{"id":"W4281717295","doi":"10.21203/rs.3.rs-1697648/v1","title":"Bioelectronic Screening Platform for Real-Time Monitoring of Tumour-derived Extracellular Vesicle-Induced Epithelial-to-Mesenchymal Transition","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Engineering and Physical Sciences Research Council; King Abdullah University of Science and Technology; University of Cambridge; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Extracellular vesicles; Epithelial–mesenchymal transition; Extracellular vesicle; Transition (genetics); Extracellular; Mesenchymal stem cell; Cell biology; Vesicle; Chemistry; Computer science; Microvesicles; Biology; Biochemistry; Membrane; 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":[0.0003154264,0.0004977128,0.0004354153,0.0004253942,0.0002334686,0.0006607289,0.0007049107,0.0009819013,0.001730616],"category_scores_gemma":[0.000378587,0.0002606,0.0002848347,0.0002935421,0.00020628,0.0003518003,0.0004931096,0.0004521872,0.0006521844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005065145,"about_ca_system_score_gemma":0.0002321366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002166886,"about_ca_topic_score_gemma":0.0003487753,"domain_scores_codex":[0.9996179,0.00003626686,0.0000142427,0.0000968665,0.0001703366,0.00006435173],"domain_scores_gemma":[0.9998505,0.00004068122,0.00003271339,0.00001952487,0.00003917704,0.00001732934],"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":[0.00004239882,0.00003021606,0.0001397884,0.00002610086,0.000004772725,0.00004123587,0.00001285825,0.0001420521,0.9969291,0.0001634105,0.0001138342,0.002354259],"study_design_scores_gemma":[0.000009087026,0.0001474147,0.0009580465,0.000003447053,0.00001197789,0.00007049921,0.00001608466,0.004198302,0.9933056,0.0000759875,0.001192608,0.00001097367],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9022946,0.001919759,0.08764122,0.0004592968,0.0003328855,0.0002025065,0.001229819,0.00123912,0.004680709],"genre_scores_gemma":[0.9585056,0.0006530037,0.03330144,0.0002108801,0.00003239934,0.0002418062,0.0005659777,0.00005041296,0.006438434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001730616,"threshold_uncertainty_score":0.005789518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05271716297865848,"score_gpt":0.3637540164985875,"score_spread":0.311036853519929,"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."}}