Attenuating Ischemia-Reperfusion Injury in Kidney Transplantation by Perfusing Donor Organs With siRNA Cocktail Solution
Bibliographic record
Abstract
BACKGROUND: Ischemia-reperfusion (I/R) injury is the major cause of delayed renal graft function in kidney transplantation. To date, there are no effective therapeutic approaches for preventing I/R injury. We previously reported that treatment of animals with small interference RNA (siRNA) would prevent warm I/R injury in nontransplant models and cold I/R injury in heart transplantation. In the present study, we further explore the feasibility of protecting grafts from extended cold I/R injury as applied to kidney transplantation by downregulating I/R-associated genes using siRNA. METHODS: Donor kidneys were intra-arterially perfused with siRNA containing solution during donor excision and preserved in siRNA containing solution. The siRNA-treated donor organs were then implanted into syngeneic recipient mice, and the 2 original kidneys were removed from the recipient. The effect of siRNA solution on extended cold I/R injury was determined by assessing renal function, histopathological change, cell apoptosis, and inflammation. RESULTS: The perfused siRNA solution knocked down the expression of complement 3, RelB, and Fas in the kidney at the mRNA and protein levels. Administration of siRNA solution reduced the levels of blood urea nitrogen and serum creatinine as compared with control groups. The siRNA cocktail decreased cell apoptosis and histopathological changes in the kidney and prolonged graft survival. The siRNA cocktail also reduced the expression of proinflammatory cytokines, IL-6, and TNFα. CONCLUSIONS: In conclusion, this is the first demonstration that perfusing donor organs with an siRNA cocktail solution can induce gene silencing in the kidney and prevent kidneys from extended cold I/R injury in kidney transplantation, highlighting the promise of the clinical application of siRNA-based therapies in the preservation of donor organs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".