Slava Epelman

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Slava Epelman

Slava Epelman

@EpelmanLab

Cardio-Immunologist, Macrophages. Associate Prof, Univ of Toronto. UHN. Research Director Peter Munk Cardiac Center. Loretta Rogers Chair

Toronto, Ontario Katılım Ekim 2013
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Lluis Montoliu
Lluis Montoliu@LluisMontoliu·
Un grupo de genetistasen EE.UU. ha investigado la pureza genética de los modelos de ratón usados en biomedicina y ha encontrado que solo un 20% de las estirpes de ratón contienen las variantes genéticas que su nombre indica. Publicado en @ScienceMagazine science.org/doi/10.1126/sc…
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Kyle G Daniels
Kyle G Daniels@ScienceKyle·
🚨What if we could reliably program macrophage polarization state?🚨 biorxiv.org/content/10.648… Macrophages are highly plastic immune cells that perform critical functions by polarizing into distinct cellular states. The polarization state of macrophages can substantially influences the progression of cancers, infections, and autoimmunity. (1/11)
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Milica Radisic
Milica Radisic@milicaruoft·
Honored to become a Fellow of the Canadian Academy of Health Sciences yesterday in Montreal! Congrats to @TohidDidar and @Arinjay Banerjee great to see folks from my alma mater @McMasterU! Thanks @UofT @UofT for the support!
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Eric Topol
Eric Topol@EricTopol·
The mechanism by which macrophage cells sense danger to drive an immune response and inflammation has been elusive. A new report highlights change in cell volume as the signal @JCellBiol rupress.org/jcb/article/22…
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Rushin Gindra
Rushin Gindra@rushin_gindra·
1/7🎉 Introducing Phoenix: generative AI predicting spatially resolved gene expression from routine H&E slides-enabling virtual spatial-omics. Zero-shot. Across cancers, organs & species. Huge thanks to my co-first @TranManuel & all collaborators! 🙌 📜: doi.org/10.64898/2026.…
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Eran Segal
Eran Segal@segal_eran·
Our new preprint is a significant milestone for us We built "HealthFormer" by training on our deeply phenotyped cohort from the Human Phenotype Project data. Healthformer is a multimodal generative transformer model that tokenizes each participant's physiological trajectory across 667 modalities (biomarkers, body comp, sleep, CGM, microbiome, wearables, meds) and is trained with a single objective: predict the next measurement Forecasting, risk stratification, and intervention-conditioned simulation all arise as queries from one shared representation Key findings: → Matches direction of effect in 41/41 published RCTs; 30/41 within the reported 95% CI → Reconstructs biomarkers at r > 0.9; forecasts 2 yrs ahead → Validation on external data from UK Biobank, NHANES, PNP3, Framingham → Outperforms Framingham CVD & PREVENT-ASCVD on 27/30 endpoints → Predicts individual 6-mo responses in a held-out RCT Paper: arxiv.org/abs/2604.27899 Great work by Guy Lutsker, Gal Sapir, Jordi Merino, Smadar Shilo, Anastasia Godneva, Eli Meirom, Shie Mannor, Hagai Rossman, Gal Chechik
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Massimo
Massimo@Rainmaker1973·
Is a chicken’s head more stable than a $5,000 camera gimbal?
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AntonioAbbate
AntonioAbbate@AbbateAntonio·
Interleukin-1 Inhibitors for Management of Recurrent Pericarditis After Thoracic Organ Transplantation | JACC: Case Reports jacc.org/doi/10.1016/j.… Good reading, thanks @garshick
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Katie Galloway
Katie Galloway@GallowayLabMIT·
So you want to engineer your hiPSCs, but targeting DNA payloads requires multiple slow, inefficient steps for each construct. What if we could accomplish multi-site integration seamlessly? Come hear about STRAIGHT-IN Dual now out at Nature Biomedical Engineering! 🧵 Link at end!
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