{"id":"W4281384029","doi":"10.1021/acs.molpharmaceut.2c00032","title":"Optimization of Lipid Nanoparticles for saRNA Expression and Cellular Activation Using a Design-of-Experiment Approach","year":2022,"lang":"en","type":"article","venue":"Molecular Pharmaceutics","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Biotalent Canada; Engineering and Physical Sciences Research Council; British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC; Canada Foundation for Innovation; University of British Columbia; Wellcome Trust; Wellcome","keywords":"Nanoparticle; Chemistry; Biophysics; Cell biology; Computational biology; Biological system; Nanotechnology; Biology; Materials 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002999233,0.001272314,0.0009543748,0.0004608947,0.0002939888,0.0007943254,0.0005141579,0.0005826984,0.000842505],"category_scores_gemma":[0.00127182,0.0004604319,0.0007862643,0.0003217499,0.0004260017,0.0004367195,0.0004482511,0.0006865564,0.000146193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000908346,"about_ca_system_score_gemma":0.001027435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005024325,"about_ca_topic_score_gemma":0.001006521,"domain_scores_codex":[0.9990369,0.0003194485,0.00008544399,0.0001674141,0.0002766487,0.0001141908],"domain_scores_gemma":[0.9994429,0.0002758047,0.0001220239,0.00004021718,0.00009738142,0.00002172525],"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":[0.0006115857,0.00062654,0.0008334744,0.0005471235,0.0001176015,0.00009309321,0.00009153812,0.07617055,0.9050345,0.002088691,0.0001369926,0.01364821],"study_design_scores_gemma":[0.0001567064,0.003670516,0.0008911497,0.0000162019,0.0001093621,0.00004751235,0.00004068832,0.1154526,0.8756028,0.0005371538,0.003425009,0.00005038786],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7470044,0.00127245,0.2439275,0.0002201426,0.00007937378,0.002192804,0.0005995157,0.0003537357,0.004350133],"genre_scores_gemma":[0.7026864,0.001210522,0.2913619,0.0001190576,0.00001455491,0.002469901,0.0003178981,0.00007581853,0.001743861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002999233,"threshold_uncertainty_score":0.01586163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05552830917390052,"score_gpt":0.3120660815288986,"score_spread":0.2565377723549981,"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."}}