{"id":"W3165483827","doi":"10.1021/acs.nanolett.0c02558","title":"Nanofluidics for Simultaneous Size and Charge Profiling of Extracellular Vesicles","year":2021,"lang":"en","type":"article","venue":"Nano Letters","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Canadian Institutes of Health Research; Canada Foundation for Innovation; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Faculty of Engineering, McGill University","keywords":"Extracellular vesicles; Nanofluidics; Nanotechnology; Population; Vesicle; Chemistry; Profiling (computer programming); Energy landscape; Biophysics; Materials science; Membrane; Biology; Cell biology; Computer 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":[0.0002040474,0.0001718789,0.0001752274,0.0001969601,0.0002486764,0.0002637855,0.0002254056,0.0004419342,0.0004651046],"category_scores_gemma":[0.0003392339,0.00009027746,0.0001182234,0.0001368671,0.0001983029,0.0003042264,0.0002893644,0.0003365923,0.0001126719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003290994,"about_ca_system_score_gemma":0.0001621641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004507441,"about_ca_topic_score_gemma":0.0007914489,"domain_scores_codex":[0.9998665,0.0000121528,0.00001092205,0.00004049746,0.00005560892,0.00001418334],"domain_scores_gemma":[0.9999052,0.0000328941,0.00002350077,0.00001064174,0.00001906135,0.000008731891],"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":[0.00001435219,0.000009102967,0.0000772453,0.00002090088,0.00000220806,0.00001784296,0.00001923321,0.00009470396,0.9963404,0.0005050278,0.0001285008,0.002770511],"study_design_scores_gemma":[0.000008755186,0.0000505774,0.0008408077,0.000005504965,0.000004439443,0.00007934096,0.00001978434,0.005253516,0.9899989,0.000234662,0.003489766,0.00001399363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.875197,0.002926076,0.1132316,0.0005710231,0.0003172419,0.0002265382,0.001291035,0.0007301592,0.005509284],"genre_scores_gemma":[0.8995633,0.0007984948,0.09703027,0.0002476859,0.00004436641,0.0002510227,0.0003261809,0.00003883549,0.001699925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004651046,"threshold_uncertainty_score":0.002387762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007631462066834922,"score_gpt":0.2328810906090516,"score_spread":0.2252496285422166,"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."}}