Combining Oxygenated Cold Perfusion With Normothermic Ex Vivo Perfusion Improves the Outcome of Donation After Circulatory Death Porcine Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Ex vivo machine perfusion is a novel preservation technique for storing and assessing marginal kidney grafts. All ex vivo perfusion techniques have advantages and shortcomings. The current study analyzed whether a combination of oxygenated hypothermic machine perfusion (oxHMP) followed by a short period of normothermic ex vivo kidney perfusion (NEVKP) could combine the advantages of both techniques. METHODS: Porcine kidneys were exposed to 30 min of warm ischemia followed by perfusion. Kidneys underwent either 16-h NEVKP or 16-h oxHMP. The third group was exposed to 16-h oxHMP followed by 3-h NEVKP (oxHMP + NEVKP group). After contralateral nephrectomy, grafts were autotransplanted and animals were followed up for 8 d. RESULTS: All animals survived the follow-up period. Grafts preserved by continuous NEVKP showed improved function with lower peak serum creatinine and more rapid recovery compared with the other 2 groups. Urine neutrophil gelatinase-associated lipocalin, a marker of kidney injury, was found to be significantly lowered on postoperative day 3 in the oxHMP + NEVKP group compared with the other 2 groups. CONCLUSIONS: A short period of NEVKP after oxHMP provides comparable short-term outcomes to prolonged NEVKP and is superior to oxHMP alone. A combination of oxHMP with end-ischemic NEVKP could be an attractive, practical strategy to combine the advantages of both preservation techniques.
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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".