{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004636719,0.0005156398,0.0003845523,0.0008524598,0.0002776667,0.0009725305,0.0003305354,0.0009838706,0.006265386],"category_scores_gemma":[0.0004886225,0.0002141506,0.0006061624,0.0006018884,0.0003094217,0.0006636411,0.000533175,0.0009933061,0.004025314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003736487,"about_ca_system_score_gemma":0.0003946343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001435578,"about_ca_topic_score_gemma":0.0003631476,"domain_scores_codex":[0.9996701,0.00004205788,0.0000365592,0.00004969535,0.0001706266,0.00003090192],"domain_scores_gemma":[0.9998595,0.00004412825,0.00003047067,0.00002082482,0.00003276329,0.00001226848],"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":[0.00008099082,0.0001112375,0.0003166969,0.003436999,0.00005128518,0.0007560548,0.0001530398,0.001765514,0.7725459,0.02646537,0.01035171,0.1839653],"study_design_scores_gemma":[0.00002220201,0.0002422484,0.0008010961,0.0005251144,0.00006000628,0.002398427,0.00006382471,0.002868068,0.4591495,0.008795015,0.5250209,0.0000535188],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06988884,0.2769126,0.4671933,0.003488262,0.006852511,0.0007840274,0.001850828,0.002869862,0.1701598],"genre_scores_gemma":[0.4325971,0.1821093,0.3108217,0.002980229,0.001084694,0.001167537,0.002138239,0.0006958046,0.06640534],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006265386,"threshold_uncertainty_score":0.02095973,"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."}}