{"id":"W4388249111","doi":"10.1021/acsnano.3c08644","title":"Lipid Nanoparticle-Mediated Hit-and-Run Approaches Yield Efficient and Safe <i>In Situ</i> Gene Editing in Human Skin","year":2023,"lang":"en","type":"article","venue":"ACS Nano","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of British Columbia","funders":"NanoMedicines Innovation Network; Canadian Institutes of Health Research; National Center for Advancing Translational Sciences; Stiftung Charité; LEO Fondet; Faculty of Pharmaceutical Sciences, University of British Columbia; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"In situ; Nanoparticle; Nanotechnology; Yield (engineering); Genome editing; Computational biology; Materials science; Human skin; Gene; Biology; Chemistry; Biochemistry; Genetics; CRISPR; Organic chemistry","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":[0.0002720006,0.0001300458,0.0001445105,0.00009660346,0.00005759805,0.00002608846,0.0001066147,0.0001302054,0.000003730496],"category_scores_gemma":[0.00008659009,0.0001269621,0.00002593648,0.0002043196,0.00006147919,0.000004445727,0.0001718842,0.0000868251,0.00001623931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000935877,"about_ca_system_score_gemma":0.00001946897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005619263,"about_ca_topic_score_gemma":0.0002652565,"domain_scores_codex":[0.9989939,0.00003917135,0.0002248401,0.0003569281,0.00008595271,0.0002992197],"domain_scores_gemma":[0.9996769,0.00002814219,0.00004392998,0.0001760076,0.00001792002,0.00005703719],"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":[0.00002314574,0.00004592091,0.008877459,0.00001445188,0.000009232082,0.00001144687,0.0002427816,0.0001568357,0.9886974,0.0000147024,0.0004545078,0.001452121],"study_design_scores_gemma":[0.0005497086,0.0001559585,0.0159345,0.00003608017,0.000005845751,0.000007082886,0.0003884987,0.0003425274,0.9817604,0.00001515907,0.0006340827,0.00017014],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998697,0.0004390328,0.000007480738,0.0002044942,0.00009601415,0.0001292181,0.000008255653,0.00001442033,0.0004041221],"genre_scores_gemma":[0.9991398,0.0001923385,0.00003345272,0.000162042,0.000161659,0.00002616199,0.00008110618,0.00001395536,0.0001894889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007057042,"threshold_uncertainty_score":0.5177364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02396653081569018,"score_gpt":0.2350946571188404,"score_spread":0.2111281263031502,"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."}}