{"id":"W4200480423","doi":"10.1016/j.ymthe.2021.12.006","title":"Anti-Cas9 immunity: A formidable challenge for muscle genome editing","year":2021,"lang":"en","type":"letter","venue":"Molecular Therapy","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health","keywords":"CRISPR; Genome editing; Duchenne muscular dystrophy; Dystrophin; Cas9; Genetic enhancement; Biology; Nuclease; Utrophin; Immune system; Acquired immune system; Gene; Computational biology; Bioinformatics; Genetics; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001876842,0.0004505528,0.0004338014,0.0000845977,0.0001353896,0.0000808789,0.0004171317,0.0008534162,0.00004913854],"category_scores_gemma":[0.00003574056,0.0004880064,0.0004491591,0.00009454686,0.00003959773,0.000003034178,0.0001338099,0.0005526692,0.000006878101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002487981,"about_ca_system_score_gemma":0.00009474051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001268486,"about_ca_topic_score_gemma":0.000004084678,"domain_scores_codex":[0.998129,0.00006799107,0.000303155,0.0006445239,0.0002153312,0.0006400584],"domain_scores_gemma":[0.9987481,0.0000178853,0.0001150492,0.0008925684,0.0001638415,0.00006252987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001348395,0.00005192273,0.000002392425,0.000189668,0.000375502,0.0001576951,0.00007402444,0.0002335015,0.9407955,0.0000077656,0.05272842,0.005370125],"study_design_scores_gemma":[0.0006708214,0.0002325706,0.00001571314,0.00003332902,0.00002901909,0.00002150257,0.00003212831,0.00004177753,0.1920207,0.00005322514,0.8063995,0.0004496564],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.2125741,0.2220459,0.295247,0.2537961,0.004281885,0.005545557,0.0008661582,0.0003092664,0.005333954],"genre_scores_gemma":[0.1862728,0.02127895,0.01265695,0.7138238,0.03031594,0.002103373,0.02403704,0.001573808,0.007937381],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.7536711,"threshold_uncertainty_score":0.9997572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166189486294856,"score_gpt":0.2796103208449728,"score_spread":0.2629913722154872,"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."}}