Prolonged Normothermic Ex Vivo Kidney Perfusion Is Superior to Cold Nonoxygenated and Oxygenated Machine Perfusion for the Preservation of DCD Porcine Kidney Grafts
Bibliographic record
Abstract
The increased usage of marginal grafts has triggered interest in perfused kidney preservation to minimize graft injury. We used a donation after circulatory death (DCD) porcine kidney autotransplantation model to compare 3 of the most frequently used ex vivo kidney perfusion techniques: nonoxygenated hypothermic machine perfusion (non-oxHMP), oxygenated hypothermic machine perfusion (oxHMP), and normothermic ex vivo kidney perfusion (NEVKP). METHODS: Following 30 min of warm ischemia, grafts were retrieved and preserved with either 16 h of non-oxHMP, oxHMP, or NEVKP (n = 5 per group). After contralateral nephrectomy, grafts were autotransplanted and animals were followed for 8 d. Kidney function and injury markers were compared between groups. RESULTS: NEVKP demonstrated a significant reduction in preservation injury compared with either cold preservation method. Grafts preserved by NEVKP showed superior function with lower peak serum creatinine (NEVKP versus non-oxHMP versus oxHMP: 3.66 ± 1.33 mg/dL, 8.82 ± 3.17 mg/dL, and 9.02 ± 5.5 mg/dL) and more rapid recovery. The NEVKP group demonstrated significantly increased creatinine clearance on postoperative day 3 compared with the cold perfused groups. Tubular injury scores on postoperative day 8 were similar in all groups. CONCLUSIONS: Addition of oxygen during HMP did not reduce preservation injury of DCD kidney grafts. Grafts preserved with prolonged NEVKP demonstrated superior initial graft function compared with grafts preserved with non-oxHMP or oxHMP in a model of pig DCD kidney transplantation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".