{"id":"W4317662131","doi":"10.1038/s41598-023-28286-9","title":"Predicting and elucidating the post-printing behavior of 3D printed cancer cells in hydrogel structures by integrating in-vitro and in-silico experiments","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Institutes of Health Research","keywords":"In silico; In vitro; 3d printed; Cancer; Computational biology; Computer science; Nanotechnology; Biology; Materials science; Biomedical engineering; Medicine; Biochemistry; Genetics; Gene","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":[0.0004342112,0.0004727479,0.0004031471,0.0002508599,0.0001604968,0.0007036602,0.0004324426,0.0007883178,0.0005377698],"category_scores_gemma":[0.0008560629,0.0003101722,0.0005687951,0.0002159113,0.0003151123,0.0004177894,0.000230547,0.000357432,0.0002119763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006001135,"about_ca_system_score_gemma":0.0004703754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002104331,"about_ca_topic_score_gemma":0.00308588,"domain_scores_codex":[0.9998237,0.00002780835,0.0000199945,0.00004189108,0.00006124857,0.00002523496],"domain_scores_gemma":[0.999447,0.0003272463,0.00007811735,0.00007631869,0.00005058317,0.00002054411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008943189,0.0001334939,0.006453346,0.0001909371,0.00003561326,0.0001995194,0.0001171472,0.5433992,0.4412991,0.0005433679,0.000101816,0.007437156],"study_design_scores_gemma":[0.000004836887,0.00008347751,0.002623729,0.000006696998,0.00002028642,0.00008049918,0.00003333342,0.7806514,0.2158949,0.0002141727,0.0003673485,0.00001939264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8516421,0.0003684576,0.1454156,0.00008241582,0.00003073296,0.00006527959,0.0004476371,0.0004741441,0.001473702],"genre_scores_gemma":[0.9556789,0.0004561667,0.04306417,0.00003134415,0.000004642885,0.00005568792,0.0001960757,0.00003723431,0.0004757057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002104331,"threshold_uncertainty_score":0.004354179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442333872227802,"score_gpt":0.293106156664838,"score_spread":0.2786828179425599,"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."}}