{"id":"W3035635204","doi":"10.1002/adfm.202000545","title":"Recapitulating Pancreatic Tumor Microenvironment through Synergistic Use of Patient Organoids and Organ‐on‐a‐Chip Vasculature","year":2020,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Science Foundation","keywords":"Organoid; Stromal cell; Tumor microenvironment; Pancreatic cancer; Cancer research; Pancreatic tumor; Cell biology; Organ-on-a-chip; Cancer-Associated Fibroblasts; Fibroblast; Biology; Medicine; Cell culture; Cancer; Materials science; Internal medicine; Tumor cells; Nanotechnology; Microfluidics","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":[0.0002131898,0.0003974392,0.0002471657,0.000206476,0.0000844722,0.000466645,0.000250508,0.0003446949,0.0005754919],"category_scores_gemma":[0.0001902152,0.0001468782,0.0002319264,0.0001421479,0.0001712471,0.0002376065,0.0003600916,0.0004137292,0.0002574972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001847117,"about_ca_system_score_gemma":0.0001762169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002598961,"about_ca_topic_score_gemma":0.0005956957,"domain_scores_codex":[0.9998308,0.00003423819,0.00001083187,0.0000499907,0.00004742255,0.00002657375],"domain_scores_gemma":[0.9998738,0.0000378635,0.00002759996,0.00002866216,0.00001217286,0.00001988739],"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":[0.00003091176,0.00002027552,0.0001473492,0.0000412394,0.000007877024,0.0001147482,0.0000259244,0.001373128,0.9951897,0.0002010762,0.00008712466,0.00276061],"study_design_scores_gemma":[0.000004829625,0.0001083029,0.0007055562,0.000004089849,0.00001415476,0.0003036519,0.00001951241,0.004230871,0.9910358,0.00005337591,0.003511418,0.000008302532],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9012727,0.001901685,0.09113726,0.0001413397,0.00008856898,0.0001030728,0.0006607803,0.0006251902,0.004069358],"genre_scores_gemma":[0.9591991,0.0008025643,0.03763682,0.00006091925,0.000007801567,0.0001383348,0.0002613906,0.00006201683,0.001831007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005754919,"threshold_uncertainty_score":0.00192517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02058425902752301,"score_gpt":0.21423278588553,"score_spread":0.193648526858007,"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."}}