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A transplant drug that has been used for decades can preserve the function of insulin-producing cells in young children who are newly diagnosed with type 1 diabetes.

The funds that the Sicilian Region has allocated to the cities of Palermo, Messina and Catania for the construction of strategic works for mobility, infrastructure ... Read more L’article Messina: 16.6 million from the Region for urban regeneration and mobility est apparu en premier sur Odnako.

It is safe for patients to receive a donor liver that has been intentionally preserved overnight using machine perfusion to enable a daytime transplant.

A groundbreaking new study, published in the journal Biochar, sheds fresh light on the intricate effects of biochar on soil emissions of nitrous oxide (N₂O), a highly potent greenhouse gas. Researchers have long touted biochar, a carbon-enriched material derived from biomass, as a promising solution to climate change due to its potential for enhancing soil […]

Robert Hart / The Verge: Utah launches a one-year pilot program allowing Legion Health's AI chatbot to renew prescriptions for 15 low-risk psychiatric maintenance medications  —  Some psychiatrists are asking what problem, exactly, this is solving. … Utah is allowing an AI system to prescribe psychiatric drugs without a doctor.

It is safe for patients to receive a donor liver that has been intentionally preserved overnight using machine perfusion to enable a daytime transplant. This is shown by a study performed at the University Medical Center Groningen (UMCG) in the Netherlands, including transplants using all types of donor organs. The post-transplant outcomes are at least as good as those for livers that were not treated with machine perfusion, or were treated for only a short time, prior to transplantation.

In a preclinical animal study, implantation of lab-grown esophageal grafts restored swallowing, offering hope for infants with long-gap esophageal atresia.

arXiv:2604.02227v1 Announce Type: cross Abstract: We consider a stopping problem and its application to the decision-making process regarding the optimal timing of organ transplantation for individual patients. At each decision period, the patient state is inspected and a decision is made whether to transplant. If the organ is transplanted, the process terminates; otherwise, the process continues until a transplant happens or the patient dies. Under suitable conditions, we show that there exists a control limit optimal policy. We propose a smoothed perturbation analysis (SPA) estimator for the gradient of the total expected discounted reward with respect to the control limit. Moreover, we show that the SPA estimator is asymptotically unbiased.

arXiv:2604.02227v1 Announce Type: cross Abstract: We consider a stopping problem and its application to the decision-making process regarding the optimal timing of organ transplantation for individual patients. At each decision period, the patient state is inspected and a decision is made whether to transplant. If the organ is transplanted, the process terminates; otherwise, the process continues until a transplant happens or the patient dies. Under suitable conditions, we show that there exists a control limit optimal policy. We propose a smoothed perturbation analysis (SPA) estimator for the gradient of the total expected discounted reward with respect to the control limit. Moreover, we show that the SPA estimator is asymptotically unbiased.

arXiv:2604.02218v1 Announce Type: cross Abstract: Ultra-narrow-linewidth lasers with suppressed high-frequency phase noise are critical for quantum control and precision metrology. While optical phase locking (OPL) is the standard technique for cloning the coherence of such sources, its effectiveness is often limited at high frequencies by feedback latency. We present a robust feedforward architecture that overcomes this limitation by recycling and demodulating the existing master-slave beat signal to drive a single electro-optic modulator for near-instantaneous noise cancellation. This approach eliminates the extraneous sidebands and transmission losses typical of more complex modulators. Through active stabilization of the beat amplitude and demodulation phase, we demonstrate robust suppression exceeding 30 dB from 10 kHz to 10 MHz. This hardware-efficient framework is readily compatible with standard OPL setups, offering a scalable solution for high-fidelity coherent control.

arXiv:2604.02227v1 Announce Type: new Abstract: We consider a stopping problem and its application to the decision-making process regarding the optimal timing of organ transplantation for individual patients. At each decision period, the patient state is inspected and a decision is made whether to transplant. If the organ is transplanted, the process terminates; otherwise, the process continues until a transplant happens or the patient dies. Under suitable conditions, we show that there exists a control limit optimal policy. We propose a smoothed perturbation analysis (SPA) estimator for the gradient of the total expected discounted reward with respect to the control limit. Moreover, we show that the SPA estimator is asymptotically unbiased.

arXiv:2604.01898v1 Announce Type: new Abstract: Artificial intelligence (AI) systems accelerate medical workflows and improve diagnostic accuracy in healthcare, serving as second-opinion systems. However, the unpredictability of AI errors poses a significant challenge, particularly in healthcare contexts, where mistakes can have severe consequences. A widely adopted safeguard is to pair predictions with uncertainty estimation, enabling human experts to focus on high-risk cases while streamlining routine verification. Current uncertainty estimation methods, however, remain limited, particularly in quantifying aleatoric uncertainty, which arises from data ambiguity and noise. To address this, we propose a novel approach that leverages disagreement in expert responses to generate targets for training machine learning models. These targets are used in conjunction with standard data labels to estimate two components of uncertainty separately, as given by the law of total variance, via a

arXiv:2604.01449v1 Announce Type: new Abstract: Artificial intelligence (AI) systems are increasingly integrated into healthcare and pharmacy workflows, supporting tasks such as medication recommendations, dosage determination, and drug interaction detection. While these systems often demonstrate strong performance under standard evaluation metrics, their reliability in real-world decision-making remains insufficiently understood. In high-risk domains such as medication management, even a single incorrect recommendation can result in severe patient harm. This paper examines the reliability of AI-assisted medication systems by focusing on system failures and their potential clinical consequences. Rather than evaluating performance solely through aggregate metrics, this work shifts attention towards how errors occur and what happens when AI systems produce incorrect outputs. Through a series of controlled, simulated scenarios involving drug interactions and dosage decisions, we analyse

Tissue repair is not a single event. It is a coordinated response involving inflammation, cellular signaling, and gradual rebuilding. Understanding how this process unfolds provides valuable insight into why recovery takes time, and why it does not always follow a predictable path.

The Information: Source: Anthropic has acquired Coefficient Bio, which was developing a platform that enables AI to run biotech tasks such as planning drug research, for ~$400M  —  Anthropic has acquired AI biotech startup Coefficient Bio for roughly $400 million, according to a person with knowledge of the deal.

Scientists at Rothamsted Research have successfully developed wheat with dramatically reduced levels of asparagine, without affecting yield, using gene editing techniques, offering a promising route to safer food production and improved regulatory compliance. Results from two years of field trials demonstrate that wheat produced using CRISPR genome editing can significantly lower concentrations of free asparagine—an amino acid that converts into acrylamide, a toxic and probably carcinogenic compound formed during everyday baking, frying, and toasting.

In a groundbreaking advancement at the intersection of cytoskeletal biology and regenerative medicine, researchers have unveiled a novel mechanism that significantly accelerates the differentiation of pluripotent stem cells into pancreatic islet cells by targeting the dynamics of the actin cytoskeleton. The study, conducted by Hogrebe, Schmidt, Augsornworawat, and colleagues and recently published in Nature Communications, […]

Artificial intelligence has already proven it can perform specific medical tasks, such as interpreting X-rays or flagging risks in patient data. But caring for patients is a dynamic process that unfolds over time, requiring clinicians to interpret signals from multiple sources and intervene as a patient's condition changes. Stabilizing a patient may require a physician to synthesize lab values and medical images, listen to lung or heart sounds, observe physical responses, and decide when to escalate care—often under severe time pressure.

Robert Hart / The Verge: Mental health startup Kintsugi is shutting down and open-sourcing its AI tech to detect depression and anxiety, after failing to secure FDA clearance  —  Instead, a mental health startup shut down and open-sourced its tech. … For the past seven years, the California-based startup Kintsugi …

Inhibiting AhR, a xenobiotic sensor protein, lifts a molecular brake on axon regeneration and pushes injured neurons from stress management towards growth in nerve and spinal cord injury models, scientists say. The post Blocking AhR Sensor Activates Regenerative Program in Injured Neurons appeared first on GEN - Genetic Engineering and Biotechnology News.

Some women with complex chronic illnesses are using chatbots to search for diagnoses or relief from their symptoms.

For the past seven years, the California-based startup Kintsugi has been developing AI designed to detect signs of depression and anxiety from a person's speech. But after failing to secure FDA clearance in time, the company is shutting down and releasing most of its technology as open-source. Some elements may even find a second life beyond healthcare, like detecting deepfake audio. Mental health assessments still largely rely on patient questionnaires and clinical interviews, rather than the lab tests or scans common in physical medicine. Instead of focusing on what someone is saying, Kintsugi's software analyzes how it is being said. Th … Read the full story at The Verge.

Hara hachi bu, a traditional Japanese practice of eating until you’re about 80% full, is gaining attention as a simple yet powerful way to improve health and reshape our relationship with food. Rather than promoting strict dieting, it encourages slowing down, tuning into hunger cues, and eating with awareness and gratitude. Research suggests it may help reduce calorie intake, support healthier food choices, and prevent long-term weight gain.

Graz University of Technology, the University of Graz and the Medical University of Graz have jointly developed an interactive system that automatically adapts evidence-based medical information to patients' prior knowledge and needs.

Erin Griffith / New York Times: How AI helped Medvi, a telehealth provider of GLP-1 weight-loss drugs with just two full-time employees, hit $401M in 2025 sales, as it tracks for $1.8B in 2026  —  Matthew Gallagher took just two months, $20,000 and more than a dozen artificial intelligence tools to get his start-up off the ground.

This week we welcome Magdalena Tyrpien, CEO of Nionyx Bio, just days after the company took first place in the BIO-Europe Spring Startup Spotlight competition in Lisbon. The post Nionyx Bio’s kidney gene therapy wins the 2026 BIO-Europe Spring Startup Spotlight appeared first on Labiotech.eu. © Labiotech UG and Labiotech.eu. Unauthorized use and/or duplication of this material without express and written permission from this site’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Labiotech UG and Labiotech.eu with appropriate and specific direction to the original content.

Jenny McCarthy dismisses Jim Carrey clone rumours, saying fans missed the real change: his happiness at the César Awards.

New results from a clinical trial show promising outcomes for a gene-edited treatment for severe sickle cell disease, a genetic blood disorder with few curative options.

In a groundbreaking advancement poised to redefine cardiovascular health diagnostics, researchers Hasan and Dhrubo have unveiled an innovative artificial intelligence (AI) framework that not only improves the accuracy of cardiovascular disease (CVD) diagnosis but also ensures the interpretability and ethical responsibility of AI applications in healthcare. Published in Scientific Reports in 2026, their work addresses […]

In a groundbreaking study set to reshape our understanding of neuronal repair, researchers have unveiled a novel mechanism by which the aryl hydrocarbon receptor (AhR) modulates axon regeneration through an intricate stress–growth switch. This work, spearheaded by Halawani and colleagues, dives deep into the cellular and molecular dynamics governing nerve injury recovery, revealing insight critical […]

If you own a Fitbit, keep an eye out for these upgrades.

In recent years, lung cancer screening programs across the United States have brought to light approximately 1.6 million suspicious lung nodules annually. This surge in detection presents a formidable challenge to pulmonologists and oncologists alike. While the overwhelming majority of peripheral pulmonary lesions identified through screening are benign, it is their malignant counterparts that remain […]

A revolutionary breakthrough in the treatment of severe sickle cell disease (SCD) has been reported from the latest data emerging from the multicenter RUBY Trial, producing highly promising outcomes that could redefine therapeutic strategies for this challenging genetic blood disorder. The findings, published in the prestigious New England Journal of Medicine, demonstrate unprecedented clinical success […]

The one-of-a-kind model gathers trial ownership, mission-driven investors, public funding, and community dollars into a single capital stack. The post Inside Cairnspring Mill’s bold new model for financing regenerative food systems appeared first on AgFunderNews.

PRISM ALS aims to develop, evaluate, and make available a diverse panel of well-characterized, patient-derived induced pluripotent stem cell (iPSC) models that capture both genetic and sporadic forms of ALS. The post Biological Complexity of ALS to Be Addressed by the Development of New Stem Cell Models appeared first on GEN - Genetic Engineering and Biotechnology News.

New results from a clinical trial show promising outcomes for a gene-edited treatment for severe sickle cell disease, a genetic blood disorder with few curative options. After research conducted as part of the multicenter RUBY Trial, researchers have published their latest findings in the New England Journal of Medicine. Remarkably, 27 out of 28 patients did not have any painful sickle cell crises after treatment, achieving what physicians call a "functional cure."

Last year's successful treatment of an infant known as Baby KJ encouraged scientists to try again. But now, five weeks after the FDA outlined its plans to make such individualized genetic medicines more accessible, researchers ...

As lung cancer screening identifies an estimated 1.6 million suspicious lung nodules each year in the U.S. alone, physicians face a challenge. Most peripheral pulmonary lesions are benign, yet the malignant minority represent the leading cause of cancer death for both men and women. Robotic bronchoscopy may provide a less invasive and more precise approach to diagnosing lung cancer, suggests a five-year, multisite Mayo Clinic study published in Mayo Clinic Proceedings.

In collaboration with researchers in South Korea, a team from The Hospital for Sick Children (SickKids) has discovered a promising therapeutic target in fat tissue that improves cellular function, reduces inflammation, and may protect against obesity-related diseases. The study was published in Nature Communications.

Experiments conducted in Brazil using laboratory rats have shown that graphene-based structures can act as a powerful ally in bone regeneration. These structures are made of sheets of the chemical element carbon that are just one atom thick. They can help heal fractures or bone loss. In the tests, the biocompatible matrix containing graphene facilitated nearly 90% repair of the damage sustained by the test subjects one month after the fracture was induced in the laboratory—a superior performance to that of other materials used in the research.

Researchers at Baylor College of Medicine and collaborating institutions took a closer look at how the gastrointestinal tissue repairs itself. They reveal in the Proceedings of the National Academy of Sciences key players and their connections in the repair process and suggest the possibility that they also may contribute to the repair of other types of tissues.

A research team from the Department of Orthopedics and Traumatology, School of Clinical Medicine, LKS Faculty of Medicine at the University of Hong Kong (HKUMed), has developed a titanium implant surface that can be activated by near-infrared (NIR). With just 15 minutes of NIR irradiation, this surface can eliminate 99.94% of Staphylococcus aureus (S. aureus) biofilms without the use of antibiotics, while simultaneously promoting bone-implant fusion.

In the rapidly evolving landscape of healthcare technology, the integration of artificial intelligence (AI) into clinical workflows offers promising avenues to address persistent challenges, notably clinician burnout. A groundbreaking large-scale observational study recently published in JAMA explores the real-world impact of AI-enabled ambient documentation systems, commonly known as “AI scribes,” on clinician time management and […]

Documenting a patient visit in the electronic health record (EHR) is essential to health care delivery, but also a major contributor to clinician burnout. Artificial intelligence (AI)-enabled ambient documentation, or "AI scribes," can automatically generate draft clinical notes for review after an appointment. While they have been shown to reduce clinician burnout, large-scale studies examining how these technologies impact clinician workflows are lacking.

Researchers from the Icahn School of Medicine at Mount Sinai have discovered a molecular switch in neurons that limits the regrowth of damaged axonal fibers. The findings, published in the journal Nature, show that blocking a protein called the aryl hydrocarbon receptor (AHR) may help neural regeneration and restore function after injuries to the peripheral nerves or spinal cord.

The Icy Box Docking and Clone Station provides the easiest way to move data between drives and to access bare drives.

A phase 1/2 trial of a novel viral vector gene therapy in adults with type 1 diabetes is scheduled to begin this year.

Mississippi has become the first U.S. state to ban cell‑cultured dairy products. The law follows a 2024 measure banning lab‑grown meat in the state.

The Copenhagen-based health AI company built Symphony on peer-reviewed research from the largest medical coding study of its kind, treating coding as a reasoning task rather than a labelling problem. It’s available via API now. Medical coding, the process of converting clinical notes, diagnoses, and procedures into standardised alphanumeric codes used for billing, reporting, and […] This story continues at The Next Web

The composition of the gut microbiome changes with age to favor inflammatory microbial species at the expense of those producing useful metabolites. Fecal microbiota transplantation is a way to permanently alter the composition of the gut microbiome, moving that of the recipient much closer to that of the donor. A number of studies in mice and other species have demonstrated that transplantation from young to old produces improved health and greater longevity, while transplantation from old to young has the opposite effect, as in the study noted here. While fecal microbiota transplantation is used in human medicine, only a few small studies have assessed outcomes in older people receiving material from young donors. The size of the effect in animal studies is promising, and thus […]

Discover how early pioneers used the winter "respite" to repair leather harnesses on stitching horses and sharpen tools for the next hard farming season. The post Winter was the time for renewal, rejuvenation and repair appeared first on Farm and Dairy.

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.