Modulatory effects of <i>Viola odorata</i> flower and leaf extracts upon oxidative stress-related damage in an experimental model of ethanol-induced hepatotoxicity
Bibliographic record
Abstract
Ethanol is the most widely abused drug in the world and its long-term use induces oxidative stress in the liver tissue. The aim of this study was to evaluate protective effect of Viola odorata against ethanol-induced hepatotoxicity in Wistar rat. Animals were divided into 9 groups as follows: control (normal saline), ethanol (10 mg/kg, intraperitoneally), ethanol with 3 doses (125, 250, and 500 mg/kg) of ethyl acetate flower and leaf extracts, and positive control (vitamin E 80 mg/kg). Animals were gavaged 30 min before ethanol injection for 28 days. Then, animals were killed and the livers were separated. Oxidative stress parameters, including reactive oxygen species, lipid peroxidation, and protein carbonyl as well as glutathione content, were evaluated. Also, histopathological examination was performed and assessment of blood alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and total antioxidant capacity were evaluated. Ethanol significantly increased oxidative stress markers in liver. Interestingly, administration of both extracts significantly decreased oxidative stress markers in liver tissue and biochemical parameters in the plasma. In addition, abnormal pathological features were improved after treatment with flower and leaf extracts. These results suggested that V. odorata can be considered a candidate for improving conditions due to ethanol-induced tissue oxidative damage because of its antioxidant activity.
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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".