{"id":"W2419387337","doi":"10.1038/npjregenmed.2016.6","title":"Targeting muscle stem cell intrinsic defects to treat Duchenne muscular dystrophy","year":2016,"lang":"en","type":"article","venue":"npj Regenerative Medicine","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; Canadian Institutes of Health Research; Stem Cell Network; Muscular Dystrophy Canada; E-Rare; Government of Ontario; Muscular Dystrophy Association","keywords":"Duchenne muscular dystrophy; Dystrophin; Muscular dystrophy; Biology; Cell biology; ITGA7; Myocyte; Skeletal muscle; Stem cell; Regeneration (biology); Wasting; Genetics; Endocrinology","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":[0.000123208,0.0003080448,0.000369689,0.0004543285,0.000158966,0.0001843168,0.0002447573,0.0003724443,0.001478485],"category_scores_gemma":[0.00005482195,0.00008647507,0.0002011714,0.0001560115,0.0002751086,0.0001283111,0.000242651,0.0006669886,0.0003220795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002331408,"about_ca_system_score_gemma":0.0002231307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004653146,"about_ca_topic_score_gemma":0.001541407,"domain_scores_codex":[0.9999099,0.0000136347,0.000007781592,0.00001524476,0.00003587064,0.00001758477],"domain_scores_gemma":[0.9999713,0.000003967928,0.000008922168,0.000002252294,0.000004446361,0.000009098274],"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":[0.0004089596,0.0005955855,0.0007567074,0.0009629782,0.00007300871,0.001066886,0.0001543834,0.0005656548,0.8828422,0.001842708,0.001946527,0.1087842],"study_design_scores_gemma":[0.000666949,0.007697624,0.01062209,0.0003307161,0.0002686803,0.006571386,0.0001488729,0.002570157,0.8077357,0.0008679479,0.162489,0.00003103919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8551857,0.1030733,0.02013322,0.002433513,0.0007104949,0.0004716299,0.0004414084,0.0008168767,0.0167338],"genre_scores_gemma":[0.9382082,0.03358689,0.01035567,0.0006533954,0.0001383514,0.0002549324,0.0005182762,0.00003853991,0.01624563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001478485,"threshold_uncertainty_score":0.004946053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009340671395278376,"score_gpt":0.2312319696918102,"score_spread":0.2218912982965318,"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."}}