{"id":"W3012171655","doi":"10.1002/jbm.a.36918","title":"Bioactive micropatterning of biomaterials for induction of endothelial progenitor cell differentiation: Acceleration of in situ endothelialization","year":2020,"lang":"en","type":"article","venue":"Journal of Biomedical Materials Research Part A","topic":"Angiogenesis and VEGF in Cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Saint-François d'Assise; Université Laval; Regroupement Québécois sur les Matériaux de Pointe","funders":"Conseil Régional Aquitaine","keywords":"Progenitor cell; Micropatterning; Endothelial progenitor cell; Cell biology; Progenitor; Materials science; Cell adhesion; Endothelial stem cell; Adhesion; In situ; Cellular differentiation; Cell; Stem cell; Biomedical engineering; In vitro; Biology; Nanotechnology; Chemistry; Medicine; Biochemistry","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":[0.000199228,0.0002767948,0.0001636528,0.000194175,0.00009974096,0.0002096053,0.0001127508,0.000314833,0.0006603597],"category_scores_gemma":[0.0001886037,0.0001347331,0.0001900786,0.0001218547,0.0001456595,0.0002115999,0.0001242694,0.0002904794,0.0002145645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001655511,"about_ca_system_score_gemma":0.0001258836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001891295,"about_ca_topic_score_gemma":0.0003886001,"domain_scores_codex":[0.9999009,0.00001089409,0.000008896121,0.00002396553,0.00003163519,0.00002368689],"domain_scores_gemma":[0.9998888,0.00003130438,0.00004169026,0.0000113691,0.00001280479,0.00001400895],"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":[0.0000130939,0.000007030826,0.00002639895,0.00001490213,0.000001065312,0.00001020494,0.000006832471,0.00004933229,0.9993598,0.0000327922,0.000006898205,0.0004716242],"study_design_scores_gemma":[0.000004725291,0.00008046463,0.0006919989,0.000004738667,0.000005813371,0.00003434955,0.000006980712,0.0007868215,0.9977381,0.00002181812,0.0006216173,0.000002539877],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862291,0.001177742,0.01148438,0.0000519772,0.00005977184,0.00004050618,0.0001245976,0.00006658125,0.0007653094],"genre_scores_gemma":[0.9839018,0.0009756608,0.01328046,0.00004432314,0.00001742775,0.00006974472,0.0001715848,0.00004042578,0.001498514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006603597,"threshold_uncertainty_score":0.002209127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07101712269173932,"score_gpt":0.3552242052703677,"score_spread":0.2842070825786284,"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."}}