{"id":"W4411846086","doi":"10.3390/ijms26136295","title":"Metabolomic Insight into Donation After Circulatory-Death Kidney Grafts in Porcine Autotransplant Model: Normothermic Ex Vivo Kidney Perfusion Compared with Hypothermic Machine Perfusion and Static Cold Storage","year":2025,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network; University of Waterloo","funders":"Narodowe Centrum Nauki","keywords":"Machine perfusion; Ex vivo; Cold storage; Perfusion; Kidney; Metabolome; Transplantation; Medicine; Kidney transplantation; Metabolomics; Cryopreservation; Organ donation; Ischemia; In vivo; Surgery; Metabolite; Bioinformatics; Biology; Internal medicine; Biotechnology","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":[0.0001769969,0.0002851194,0.0003807137,0.0002997177,0.0001892708,0.0004468081,0.0001228228,0.0002845532,0.0009307106],"category_scores_gemma":[0.000143578,0.00009133456,0.0003588659,0.0003155395,0.0002073895,0.0003687638,0.0002552081,0.000537227,0.00009830185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001499603,"about_ca_system_score_gemma":0.0003117375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004491219,"about_ca_topic_score_gemma":0.0007693304,"domain_scores_codex":[0.999893,0.00001575811,0.000007181419,0.00003357382,0.00002915103,0.00002136573],"domain_scores_gemma":[0.9999278,0.000009029282,0.00002269864,0.000007097688,0.00001718402,0.0000162682],"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":[0.001328725,0.0001026135,0.005214451,0.0001783369,0.00006020017,0.000150216,0.00006207316,0.0002157389,0.9861437,0.00009739985,0.0001018309,0.006344603],"study_design_scores_gemma":[0.00006176664,0.003065855,0.1480669,0.00006549854,0.0004096108,0.0009812071,0.0004174685,0.004189197,0.8372568,0.0007336528,0.004679136,0.00007283083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989965,0.003442151,0.004635571,0.0001157682,0.00005731558,0.0000429529,0.0009523378,0.00002755385,0.0007612452],"genre_scores_gemma":[0.9909307,0.002304164,0.00398695,0.0001791028,0.00002104668,0.00009341836,0.001299391,0.00001821894,0.001166931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009307106,"threshold_uncertainty_score":0.003113508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00852304905882868,"score_gpt":0.2664251945667351,"score_spread":0.2579021455079065,"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."}}