{"id":"W4210648601","doi":"10.1002/jev2.12184","title":"Optimization of small extracellular vesicle isolation from expressed prostatic secretions in urine for in‐depth proteomic analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Extracellular Vesicles","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"National Cancer Institute","keywords":"Ultracentrifuge; Nanoparticle tracking analysis; Urine; Prostate cancer; Tamm–Horsfall protein; Extracellular vesicle; Biomarker discovery; Dithiothreitol; Chemistry; Microvesicles; Extracellular vesicles; Vesicle; Chromatography; Biomarker; Proteomics; Biology; Biochemistry; Cancer; Cell biology; Medicine; Internal medicine; Enzyme; Gene; microRNA","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008448098,0.000532073,0.0004844144,0.00042259,0.0003749808,0.000691365,0.0003485556,0.0004593136,0.0005038533],"category_scores_gemma":[0.000814135,0.0002034056,0.0003819123,0.0003180226,0.0002827454,0.0003131169,0.0005519725,0.0005501288,0.0004438279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001776975,"about_ca_system_score_gemma":0.0004320779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005347662,"about_ca_topic_score_gemma":0.0008475395,"domain_scores_codex":[0.9994687,0.0001410864,0.00005633824,0.0001027147,0.0001603,0.0000709145],"domain_scores_gemma":[0.9997863,0.00006149766,0.00003362619,0.00002335629,0.0000629678,0.00003230112],"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":[0.00003079531,0.00002557244,0.0002137161,0.00005521555,0.000007887423,0.00004166569,0.00001981739,0.0001540285,0.9966432,0.0000622271,0.00004065256,0.002705324],"study_design_scores_gemma":[0.000005835366,0.000140093,0.001749808,0.00001660082,0.00001961412,0.0002003653,0.000037006,0.002037612,0.9928818,0.00007049843,0.00282867,0.00001209302],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7735696,0.003380744,0.2179787,0.0003758128,0.0001535946,0.0009872421,0.0009135897,0.0006858038,0.001954797],"genre_scores_gemma":[0.7488019,0.003765016,0.2402578,0.0002904914,0.00004832341,0.0006795267,0.002460058,0.0003201989,0.003376694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008448098,"threshold_uncertainty_score":0.004467845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327634924011987,"score_gpt":0.2448349863613903,"score_spread":0.2315586371212704,"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."}}