Modulation of oxidative–nitrosative stress and inflammatory response by rapamycin in target and distant organs in rats exposed to hindlimb ischemia–reperfusion: the role of mammalian target of rapamycin
Bibliographic record
Abstract
Mammalian target of rapamycin (mTOR) has been recognized with potential immunomodulatory properties playing an important role in various physiopathological processes including ischemia–reperfusion (I/R) injury. I/R injury stimulate reactive oxygen and nitrogen species by activating nicotinamide adenine dinucleotide phosphate oxidase and inducible nitric oxide synthase, respectively. Controversial results have been obtained in different I/R models following localized I/R; however, the precise role of the mTOR signaling pathway remains undefined. The objective of the current study was to evaluate the role of the mTOR in oxidative–nitrosative stress and inflammation in hindlimb I/R-induced injury in target and remote organ injuries. In rats subjected to I/R, an increased expression of ribosomal protein S6 (rpS6), inhibitor κB (IκB)-α, nuclear factor-κB (NF-κB) p65, inducible nitric oxide synthase, cyclooxygenase 2, gp91phox, and levels of tumor necrosis factor α, nitrite, nitrotyrosine, malondialdehyde and the activities of myeloperoxidase and catalase in the tissues and (or) sera were detected. Treatment with rapamycin, a selective inhibitor of mTOR, reversed all the I/R-induced changes as manifested by its anti-inflammatory and antioxidant effects in kidney and gastrocnemius muscle of rats. Collectively, these findings suggest that rapamycin protects against I/R-induced oxidative–nitrosative stress and inflammation leading to organ injuries via suppression of mTOR/IκB-α/NF-κB signaling pathway.
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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 | 0.001 |
| 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".