{"id":"W3125409058","doi":"10.1039/d0nr05525e","title":"Nanoscale flow cytometry for immunophenotyping and quantitating extracellular vesicles in blood plasma","year":2021,"lang":"en","type":"article","venue":"Nanoscale","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Biotech (Canada); University of British Columbia","funders":"Congressionally Directed Medical Research Programs; Michael Smith Health Research BC; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Flow cytometry; Extracellular vesicles; Immunophenotyping; Nanoscopic scale; Microvesicles; Chemistry; Cytometry; Plasma; Nanotechnology; Materials science; Molecular biology; Biology; Cell biology; Biochemistry; Physics; microRNA","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":[0.0002873216,0.0002097223,0.0002341032,0.00007147279,0.0001366382,0.00006153486,0.0001721032,0.0002240683,0.00002326254],"category_scores_gemma":[0.0004739603,0.0002436658,0.0001166027,0.0002100511,0.00009869273,0.0000120859,0.0001736087,0.0001249066,0.000006653998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001481604,"about_ca_system_score_gemma":0.0001023466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000666841,"about_ca_topic_score_gemma":0.00005574105,"domain_scores_codex":[0.9984097,0.00009251109,0.0003580063,0.0005927133,0.0001422399,0.0004047664],"domain_scores_gemma":[0.9992099,0.00008057574,0.00008836731,0.0004106087,0.00009488078,0.0001156658],"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":[0.00009138165,0.0002214612,0.008975744,0.0001197911,0.00004622264,0.00004778775,0.00008243158,0.00005041606,0.9855602,0.0002132599,0.0002098667,0.004381475],"study_design_scores_gemma":[0.001803574,0.0001358761,0.003733027,0.00006574189,0.00005211762,0.00003955535,0.0002905099,0.001336938,0.9854409,0.000282629,0.006489735,0.0003294685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724693,0.02307166,0.00365525,0.0000791913,0.0001632322,0.0002465889,0.00006840323,0.0000210726,0.0002252893],"genre_scores_gemma":[0.9694921,0.0003132505,0.02861252,0.00008968338,0.0001475076,0.00005600381,0.0001895465,0.00005399957,0.001045369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02495727,"threshold_uncertainty_score":0.9936402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162589422582514,"score_gpt":0.2528794605862053,"score_spread":0.2412535663603802,"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."}}