{"id":"W1797688421","doi":"10.1186/s12951-015-0125-1","title":"A flow cytometric approach to study the mechanism of gene delivery to cells by gemini-lipid nanoparticles: an implication for cell membrane nanoporation","year":2015,"lang":"en","type":"article","venue":"Journal of Nanobiotechnology","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Viability assay; Flow cytometry; Transfection; Lipofectamine; Gene delivery; Cell; Molecular biology; Chemistry; Stain; Biophysics; Cell biology; Staining; Biology; Biochemistry; Gene; Vector (molecular 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":[0.0007624452,0.0001570736,0.0002739479,0.0003008784,0.00005933824,0.00002081557,0.0006219106,0.0002589092,0.000001499485],"category_scores_gemma":[0.0001105927,0.0001184768,0.0000912352,0.0004894707,0.00003668204,0.0000150036,0.0001022316,0.000112524,0.000004834749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003593358,"about_ca_system_score_gemma":0.0001118881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001000339,"about_ca_topic_score_gemma":0.000003793551,"domain_scores_codex":[0.9986777,0.00008167236,0.0005353229,0.0002834943,0.0001959929,0.0002258741],"domain_scores_gemma":[0.9984092,0.00001748486,0.0003682545,0.0004571624,0.0006122026,0.0001356825],"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":[0.0003847227,0.0007057585,0.0000190541,0.000007118698,0.0000632582,8.032519e-7,0.0003290724,0.0005093709,0.9927686,0.00001822306,0.00216812,0.003025913],"study_design_scores_gemma":[0.0009739946,0.008497231,0.0000188631,0.000003745079,0.00006324326,0.00003169445,0.001859656,0.0002704354,0.9867178,0.00005726282,0.001374875,0.0001312092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9560114,0.0002372012,0.04247279,0.0003367355,0.0001892787,0.0006945318,0.00003372996,0.000007896271,0.00001639461],"genre_scores_gemma":[0.9865927,0.00006226717,0.01290154,0.0001997239,0.0001061995,0.00004858854,0.0000194075,0.00001972768,0.00004983493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03058126,"threshold_uncertainty_score":0.4831343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331689693720883,"score_gpt":0.2620382507530398,"score_spread":0.2387213538158309,"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."}}