{"id":"W3089991419","doi":"10.1016/j.cjca.2020.07.196","title":"OPTIMIZATION OF ACELLULAR MATRIX BIOMATERIAL FOR CARDIAC REGENERATION","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Cardiology","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Calgary Laboratory Services","funders":"","keywords":"Decellularization; Medicine; Regeneration (biology); Fibroblast growth factor; Fibroblast; Biomaterial; Extracellular matrix; Biomedical engineering; Tissue engineering; Regenerative medicine; Cell biology; Internal medicine; In vitro; Stem cell; Biochemistry; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003246364,0.0003556779,0.0001835828,0.0003051644,0.0001604185,0.0004053453,0.0001867776,0.0003006917,0.0008248911],"category_scores_gemma":[0.0003522745,0.0001179149,0.000253509,0.0002449345,0.0001457024,0.0002535566,0.0001937591,0.0002382335,0.0002398128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002588269,"about_ca_system_score_gemma":0.000245589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003673913,"about_ca_topic_score_gemma":0.001330495,"domain_scores_codex":[0.9997936,0.00003072117,0.00001703442,0.00003821451,0.00008886683,0.00003159507],"domain_scores_gemma":[0.9998223,0.00005075141,0.00004253286,0.00001266192,0.0000502536,0.00002148173],"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":[0.00003881696,0.00003852411,0.0001238699,0.00006087321,0.000005318277,0.00002298586,0.00001114379,0.0008259129,0.9963069,0.0001050657,0.00002663311,0.002433918],"study_design_scores_gemma":[0.000006785524,0.0001889782,0.001153136,0.000008762257,0.00002345299,0.0000564635,0.00002635134,0.003256702,0.9936125,0.00006141665,0.001596433,0.00000902777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9801039,0.001841418,0.01361523,0.00005452612,0.00005808661,0.00005773024,0.0001777799,0.00007208507,0.004019289],"genre_scores_gemma":[0.9834822,0.0008214801,0.01398059,0.00003552295,0.0000116105,0.00005729787,0.0001381975,0.00002961988,0.001443448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008248911,"threshold_uncertainty_score":0.002759576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976514317641261,"score_gpt":0.2482788870866387,"score_spread":0.2285137439102261,"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."}}