{"id":"W2959908112","doi":"10.1016/j.msec.2019.109973","title":"Polysaccharide-based tissue-engineered vascular patches","year":2019,"lang":"en","type":"article","venue":"Materials Science and Engineering C","topic":"Electrospun Nanofibers in Biomedical Applications","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Biomedical engineering; Pectin; Materials science; Tissue engineering; Vascular tissue; Biocompatible material; Polysaccharide; Polymer; Thrombogenicity; Vascular smooth muscle; Chemistry; Smooth muscle; Composite material; Surgery; Medicine; Biochemistry; Thrombosis","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":[0.001072729,0.0001728484,0.0002280541,0.000171516,0.0001271554,0.0002587237,0.0004620958,0.00007022457,0.0005329248],"category_scores_gemma":[0.00006990757,0.0001511566,0.00001866241,0.0005636147,0.0002139055,0.0002741574,0.0001043446,0.00005645764,0.0004114988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007687639,"about_ca_system_score_gemma":0.0001162011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004304812,"about_ca_topic_score_gemma":3.493384e-7,"domain_scores_codex":[0.9982302,0.0000110925,0.0002380818,0.0004572801,0.0005488848,0.0005144215],"domain_scores_gemma":[0.9992175,0.00005285513,0.00004660958,0.0004174343,0.00009004815,0.000175523],"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":[0.000002567601,0.00001234829,0.00002708593,0.00005111512,0.000001526436,8.678242e-7,0.00003399942,0.000154006,0.9983454,0.001136651,0.00005629225,0.0001781137],"study_design_scores_gemma":[0.0001789992,0.00005269862,0.00141991,0.0000312335,0.000007129087,0.000004474556,0.000006945437,0.0008292493,0.9945908,0.00008447433,0.002584514,0.0002095862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992056,0.0001022306,0.006267868,0.0002664198,0.0007181469,0.0002605784,0.00001607318,0.0002445592,0.00006816713],"genre_scores_gemma":[0.9850118,0.00001127047,0.01462896,0.000136682,0.00008151667,0.00004992596,0.000003710711,0.00002025074,0.00005592691],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008361095,"threshold_uncertainty_score":0.6163988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004334628944658391,"score_gpt":0.2035137916073509,"score_spread":0.1991791626626926,"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."}}