Static Cold Storage with Mitochondria-Targeted Hydrogen Sulfide Donor Improves Renal Graft Function in an Ex Vivo Porcine Model of Controlled Donation-after-Cardiac-Death Kidney Transplantation
Bibliographic record
Abstract
The global donor kidney shortage crisis has necessitated the use of suboptimal kidneys from donors-after-cardiac-death (DCD). Using an ex vivo porcine model of DCD kidney transplantation, the present study investigates whether the addition of hydrogen sulfide donor, AP39, to University of Wisconsin (UW) solution improves graft quality. Renal pedicles of male pigs were clamped in situ for 30 min and the ureters and arteries were cannulated to mimic DCD. Next, both donor kidneys were nephrectomized and preserved by static cold storage in UW solution with or without AP39 (200 nM) at 4 °C for 4 h followed by reperfusion with stressed autologous blood for 4 h at 37 °C using ex vivo pulsatile perfusion apparatus. Urine and arterial blood samples were collected hourly during reperfusion. After 4 h of reperfusion, kidneys were collected for histopathological analysis. Compared to the UW-only group, UW+AP39 group showed significantly higher pO2 (p < 0.01) and tissue oxygenation (p < 0.05). Also, there were significant increases in urine production and blood flow rate, and reduced levels of urine protein, serum creatinine, blood urea nitrogen, plasma Na+ and K+, as well as reduced intrarenal resistance in the UW+AP39 group compared to the UW-only group. Histologically, AP39 preserved renal structure by reducing the apoptosis of renal tubular cells and immune cell infiltration. Our finding could lay the foundation for improved graft preservation and reduce the increasingly poor outcomes associated with 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".