Subnormothermic Oxygenated Perfusion Optimally Preserves Donor Kidneys Ex Vivo
Bibliographic record
Abstract
INTRODUCTION: The current methods of preserving donor kidneys in nonoxygenated cold conditions minimally protect the kidney against ischemia-reperfusion injury (IRI), a major source of complications in clinical transplantation. However, preserving kidneys with oxygenated perfusion is not currently feasible due to the lack of an ideal perfusion mechanism that facilitates perfusion with blood at warm temperature. Here, we have designed an innovative renal pump circuit system that can perfuse blood or acellular oxygen carrier under flexible temperatures, pressures, and oxygenation. We have tested this apparatus to study optimal conditions of storage of our porcine model of donation after cardiac death (DCD) kidneys. METHODS: . Cessation of blood mimics postcardiac death in humans and simulates DCD warm ischemic injury. Procured kidneys were flushed and subjected to static cold storage (SCS) for 4 hours. For warm perfusion, kidneys were cannulated for pulsatile oxygenated perfusion with blood:PlasmaLyte for 4 hours at 15 °C, 22 °C, and 37 °C. To mimic posttransplant scenario, all kidneys were reperfused with blood for an additional 4 hours at 37 °C. RESULTS: Compared with all other groups, 22 °C perfusion resulted in significant reduction of acute tubular necrosis (ATN), apoptosis, kidney damage markers, Toll-like receptor signaling, and cytokine production. It was associated with maximal renal blood flow and urine output. Kidneys stored at 15 °C thrombosed within 2 hours under this condition. Martius Scarlet Blue staining confirmed that 22 °C was the optimal temperature to minimize hemorrhage and blood clots. CONCLUSION: Our novel study shows that oxygenated perfusion at near-room-temperature provides optimal donor kidney storage conditions.
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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".