{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008777187,0.000745772,0.0005167609,0.0008625609,0.000440875,0.0005067902,0.0006544627,0.001081586,0.002069012],"category_scores_gemma":[0.0003633015,0.0001753236,0.0003314426,0.0006601655,0.0004826503,0.000508942,0.0002455438,0.001351941,0.0005680808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007130916,"about_ca_system_score_gemma":0.0003174489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003695651,"about_ca_topic_score_gemma":0.0004246866,"domain_scores_codex":[0.9995961,0.00008071115,0.00003378338,0.0001273706,0.0001148324,0.00004716023],"domain_scores_gemma":[0.9996815,0.0001383723,0.00005083876,0.00002451325,0.00008327071,0.00002143216],"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":[0.00007136289,0.0000667873,0.0001769487,0.00009862232,0.000005084106,0.00002337676,0.00004326727,0.00009435627,0.9964385,0.0003885019,0.00004561444,0.002547491],"study_design_scores_gemma":[0.00001236517,0.0001787147,0.0008061131,0.000006755823,0.0000127953,0.0001376228,0.00002587063,0.004548673,0.9925282,0.0002175485,0.001515634,0.0000097946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5164725,0.004740274,0.4690795,0.0007088943,0.0002800375,0.0009306975,0.001484627,0.001502467,0.004801019],"genre_scores_gemma":[0.6751522,0.003903343,0.3125469,0.0003522059,0.000111979,0.002052865,0.0009453741,0.00008856184,0.004846632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002069012,"threshold_uncertainty_score":0.006921589,"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."}}