{"id":"W2955407204","doi":"10.1186/s13036-019-0185-0","title":"Cardiac tissue engineering: state-of-the-art methods and outlook","year":2019,"lang":"en","type":"review","venue":"Journal of Biological Engineering","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institutes of Health; National Science Foundation","keywords":"Tissue engineering; Scaffold; Computer science; Process (computing); Genome editing; Construct (python library); Computational biology; Artificial intelligence; Genome; Nanotechnology; Biology; Biomedical engineering; Gene; Engineering; Materials science; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001137824,0.001009322,0.001303915,0.002809902,0.00037383,0.001419242,0.000934017,0.001272126,0.003446091],"category_scores_gemma":[0.0009101393,0.0004522098,0.0005999371,0.002186038,0.0006949572,0.002054181,0.0007809492,0.001893364,0.002351927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005620005,"about_ca_system_score_gemma":0.0009924826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000651168,"about_ca_topic_score_gemma":0.0008605639,"domain_scores_codex":[0.9996234,0.00004476014,0.00005164862,0.0000759602,0.0001685868,0.00003563933],"domain_scores_gemma":[0.9993024,0.0004049388,0.00007125241,0.00002509684,0.0001544305,0.00004190307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004183676,0.00008119841,0.0001449423,0.01946293,0.00004740066,0.0001758924,0.0000871298,0.000611014,0.005730079,0.008646654,0.01358256,0.9513885],"study_design_scores_gemma":[0.000006630863,0.0001159378,0.000490209,0.003338237,0.00007095507,0.001198958,0.00008329869,0.0002344895,0.002555371,0.004784361,0.9870858,0.00003582646],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000179697,0.9974768,0.0005238454,0.0002059903,0.0001930223,0.000005167036,0.00001531773,0.00001126763,0.001388947],"genre_scores_gemma":[0.0008070163,0.9976938,0.0006217023,0.0001251617,0.0001903839,0.000008335203,0.00002603677,0.000003048473,0.0005245039],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003446091,"threshold_uncertainty_score":0.01152837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06501414554587766,"score_gpt":0.3780962293836067,"score_spread":0.313082083837729,"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."}}