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Another crazy longevity breakthrough Scientists found a way to selectively kill aging cells and actually improve health. A new study reveals a hidden survival weakness in senescent cells, the damaged “zombie” cells that build up as we age and drive inflammation, fibrosis and age related disease. Researchers discovered that these aging cells rely on an abnormal protein partnership between PGAM1 (a glycolysis enzyme) and Chk1 (a DNA damage checkpoint protein). This interaction supercharges sugar metabolism and survival signals, keeping senescent cells alive when they should have exited the building. Instead of blasting all cells with toxic drugs, scientists used a small molecule to break this specific PGAM1–Chk1 interaction. When that link was disrupted, senescent cells lost their metabolic lifeline, accumulated DNA damage and triggered self-destruction, while healthy cells were largely unaffected. In aged mice, clearing these cells led to measurable improvements in tissue health, including reduced lung fibrosis and lower markers of chronic inflammation. No immortality serum, but real physiological benefits tied directly to removing harmful aging cells. if this strategy ever translates to humans, it could represent a new class of precision senolytic therapies, targeting aging at its cellular roots instead of just managing symptoms. Still early, but this is one of those rare aging studies that shows a clean mechanism, a clear target, and real functional improvement.

Bad news for AI-based radiology. 🤔 It checks if chatbots can diagnose hard radiology images like experts. Finds that board-certified radiologists scored 83%, trainees 45%, but the best performing AI from frontier labs, GPT-5, managed only 30%. 😨 Claims “doctor-level” AI in medicine is still far away. The team built 50 expert level cases across computed tomography (CT), magnetic resonance imaging (MRI), and X-ray. Each case had one clear diagnosis and no extra clinical history. They tested GPT-5, OpenAI o3, Gemini 2.5 Pro, Grok-4, and Claude Opus 4.1 in reasoning modes. Blinded radiologists graded answers as exact, partial, or wrong, then averaged scores. Experts beat trainees, and trainees beat every model by a wide margin. More reasoning barely helped accuracy, but it made replies about 6x slower. Models did best on MRI and struggled more on CT and X-ray. To explain mistakes, the authors built an error map covering missed or false findings, wrong location or meaning, early closure, and contradictions with the final answer. They also saw thinking traps like anchoring, availability bias, and skipping relevant regions. General purpose models are not ready to read hard cases without expert oversight.

Paracetamol is the first choice of painkiller if you're pregnant and is still considered the safest choice of pain relief during pregnancy. There is no evidence it causes autism in children. Kate Brintworth, our Chief Midwifery Officer, explains more 🎥