An implantable, wirelessly powered electronic device could offer a drug-free approach to managing the intertwined metabolic disorders of obesity and type 2 diabetes. The device uses electrocatalysis to remove excess glucose from the bloodstream on demand. Obesity and diabetes are closely interconnected metabolic disorders that reinforce one another through complex hormonal, inflammatory, and energy-regulation processes, together contributing to the global rise of “diabesity.” Existing treatments rely heavily on long-term medication and lifestyle changes, while current glucose-monitoring and insulin-delivery systems face limitations in accuracy, reliability, personalization, and cost. Emerging approaches, including engineered therapeutic cells and drugs that target both weight and blood glucose, offer promise but remain constrained by concerns of side effects, safety, and affordability. Another emerging strategy involves electrocatalysis, which uses electrode surfaces to drive chemical reactions that convert glucose into other compounds.

 

Using electrocatalysis, Debasis Maity and colleagues developed an implantable device called electrocatalytic closed-loop health optimization (ECHO). The wirelessly powered device uses a platinum-decorated gold catalyst to convert circulating glucose into gluconate, which can be excreted through the kidneys. Connected to a continuous glucose monitor via a smartphone, ECHO automatically activates when blood sugar exceeds a preset threshold, enabling targeted glucose removal without external insulin or medication. In a proof-of-concept study in obese mice with type 2 diabetes, Maity et al. report that wireless activation of the device at low power (0.75 volts and 10 milliamperes) restored normal blood glucose levels within an hour and shifted the body’s energy use from glucose toward fat. Over six weeks of treatment, the mice experienced reductions in body weight and triglyceride levels, alongside increases in circulating free fatty acids and ketone bodies. These changes occurred without detectable episodes of low blood sugar or adverse effects on physiological function, inflammation, or behavior. “Current bioelectronics devices provide notable access to the nervous and neuromuscular systems,” write William Bentley and Gregory Payne in a related Perspective. “Therefore, future electronic intervention devices might help to manage diseases other than diabetes, modulate human-microbiome interactions, or maintain redox homeostasis in stress conditions, among many other applications.”

An artificial intelligence (AI) system designed to mimic a panel of medical experts – continually reviewing new information as it becomes available – outperformed participating human doctors in diagnosing clinical cases, while often reducing estimated testing costs. Previous evaluations of medical AI tools “have not tested diagnosis as an active, sequential information-gathering process that jointly considers diagnostic accuracy and diagnostic workup costs,” the study’s authors say. Clinical diagnosis is an iterative process in which physicians gather information from patient histories, physical examinations, and diagnostic tests to identify a medical condition. As new evidence emerges, they continually revise potential diagnoses and determine whether sufficient certainty exists to reach a conclusion or initiate treatment. Although language models (LMs) have demonstrated impressive medical reasoning abilities on written examinations and structured clinical cases, conventional evaluations typically provide complete case information upfront. “By compressing diagnosis into one inference step,” say the authors, “static benchmarks cannot measure whether a system asks the right questions, refrains from indiscriminate testing, updates the differential diagnosis as evidence accrues, or recognizes when sufficient certainty has been achieved to conclude the workup.” This could lead them to overstate diagnostic competence. To address this gap, Harsha Nori and colleagues developed the Sequential Diagnosis Benchmark (SDBench), transforming 629 clinical cases from The New England Journal of Medicine and emergency department encounters into interactive, step-by-step diagnostic challenges.

 

Using SDBench, Nori et al. evaluated Diagnostic Orchestrator (DxO), an AI system they created to guide diagnostic reasoning; it selects questions and tests with cost considerations in mind. Powered by GPT-5, DxO established a more favorable balance between diagnostic accuracy and testing costs than participating physicians across the evaluated cost-accuracy range. Depending on budget constraints, the system achieved accuracies of 79.6% to 84.5%, with average costs ranging from $1,303 to $8,945 per case. By comparison, physicians assessing curated clinical conference cases achieved average diagnostic accuracies of 22.3% without reference tools and 36.1% with them. Even the highest-performing physician, whose accuracy reached 51.8%, was surpassed by DxO in both accuracy and cost. The system also followed a distinct diagnostic strategy, gathering substantially more patient-history information before ordering its first test than physicians typically did. On routine emergency department cases, DxO achieved 80.3% accuracy at an average cost of $4,980 per case without budget constraints. Although these findings suggest that AI orchestration could support more efficient diagnosis, the authors argue that further evaluation is needed to establish its effectiveness in real-world clinical practice and its impact on patient outcomes. Morover, Nori et al. stress that the model cannot perform physical exams or observe patients directly. However, the system shows promise in supplementing a physician’s workflow.

 

For more insights, a “SciPak: Unpak’d” video interview with the lead and senior authors is available upon request from scipak@aaas.org.

The study of biological aging, how our cells, tissues, and organs change over time, independent of how many years have passed, is a growing area of research that aims to help scientists understand the root causes of chronic illnesses and find ways to enhance longevity. A review article publishing in the Cell Press journal Trends in Endocrinology & Metabolism on October 22 discusses the connections between pregnancy and biological aging. The authors hypothesize that when pregnancies occur too closely together, the body may not have enough time to recover—causing changes related to biological aging to accumulate. They hope that studying the biological changes that occur during pregnancy can help improve maternal health. 

Researchers from Mass General Brigham investigated women’s biological aging data during pregnancy and postpartum, analyzed population-level studies on birth spacing and long-term maternal health, and formulated the “recovery period hypothesis” framework linking the postpartum recovery period with long-term health outcomes and biological aging.

The results of numerous high-impact phase 3 clinical trials that could affect kidney-related medical care will be presented in-person at American Society of Nephrology Kidney Week 2026 October 22–25.

In two studies, University of Michigan researchers have found that targeting PP2A using a ‘molecular glue’ that helps the protein to retain its structure can help suppress the growth of cancer cells.