{"id":"W4294408764","doi":"10.1016/j.biomaterials.2022.121786","title":"Decellularized extracellular matrix: New promising and challenging biomaterials for regenerative medicine","year":2022,"lang":"en","type":"review","venue":"Biomaterials","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":259,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Decellularization; Regenerative medicine; Extracellular matrix; Tissue engineering; Biomaterial; Self-healing hydrogels; Scaffold; Materials science; Nanotechnology; Biomedical engineering; Regeneration (biology); Cell biology; Stem cell; Engineering; Biology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001526347,0.001052568,0.004984827,0.0006826505,0.0003441262,0.00008347072,0.0002280261,0.0005163532,0.00134193],"category_scores_gemma":[0.0005497613,0.0007293296,0.0003720067,0.0003586026,0.0002023389,0.00006673828,0.0001607071,0.00006152273,0.00001516095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339518,"about_ca_system_score_gemma":0.0003776865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005306673,"about_ca_topic_score_gemma":6.105216e-7,"domain_scores_codex":[0.9957656,0.0003669554,0.001693895,0.001065553,0.0004485186,0.0006594439],"domain_scores_gemma":[0.9976714,0.0003671249,0.0007569602,0.0006532018,0.00007015129,0.0004812165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002305491,0.0001064706,2.953954e-7,0.05682851,0.001262052,0.0003374699,0.0008734904,0.000001628032,0.6329783,0.002159043,0.004693497,0.3005287],"study_design_scores_gemma":[0.002673651,0.0007616821,3.874546e-7,0.01383831,0.002881522,0.0009178799,0.00007432288,0.00002278588,0.01099013,0.00003430807,0.967143,0.0006620454],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000495387,0.9862457,0.003952364,0.0004777269,0.003793073,0.004516696,0.0001738107,0.0003257398,0.00001953914],"genre_scores_gemma":[0.0000422956,0.9690731,0.007621871,0.00001009122,0.005078956,0.0006748375,0.001846362,0.0002971715,0.0153553],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9624495,"threshold_uncertainty_score":0.999571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1737516960124995,"score_gpt":0.40956120862948,"score_spread":0.2358095126169804,"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."}}