{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003981286,0.00006053853,0.00008253541,0.0000348588,0.00003850605,0.00002053358,0.00004726072,0.00001427505,0.000004302442],"category_scores_gemma":[0.00002706805,0.00005509741,0.00003026057,0.00006392542,0.00001628335,0.0001703625,0.00002241536,0.00003052338,0.000007328284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000491304,"about_ca_system_score_gemma":0.000009340063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.013062e-7,"about_ca_topic_score_gemma":5.226363e-8,"domain_scores_codex":[0.9994907,0.000004617216,0.00008594555,0.0002103049,0.00009122826,0.0001171445],"domain_scores_gemma":[0.9997466,0.00003683717,0.00002151603,0.0001163049,0.000009482837,0.000069293],"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":[0.000008194635,0.000007213575,0.0000219038,0.00003350323,1.244304e-7,0.000001113715,0.00005667303,0.004211956,0.9948967,0.0004795229,0.00003887098,0.0002442584],"study_design_scores_gemma":[0.00009207409,0.00002866154,0.0000106412,0.000004553543,0.000001718133,5.120976e-7,0.000006083906,0.3469966,0.6516024,0.00006221449,0.001143714,0.00005080951],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977311,0.0001858534,0.001170607,0.00002742343,0.0003694688,0.0002363518,0.00001241358,0.00002028839,0.0002464879],"genre_scores_gemma":[0.9991211,0.0001978027,0.00008661228,0.00005745652,0.00005867257,0.000009184427,0.000001317374,0.000009251258,0.0004586583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3432943,"threshold_uncertainty_score":0.2246807,"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."}}