{"id":"W2944203705","doi":"10.1039/c8lc01037d","title":"Effective bioprinting resolution in tissue model fabrication","year":2019,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":219,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia; National Cancer Institute; National Institutes of Health","keywords":"3D bioprinting; Nanotechnology; Fabrication; Materials science; High resolution; Computer science; Engineering; Biomedical engineering; Tissue engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0007701064,0.0004985257,0.0003258798,0.0004312577,0.000227846,0.0007666105,0.0005211974,0.0007958668,0.001259074],"category_scores_gemma":[0.0007560685,0.0004505992,0.0003848932,0.0003234507,0.0003595457,0.000833324,0.0006512363,0.0008676874,0.0006264909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004168102,"about_ca_system_score_gemma":0.0002437678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002376978,"about_ca_topic_score_gemma":0.0005155197,"domain_scores_codex":[0.9993861,0.0000936713,0.00003626439,0.0001425358,0.0002861136,0.00005528557],"domain_scores_gemma":[0.9995584,0.0002285626,0.00006571399,0.00008309523,0.00004758846,0.00001675514],"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":[0.00002364909,0.00001680888,0.00007821475,0.0001219931,0.000005487098,0.00007366222,0.00005626574,0.002530812,0.9867858,0.00173545,0.0002046394,0.008367106],"study_design_scores_gemma":[0.000003422624,0.00005091883,0.0003380711,0.00001940884,0.00000982172,0.0002626886,0.00001404348,0.01288139,0.9801806,0.0005110362,0.005714888,0.00001376517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2815141,0.01022214,0.6925686,0.0003045944,0.0001619798,0.0001198986,0.0004063764,0.001731324,0.01297094],"genre_scores_gemma":[0.6104848,0.005853062,0.3763833,0.000165542,0.00004291509,0.0001635843,0.0004248232,0.0003900715,0.006091791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001259074,"threshold_uncertainty_score":0.004212081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01197389821802116,"score_gpt":0.2736326931588679,"score_spread":0.2616587949408468,"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."}}