Hepatoprotective influence of quercetin and ellagic acid on thioacetamide-induced hepatotoxicity in rats
Bibliographic record
Abstract
Despite all the studies performed to date, therapy choices for liver injuries are very few. Therefore, the search for a new treatment that could safely and effectively block or reverse liver injuries remains a challenge. Quercetin (QR) and ellagic acid (EA) had potent antioxidant and anti-inflammatory activities. The current study aimed at evaluating the potential hepatoprotective influence of QR and EA against thioacetamide (TAA)-induced liver toxicity in rats and the underlying mechanism using silymarin as a reference drug. Fifty mature male rats were orally treated daily with EA and QR in separate groups for 45 consecutive days, and then were injected with TAA twice with 24 h intervals in the last 2 days of the experiment. Administration of TAA resulted in marked elevation of liver indices, alteration in oxidative stress parameters, and significant elevation in expression level of fibrosis-related genes (MMP9 and MMP2). Administration of QR and EA significantly attenuated the hepatic toxicity through reduction of liver biomarkers, improving the redox status of the tissue, as well as hampering the expression level of fibrosis-related genes. In this study, QR and EA were proved to attenuate the hepatotoxicity through their antioxidant, metal-chelating capacity, and anti-inflammatory effects.
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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 | 0.000 | 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".