{"id":"W2809083129","doi":"10.3390/mi9060306","title":"Rapid Detection and Trapping of Extracellular Vesicles by Electrokinetic Concentration for Liquid Biopsy on Chip","year":2018,"lang":"en","type":"article","venue":"Micromachines","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; Abu Dhabi Education Council; New York University Abu Dhabi","keywords":"Liquid biopsy; Microvesicles; Nanoparticle tracking analysis; Extracellular vesicle; Vesicle; Extracellular; Detection limit; Chemistry; Extracellular vesicles; Electrokinetic phenomena; Extracellular fluid; Microfluidics; Chromatography; Biophysics; Nanotechnology; Membrane; Materials science; Cancer; Biology; Biochemistry; Cell biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001364935,0.0001485957,0.0001204045,0.00002928981,0.00009675045,0.00001635565,0.00009550929,0.0001040654,0.000008403827],"category_scores_gemma":[0.00006384488,0.000146033,0.00006505103,0.00005784707,0.0001668463,0.000004850189,0.00002023879,0.00004349614,0.000001399158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007282705,"about_ca_system_score_gemma":0.00001754078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006200876,"about_ca_topic_score_gemma":0.00000486885,"domain_scores_codex":[0.9991798,0.0000507838,0.0002092883,0.0003070712,0.00006513284,0.0001878884],"domain_scores_gemma":[0.9995548,0.00001827511,0.0001083354,0.0001978005,0.00006454161,0.00005626849],"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":[0.0005997678,0.00007651046,0.0001040908,0.00004046619,0.00003273434,3.985191e-7,0.00003894126,0.000001157955,0.990615,0.00004153495,0.0002437608,0.00820562],"study_design_scores_gemma":[0.0006690152,0.001630786,0.0004958648,0.00001685745,0.00003026382,0.00001286847,0.00001513663,0.0003343948,0.9911417,0.00007061794,0.005428134,0.000154297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821119,0.008200744,0.009094247,0.00006606523,0.00008226038,0.0002950858,0.00004409557,0.00001510485,0.00009043732],"genre_scores_gemma":[0.9987554,0.0001346467,0.0005706208,0.000054488,0.0002440227,0.00002143404,0.0001053974,0.00002594456,0.00008804569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01664344,"threshold_uncertainty_score":0.5955052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006820241428848341,"score_gpt":0.2341503182612669,"score_spread":0.2273300768324186,"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."}}