{"id":"W2911534815","doi":"10.1016/j.cell.2018.11.042","title":"A Platform for Generation of Chamber-Specific Cardiac Tissues and Disease Modeling","year":2019,"lang":"en","type":"article","venue":"Cell","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":621,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; MaRS; Toronto Public Health; McGill University; Toronto General Hospital; University Health Network; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Ranolazine; Biology; Induced pluripotent stem cell; Biomedical engineering; Tissue engineering; Neuroscience; Pharmacology; Gene; Medicine; Embryonic stem cell","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003257034,0.0004589335,0.0003054671,0.0003093312,0.0003283538,0.0006174828,0.0008082087,0.0006868643,0.002625643],"category_scores_gemma":[0.0002846759,0.0003610875,0.0003671595,0.0001676983,0.0002173968,0.0003711512,0.000747068,0.0009752496,0.001283828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001689781,"about_ca_system_score_gemma":0.0003669677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003382205,"about_ca_topic_score_gemma":0.0006010056,"domain_scores_codex":[0.9998642,0.0000108089,0.000006786222,0.00003027252,0.00007157437,0.00001640041],"domain_scores_gemma":[0.9998604,0.00003271486,0.00002483642,0.00003543539,0.00002188398,0.00002468392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003424741,0.00005721737,0.000141628,0.00006659929,0.00001779349,0.00018933,0.00004904209,0.007361668,0.9717923,0.005162803,0.00148054,0.01364679],"study_design_scores_gemma":[0.00003138886,0.0001970961,0.0006495009,0.00002560428,0.00004112779,0.0004998078,0.00003062255,0.07818202,0.8681319,0.003325946,0.04884919,0.00003590691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.123852,0.0005565106,0.8567935,0.0003456643,0.0005621134,0.0002296171,0.001432476,0.005108621,0.01111948],"genre_scores_gemma":[0.4648918,0.001318309,0.5146526,0.0002188785,0.00007509361,0.0006223695,0.002867763,0.001434019,0.01391921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002625643,"threshold_uncertainty_score":0.008783638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06877104564724223,"score_gpt":0.255753358836644,"score_spread":0.1869823131894018,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}