of protein-ligand complexes at atomic resolution. Using this approach, we investigate resistance to the antiviral Darunavir in a multi-mutant HIV-1 protease variant. By mapping the network of electronic interactions across the

arXiv:2603.25821v1 Announce Type: new Abstract: We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physician-patient interactions. Unlike traditional medical benchmarks that rely on solving standardized test questions, the proposed approach models a multi-step clinical dialogue in which either a physician or an AI system must collect medical history, analyze attached materials (including laboratory reports, images, and medical documents), formulate differential diagnoses, and provide personalized recommendations. System performance is evaluated using the D.O.T.S. metric, which consists of four components: Diagnosis, Observations/Investigations, Treatment, and Step Count, enabling assessment of both clinical correctness and dialogue efficiency. The system also incorporates a multi-level testing and quality monitoring architecture designed to detect model degradation during both development and

Conditional approval of the intravesical gene therapy was supported by clinical evidence demonstrating a 53.4% complete response rate in patients with treatment-resistant non-muscle invasive bladder cancer.

Drug discovery is like molecular Tetris. Chemists snap atoms together, adjusting the pieces until everything fits and suddenly,

Researchers showed that chronic colitis leaves a long-lasting epigenetic memory in colonic stem cells, persisting for more than 100 days after recovery in mice. This memory is marked by durable AP-1-linked chromatin changes and later amplifies tumour outgrowth after oncogenic mutation.

Insilico Medicine has signed a potential multibillion-dollar deal with Eli Lilly to license out an early-stage drug pipeline and provide its artificial intelligence platform services in research and development collaborations. Under the agreement announced on Monday, US-headquartered, Hong Kong-listed Insilico will receive an upfront payment of US$115 million. The deal could have a total value of around US$2.75 billion tied to development, regulatory and commercial milestones, plus tiered...

Wildfire smoke is teeming with them. Researchers have employed them to develop energy-dense biofuels like rocket, marine, and jet fuels. Scientists have engineered rice paddies that interact differently with them, causing lower methane "burping." They can be used to extract valuable metals like lithium and copper from plants like seaweed.

The latest news and headlines from Yahoo! News. Get breaking news stories and in-depth coverage with videos and photos.

In a groundbreaking study published in Communications Biology, researchers from the Institute of Science Tokyo have unveiled a remarkable cellular mechanism underlying intestinal regeneration. This discovery illuminates how the intestine efficiently repairs itself after damage without depleting its vital stem cell reserve. By exploring the dynamic interplay between specialized revival stem cells and conventional intestinal […]

In a groundbreaking advancement that bridges biology, materials science, and robotics, researchers at The University of Manchester have secured nearly £1 million in funding from UK Research and Innovation (UKRI) to develop innovative soft robots inspired by the locomotion of snails. These microscopic robots are specifically engineered to revolutionize the delivery of anti-cancer drugs with […]

Results of a Phase I clinical trial show that patients who undergo a blood stem cell transplant involving a donor have a lower risk of relapse and lower rates of graft-versus-host disease when they receive the targeted therapy VIC-1911 along with the standard-of-care regimen that includes post-transplant cyclophosphamide and sirolimus.

Eli Lilly's deal involves an upfront payment of $115 million to Insilico, with additional payments tied to milestones. Importance Rank:  1 read more

As the global population continues its inexorable rise, the pressure on agriculture to meet increasing food demands has never been more intense. In this context, the promise of regenerative farming practices emerges not only as a beacon of hope but also as a scientifically grounded approach to reconciling yield enhancement with environmental sustainability. A groundbreaking […]

Evelyn Cheng / CNBC: Hong Kong-listed AI drug discovery company Insilico Medicine and Eli Lilly sign a drug co-development deal worth up to $2.75B, with $115M in upfront payments  —  BEIJING — U.S. pharmaceutical giant Eli Lilly has reached a $2.75 billion deal to bring drugs developed using artificial intelligence …

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U.S. pharmaceutical giant Eli Lilly will give Hong Kong-listed Insilico $115 million upfront to bring some of its AI-discovered drugs to the global market.

U.S. pharmaceutical giant Eli Lilly will give Hong Kong-listed Insilico $115 million upfront to bring some of its AI-discovered drugs to the global market.

U.S. pharmaceutical giant Eli Lilly will give Hong Kong-listed Insilico $115 million upfront to bring some of its AI-discovered drugs to the global market.

After a stroke, the brain may do something surprisingly hopeful—it can “refresh” parts of itself. Researchers analyzing brain scans from over 500 stroke survivors found that while the damaged side of the brain appears to age faster, the opposite, unaffected side can actually look younger. This unexpected shift seems to reflect the brain’s effort to rewire itself, strengthening healthy regions to compensate for lost function.

Ученые обнаружили, что главная проблема генной терапии может быть связана не с ошибками самих инструментов редактирования. Причину нашли в физической форме ДНК. Оказалось, что скручивание ДНК напрямую влияет на то, как CRISPR находит и разрезает гены, и именно это может объяснять неожиданные ошибки при лечении.

Heather Landi / Fierce Healthcare: Qualified Health, which helps health systems evaluate and adopt AI tools, raised a $125M Series B led by NEA at a valuation of between $500M and $1B  —  Qualified Health Artificial Intelligence New Enterprise Associates SignalFire  —  Qualified Health, a startup that works with health systems …

Dr. Donald Kohn has been developing gene therapies for rare pediatric immune disorders for over 30 years. This week, his role in a clinical trial has culminated in the first-ever U.S. Food and Drug Administration–approved therapy for severe leukocyte adhesion deficiency-I—a genetic condition characterized by recurrent infections and, often, early death.

Researchers are exploring the prospect of using gut bacteria to boost muscle strength, after zeroing in on a microbe that does this in mice

Dr. Donald Kohn has been developing gene therapies for rare pediatric immune disorders for over 30 years.

In the months before his wedding, Queens resident Roberto Pineda Sanchez noticed persistent rectal bleeding, a dangerous symptom he almost ignored.

Dynamic switching between revival stem cells and conventional intestinal stem cells enables efficient tissue repair without exhausting the stem cell pool, report researchers from Institute of Science Tokyo. Using organoid and mouse disease models, the researchers uncovered how flexible stress-tolerant cell states contribute to intestinal repair—providing a better understanding of the biological mechanisms driving intestinal regeneration.

AI-enabled medical devices promise improved medical care and support for health care professionals. However, the safety and performance of such systems not only depends on algorithms or technical specifications. It is equally important how people use these devices and applications. In a recent publication in the journal NEJM AI, a research team led by Prof. Stephen Gilbert from Else Kröner Fresenius Center (EKFZ) for Digital Health at TUD Dresden University of Technology systematically analyzes risks that can arise in human-AI interactions and makes recommendations for manufacturers and regulatory evaluators.

SiliconRepublic.com spoke with experts at Amgen to explore how early career guidance can set the foundations for a happy and productive career. Read more: How does mentorship impact a professional’s career longevity?

Marne-cel now offers an option to children with the rare immune disorder who lack a sibling donor for stem cell transplantation.

A rare disease gene therapy from Rocket Pharmaceuticals has garnered FDA approval after an earlier rejection for manufacturing problems. The FDA on Thursday granted accelerated approval to Rocket Pharma’s gene therapy ...

This GEN Live show will focus on new areas of stem cell research, including aging. It will bring together a panel of leading experts to unpack the latest advances and ongoing challenges in stem cells. The post Stem Cells at the Frontier: Breakthroughs, Barriers, and What’s Next appeared first on GEN - Genetic Engineering and Biotechnology News.

Artificial intelligence (AI) could help physicians determine if survivors of childhood cancer need extra support—and the more information included in AI prompting, the better its performance. This finding, published in Communications Medicine by scientists from St. Jude Children's Research Hospital, may guide future integration of AI into clinical workflows.

A US jury has ruled that deliberately addictive features of apps owned by Meta and Google harmed a young user’s mental health.The ruling, by the California Superior Court of Los Angeles, found that the platforms were deliberately designed in ways that contributed to the anxiety, depression, and body dysmorphia of the plaintiff KGM, as she is referred to in court documents, now a 20 year old woman.KGM’s lawyers argued that algorithmic recommendations, “likes,” push notifications, and autoplaying videos, among other features, were engineered to encourage compulsive use and exploited developmental vulnerabilities in KGM. This, they claimed, had contributed to a deterioration in her mental health.Meta reportedly argued that KGM’s mental health problems, including body dysmorphia and thoughts of self-harm, were caused by familial abuse and turmoil.1 However, the company added that it no longer set internal targets for user time spent on its platform.The video sharing platform YouTube, owned

Discover the future of pet safety with SATELLAI. From 5-way GNSS satellite tracking to PetSense AI health analysis, these smart collars offer precision and peace of mind for every dog and terrain.

arXiv:2603.24986v1 Announce Type: new Abstract: Large language model based health agents are increasingly used by health consumers and clinicians to interpret health information and guide health decisions. However, most AI systems in healthcare operate in siloed configurations, supporting individual users rather than the multi-stakeholder relationships central to healthcare. Such use can fragment understanding and exacerbate misalignment among patients, caregivers, and clinicians. We reframe AI not as a standalone assistant, but as a collaborator embedded within multi-party care interactions. Through a clinically validated fictional pediatric chronic kidney disease case study, we show that breakdowns in adherence stem from fragmented situational awareness and misaligned goals, and that siloed use of general-purpose AI tools does little to address these collaboration gaps. We propose a conceptual framework for designing AI collaborators that surface contextual information, reconcile

Japanese news outlet Akiba PC Hotline! examines a fake Samsung 990 Pro SSD that's starting to show up in the Japanese market.

This week I reported on some rather unusual research that focuses on the brain of L. Stephen Coles. Coles was a gerontologist who died from pancreatic cancer in 2014. He had spent the latter part of his career specializing in human longevity. And before he died, he decided to have his brain preserved by a…

Lily Mae Lazarus / Fortune: NY-based Blossom Health, which makes an “AI copilot” to augment psychiatrists' clinical decisions and automate office tasks, raised $20M in seed and Series A  —  Blossom Health has raised $20 million in seed and Series A funding to bring an AI “copilot” for psychiatry to patients nationwide, Fortune has exclusively learned.

Despite an urgent demand for kidney donors in the U.S., about 1 in 4 donor kidneys goes unused. In 2022, more than 71,000 people were on the kidney transplant waiting list, with waitlists often extending as long as five years for a donor kidney.

CRISPR is a powerful DNA-editing tool that has underpinned huge advancements in human health care in the last decade. It is a precision tool, but is not perfect, and misplaced DNA edits can compromise safety and efficacy, costing billions each year. Researchers at the MRC Laboratory of Medical Sciences (LMS), Imperial College London and the University of Sheffield have published research in Nature showing that the physical twisting of DNA plays an important role in these mistakes. Using a newly developed platform of tiny (nanometer-sized) DNA circles, called DNA minicircles, the team captured never-before-seen interactions between CRISPR and DNA, providing insights that could help eradicate errors altogether.