{"id":"W2883089449","doi":"10.1016/j.biomaterials.2018.07.013","title":"Fibronectin promotes elastin deposition, elasticity and mechanical strength in cellularised collagen-based scaffolds","year":2018,"lang":"en","type":"article","venue":"Biomaterials","topic":"Connective tissue disorders research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Réseau de Recherche en Santé Buccodentaire et Osseuse; Natural Sciences and Engineering Research Council of Canada; Fonds Wetenschappelijk Onderzoek; Heart and Stroke Foundation of Canada","keywords":"Elastin; Lysyl oxidase; Tropoelastin; Fibronectin; Extracellular matrix; Fibulin; Elastic fiber; Tissue engineering; Elasticity (physics); Materials science; Elastic modulus; Scaffold; Biophysics; Biomedical engineering; Viscoelasticity; Fibrillin; Chemistry; Composite material; Biochemistry; Pathology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003086054,0.0003301168,0.0001348605,0.0002025062,0.0001590705,0.0003314148,0.0001303157,0.000337768,0.0009929532],"category_scores_gemma":[0.0002855811,0.0001770074,0.0001596552,0.0001148957,0.0002389786,0.0002699603,0.0002343169,0.0002985818,0.0001679169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002363041,"about_ca_system_score_gemma":0.0001737243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001078697,"about_ca_topic_score_gemma":0.002542428,"domain_scores_codex":[0.9997705,0.0000514149,0.00002025806,0.0000303721,0.000067088,0.00006044006],"domain_scores_gemma":[0.9997169,0.0001134901,0.00004283169,0.00002116486,0.00003308912,0.00007243059],"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":[0.00006561844,0.000008103627,0.00004150068,0.00001326923,0.000001859992,0.00002284468,0.00001242583,0.00003867171,0.9995081,0.00001600954,0.000008700126,0.0002627365],"study_design_scores_gemma":[0.00001166115,0.0001501033,0.00241555,0.000004583931,0.00001050362,0.00008399963,0.0000239338,0.000595765,0.9959663,0.00002177313,0.000710827,0.000005058806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957872,0.0008890894,0.001422591,0.00003766279,0.00002605406,0.00001350961,0.00009491234,0.00001984481,0.001709181],"genre_scores_gemma":[0.9955598,0.0003323945,0.001294925,0.00003721057,0.00001058064,0.00001309494,0.0001230233,0.00001271387,0.00261619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001078697,"threshold_uncertainty_score":0.003321767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231809048428445,"score_gpt":0.2749400136351691,"score_spread":0.2626219231508846,"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."}}