{"id":"W3198633396","doi":"10.1016/j.bios.2021.113585","title":"Magnetic particle based liquid biopsy chip for isolation of extracellular vesicles and characterization by gene amplification","year":2021,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Atlantic Cancer Research Institute; Saint John Regional Hospital; Concordia University","funders":"","keywords":"Nanoparticle tracking analysis; Liquid biopsy; Magnetic nanoparticles; Vesicle; Extracellular vesicles; Digital polymerase chain reaction; DNA; Chemistry; Particle (ecology); Circulating tumor cell; Housekeeping gene; Materials science; Nanotechnology; Microvesicles; Chromatography; Biophysics; Nanoparticle; Gene; Gene expression; Cell biology; Biology; Polymerase chain reaction; Biochemistry; microRNA; Cancer","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":[0.000256313,0.0003422484,0.0004029934,0.0003370601,0.0003335617,0.0004730783,0.0006492686,0.001000526,0.001473883],"category_scores_gemma":[0.0003993638,0.0002454195,0.0002491931,0.0001347913,0.0002382399,0.0003454412,0.0003728553,0.0004873491,0.00135705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003310529,"about_ca_system_score_gemma":0.0003453939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004528709,"about_ca_topic_score_gemma":0.0009225577,"domain_scores_codex":[0.9996773,0.00003879337,0.00001650592,0.00008196195,0.0001448108,0.00004064959],"domain_scores_gemma":[0.999787,0.00006693387,0.0000316599,0.00002593886,0.00006065454,0.00002775895],"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":[0.00002190512,0.00001902004,0.00005978474,0.00002034824,0.00000331622,0.00002709488,0.00000994304,0.00003909227,0.9979679,0.000132092,0.00007472459,0.001624792],"study_design_scores_gemma":[0.000008029363,0.0001113022,0.0007547351,0.000005189004,0.000009685427,0.0002073422,0.00001601912,0.003614806,0.9927683,0.00007314944,0.002421365,0.00001008998],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5630091,0.002609403,0.4239787,0.0007088539,0.0003197137,0.0005467713,0.000904127,0.002199404,0.005723945],"genre_scores_gemma":[0.7068007,0.001010265,0.2794021,0.0006413148,0.00006546138,0.0005390294,0.0009876766,0.000129857,0.0104236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001473883,"threshold_uncertainty_score":0.004930615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007863359688513946,"score_gpt":0.2212454756147675,"score_spread":0.2133821159262536,"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."}}