{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000317137,0.0001618128,0.0001868286,0.00008546489,0.00009577932,0.0000621207,0.0001281241,0.000198449,0.0002235753],"category_scores_gemma":[0.000412127,0.000159479,0.0000304767,0.0001446477,0.0001546036,0.000004015565,0.0001257382,0.000007894934,0.00002354449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002157135,"about_ca_system_score_gemma":0.00008788624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001319992,"about_ca_topic_score_gemma":0.0003777632,"domain_scores_codex":[0.9986446,0.0002404063,0.000242957,0.0004369764,0.0001311078,0.0003039035],"domain_scores_gemma":[0.9994619,0.00005174478,0.00006152983,0.0002225899,0.0001099021,0.00009228846],"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":[0.0003461877,0.0001156295,0.002380151,0.0000368014,0.00001679333,0.000004850166,0.00002102839,8.318545e-7,0.9964973,0.0000287531,0.0001060338,0.0004456697],"study_design_scores_gemma":[0.001009303,0.0007798754,0.01061944,0.00003031982,0.000007934959,0.00000573099,0.00002512611,0.0003645128,0.9865526,0.0000644163,0.0003572354,0.0001834866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979942,0.0001372668,0.00105946,0.0001239465,0.0001100385,0.0004080805,0.00004605539,0.00001841537,0.0001025476],"genre_scores_gemma":[0.998692,0.000007447816,0.000899853,0.00002903992,0.0001524383,0.00004873144,0.00009839748,0.00002348331,0.00004863799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009944648,"threshold_uncertainty_score":0.6503363,"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."}}