{"id":"W2959319687","doi":"10.1088/1758-5090/ab30b4","title":"Micropocket hydrogel devices for all-in-one formation, assembly, and analysis of aggregate-based tissues","year":2019,"lang":"en","type":"article","venue":"Biofabrication","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Cancer Society Research Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Biofabrication; Multicellular organism; Aggregate (composite); Scalability; Simple (philosophy); Biological system; Nanotechnology; Computer science; Spheroid; Human breast; 3D cell culture; Materials science; Tissue engineering; Biomedical engineering; Cell culture; Biology; Cell; Cancer cell; Engineering; Cancer","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":[0.0004152382,0.000556451,0.0003085139,0.0004121737,0.000270677,0.0004756829,0.0005992191,0.0005547548,0.001752304],"category_scores_gemma":[0.0003759991,0.0003167436,0.0003545613,0.0002510551,0.0002687371,0.000569866,0.0005374687,0.00069812,0.0009097622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004138166,"about_ca_system_score_gemma":0.0002894507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001773383,"about_ca_topic_score_gemma":0.0005484131,"domain_scores_codex":[0.9996355,0.00003634782,0.00003911829,0.00008934893,0.0001557129,0.0000439454],"domain_scores_gemma":[0.9995642,0.0001671635,0.0001359844,0.00006961889,0.00002947267,0.00003359468],"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":[0.0000333203,0.00002055047,0.000114984,0.0001432678,0.00000639571,0.00006012584,0.00003191156,0.0002888579,0.9903609,0.0007104938,0.0004323909,0.007796806],"study_design_scores_gemma":[0.000009523749,0.00008374273,0.0004892167,0.000008428849,0.000009139627,0.000192814,0.000009962213,0.002625407,0.9884901,0.0001348686,0.007934806,0.0000118498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3802465,0.01152664,0.5863869,0.0006589693,0.0007832428,0.0006071248,0.002670462,0.00474799,0.0123721],"genre_scores_gemma":[0.682005,0.005949766,0.3002711,0.0003680391,0.0001587538,0.001075193,0.001134305,0.0003242775,0.008713526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001752304,"threshold_uncertainty_score":0.005862057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03155344510436064,"score_gpt":0.3025669808136882,"score_spread":0.2710135357093275,"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."}}