Small Interfering RNA Targeting RelB Protects Against Renal Ischemia-reperfusion Injury
Bibliographic record
Abstract
BACKGROUND: Nuclear factor kappaB (NF-kappaB) has been found to be critical to the pathogenesis of renal ischemia-reperfusion injury (IRI). Using small interfering RNA (siRNA) to silence the expression of RelB, a component of the transcription factors Rel/nuclear factor kappaB, may protect renal IRI. Here, we report an siRNA-based treatment of preventing IRI. METHODS: Renal IRI was induced in mice by clamping the left renal pedicle for 25 or 35 min. The therapeutic effects of siRNA were evaluated in renal function, histologic examination, and overall survival after lethal IRI. RESULTS: A single injection of RelB siRNA resulted in knockdown of renal RelB expression. In comparison with control mice, levels of blood urea nitrogen and serum creatinine were significantly decreased in mice treated with siRNA. Pathologic examination demonstrated that tissue injury caused by IRI was markedly reduced as a result of RelB siRNA treatment. Additionally, with RelB siRNA treatment, immunohistochemistry showed a significant attenuation of tumor necrosis factor-alpha expression. Furthermore, survival experiments revealed that more than 90% of control mice died from lethal IRI, whereas 80% of siRNA-pretreated mice survived until the end of the 8-day observation period. CONCLUSION: Silencing RelB, using siRNA, can significantly attenuate IRI-induced renal dysfunction and protect mice against lethal kidney ischemia, highlighting the potential for siRNA-based clinical therapy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".