15th July 2026, Wednesday

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HealthTech News

  • New study examines how and why harm reduction is misunderstood on college campuses
    on July 15, 2026 at 7:40 pm

    A new study found that students and staff involved with collegiate recovery programs had very different definitions and perceptions of harm reduction and its role in these programs, suggesting opportunities to reframe this public health approach in collegiate settings to build common ground and meet students’ varying substance use needs and recovery goals.

  • Molecular map of liver disease could transform how disease is diagnosed and monitored
    on July 15, 2026 at 7:40 pm

    A study led by researchers at Open Targets, EMBL’s European Bioinformatics Institute (EMBL-EBI), the University of Cambridge and others has identified a set of plasma proteins that could be used to diagnose and monitor patients with metabolic dysfunction-associated steatotic liver disease (MASLD). It was published today in Nature Metabolism.

  • The long shadow of a short fuse: Study finds parent-teen conflict echoes across generations
    on July 15, 2026 at 7:20 pm

    Parent-teen tension is often treated as a phase—something to be endured, then forgotten. A common byproduct of growing up.

  • Skeletal muscle signals to brain, brown fat to control aging in mice
    on July 15, 2026 at 7:20 pm

    Open lines of communication between the body’s organs are important to health and often falter with age. A new study in mice by researchers at WashU Medicine shows how signals that travel from skeletal muscle to the brain and then activate brown fat and control core body temperature are weakened in elderly mice. The research suggests that finding ways to restore these signals could offer new opportunities to support healthy aging.

  • AI flags heart failure risk five years early from routine ECG recordings
    on July 15, 2026 at 7:00 pm

    Researchers at the Technion Faculty of Biomedical Engineering have achieved a breakthrough in the early detection of heart failure. They developed DeepHHF, an artificial intelligence model that identifies patients at high risk of developing heart failure years before the onset of clinical disease, enabling preventive interventions that could spare patients significant suffering and potentially save lives.