{"id":"W4401602181","doi":"10.1016/j.atherosclerosis.2024.118496","title":"An investigational in vivo base editing medicine targeting ANGPTL3, VERVE-201, achieves precise and durable liver editing in nonclinical studies","year":2024,"lang":"en","type":"article","venue":"Atherosclerosis","topic":"Endoplasmic Reticulum Stress and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut universitaire de cardiologie et de pneumologie de Québec","funders":"","keywords":"Image editing; In vivo; Medicine; Computer science; Biology; Artificial intelligence; Genetics","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":[0.0006067916,0.0003612429,0.0004141921,0.0001993579,0.0002578446,0.0006733952,0.0004265913,0.0007450876,0.001978979],"category_scores_gemma":[0.0003329745,0.0002020518,0.0002093463,0.0001070428,0.0004240517,0.0003926726,0.0002513243,0.001253016,0.0005618971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002216694,"about_ca_system_score_gemma":0.000251648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000250594,"about_ca_topic_score_gemma":0.0005755533,"domain_scores_codex":[0.9997684,0.00005865642,0.00001409374,0.00005086409,0.00006799671,0.00003993944],"domain_scores_gemma":[0.9998112,0.0000405857,0.00005425261,0.00003534267,0.00001793371,0.00004072881],"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":[0.0003045805,0.00006439245,0.0001135334,0.00004524008,0.00001493109,0.0001094781,0.00002481033,0.0001642904,0.9893698,0.0009204835,0.0004549121,0.00841364],"study_design_scores_gemma":[0.00008676194,0.0006650762,0.0004553342,0.000008096404,0.00004128924,0.0008998009,0.00002167438,0.001206342,0.9872702,0.0003221553,0.009010516,0.00001281989],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8822093,0.00600425,0.08996496,0.00175235,0.0004710078,0.0002444675,0.0009830857,0.00185769,0.01651292],"genre_scores_gemma":[0.9765862,0.001231827,0.0149793,0.0003478465,0.00004624289,0.00006979984,0.0004516564,0.000185162,0.00610192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001978979,"threshold_uncertainty_score":0.006620407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03390876611817322,"score_gpt":0.3131286724051624,"score_spread":0.2792199062869892,"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."}}