{"id":"W2925880270","doi":"10.11159/nddte19.117","title":"A Microfluidic 3D Cell Culture System for Drug Discovery Studies","year":2019,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Recent Advances in Nanotechnology","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Microfluidics; Drug discovery; Computer science; 3D cell culture; Drug; Cell; Nanotechnology; Chemistry; Materials science; Bioinformatics; Medicine; Pharmacology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007907919,0.0006415576,0.0008445437,0.0006392996,0.0004963718,0.0007623426,0.0009527626,0.001048058,0.001483775],"category_scores_gemma":[0.0004715671,0.0003850199,0.0009577496,0.0003938758,0.0003270956,0.0004546109,0.0006583049,0.0007164532,0.001291995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005252052,"about_ca_system_score_gemma":0.0009983191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006880403,"about_ca_topic_score_gemma":0.001092714,"domain_scores_codex":[0.9993972,0.00008894094,0.00005734032,0.0001580878,0.0002468055,0.00005169369],"domain_scores_gemma":[0.9996099,0.0001262995,0.00006040177,0.00007578017,0.00007563136,0.00005195362],"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":[0.00008220379,0.00004306548,0.0003460041,0.0002987074,0.00003381625,0.000147237,0.00003717096,0.0007809345,0.9787234,0.001053688,0.001236698,0.01721701],"study_design_scores_gemma":[0.00005928303,0.0004688684,0.002125974,0.00004224599,0.0001348557,0.001173236,0.00001979323,0.01102834,0.9127546,0.0006717503,0.07140725,0.0001138632],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1812986,0.0198332,0.7696283,0.001677852,0.002325982,0.001398199,0.009808324,0.00622469,0.007804947],"genre_scores_gemma":[0.2736225,0.01002654,0.6998485,0.001195244,0.0003981008,0.002934916,0.005336704,0.0001833698,0.006454143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001483775,"threshold_uncertainty_score":0.004963696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008732759716089404,"score_gpt":0.2704830324968969,"score_spread":0.2617502727808075,"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."}}