Rosiglitazone protects rat liver against acute liver injury associated with the NF-κB signaling pathway
Bibliographic record
Abstract
Rosiglitazone, which is mainly used in the treatment of diabetes mellitus, is also involved in the regulation of inflammation. The peroxisome proliferator-activated receptor (PPAR)-γ receptor subtype appears to play a pivotal role in the regulation of inflammation. However, the exact mechanism for the protective effects of rosiglitazone against inflammation such as liver injury remains unclear. The aim of this study was to investigate the effects of rosiglitazone on inflammation in the liver of rats treated with D-GaIN/LPS. Male Sprague-Dawley rats were injected with D-GaIN/LPS with or without pre-administration of rosiglitazone (3, 10, or 30 mg/kg, intraperitoneal injection). Our data showed that rosiglitazone significantly inhibited D-GaIN/LPS-induced hepatotoxicity in a dose-dependent manner, as indicated by both diagnostic indicators of liver damage (serum aminotransferase activities) and histopathological analysis. Western blot analysis showed that rosiglitazone significantly decreased protein expression levels of COX-2 and production of pro-inflammatory markers, including TNF-α and IL-6, in D-GaIN/LPS-treated rat liver. The results indicated that the inhibition of D-GaIN/LPS-induced inflammation by rosiglitazone can be attributed, at least partially, to its capacity to regulate the the immunoregulatory transcription factor nuclear factor kappa B (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 | 0.000 |
| 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) | 0.001 | 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".