{"id":"W4389388944","doi":"10.1038/s41551-023-01132-z","title":"Optimization of base editors for the functional correction of SMN2 as a treatment for spinal muscular atrophy","year":2023,"lang":"en","type":"article","venue":"Nature Biomedical Engineering","topic":"Neurogenetic and Muscular Disorders Research","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"National Institute on Deafness and Other Communication Disorders; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Cancer Institute; National Institutes of Health; Muscular Dystrophy Canada; Charles A. King Trust; St. Jude Children's Research Hospital; National Institute of Neurological Disorders and Stroke; Massachusetts General Hospital; National Institute of Allergy and Infectious Diseases; Muscular Dystrophy Association","keywords":"SMN1; Spinal muscular atrophy; Exon; SMA*; Biology; Mutation; Point mutation; Motor neuron; Molecular biology; Genetics; Cancer research; Gene; Neuroscience; Spinal cord; Computer science","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":[0.0001617305,0.00008857687,0.0001615647,0.0001838275,0.00003758272,0.000002868612,0.00004704066,0.0001666645,0.00002503421],"category_scores_gemma":[0.0005906236,0.00005879139,0.0002010303,0.0004090315,0.00004927551,0.00001301022,0.00001335488,0.0001261207,9.167591e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000410092,"about_ca_system_score_gemma":0.0001124554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007861267,"about_ca_topic_score_gemma":2.367393e-7,"domain_scores_codex":[0.9991655,0.000006681335,0.0001627661,0.0001448528,0.0003630992,0.000157105],"domain_scores_gemma":[0.999424,0.0001941156,0.00003358065,0.0001253995,0.0001427636,0.00008009108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004947077,0.001147291,0.0002761116,0.003103784,0.001941948,0.00002722792,0.0002437686,0.5806798,0.2080018,0.0007046763,0.0411539,0.1577727],"study_design_scores_gemma":[0.003439664,0.002521852,0.001965456,0.0001210576,0.000229856,0.00001378408,0.00008556638,0.8844916,0.013851,0.00000929496,0.09318088,0.00008998565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08609561,0.002349709,0.8965569,0.002846089,0.009103953,0.002803862,0.00007081999,0.0001388625,0.00003422561],"genre_scores_gemma":[0.989239,0.0003426549,0.005864064,0.00006087594,0.003249001,0.0004607867,0.0003924269,0.00005297713,0.0003382271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9031433,"threshold_uncertainty_score":0.2397443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530269227728035,"score_gpt":0.2993428286397499,"score_spread":0.2840401363624696,"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."}}