{"id":"W4206385286","doi":"10.3390/cells11030351","title":"In-Cell Labeling Coupled to Direct Analysis of Extracellular Vesicles in the Conditioned Medium to Study Extracellular Vesicles Secretion with Minimum Sample Processing and Particle Loss","year":2022,"lang":"en","type":"article","venue":"Cells","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Canadian Glycomics Network; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Nanoparticle tracking analysis; Extracellular vesicles; Flow cytometry; Extracellular; Vesicle; Secretion; Cell; Microvesicles; Extracellular vesicle; Cell biology; Chemistry; Cell culture; Biophysics; Biology; Biochemistry; Immunology; Membrane; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001175912,0.001136954,0.0009069161,0.000624047,0.0005340111,0.001062295,0.000807022,0.0009525343,0.001899683],"category_scores_gemma":[0.0009882546,0.0003818595,0.0005400888,0.0004709173,0.0007265128,0.0006645937,0.0007001708,0.002007671,0.001482178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008754489,"about_ca_system_score_gemma":0.000805449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009610073,"about_ca_topic_score_gemma":0.001400224,"domain_scores_codex":[0.9988253,0.0001808066,0.0001043164,0.0003189097,0.0004098449,0.0001608458],"domain_scores_gemma":[0.999247,0.0002330986,0.0001456923,0.0001477533,0.0001688768,0.00005767994],"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":[0.00002712117,0.00002115428,0.00007379769,0.00004019783,0.000004424188,0.00001877376,0.00001937091,0.00007727929,0.9985152,0.000143476,0.00004765736,0.001011533],"study_design_scores_gemma":[0.000004811706,0.00003334025,0.0005067039,0.000006345804,0.00000898903,0.00004802439,0.000008864366,0.001146299,0.9964695,0.00006452935,0.001695641,0.000006826049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4189458,0.002336559,0.5684355,0.0005553727,0.0003881209,0.0009930036,0.002511781,0.001474025,0.004359925],"genre_scores_gemma":[0.5437153,0.00441656,0.4317279,0.000778601,0.0001449147,0.002983492,0.005164619,0.001338189,0.009730416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001899683,"threshold_uncertainty_score":0.006355107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103820050478506,"score_gpt":0.2518625823270075,"score_spread":0.2414805772791569,"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."}}