{"id":"W2751861876","doi":"10.1016/j.biomaterials.2017.08.041","title":"Tissue-engineered human 3D model of bladder cancer for invasion study and drug discovery","year":2017,"lang":"en","type":"article","venue":"Biomaterials","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"Canadian Urological Association Scholarship Fund; Canadian Urological Association; Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Bladder Cancer Canada; Université Laval","keywords":"Stromal cell; Urothelium; Tumor microenvironment; Bladder cancer; Cancer research; Cancer cell; Carcinogenesis; Cancer; Drug discovery; Biology; Basement membrane; Cell culture; Cell biology; Bioinformatics; Tumor cells; Anatomy","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":[0.0005092756,0.0004010843,0.0003334297,0.0005159803,0.000329164,0.0005816967,0.0004799389,0.0008543814,0.001708283],"category_scores_gemma":[0.000185893,0.0003303202,0.0007287626,0.0005415967,0.0002909026,0.0003147403,0.0003421599,0.0007618883,0.0006043271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323193,"about_ca_system_score_gemma":0.0009798855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003115876,"about_ca_topic_score_gemma":0.004334878,"domain_scores_codex":[0.9997703,0.00003233809,0.00001911931,0.00003462657,0.000105366,0.00003832984],"domain_scores_gemma":[0.9997844,0.00005765612,0.00003449716,0.00006133349,0.00002491034,0.00003727262],"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":[0.0002824264,0.000179409,0.001085822,0.0002643387,0.00004380536,0.0006056748,0.0001663434,0.006630116,0.9797392,0.002309874,0.0008591134,0.007833833],"study_design_scores_gemma":[0.00009903245,0.0006428681,0.006754747,0.00006139525,0.0001763059,0.002713313,0.0001562612,0.05318058,0.9049711,0.0008865727,0.03028861,0.00006920294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8409019,0.004549161,0.1317907,0.0007387401,0.0003923775,0.0003631461,0.00678816,0.001017717,0.01345816],"genre_scores_gemma":[0.923553,0.001528042,0.06622869,0.0001964578,0.0000195085,0.0003193638,0.002266142,0.00008652938,0.005802274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003115876,"threshold_uncertainty_score":0.006195426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06018854472556164,"score_gpt":0.3551043999910899,"score_spread":0.2949158552655283,"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."}}