{"id":"W4390939128","doi":"10.1016/j.bioadv.2024.213775","title":"Biomaterial engineering for cell transplantation","year":2024,"lang":"en","type":"article","venue":"Biomaterials Advances","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Transplantation; Regenerative medicine; Regeneration (biology); Cell therapy; Tissue engineering; Medicine; Biomaterial; Function (biology); Cell; Stem cell; Immune system; Neuroscience; Biology; Immunology; Cell biology; Biomedical engineering; Surgery","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002340638,0.0001455381,0.000143047,0.000144495,0.00003237605,0.0001949211,0.0001409925,0.00008143111,0.000102892],"category_scores_gemma":[0.00002347666,0.000130217,0.00004965092,0.0001541238,0.00002848926,0.0002286815,0.00001191897,0.00001832438,0.00009025342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003832009,"about_ca_system_score_gemma":0.00001457872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002798314,"about_ca_topic_score_gemma":6.072721e-7,"domain_scores_codex":[0.9990917,0.00001008799,0.0002396051,0.0001937943,0.0001512407,0.0003135622],"domain_scores_gemma":[0.9996711,0.0001337898,0.000009595595,0.0000986677,0.00001679044,0.00007006736],"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":[0.00001767628,0.000005179828,0.000002558408,0.003082482,0.00001393805,0.00001071621,0.00005268027,0.0002263224,0.9885303,0.0002457386,0.000288869,0.007523526],"study_design_scores_gemma":[0.0001624213,0.00003466901,0.00006436468,0.000122485,0.00001054855,0.000007244579,0.000004106217,0.006995289,0.8822603,0.0001761336,0.1100045,0.0001579334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9295085,0.00455793,0.05400115,0.00006393125,0.008649348,0.0004371461,0.0003154559,0.002042516,0.0004240134],"genre_scores_gemma":[0.9885089,0.0009533062,0.009543004,0.000004027696,0.0006635456,0.0001515275,0.00008034207,0.00006053774,0.00003477892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1097156,"threshold_uncertainty_score":0.5310094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00976965298454501,"score_gpt":0.2671809732597106,"score_spread":0.2574113202751656,"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."}}