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Machine-learning framework to predict the performance of lipid nanoparticles for nucleic acid delivery

This work was supported by the Wilmot Cancer Institute Predoctoral Fellowship (to G.A.), the American Association for Cancer Research and Breast Cancer Research Foundation (20-20-26-HARR to I.S.H.), the Breast Cancer Coalition of Rochester (to I.S.H.), the American Cancer Society (RSG-23-971782-01-TBE to I.S.H.), the Ludwig Center for Metastasis Research (to A.M.), the University of Chicago Comprehensive Cancer Center (to A.M.), the Cancer Research Foundation (to A.M.), the V Foundation (to A.M.), the Pancreatic Cancer Action Network (to G.C.), the Lung Cancer Research Foundation (to J.C.), and NIH grants R01CA269813 (to I.S.H.), R01CA269813-S1 (to M.Z.), R37CA230042 (to G.M.D.), R24AA022057 (to V.V.), R01AA028859 (to Y.C.), R01CA276461 (to A.M.), T32CA009594 and F31CA278362 (to C.S.), and AI150698 (to J.M.)

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It nearly impossible to determine what's actually causing our results Because when I tried BPC-157, I made some classic mistakes that created a messy data set

This higher range is designed to saturate the tissues and provide a more robust defense against oxidative stress