Remote ischemic per-conditioning protects against renal ischemia–reperfusion injury via suppressing gene expression of TLR4 and TNF-α in rat model
Bibliographic record
Abstract
The pathogenesis of renal ischemia-reperfusion injury (IRI) involves both inflammatory processes and oxidative stress in the kidney. This study determined whether remote ischemic per-conditioning (RIPerC) is mediated by toll-like receptor 4 (TLR4) signaling pathway in rats. Renal IR injury was induced by occluding renal arteries for 45 min followed by 24 h of reperfusion. RIPerC included 4 cycles of 2 min of ischemia of the left femoral artery followed by 3 min of reperfusion performed at the start of renal ischemia. Rats were divided into sham, IR, and RIPerC groups. At the end of the reperfusion period, urine, blood and tissue samples were gathered. IR created kidney dysfunction, as ascertained by a significant decrease in creatinine clearance and a significant increase in sodium fractional excretion. These changes occurred in concert with a decrease in the activities of glutathione peroxidase, catalase, and superoxide dismutase with an increment in malondialdehyde levels, mRNA expression levels of TLR4 and tumor necrosis factor α (TNF-α), and histological damage in renal tissues. RIPerC treatment diminished all these changes. This study demonstrates that RIPerC has protective effects on the kidney after renal IR, which might be related to the inhibition of the TLR4 signaling pathway and augmentation of antioxidant systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".