{"id":"W4413088538","doi":"10.1002/adfm.202515436","title":"Engineering Highly Cellularized Living Materials via Mechanical Agitation","year":2025,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Biofabrication; Materials science; Tissue engineering; Self-healing hydrogels; Biomedical engineering; Toughness; 3D bioprinting; Nanotechnology; Cell encapsulation; Biocompatible material; Hemostasis; Composite material; Surgery","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006608596,0.0002252548,0.0003195793,0.0002370547,0.00008447083,0.0001202207,0.000193406,0.0001720468,0.001676495],"category_scores_gemma":[0.001048634,0.000234284,0.00004672282,0.0003130039,0.00003298072,0.0001817471,0.0001250031,0.0001437858,0.0003280953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001664732,"about_ca_system_score_gemma":0.00003313896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008959518,"about_ca_topic_score_gemma":7.04312e-7,"domain_scores_codex":[0.9983365,0.00006515097,0.0005156959,0.0003054787,0.0003544925,0.000422708],"domain_scores_gemma":[0.9989137,0.0005476174,0.00004368127,0.0002607307,0.0001403866,0.00009387327],"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":[0.00004070962,0.00001474785,0.000003436135,0.000223528,0.00005871681,0.000003767193,0.000008831837,0.004424307,0.9865599,0.00658463,0.0003448423,0.001732531],"study_design_scores_gemma":[0.0003592939,0.00002073571,0.001679257,0.0002206034,0.00001649853,0.0000041086,0.000007544705,0.001811511,0.9913338,0.002185158,0.002140047,0.0002214017],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7190916,0.0001054987,0.2707364,0.0002274579,0.007677863,0.0003904168,0.00004196171,0.0009517484,0.0007770415],"genre_scores_gemma":[0.9909215,0.00003891634,0.007909352,0.00009915118,0.0002703953,0.0001539344,0.00008173371,0.000048973,0.0004760676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2718299,"threshold_uncertainty_score":0.9992361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007481892338785275,"score_gpt":0.2277352484811236,"score_spread":0.2202533561423383,"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."}}