Curcumin alleviates ischemia reperfusion-induced acute kidney injury through NMDA receptor antagonism in rats
Bibliographic record
Abstract
OBJECTIVE: The present study investigated the role of N-methyl-d-aspartate (NMDA) receptors in curcumin-mediated renoprotection against ischemia reperfusion (I/R)-induced acute kidney injury (AKI) in rats. METHODS: Rats were subjected to bilateral renal I/R (40 min I, 24 hours R) to induce AKI. Kidney injury was assessed by measuring creatinine clearance, blood urea nitrogen, plasma uric acid, potassium level, fractional excretion of sodium, and macroproteinuria. Oxidative stress in renal tissues was assessed by measuring myeloperoxidase activity, thiobarbituric acid reactive substances, superoxide anion generation, and reduced glutathione content. Hematoxylin & eosin staining was done to assess histological changes in renal tissues. Curcumin (30 and 60 mg/kg) was administered one hour before subjecting rats to AKI. In separate groups, NMDA receptor agonists, glutamic acid (200 mg/kg), and spermidine (20 mg/kg) were administered prior to curcumin treatment in rats followed by AKI. RESULTS: I/R-induced AKI was demonstrated by significant change in plasma and urine parameters along with marked increase in oxidative stress and histological changes in renal tissues that were aggravated with pretreatment of glutamic acid and spermidine in rats. Administration of curcumin resulted in significant protection against AKI. However, glutamic acid and spermidine pretreatments prevented curcumin-mediated renoprotection. CONCLUSION: It is concluded that NMDA receptor antagonism significantly contributes towards curcumin-mediated protection against I/R-induced AKI.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".