{"id":"W2905954642","doi":"10.1039/c8nr06442c","title":"Tuning optimum transfection of gemini surfactant–phospholipid–DNA nanoparticles by validated theoretical modeling","year":2018,"lang":"en","type":"article","venue":"Nanoscale","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regional Municipality of Waterloo; Fields Institute for Research in Mathematical Sciences; Emmanuel Bible College; University of Waterloo","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Transfection; Gene delivery; Nanoparticle; Pulmonary surfactant; Phospholipid; DNA; Nanotechnology; Materials science; Biophysics; Chemistry; Gene; Membrane; Biology; Biochemistry","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":[0.0003160028,0.0006119655,0.0005297225,0.0005599684,0.0003825695,0.0006123745,0.0007597908,0.00102979,0.0009481564],"category_scores_gemma":[0.0009127333,0.0003167976,0.0006023728,0.0004014579,0.0003439544,0.0004972199,0.0002770255,0.0003922175,0.0002535634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162458,"about_ca_system_score_gemma":0.0008823019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008708987,"about_ca_topic_score_gemma":0.005136345,"domain_scores_codex":[0.9999006,0.00002577402,0.00000515388,0.00001784215,0.00003046655,0.00002017805],"domain_scores_gemma":[0.9997186,0.0001737259,0.00002511975,0.00001394155,0.0000578013,0.00001070361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000024165,0.00003129493,0.0003566999,0.00005095893,0.000008634558,0.00004984232,0.00003130925,0.9905791,0.005631277,0.00190865,0.00009761109,0.00123052],"study_design_scores_gemma":[0.000003561946,0.000007116033,0.00005195331,0.000002340316,0.000002028208,0.000002185242,0.000005040607,0.9986284,0.00094204,0.0002480625,0.0001049882,0.000002323242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8087917,0.000916663,0.1649769,0.0004471231,0.00004921402,0.00016007,0.0007225439,0.000452178,0.02348361],"genre_scores_gemma":[0.9747375,0.0003385044,0.02252164,0.00004226309,0.000008835741,0.000236079,0.0002985582,0.00007672019,0.001739961],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.008708987,"threshold_uncertainty_score":0.01731658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047853006542836,"score_gpt":0.2441809089895384,"score_spread":0.23370237892411,"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."}}