Piroxicam and meloxicam ameliorate hepatic oxidative stress and protein carbonylation in Kupffer and sinusoidal endothelial cells promoted by ischemia-reperfusion injury
Bibliographic record
Abstract
The present study was aimed to assess the effect of protein carbonylation (PC) in hepatic cells and effects of nonsteroidal anti-inflammatory drugs (NSAIDs) on indicators of tissue damage induced by liver ischemia-reperfusion injury (LIRI). Warm ischemia was performed by partial vascular occlusion during 90 min in Wistar rats. In serum, we determined the catalytic activity of Alanine Aminotransferase, Aspartate Aminotransferase, Lacticate Dehydrogenase, and Ornithine Carbamoyltransferase. In liver samples, we studied cellular alterations by means of histologic studies, lipid peroxidation, PC by immunohistochemistry, apoptosis and reactive oxygen species in bile by electron paramagnetic resonance. Based on PC data, sinusoidal endothelial cells (SEC) and Kupffer cells (KC) were the first to exhibit LIRI-associated oxidative damage and prior to parenchymal cells. Administration of piroxicam or meloxicam during the pre-ischemic period produced a highly significant decrease in all studied injury indicators. No significant differences were revealed between the protective action of the two drugs. The data shown here suggest the potential use of NSAIDs such as piroxicam or meloxicam in minimizing ischemic event-caused damage in liver. We also propose that PC may be employed as an adequate tool to assess tissue damage after oxidative stress.
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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) | 0.000 | 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 | 0.000 |
| 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".