{"id":"W2017381676","doi":"10.1038/ncomms2853","title":"InVERT molding for scalable control of tissue microarchitecture","year":2013,"lang":"en","type":"article","venue":"Nature Communications","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":false,"ca_institutions":"Heart and Stroke Foundation; University of Toronto; University Health Network","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Diabetes and Digestive and Kidney Diseases; National Science Foundation; National Institutes of Health; National Cancer Institute; Agency for Science, Technology and Research; Howard Hughes Medical Institute","keywords":"Microscale chemistry; Induced pluripotent stem cell; Cell type; Cell; Cell biology; Biology; Tissue engineering; Stem cell; 3D bioprinting; Computational biology; Embryonic stem cell; Gene; Biochemistry; Genetics","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":[0.0001403298,0.0003490312,0.0001739804,0.0001788787,0.0001461664,0.0003827912,0.0004684044,0.0001881789,0.001493747],"category_scores_gemma":[0.0002070339,0.0002491453,0.0001561723,0.0001684811,0.0003640949,0.0003067109,0.0005049519,0.0005908399,0.0002407824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000275921,"about_ca_system_score_gemma":0.0002196755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003117696,"about_ca_topic_score_gemma":0.001577586,"domain_scores_codex":[0.9999012,0.000005211504,0.000005892879,0.00002332073,0.00004989885,0.00001450595],"domain_scores_gemma":[0.9998344,0.0000499067,0.00005784064,0.00003608783,0.00001229523,0.000009458258],"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":[0.00002289605,0.00002125443,0.0001318634,0.00005648217,0.000006483663,0.00004532056,0.00005276786,0.003241714,0.9821157,0.002222382,0.0003063174,0.01177681],"study_design_scores_gemma":[0.00002036102,0.0001239024,0.0008510764,0.000007564738,0.00001056112,0.0000856592,0.00001848051,0.03820228,0.9542534,0.0008946121,0.005518062,0.0000140414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6863135,0.001799411,0.2851695,0.0003725019,0.0003719556,0.0001222736,0.0004340514,0.00210835,0.0233085],"genre_scores_gemma":[0.9434392,0.0006153943,0.0518445,0.0001076096,0.00003394894,0.00006392784,0.0001095237,0.0002158567,0.00356999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001493747,"threshold_uncertainty_score":0.004997075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470088646568342,"score_gpt":0.3005791092623795,"score_spread":0.2858782227966961,"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."}}