{"id":"W3128295243","doi":"10.1007/s10237-021-01426-8","title":"A numerical study on tumor-on-chip performance and its optimization for nanodrug-based combination therapy","year":2021,"lang":"en","type":"article","venue":"Biomechanics and Modeling in Mechanobiology","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Chip; Microfluidics; Inlet; Materials science; Shear stress; Viscosity; Computer simulation; Performance improvement; Flow (mathematics); Range (aeronautics); Computer science; Mechanical engineering; Nanotechnology; Mechanics; Simulation; Engineering; Composite material","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":[0.0001889489,0.0002629285,0.0002889616,0.0001975955,0.0002140795,0.0004124254,0.0003251329,0.0006238323,0.001642353],"category_scores_gemma":[0.0008683127,0.0001621996,0.0002398684,0.0002825295,0.0002257827,0.0002963089,0.0001830872,0.000169275,0.000178227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004213583,"about_ca_system_score_gemma":0.000342418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001962768,"about_ca_topic_score_gemma":0.002077834,"domain_scores_codex":[0.9999129,0.00001278771,0.000003251339,0.00001678878,0.0000321971,0.00002214682],"domain_scores_gemma":[0.99959,0.0002513237,0.00003597579,0.00002999018,0.00007905174,0.00001368165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001158954,0.0001080281,0.001517728,0.0001201777,0.00002320426,0.00008891718,0.00003721755,0.9325795,0.05409905,0.001345523,0.0006355658,0.009329129],"study_design_scores_gemma":[0.000005858058,0.00005180652,0.0006366926,0.000002944614,0.000009563203,0.00001879494,0.00001683364,0.9866252,0.01216908,0.0001216063,0.0003345686,0.000007109589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9135383,0.0009036186,0.06152121,0.0004122549,0.00007576444,0.00004459151,0.0003399801,0.0003082215,0.02285613],"genre_scores_gemma":[0.9919125,0.00011655,0.006511122,0.00002493709,0.000004108826,0.00001971832,0.00005639024,0.00002453053,0.001330173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001962768,"threshold_uncertainty_score":0.005494177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05701363791502601,"score_gpt":0.2942876881818927,"score_spread":0.2372740502668667,"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."}}