{"id":"W2560266955","doi":"10.1038/mt.2016.191","title":"Gene Editing for Duchenne Muscular Dystrophy Using the CRISPR/Cas9 Technology: The Importance of Fine-tuning the Approach","year":2016,"lang":"en","type":"letter","venue":"Molecular Therapy","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval","funders":"","keywords":"Dystrophin; Duchenne muscular dystrophy; Exon; Genome editing; Nonsense mutation; Muscular dystrophy; CRISPR; Genetics; Biology; Exon skipping; Gene; Molecular biology; Utrophin; Mutation; Alternative splicing; Missense mutation","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004876963,0.001104563,0.001769942,0.002517218,0.001068431,0.003989513,0.002772518,0.00594161,0.01141869],"category_scores_gemma":[0.02181988,0.000555468,0.00132823,0.001565663,0.001386875,0.003341876,0.001365034,0.008202894,0.004921326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001852183,"about_ca_system_score_gemma":0.003202902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340722,"about_ca_topic_score_gemma":0.003142071,"domain_scores_codex":[0.9966722,0.0005622393,0.0007744362,0.0003754885,0.001342147,0.0002735984],"domain_scores_gemma":[0.9782551,0.007410779,0.001782969,0.0004156737,0.01050738,0.001628003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007760225,0.00001656577,0.0001088326,0.002021185,0.00006651271,0.000355091,0.00005394105,0.00003752027,0.0004591426,0.0004684909,0.9687849,0.02755019],"study_design_scores_gemma":[0.00003634682,0.0000440978,0.0003289531,0.001499645,0.0001134447,0.000916884,0.0001033751,0.0001124067,0.0007657555,0.0006273846,0.9954184,0.00003322742],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.000407255,0.04249426,0.0009086928,0.1731373,0.7808474,0.00003817079,0.000272645,0.0001085209,0.001785755],"genre_scores_gemma":[0.006143359,0.09024137,0.001649233,0.1876325,0.6856525,0.000113269,0.0003662472,0.0003071111,0.02789445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01141869,"threshold_uncertainty_score":0.03819937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367872603796289,"score_gpt":0.2802106464241298,"score_spread":0.2665319203861669,"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."}}