{"id":"W2985931829","doi":"10.1021/acsbiomaterials.9b00992","title":"Customizable Composite Fibers for Engineering Skeletal Muscle Models","year":2019,"lang":"en","type":"article","venue":"ACS Biomaterials Science & Engineering","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Heart, Lung, and Blood Institute; Fundação para a Ciência e a Tecnologia; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Composite number; Tissue engineering; Microstructure; Textile; Myogenesis; Biomedical engineering; Nanotechnology; Fiber; Biocompatible material; Cell adhesion; Adhesion; Skeletal muscle; Composite material; Anatomy; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.001267974,0.0003365329,0.0003841542,0.0005224196,0.0001147411,0.0003413926,0.0009410936,0.0001413893,0.00006408194],"category_scores_gemma":[0.0001403604,0.0003466368,0.00009192983,0.0008852796,0.0001205359,0.0008506121,0.0002441761,0.0001094837,0.0001586018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000263608,"about_ca_system_score_gemma":0.00006717067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001564762,"about_ca_topic_score_gemma":2.605906e-7,"domain_scores_codex":[0.9971195,0.000008434195,0.000440606,0.0005176239,0.000655646,0.001258182],"domain_scores_gemma":[0.998819,0.0001776274,0.00003843016,0.000526041,0.0001094833,0.000329437],"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":[0.000002171825,0.000006915446,0.000009872761,0.0001481701,0.00001279838,0.000001726588,0.0000663703,0.2550523,0.7431123,0.0009849159,0.0000612455,0.0005412009],"study_design_scores_gemma":[0.0002498738,0.0000255924,0.00024084,0.00007636226,0.000005252548,0.000005913168,0.000007330333,0.4761969,0.5193836,0.00003437262,0.003487875,0.0002860195],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556377,0.0001034517,0.03995828,0.0000323097,0.002296069,0.0005885815,0.00003066537,0.001024431,0.0003285172],"genre_scores_gemma":[0.9804689,0.00002392923,0.01905742,0.00001390443,0.0001781184,0.00009254259,0.00001384907,0.00009951108,0.00005179277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2237287,"threshold_uncertainty_score":0.9998986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183398276356227,"score_gpt":0.2377778514877609,"score_spread":0.2259438687241987,"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."}}