{"id":44932,"topic":"ai","source":"Google DeepMind","title":"Accelerating discovery of liver disease mechanisms - Google DeepMind","url":"https://deepmind.google/blog/accelerating-discovery-of-liver-disease-mechanisms/","url_hash":"763e0f32f0957ef5e9acd43586b5b51ed6a25b1f","author":"","summary":"<a href=\"https://news.google.com/rss/articles/CBMihwFBVV95cUxNXzFhMzJTenRBcExrcFB6clJpWDNkQ2l4Y3pSTlNweWxDM090WGZKdG9jcXFERm1sQ3ozMXBlUjc5eGxJS2hhUjhSTnRBQ2lkYzhianV3V0dRNFNkYzh4SGstSks5S3dXdzVLRVlKaURNd2dvZkFRZlF1aUtlRlAweGJib3FEUUk?oc=5\" target=\"_blank\">Accelerating discovery of liver disease mechanisms</a>&nbsp;&nbsp;<font color=\"#6f6f6f\">Google DeepMind</font>","content":"Biomedical research produces a flood of information that no scientist can realistically absorb. At the University of Edinburgh, bioengineer Filippo Menolascina is using Co-Scientist to comb the literature for overlooked links and generate new hypotheses.\nHis team focused on a common liver disease called metabolic dysfunctionâassociated steatohepatitis (MASH). Developing treatments is challenging because MASH involves intertwined biological processes, including liver inflammation and metabolism, meaning singleâtarget drugs fall short. That pushes researchers toward combination treatments, but the number of potential drug pairings is overwhelming.\nFaced with that combinatorial explosion, Menolascina used CoâScientist to narrow the search. 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In his hands, CoâScientist synthesised evidence across liver biology and pharmacology, highlighted mechanisms worth focusing on, and flagged candidate combination therapies that his team could test.\nIn one emblematic case, CoâScientist tackled a live, practical question: Why does the drug resmetirom â a recently approved treatment prescribed for a specific stage of MASH â only help a narrow slice of those eligible patients? The system produced a hypothesis pinpointing the NLRP3 inflammasome as the specific molecular bridge coupling inflammation and metabolism in the disease â a connection never previously pulled together into a single, actionable explanation. The hypothesis, later experimentally verified, could pave the way for targeted dual-therapies.","excerpt":"Biomedical research produces a flood of information that no scientist can realistically absorb. 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