{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001468435,0.000144479,0.0001588393,0.00001777048,0.00006195359,0.00001795061,0.0001213873,0.000112749,0.00001421474],"category_scores_gemma":[0.0005816055,0.0001567132,0.0001084758,0.00006127174,0.0001095291,0.000003881979,0.00007851702,0.00004108686,0.000002159811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008498,"about_ca_system_score_gemma":0.00006420889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001172844,"about_ca_topic_score_gemma":7.380541e-7,"domain_scores_codex":[0.9989911,0.00004709746,0.0002381254,0.0003668667,0.0001200277,0.0002367454],"domain_scores_gemma":[0.9992855,0.0001093483,0.00008958076,0.000329504,0.0001033222,0.00008275211],"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":[0.00007499918,0.0000666597,0.0006430173,0.0001303592,0.00005054661,0.00003971748,0.00003557197,0.00002867333,0.9971206,0.000151538,0.000562347,0.001096013],"study_design_scores_gemma":[0.0006166663,0.00007019444,0.00003935661,0.00001959116,0.00004041077,0.00002039167,0.00003941382,0.00009635077,0.987267,0.00003635203,0.01157915,0.0001751203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850186,0.01025122,0.003807978,0.000452787,0.0001147198,0.0002380092,0.00006352267,0.00001202945,0.00004118287],"genre_scores_gemma":[0.9874123,0.0001980096,0.010927,0.0007356884,0.0001555261,0.00002256991,0.00009290197,0.00003648005,0.0004194819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01101681,"threshold_uncertainty_score":0.639058,"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."}}