{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001908072,0.0002696936,0.0002903567,0.00007398758,0.0001351911,0.000005531682,0.0001885948,0.0001479484,0.0001393306],"category_scores_gemma":[0.0001238473,0.0001654865,0.00008293088,0.0001466645,0.000197559,0.000006118037,0.0001056893,0.00008063659,0.00006679328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002812762,"about_ca_system_score_gemma":0.00004718345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002556644,"about_ca_topic_score_gemma":0.0000155297,"domain_scores_codex":[0.9984578,0.0001775132,0.0002475627,0.0005898259,0.0001490354,0.0003781956],"domain_scores_gemma":[0.9991059,0.00003883068,0.00008626788,0.0004132542,0.0001434788,0.0002122224],"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":[0.0001343236,0.00004763514,0.0003085153,0.00002102657,0.00002361174,0.000007847357,0.0001725674,0.00005607137,0.9752776,0.0002178191,0.0132966,0.0104364],"study_design_scores_gemma":[0.002696376,0.001928381,0.002197061,0.00006251737,0.00004727936,0.000003417634,0.0003281312,0.000007701362,0.9511552,0.0001256721,0.04107678,0.0003714614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854234,0.002159937,0.008748435,0.001486665,0.0002724627,0.0003034225,0.00001369928,0.00002629877,0.001565633],"genre_scores_gemma":[0.9948761,0.0002256762,0.0003669056,0.0009685788,0.0009971249,0.00006347814,0.00006972446,0.00003223741,0.002400175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02778018,"threshold_uncertainty_score":0.6748344,"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."}}