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Organ-on-Chip Method Designed to Zero In on Connection Between Diabetes and Dementia

A University of Bath-led research effort received £500,000 to develop an organ-on-chip device that replicates connections between the brain, gut, and pancreas. The GlucoBrain project is designed to allow researchers to track how signals move between the organs and uncover why diabetes may lead to changes in memory and cognition.

Collaborators include investigators from the University of Oxford and Johns Hopkins. Their findings could pave the way for new treatments to improve the lives of millions of people affected by diabetes, dementia, or both, notes the team.

Diabetes and Alzheimer’s disease are two of the world’s most pressing health problems, especially in aging societies. While diabetes is widely known to affect the heart, kidneys, and eyes, growing evidence suggests it is also linked with problems in memory, learning, and brain function. However, the biological mechanisms behind this link remain poorly understood.

“Our gut, pancreas, and brain are constantly communicating via a network of signals, helping us regulate hunger and blood sugar,”  says Despina Moschou, PhD, project lead. “But we still don’t fully understand how these signals interact at a cellular level and why glucose levels are linked to cognitive decline. “By creating a connected system on a chip, we can study in real time how signals travel between organs, how diabetes may impair brain function, and how new drugs could help.”

Most current knowledge on the link between diabetes and dementia comes from animal studies, simple cell cultures, and patient studies. While these are useful, they don’t fully and accurately capture all the complex interactions between our organs, hormones, and cells, points out Moschou.

Organ-on-chip technology uses living human cells in miniature devices that mimic how organs work in the body. Unlike cell cultures grown in a petri dish, these devices allow cells to grow in three dimensions, receive a controlled supply of nutrients and interact more naturally. Researchers will also be able to isolate these individual organs and cell types to understand exactly how they communicate at a molecular level.

The three-year project starts in October, bringing together engineers, clinicians, biologists and computer scientists to model the complex disease interactions. The team will first develop individual chip models for the gut, pancreas, and brain, before connecting them into a multi-organ system. They will gradually increase complexity and measure how each organ responds to glucose, hormones and different drug treatments.

Researchers from the University of Oxford will provide core clinical expertise in diabetes and metabolic disease, ensuring models are physiologically accurate. The team from Johns Hopkins University brings specialist expertise in Alzheimer’s disease and brain organoids.

GlucoBrain is a pilot project established to help researchers understand exactly how diseases like diabetes and dementia work at a deeper, biological level. This early-stage research will build the foundations for even more advanced and realistic models, bringing together more organs and cell types, explain team members. By harnessing the power of artificial intelligence, the devices have the potential to reveal new insights into how diseases emerge and develop.

“Not only would these devices give us an unprecedented way to study diseases, but they could help speed up drug discovery and testing, reducing reliance on animal models and making results more relevant to humans,” continues Moschou. “In the long term, they could pave the way for personalized medicine, using a patient’s own cells to identify the most effective treatment.”

The project is funded by the Engineering and Physical Sciences Research Council (EPSRC) Health Technologies Connectivity Awards.

 

The post Organ-on-Chip Method Designed to Zero In on Connection Between Diabetes and Dementia appeared first on GEN – Genetic Engineering and Biotechnology News.

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STAT+: Trump’s CMS to allow skimpier plans on ACA exchanges

The Trump administration is opening the floodgates for Affordable Care Act exchanges to feature plans that offer bare-bones coverage and fewer protections, but which have lower monthly premiums.

The Centers for Medicare and Medicaid Services on Friday finalized its proposal from February to pare back the requirements that ACA plans have to meet beginning for 2027, like offering standardized plans and restricting high-deductible, catastrophic plans to a small subset of members. 

The changes are aimed at making policies cheaper, in line with President Trump’s affordability agenda, but they also fit within the conservative playbook of loosening market restrictions and consumer protections in favor of offering people the freedom to choose policies that offer less coverage. The obvious drawback, health policy experts say, is that those policies put individuals and families on the hook for potentially massive bills if they actually need care. 

Continue to STAT+ to read the full story…

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AI Model Predicts Alzheimer’s Progression from a Single MRI Scan

Researchers at the University of California, San Francisco (UCSF) have developed an artificial intelligence model capable of predicting cognitive impairment and Alzheimer’s disease progression using only a single baseline MRI scan and basic demographic information. The approach, published in Nature Aging, could help make early Alzheimer’s assessment faster, more accessible, and less dependent on costly specialized testing.

Alzheimer’s diagnosis remains complex and resource-intensive

Alzheimer’s disease accounts for approximately 60% to 70% of dementia cases worldwide. Although structural brain changes and cognitive decline are hallmarks of the disease, accurately forecasting who will develop progressive impairment remains difficult.

Current diagnostic workflows often rely on multiple complementary techniques, including PET imaging, cerebrospinal fluid or blood biomarkers, genetic testing, and comprehensive neuropsychological assessments. While effective, these approaches can be expensive, time-consuming, and inaccessible in many healthcare settings.

MRI scans are among the most widely available clinical imaging tools for neurological assessment, but MRI data alone has historically struggled to capture the complexity and heterogeneity of Alzheimer’s disease progression when used in conventional AI frameworks.

To address this challenge, the UCSF team designed a multitask deep learning framework that combines domain-specific imaging knowledge with advanced machine learning methods to predict cognitive outcomes directly from structural MRI scans.

AI framework predicts cognition without invasive testing

Unlike many earlier Alzheimer’s prediction models, the new system does not require longitudinal imaging data, baseline cognitive testing, PET scans, or molecular biomarker analysis.

The researchers instead focused on extracting clinically meaningful information from a single baseline MRI scan. The framework was trained to perform several related tasks simultaneously, including tissue segmentation, Alzheimer’s diagnosis prediction, and estimation of both present and future cognitive performance.

A key innovation of the study was the development of a specialized image model that segments brain tissue into gray matter, white matter, and cerebrospinal fluid before generating cognitive predictions. According to the authors, this task-specific segmentation step allowed the model to learn biologically relevant spatial brain features more effectively than standard transfer-learning approaches.

Senior study author Ashish Raj, PhD, professor of radiology and biomedical imaging at UCSF, said the goal was to create a system that could be realistically implemented in routine clinical environments.

“Unlike previous approaches, our model does not require baseline cognitive assessment, specialized image pipelines, expensive PET scans, genetic analysis, or fluid proteomics, making it a fast, accurate, and easily implementable tool for most clinical settings,” Raj said in a statement.

Large imaging datasets improved robustness and generalizability

To train and validate the framework, the researchers used imaging and clinical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including MRI scans, demographic information, diagnoses, and cognitive assessments.

The team also incorporated MRI data from the Human Connectome Project Young Adult cohort, which contains scans from healthy younger adults with minimal age-related brain atrophy. According to the authors, exposing the model to healthy brain anatomy improved its ability to distinguish pathological neurodegeneration from normal aging.

An external validation cohort from the Dallas Lifespan Brain Study was additionally used to test the generalizability of the framework across independent datasets.

The researchers reported that the multitask framework outperformed existing AI methods, including standard transfer-learning approaches, in predicting clinically relevant outcomes. The model generated accurate predictions for Alzheimer’s diagnosis, tissue segmentation, current cognitive function, and future cognitive decline using only baseline MRI data.

The study also reported improvements in computational efficiency and processing speed compared with more complex MRI morphometry pipelines commonly used in neuroimaging research.

First author Daren Ma, MSc, a machine learning specialist in the Raj Lab at UCSF, said the framework could help clinicians identify at-risk patients earlier and streamline referrals for advanced neurological evaluation.

“We reported meaningful gains in speed and performance over other pipelines, which could prove valuable in developing a quick clinical prediction of cognitive impairment prior to referring the patient to a more advanced imaging lab and/or a full neuroradiology report,” Ma said.

Potential implications beyond Alzheimer’s disease

The researchers believe the framework could eventually be adapted for other neurodegenerative disorders characterized by structural brain changes and progressive cognitive decline.

Potential future applications include Parkinson’s disease, amyotrophic lateral sclerosis (ALS), and Huntington’s disease. The ability to estimate cognitive impairment using minimal baseline data may also prove useful in community healthcare settings where access to specialist neuropsychological testing is limited.

In addition, the model may have implications for clinical trial design. Identifying likely disease progressors early could help reduce trial size requirements and improve patient selection for studies evaluating disease-modifying therapies.

“The ability to correctly predict progressors from non-progressors using only baseline data can dramatically reduce sample sizes and cost,” Raj said.

The authors emphasized, however, that further validation will be necessary before the model can be broadly implemented in routine clinical practice. Future iterations of the framework may incorporate additional clinical measurements where available, including longitudinal MRI imaging, PET scans, genetics, and blood or cerebrospinal fluid biomarkers.

The study highlights the growing role of AI-driven imaging analysis in neurology and suggests that clinically accessible tools such as MRI may eventually support earlier and more scalable prediction of Alzheimer’s disease progression.

The post AI Model Predicts Alzheimer’s Progression from a Single MRI Scan appeared first on Inside Precision Medicine.

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Microbiome Therapy Could Help Drug-Resistant Melanoma Patients

Microbiotica, a microbiome-focused biotech based in Cambridge in the U.K., has achieved good Phase Ib results in a trial of its microbiome therapy for patients with advanced melanoma skin cancer.

The therapy, currently known as MB097, is designed to be given to patients who have not previously responded to immunotherapy in addition to a checkpoint inhibitor pembrolizumab. MB097 was developed to reverse the drug resistance seen in these patients and is based on research looking into the gut microbiome of melanoma patients who do respond to this kind of immunotherapy.

The primary endpoint of the trial, which included 41 patients from the U.K., France, Italy, and Spain, who had previously shown resistance to anti-PD-1 drugs, was safety and tolerability of MB097. Several secondary endpoints including response rate, duration of response, and overall survival were also included. The therapy, which contains nine beneficial strains of gut bacteria, met both its primary and secondary endpoints in the study, according to the company, although precise details will be released at a scientific conference later this year.

“There is increasing evidence that the microbiome plays a crucial role in patients’ response to immune checkpoint inhibitors. Clinical benefit has been reported with fecal microbiota transplantations, while MB097 capsules taken orally each day affords an easy and reproducible way of modifying the microbiome,” said the national coordinating investigator for the study, Pippa Corrie, MD, PhD, a clinician and researcher from Cambridge University Hospitals NHS Foundation Trust, in a press statement.

“The MELODY-1 study results show that MB097 is well tolerated, with encouraging early signs of efficacy in a very difficult to treat metastatic melanoma patient population with primary resistance to anti-PD-1 based immunotherapy, in whom there is a significant unmet need.”

Up to half of all advanced melanoma patients fail to respond to anti-PD-1 immunotherapy, leaving them with very few options. A growing body of research, including a 2021 study showing fecal transplant can overcome resistance to anti-PD-1 immunotherapy, shows that the gut microbiome plays an important role in whether a patient’s immune system mounts an effective anti-tumor response when given these therapies.

The make-up of MB097 is based on detailed research looking at strains of bacteria linked to effective response to immunotherapy. Preclinical work showed that the bacteria in the therapy directly activate cytotoxic T cells and counter immunosuppressive tumor macrophages. If larger controlled trials confirm these initial results MB097 could become a standard add-on to immunotherapy.

Microbiotica has another clinical program in ulcerative colitis, which also reported good results earlier this year in another Phase Ib trial. In total, 63% of those in the treatment group achieved clinical disease remission versus 30% in the placebo group and all were also taking standard therapy for the autoimmune disease.

The company now plans to move both its programs to larger controlled studies with a view to moving closer to market approval with both therapies.

The post Microbiome Therapy Could Help Drug-Resistant Melanoma Patients appeared first on Inside Precision Medicine.

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<![CDATA[See how overlooked birth trauma can mimic postpartum depression.]]>
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STAT+: Why the WakeMed – Atrium Health hospital merger matters

This is the online version of STAT’s weekly email newsletter Health Care Inc. Sign up here.

Thanks for being here! So much to read, including a new series from my colleagues Lev Facher and Isa Cueto. They delved into the perils of alcohol despite its ubiquity in American culture. The opening piece has several passages and quotes that will make you stop and think in a gratifying, educational way. Give it a read, and as always, pour your thoughts and tips my way: bob.herman@statnews.com.

Wide a-Wake(Med)

Most of you reading this do not live in North Carolina. But a hospital system merger in that state that has created local rage has national relevance.

Continue to STAT+ to read the full story…

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What to expect from Google this week

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

When Google opens its doors tomorrow for its annual developer conference, I/O, it will do so as a clear third place in the foundation model race. A year ago, at Google I/O 2025, the situation looked very different: The company was still riding high from the launch of Gemini 2.5 Pro that March, and distinguishing among the top-tier large language models often felt like a subjective splitting of hairs. 

But a foundation model’s reputation these days rests largely on its coding capabilities, and for months Google’s coding tools have been outgunned by Anthropic’s Claude Code and OpenAI’s Codex. Those systems are so dramatically superior to Google’s own offerings that the company has reportedly had to allow some engineers at DeepMind, its AI division, to use Claude for their work—lest they fall farther behind.

So when I arrive at the conference in Mountain View, California tomorrow, I’ll certainly be on the lookout for any efforts Google is making to claw its way back into frontrunner position. But I’m also eager to see new developments in areas where Google shapes the cutting edge, such as AI for science. The company’s moves there might receive less attention, but they will be no less consequential. 

Here are three things I’ll be paying particular attention to over the next two days.

An attempted coding comeback

Google is taking its AI coding crisis seriously. According to reporting from The Information, there’s a new AI coding team at DeepMind. And the Los Angeles Times has reported that John Jumper, who shared a 2024 Nobel Prize in chemistry with DeepMind CEO Demis Hassabis for their work on the protein structure prediction software AlphaFold, is lending his talents to the efforts. I would be surprised if we don’t see a major new coding release at I/O, perhaps in the form of an update to the company’s Antigravity agentic coding platform.

That said, we shouldn’t expect anything transformative here. Googlers have access to models and products that are substantially ahead of those released to the public, yet they were still reportedly fighting over who got access to Claude Code last month. Unless the company has made astonishing progress since then, Google probably won’t make it back to the coding frontier in the next two days.

Science and health

Coding might be Google DeepMind’s weakness, but science is its conspicuous strength. It is the only frontier AI company to have earned a Nobel Prize. And as LLMs have come to dominate the AI-for-science landscape, Google has only solidified its lead. Last year, the company released multiple scientific AI tools, including the AI co-scientist, which formulates hypotheses and research plans in response to user questions and has been described as an “oracle” by one Stanford scientist, and AlphaEvolve, a system that iteratively discovers new solutions for mathematical and computational problems. If any new scientific tools are announced at I/O, they’ll be worth noting.

I’ll also be paying close attention to any moves Google makes in health and medicine. Google is doing some of the best research out there on LLM-based health tools, but OpenAI has defined the health AI conversation since the release of ChatGPT Health in January. Google has announced that it will be making its AI-powered Health Coach publicly available tomorrow, but promotional material suggests that the tool is geared more toward providing advice on topics such as fitness and diet than to addressing users’ medical concerns. Is this another area where Google has fallen behind, or is the company exercising appropriate caution in a high-stakes domain? 

The drama

While Google fans congregate down in Mountain View, roughly 30 miles north in Oakland the Elon Musk v. Sam Altman trial will be wrapping up. The past few months have seen more than their fair share of AI CEO drama—before the trial, the animosity between Altman and Anthropic CEO Dario Amodei took center stage as Anthropic and OpenAI worked to negotiate deals with the US Department of Defense. But DeepMind’s Hassabis has, for the most part, steered clear of such drama. He effectively presents himself as a Nobel Prize-winning nerd, and if he has written screeds about any of his peers, they haven’t been leaked to the press or appeared in legal discovery.

That’s not to say that Google is controversy free. Last month, a group of 600 employees, many of whom work for DeepMind, sent a letter to CEO Sundar Pichai protesting an impending DoD deal. Google signed that deal the next day. Hassabis, Pichai, and all the other big names will surely do their best to skirt these and other touchy subjects while on stage, but controversies will worm their way in regardless. It will be interesting to see whether Google can maintain its veneer of neutrality.

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<![CDATA[Why schizophrenia shortens life: smoking, metabolic risks, and care gaps drive a 15-year mortality loss—and clinicians can help close it.]]>
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Inside Anduril and Meta’s quest to make smart glasses for warfare

The defense-tech company Anduril has shared new details about the augmented-reality headset for the military it’s prototyping with Meta, including a vision for ordering drone strikes via eye-tracking and voice commands.

Quay Barnett, who leads the efforts as a vice president at Anduril following a career in the Army’s Special Operations Command, says his fundamental goal is to optimize “the human as a weapons system.” The vision is undoubtedly cyborg-inspired: Barnett wants drones and soldiers to see together, share information seamlessly, and make decisions as one. 

Anduril actually has two such projects in the works. The first is the Army’s Soldier Born Mission Command, or SBMC, for which the company won a $159 million prototyping contract last year to work with Meta on augmented-reality glasses to attach to existing military helmets. But Anduril has also embarked on a self-funded side quest, announced in October, to design its own helmet and headset combo called EagleEye. This is something the military has not asked for, but Anduril insists it will prefer it and purchase it in the end.

So far, both systems are years away. The Army isn’t expected to move its top choice for the SBMC program into production until 2028, if it picks one at all (the previous lead for the effort, Microsoft, was set to receive a $22 billion production contract that was ultimately cancelled when the glasses didn’t prove viable). But Barnett told MIT Technology Review about where both Anduril’s prototypes are headed.

Depending on the situation, the glasses for either prototype will overlay certain information onto a soldier’s field of view. This might be as simple as a compass or as complex as an entire map of the area, information about where nearby drones are flying, or AI-driven recognition of a target like a truck. 

The soldier would then speak to the interface in plain language—for example, to order an evacuation for someone who’s been injured or to plan a route taking into account which areas are off limits. A large language model—Anduril is in tests with Google’s Gemini, Meta’s Llama, and even Anthropic’s Claude, despite the company’s conflict with the Pentagon—will be used to help translate a soldier’s speech into commands the software can follow. And the engine for it all will be Anduril’s software Lattice, which incorporates data from lots of different military hardware into one picture. The Army announced in March that it would spend $20 billion to integrate Lattice with essentially its entire infrastructure.

Barnett’s team is designing the headset to carry out multi-step tasks. A soldier might send a drone to surveil an area and instruct it to come back once it’s found something that looks like an artillery unit; then the system would recommend courses of action, like sending a nearby drone to strike, that would have to be approved by the normal chain of command. Leading the system through this, if all goes to plan, might not even require speech; the soldier could instead communicate through tracked eye movements and subtle taps.

That’s the idea, anyway. It’s worked on early prototypes, Barnett says, but there aren’t yet versions ready for the Army to test at scale. The component parts began arriving in March. Because of federal military contracting rules, these parts—unlike Meta’s commercial smart glasses—required new supply chains that don’t rely on Chinese companies.

It’s a lot for soldiers already bogged down in information overload, says Jonathan Wong, a former US Marine who works as a senior policy researcher at RAND on Army efforts to buy new tech. Both smart glasses projects aim to create a clean interface that presents only the right information at the right time. But it’s a product that soldiers will reject if it costs more of their attention than it saves. “How much mental bandwidth do you have to be both aware of your surroundings and to operate this technology in a way that makes you and your whole unit better?” he says.

Wong recalls that as a platoon commander, for example, he had a radio that operated on three different channels at once. “The moment that two people were on different channels talking at the same time, I immediately couldn’t comprehend anything that either one of them was trying to tell me, and I was probably not aware of my own surroundings,” he says. “I think there are limits to what you can take in.”

Ideally, Barnett says, smart glasses can ease that information overload. Anduril’s approach is to get creative with ways the user can access necessary information quickly. Voice commands and eye tracking are a piece of that strategy. But even if it’s all technically feasible, it might take years of field testing to know if the system is actually useful for soldiers, Wong says. 

Such a system would mark a major escalation in how closely soldiers rely on imperfect AI systems. While computer vision models used to identify objects have long been employed by militaries, and chatbots have recently entered decision-making during the war in Iran, these technologies have not yet made their way to most frontline soldiers. A smart glasses system tasked with identifying threats and recommending strikes would introduce massive new risks of errors. 

Anduril is not the only one competing to develop smart goggles for combat. Rivet, which specializes in wearable sensors for the military, received a $195 million prototyping contract the same time, and in March the Israeli defense-tech company Elbit received its own $120 million contract. This all comes after Microsoft lost its role leading the Army’s smart glasses effort, following a Pentagon audit that found the Army wasn’t properly testing the glasses, a mistake that could have wasted $22 billion.

For both Anduril’s prototypes, the company is testing a new system for digital night vision, which uses electronic sensors and algorithms to boost low levels of light. It’s been a promised technology for decades but has tended to work too slowly for practical use and produce grainy images. Anduril says it has found improvements over previous prototypes through techniques rooted in both new generative AI and older machine learning. 

Much of the other hardware for both projects is being built by Meta, including the displays and the waveguides that send visuals to the user’s eye without blocking the view. That might be a surprise to anyone who knows the backstory: In 2017, Facebook (now Meta) ousted Anduril founder Palmer Luckey following an internal conflict involving his support for Donald Trump. The two are now back in the augmented-reality business together, while Mark Zuckerberg has also adopted a friendlier posture toward the second Trump administration.

For the Army initiative, this suite of smart glasses, night vision, and sensors will be attached to the helmets and other gear soldiers already wear, with a separate battery pack. The EagleEye version will instead incorporate the tech into the helmet itself. Even if the Army doesn’t prefer EagleEye in the end, Barnett says, Anduril will attempt to sell the system to foreign militaries.

Multiple challenges must still be overcome. Unlike Meta’s Ray-Ban glasses, the prototypes have to operate in an environment full of dust, explosions, and smoke. Adding the computing power and battery life they need also means more weight for soldiers already carrying upwards of 100 pounds. Then the technology has to work in environments without ubiquitous 5G cell connections; powerful computer vision and AI models will need to run locally on the device.

For the Army to want to buy it at scale, “it’s got to work, and it’s got to be pretty seamless,” Wong says. “It’s a high bar.”