{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005606996,0.000490259,0.0003832605,0.00008170865,0.0003242041,0.00005197434,0.001243236,0.0007011014,0.00000612505],"category_scores_gemma":[0.00009907912,0.000244406,0.0004855272,0.0002261063,0.0003713571,0.000003434008,0.0002011732,0.0006920294,7.496623e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002405602,"about_ca_system_score_gemma":0.00008457225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008847638,"about_ca_topic_score_gemma":0.000001658945,"domain_scores_codex":[0.9978549,0.0001329342,0.0004487573,0.0006278657,0.0003103321,0.0006252328],"domain_scores_gemma":[0.9976433,0.00006141322,0.0003560173,0.001695564,0.000215441,0.00002828064],"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":[0.00003237583,0.00001536843,0.00005496235,0.00006862634,0.0005934111,0.00001831291,0.00007058851,0.001145049,0.9731597,0.00005725927,0.02086128,0.003923086],"study_design_scores_gemma":[0.0006115366,0.0001281335,0.000008565987,0.00004495015,0.0001173577,0.00008373089,0.00009236157,0.0007299697,0.5266008,0.0002594964,0.470962,0.0003610193],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1172031,0.03077593,0.6910694,0.1578629,0.0006728004,0.002121974,0.0001443445,0.00005043377,0.00009913856],"genre_scores_gemma":[0.6424182,0.003000967,0.04607447,0.2730372,0.02926728,0.002423277,0.001296782,0.001296319,0.001185497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6449949,"threshold_uncertainty_score":0.9966588,"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."}}