Protective effect of irbesartan against doxorubicin-induced nephrotoxicity in rats: implication of AMPK, PI3K/Akt, and mTOR signaling pathways
Bibliographic record
Abstract
Nephrotoxicity is one of the serious undesirable effects related to doxorubicin (DOX). Herein, we have investigated the potential protective effect of irbesartan (IRB) against chronic nephrotoxicity induced by DOX, and the implication of different mechanistic pathways underlying these effects. Rats were treated with either DOX (2.5 mg/kg i.p., 3 times/week) for 2 weeks, and (or) IRB (40 mg/kg, daily) for 3 weeks. IRB prohibited nephrotoxicity induced by DOX, which was evident by the increase in blood urea nitrogen and creatinine levels and histopathological changes. IRB improved DOX-induced alterations in oxidative status by diminishing lipid peroxidation and upregulating the antioxidant enzymes. Also, upon DOX treatment, the renal expression of tumor necrosis factor-α, interleukin-6, and caspase-3 were significantly increased; IRB diminished DOX-induced alterations in these parameters. Moreover, DOX significantly decreased the expression level of AMP-activated protein kinase (AMPK). Meanwhile, DOX induced activation of phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt/PKB) and mammalian target of rapamycin (mTOR) pathways that cross talked with AMPK. On the contrary, IRB successfully counterbalanced all these effects. Collectively, these outcomes suggest that the modulation of AMPK, PI3K, Akt, and mTOR pathways plays a critical role in conferring the protective effects of IRB against DOX nephrotoxicity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".