{"id":"W4388750824","doi":"10.1101/2023.11.16.567470","title":"Vascularized Liver Tissue Embedded Bioprinting Utilizing GelMA/Nanoclay-based Composite hydrogels","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Self-healing hydrogels; Tissue engineering; Biocompatible material; Materials science; Biomedical engineering; Nanocomposite; Regenerative medicine; Liver tissue; 3D bioprinting; Regeneration (biology); Rheology; Population; Nanotechnology; Chemistry; Engineering; Composite material; Medicine; Cell biology; Polymer chemistry","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":[0.0001875346,0.0003494116,0.0001241657,0.000223966,0.000086101,0.0002452565,0.0001494325,0.0003153711,0.0007351338],"category_scores_gemma":[0.0001362863,0.0001396869,0.0001957747,0.0001038859,0.00016327,0.0002655666,0.0001417645,0.000200718,0.0001913109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002557588,"about_ca_system_score_gemma":0.0001221024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004931077,"about_ca_topic_score_gemma":0.001032306,"domain_scores_codex":[0.999931,0.000006517458,0.000005178455,0.00002311759,0.00001812844,0.00001619682],"domain_scores_gemma":[0.9998553,0.00004200538,0.00005508522,0.00001482242,0.0000139141,0.00001890338],"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":[0.00001445744,0.000006635899,0.00002244986,0.00001679437,0.000001527395,0.00002907805,0.000007308851,0.0001822931,0.999109,0.00003651837,0.00001370914,0.0005602082],"study_design_scores_gemma":[0.000002873098,0.0000505863,0.0003077267,0.000002974749,0.000003386618,0.00003492114,0.000003764655,0.001319488,0.99784,0.00001111798,0.000420044,0.000003042292],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825905,0.001082345,0.01368601,0.00004830125,0.00005120355,0.0000327964,0.0001277668,0.0002468058,0.002134337],"genre_scores_gemma":[0.985394,0.0004147824,0.01128457,0.00002979614,0.00001303713,0.00002278609,0.00006799045,0.00004749229,0.002725506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007351338,"threshold_uncertainty_score":0.002459288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02719093468105049,"score_gpt":0.2541175636887846,"score_spread":0.2269266290077341,"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."}}