{"id":"W2900564760","doi":"10.3390/jpm8040038","title":"Applications of CRISPR/Cas9 for the Treatment of Duchenne Muscular Dystrophy","year":2018,"lang":"en","type":"review","venue":"Journal of Personalized Medicine","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Muscular Dystrophy Canada; University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"CRISPR; Genome editing; Duchenne muscular dystrophy; Dystrophin; Cas9; Exon skipping; Medicine; Muscular dystrophy; Genetic enhancement; Bioinformatics; Computational biology; Biology; Genetics; Gene; Exon; Alternative splicing","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":[0.0003960631,0.0005277477,0.0005834834,0.0008329343,0.0003358934,0.0006672334,0.0004044094,0.0008693588,0.00197252],"category_scores_gemma":[0.0003915407,0.0002195944,0.0006599843,0.0004504721,0.0003141551,0.0003939791,0.0005345535,0.001210728,0.0008439079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005167993,"about_ca_system_score_gemma":0.0005746057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001167907,"about_ca_topic_score_gemma":0.001785797,"domain_scores_codex":[0.9995669,0.00007209928,0.00006146839,0.00008788262,0.0001680123,0.00004362225],"domain_scores_gemma":[0.9998873,0.00004231964,0.00002309523,0.000004847149,0.00002769663,0.00001468494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003296375,0.000156728,0.0008502728,0.01250426,0.0002146438,0.002099288,0.0003105789,0.002139457,0.363785,0.009469047,0.0272902,0.5808508],"study_design_scores_gemma":[0.00007619768,0.0009050749,0.00280087,0.001487909,0.0002948455,0.00694478,0.000142461,0.002614892,0.1876165,0.002864076,0.7941221,0.0001302103],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.02311882,0.920359,0.02967355,0.002525427,0.001237937,0.0002039869,0.0005549169,0.0007286135,0.0215978],"genre_scores_gemma":[0.1282423,0.8262742,0.02935319,0.001950709,0.0004053136,0.0002095042,0.001116069,0.0001000057,0.01234869],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00197252,"threshold_uncertainty_score":0.006598771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03045498754806557,"score_gpt":0.4020737553894709,"score_spread":0.3716187678414053,"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."}}