Inhibiting inducible nitric oxide synthase with rutin reduces renal ischemia/reperfusion injury
Bibliographic record
Abstract
BACKGROUND: Nitric oxide (NO) seems to play an important role during renal ischemia/reperfusion (I/R) injury. We investigated whether rutin inhibits inducible nitric oxide synthase (iNOS) and reduces 3-nitrotyrosine (3-NT) formation in the kidneys of rats during I/R. METHODS: Wistar albino rats were nephrectomized unilaterally and, 2 weeks later, subjected to 45 minutes of left renal pedicle occlusion followed by 3 hours of reperfusion. We intraperitoneally administered L-N6-(1-iminoethyl)lysine (L-NIL; 3 mg/kg) for 30 minutes or rutin (1 g/kg) for 60 minutes before I/R. After reperfusion, kidney samples were taken for immunohistochemical analysis of iNOS and 3-NT. We measured plasma nitrite/nitrate and cyclic guanosine monophosphate (cGMP) to evaluate NO levels. RESULTS: Ischemia/reperfusion caused plasma cGMP to increase significantly. Similarly, plasma nitrite/nitrate was elevated in the I/R group compared with the control group. Histochemical staining was positive for iNOS and 3-NT in the I/R group. Pretreatment with L-NIL or rutin significantly mitigated the elevation of plasma cGMP and nitrite/nitrate. These changes in biochemical parameters were also associated with changes in immunohistochemical appearance. Pretreatment with L-NIL or rutin significantly decreased the incidence and severity of iNOS and 3-NT formation in the kidney tissues. CONCLUSION: Our findings suggest that high activity of iNOS causes renal I/R injury, and that rutin exerts protective effects, probably by inhibiting iNOS.
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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) | 0.001 | 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) | 0.001 | 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".