{"id":"W4323649788","doi":"10.1039/d2bm02014a","title":"Optimization of precision nanofiber micelleplexes for DNA delivery","year":2023,"lang":"en","type":"article","venue":"Biomaterials Science","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Victoria","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Engineering and Physical Sciences Research Council; Canada Foundation for Innovation; University of Victoria","keywords":"Nanofiber; Transfection; Nucleic acid; Nanotechnology; Polymer; Gene delivery; Chemistry; Methacrylate; DNA; Biophysics; Materials science; Biochemistry; Monomer; Organic chemistry; Biology","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":[0.0004216845,0.00007421088,0.00009199553,0.0001002462,0.00009052491,0.00003667934,0.0002778762,0.00005656363,0.00002167084],"category_scores_gemma":[0.00009129565,0.00006414857,0.00004313798,0.0002444532,0.0001733434,0.00001128397,0.0001304531,0.000003228404,0.00001764597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005858109,"about_ca_system_score_gemma":0.00008467589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008437338,"about_ca_topic_score_gemma":0.00000173782,"domain_scores_codex":[0.9992105,0.00001575871,0.0001866761,0.0002756293,0.0001286788,0.0001827609],"domain_scores_gemma":[0.9994741,0.0000116209,0.00008261348,0.0002102183,0.0001861633,0.00003530125],"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":[0.00007206386,0.00001097654,0.00005721525,0.00001383807,0.000003838326,1.745747e-7,0.0000235745,0.0007623649,0.9970663,0.000009101027,0.001437927,0.0005425998],"study_design_scores_gemma":[0.000150706,0.0001946871,0.0005087575,0.00001921347,0.000004348842,0.000001475963,0.00003371289,0.001169186,0.9969454,0.00002983688,0.0008601472,0.00008250464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962474,0.00006270501,0.002805536,0.00002666121,0.000525816,0.0001792239,0.0000549842,0.00001665698,0.00008108071],"genre_scores_gemma":[0.9946498,0.0001547779,0.004700359,0.00003104494,0.00008417165,0.0000220037,0.00008050234,0.000007817944,0.0002694649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001894824,"threshold_uncertainty_score":0.2615903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01951790603763407,"score_gpt":0.2783781587780134,"score_spread":0.2588602527403793,"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."}}