{"id":"W2891717954","doi":"10.1039/c8lc00852c","title":"Development of a biomimetic liver tumor-on-a-chip model based on decellularized liver matrix for toxicity testing","year":2018,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; National Science and Technology Major Project; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Decellularization; Tumor microenvironment; Scaffold; Liver cancer; Microfluidic chip; Cancer research; Toxicity; 3D cell culture; Extracellular matrix; Nanotechnology; Microfluidics; Biomedical engineering; Medicine; Chemistry; Cell; Biology; Materials science; Cell biology; Tumor cells; Internal medicine; Hepatocellular carcinoma","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":[0.000309218,0.0005172914,0.0003551722,0.0002609564,0.0001607338,0.0002561263,0.00070313,0.0006696932,0.00061636],"category_scores_gemma":[0.000183145,0.00031985,0.0003723701,0.0001726102,0.0002029649,0.0003226868,0.0002007596,0.000498822,0.0002607405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003402053,"about_ca_system_score_gemma":0.0005232293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001903073,"about_ca_topic_score_gemma":0.002880959,"domain_scores_codex":[0.9998341,0.0000134915,0.00001243281,0.00004980138,0.00006284357,0.0000274305],"domain_scores_gemma":[0.9998189,0.00004260042,0.00003752865,0.00002566992,0.00005154465,0.00002382479],"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":[0.00003361902,0.00003792682,0.0001637904,0.00007931443,0.000006741192,0.00005394515,0.00001411578,0.002732972,0.9945661,0.0002076939,0.00008428215,0.002019463],"study_design_scores_gemma":[0.0000141079,0.0002492958,0.0009595349,0.000007032124,0.00002280554,0.00009163851,0.0000107871,0.04512728,0.9512429,0.00006895174,0.002186302,0.00001941711],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7371006,0.002287166,0.2528945,0.000311958,0.0002249107,0.0004712618,0.001389421,0.001491979,0.003828159],"genre_scores_gemma":[0.804984,0.001393944,0.1879985,0.0001794752,0.00001958438,0.0009026098,0.001042641,0.00009002213,0.003389267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001903073,"threshold_uncertainty_score":0.003783941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05213614965651931,"score_gpt":0.292253593951297,"score_spread":0.2401174442947777,"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."}}