{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003448119,0.0003768617,0.0001829991,0.0001787382,0.0001632854,0.000333343,0.0002396121,0.0003890421,0.001482774],"category_scores_gemma":[0.0002071328,0.0001970315,0.0002312949,0.00009217521,0.000210384,0.0001993604,0.0002355669,0.0003736025,0.0006251375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001729148,"about_ca_system_score_gemma":0.0001719715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005254561,"about_ca_topic_score_gemma":0.0008130442,"domain_scores_codex":[0.9998021,0.00003586516,0.00001520051,0.00007227998,0.00004798483,0.00002656026],"domain_scores_gemma":[0.9998832,0.000041874,0.00002608893,0.0000198387,0.00001531862,0.00001362133],"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":[0.00002114605,0.0000054612,0.00004665658,0.00001957369,0.000002504685,0.00004480839,0.00001781248,0.00008901309,0.9986523,0.0000344601,0.00003203576,0.001034222],"study_design_scores_gemma":[0.000002069102,0.00004296786,0.00032952,0.000001718383,0.000002663465,0.00008223652,0.00001022772,0.0005371533,0.9984843,0.00001632585,0.0004880112,0.000002985984],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9270961,0.0008917921,0.06587507,0.0001674788,0.0000577239,0.0001429213,0.0005213426,0.0008072109,0.00444034],"genre_scores_gemma":[0.9485968,0.0008270655,0.04471818,0.00007326971,0.00000794316,0.0001171269,0.0002931345,0.000248323,0.005118079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001482774,"threshold_uncertainty_score":0.004960418,"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."}}