Polydatin protects against acetaminophen-induced hepatotoxicity in mice <i>via</i> anti-oxidative and anti-apoptotic activities
Bibliographic record
Abstract
Acetaminophen (APAP) is commonly used to relieve pain and fever in a clinical setting, but its excessive use can lead to serious hepatotoxicity. Our previous study demonstrated that polydatin (PD) can effectively attenuate d-galactose- and alcohol-induced hepatotoxicity, however, its effect on APAP-induced hepatotoxicity is still unknown. In this study, we explore the protective effect and potential mechanism of PD against APAP-induced hepatotoxicity in mice. The results indicate that PD effectively improves the survival of mice with APAP-induced hepatotoxicity, significantly alleviating histopathologic alterations in the liver, and decreasing the serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). PD significantly and dose-dependently reduces oxidative stress by lowering the content of oxidized glutathione (GSSG), reactive oxygen species (ROS), nitric oxide (NO) and malonaldehyde (MDA), while enhancing the hepatic activities of glutathione (GSH), glutathione peroxidase (GSH-Px) and the GSH/GSSG ratio. Meanwhile, PD also substantially inhibits the levels and mRNA expressions of inducible nitric oxide synthase (iNOS) and NADPH oxidase 2 (NOX2). Additionally, PD markedly arrests apoptosis by assuaging TUNEL-positive hepatocytes and the apoptotic index, decreasing the levels and expression of cytochrome c (CytC), cleaved-caspase-9, apoptotic protease activating factor 1 (Apaf-1), cleaved-caspase-3, and Bax and increasing the level and expression of Bcl-2. Overall, PD pretreatment shows a potent protective effect against APAP-induced hepatotoxicity by relieving oxidative stress and inhibiting apoptosis.
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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) | 0.000 | 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 | 0.001 | 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".