{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002737214,0.0003537734,0.00009843685,0.0002919685,0.0001254881,0.0001711125,0.0001875875,0.0003776423,0.001544905],"category_scores_gemma":[0.0001151658,0.0001784068,0.00020083,0.0001140512,0.0001217067,0.0002293278,0.0001475126,0.0003305653,0.0004310135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001568268,"about_ca_system_score_gemma":0.0001365095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000348557,"about_ca_topic_score_gemma":0.001551373,"domain_scores_codex":[0.9999104,0.00001270151,0.000007407264,0.00002467735,0.00003292038,0.00001195512],"domain_scores_gemma":[0.9999275,0.00001481976,0.00002209885,0.00001158846,0.00001071629,0.00001327662],"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":[0.00001626778,0.00002252431,0.00004638976,0.00003399908,0.000002520377,0.00002817782,0.00001061437,0.0003660799,0.9968816,0.0001438926,0.00007741119,0.002370578],"study_design_scores_gemma":[0.00001840505,0.0003114549,0.001159273,0.00001745822,0.00001331102,0.0002176829,0.00001043887,0.00479125,0.9809577,0.0001092575,0.01238495,0.000008789855],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8161555,0.003194946,0.1663263,0.0002509532,0.0003231631,0.0004972983,0.001474538,0.001048292,0.01072884],"genre_scores_gemma":[0.8117267,0.002162748,0.1727518,0.0001388529,0.00003386633,0.000682315,0.001226431,0.0001870681,0.01109012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001544905,"threshold_uncertainty_score":0.0051682,"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."}}