{"id":"W4410521520","doi":"10.1038/s41551-025-01399-4","title":"Treatment of a metabolic liver disease in mice with a transient prime editing approach","year":2025,"lang":"en","type":"article","venue":"Nature Biomedical Engineering","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acuitas Therapeutics (Canada)","funders":"National Institute of Allergy and Infectious Diseases; Universität Zürich; Novartis Stiftung für Medizinisch-Biologische Forschung; Eidgenössische Technische Hochschule Zürich; Staatssekretariat für Bildung, Forschung und Innovation; Novartis Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Institutes of Health; National Science Foundation","keywords":"Genome editing; RNA editing; RNA; Messenger RNA; Locus (genetics); In vivo; Molecular biology; Computational biology; Chemistry; Biology; Gene; Genetics; Biochemistry; Genome","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":[0.00005017727,0.0001512595,0.0001810721,0.0001404664,0.00001086343,0.000005008377,0.0000921973,0.0001801043,0.000002108144],"category_scores_gemma":[0.00004185528,0.0001208427,0.00006439268,0.0002765783,0.00003221483,0.000001997153,0.00001998099,0.0001255354,2.06486e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001606495,"about_ca_system_score_gemma":0.00006062523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001439314,"about_ca_topic_score_gemma":0.000003532935,"domain_scores_codex":[0.9992594,0.000008132075,0.000159433,0.0002438709,0.000128483,0.0002006556],"domain_scores_gemma":[0.9996945,0.00001099259,0.00001816036,0.0001518171,0.00002272052,0.0001018454],"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":[0.0004282458,0.001251621,0.0035481,0.0008807142,0.0005103023,0.0000887581,0.0004958597,0.04415402,0.9257665,0.001144018,0.0001482722,0.02158361],"study_design_scores_gemma":[0.007045858,0.0008328093,0.0960248,0.0005122127,0.0003628947,0.00003860946,0.0001297774,0.1916515,0.4774779,0.000005802224,0.2249671,0.0009507911],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7388431,0.04307017,0.2167292,0.0001783279,0.0003458687,0.0004654579,0.0000332085,0.00003768222,0.0002969361],"genre_scores_gemma":[0.9931905,0.0002340876,0.006256121,0.00001857712,0.0001286341,0.00003766949,0.00006037542,0.00001317078,0.00006088068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4482886,"threshold_uncertainty_score":0.4927821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002291410172772034,"score_gpt":0.2384732232663218,"score_spread":0.2361818130935498,"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."}}