{"id":"W2619325259","doi":"10.1089/ten.tec.2017.0190","title":"Optical Method to Quantify Mechanical Contraction and Calcium Transients of Human Pluripotent Stem Cell-Derived Cardiomyocytes","year":2017,"lang":"en","type":"article","venue":"Tissue Engineering Part C Methods","topic":"Pluripotent Stem Cells Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute","funders":"National Heart, Lung, and Blood Institute","keywords":"Contractility; Induced pluripotent stem cell; Calcium; Contraction (grammar); Stem cell; Biomedical engineering; Calcium in biology; Calcium signaling; Cytochalasin D; Cell biology; Biophysics; Live cell imaging; Biology; Cell; Intracellular; Medicine; Internal medicine; Embryonic stem cell; Biochemistry; Cytoskeleton; Endocrinology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001546201,0.0002205355,0.0004176495,0.0000769539,0.0001611782,0.00005668226,0.0003226932,0.0002152878,0.000009628252],"category_scores_gemma":[0.0001604252,0.0002232725,0.0001001943,0.00004614156,0.00005487594,0.000009652927,0.0002172094,0.0001857603,0.000004449609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001978702,"about_ca_system_score_gemma":0.00002436595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003886329,"about_ca_topic_score_gemma":0.000001621636,"domain_scores_codex":[0.9983163,0.0002494746,0.0003340183,0.0004846262,0.0002392062,0.0003763075],"domain_scores_gemma":[0.9987828,0.0000919201,0.00009188597,0.0006590385,0.00009680266,0.0002775683],"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":[0.00005603129,0.00003599366,0.00002675715,0.0001140598,0.00007653145,0.000008490128,0.00004656016,0.001180558,0.9853917,0.0001249624,0.00003714271,0.01290116],"study_design_scores_gemma":[0.0006288034,0.0003162375,0.001827167,0.00003543516,0.00004855605,0.00001708905,0.00002131767,0.001696809,0.9788452,0.000005547631,0.0163325,0.0002253222],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3726546,0.0003481003,0.6261752,0.00009427705,0.0002381344,0.0003004859,0.00001232,0.00001774855,0.0001591467],"genre_scores_gemma":[0.7638727,0.00003931301,0.2353111,0.000008711429,0.000154656,0.00004133438,0.00001326637,0.00003961297,0.0005192937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3912182,"threshold_uncertainty_score":0.9104789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05024727260289585,"score_gpt":0.3958706326251598,"score_spread":0.345623360022264,"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."}}