{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001277359,0.0002438036,0.0005136885,0.0006268623,0.0001026905,0.00003286467,0.0004615314,0.0001273737,0.0001034016],"category_scores_gemma":[0.0002895496,0.0002731429,0.000404743,0.0007794218,0.00008267287,0.0000385856,0.0001300645,0.0002853294,4.989251e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233326,"about_ca_system_score_gemma":0.000230184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005848792,"about_ca_topic_score_gemma":0.00007153134,"domain_scores_codex":[0.9970543,0.0004171501,0.001417364,0.0004146178,0.0003749552,0.0003215768],"domain_scores_gemma":[0.9979464,0.0001025136,0.001166117,0.0004591528,0.0002062921,0.0001195491],"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":[0.0006440236,0.000596764,0.01493602,0.00004145991,0.0001668686,0.00003094558,0.000254755,0.3391308,0.6437489,0.00005413193,0.0000253815,0.0003699826],"study_design_scores_gemma":[0.005650699,0.00116589,0.01887865,0.00009844939,0.0007556353,0.00002245234,0.001371604,0.1886658,0.780516,0.001679389,0.0006035084,0.0005919349],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8032958,0.005984029,0.1897012,0.0001092429,0.00008289994,0.0006941662,0.0001105797,0.000005499004,0.00001664932],"genre_scores_gemma":[0.9454471,0.0001121815,0.05362747,0.00001778211,0.0001102006,0.0001107291,0.0003953527,0.00005009201,0.0001291237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.150465,"threshold_uncertainty_score":0.9999721,"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."}}