{"id":"W3154523717","doi":"10.1038/s41598-021-87526-y","title":"A multiparametric extraction method for Vn96-isolated plasma extracellular vesicles and cell-free DNA that enables multi-omic profiling","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Beatrice Hunter Cancer Research Institute; Université de Moncton; Health Canada; Fisheries and Oceans Canada; Atlantic Cancer Research Institute","funders":"Atlantic Canada Opportunities Agency","keywords":"Extracellular vesicles; Profiling (computer programming); Computational biology; DNA; Extracellular; Vesicle; Chemistry; DNA extraction; Cell biology; Biology; Computer science; Biochemistry; Membrane; Gene; Polymerase chain reaction","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":[0.0005089789,0.0007449591,0.0003925887,0.0005361714,0.000348983,0.0004282458,0.0005131341,0.0008464101,0.000935286],"category_scores_gemma":[0.0007265156,0.0003151514,0.0004198822,0.0003392078,0.0002640263,0.0003723872,0.0007037317,0.0009845941,0.00101637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002325283,"about_ca_system_score_gemma":0.0003417188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002535931,"about_ca_topic_score_gemma":0.0004735518,"domain_scores_codex":[0.999319,0.00009848565,0.00007549609,0.0002098262,0.0002429094,0.00005426004],"domain_scores_gemma":[0.999669,0.0001135559,0.00007216608,0.00004963665,0.00006873631,0.00002686242],"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":[0.00001625391,0.00001220662,0.00008968014,0.00004841038,0.00000483857,0.0000367442,0.00001703013,0.0001303652,0.9945765,0.0001107913,0.00006064261,0.004896506],"study_design_scores_gemma":[0.000005498791,0.00006278958,0.0005002939,0.00001097014,0.000008179295,0.0002392347,0.000009772022,0.001358033,0.9939519,0.00008331185,0.003757076,0.0000130225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2171294,0.00280126,0.7737071,0.0003266929,0.0002081706,0.0009225972,0.001293601,0.000842091,0.002769167],"genre_scores_gemma":[0.3102864,0.001831775,0.6772267,0.0003996614,0.00005899691,0.00157438,0.00258761,0.0002083285,0.005826264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000935286,"threshold_uncertainty_score":0.003128827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134642165623722,"score_gpt":0.2856943575477862,"score_spread":0.2643479358915489,"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."}}