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  "text": "What if you could test a drug on a human organ and simultaneously know what would have happened without it?\n\nThat's what we built.\n\nProud to share our work in @NatureBiotech: digital twins of human lungs 🫁🔥 (paper: https://t.co/Y8wzxNwWfd)\n\nWe created digital twins of ex vivo human lungs: multimodal AI models trained on 951 human lungs from the world's largest EVLP dataset at @UHN. Physics-informed ML across physiology, biochemistry, transcriptomics, proteomics, metabolomics, and imaging, all forecasting together.\n\nThe key insight: the physical lung receives the treatment. The twin is the untreated control. Paired causal inference on the same organ. No separate cohort. No intersubject noise.\n\nResult: we detected drug efficacy with 6 lungs. Traditional methods would need 18.\n\nThis is what precision preclinical evaluation looks like.\n\nFrom Virtual Cells → Virtual Organs → Virtual Patients. \nOne step toward virtual organs replacing animal testing.\n\nHuge congratulations to Elly Zhou (a phd student I co-supervise) for leading this work with exceptional rigor, and to Andrew Sage and @SKeshavjee  for building the foundation that made it possible.",
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