Hepato-protective Effect of Clove Bud Polyphenols (Syzygium aromaticum L.) (Clovinol®) by Modulating Alcohol Induced Oxidative Stress and Inflammation
Bibliographic record
Abstract
The pathogenesis of alcoholic liver diseases progresses from fatty liver to hepatic inflammation and necrosis to fibrosis. Clove buds (Syzygium aromaticum L.) are one of the richest sources of dietary polyphenols with many traditional medicinal uses. Hence, the present work attempted to evaluate the hepatoprotective activity of a standardized polyphenol-rich extract of clove buds (Clovinol). The experiment was conducted on Wistar rats designated into three groups. First group was vehicle control and hepatotoxicity was induced to the second group by the administration of ethanol at the fixed dose of 12.5 g/kg body wt., orally for 30 days. Clovinol (100 mg/kg body wt.) was co-administered with ethanol in the third group. The liver toxicity was assessed by the estimation of liver marker enzymes in addition to which, assays of antioxidant enzymes, inflammatory markers, and liver histopathology studies were also conducted. Ethanol treatment significantly increased (p<0.05) liver function markers (SGOT and SGPT) and reduced (p<0.05) the antioxidant enzymes (SOD, CAT, and GPx) and GSH. It also increased the WBC count, inflammatory markers (nitrite, CRP, COX-2, IL-6 and TNF-α) and lipid peroxidation, significantly (p<0.05). Supplementation of Clovinol showed significant (p<0.05) reversal of all these biochemical and molecular variables indicating the efficacy of Clovinol in the downregulation of alcohol-induced oxidative stress and inflammatory changes, bringing about a significant reduction in the associated liver pathology. To summarize, we found that Clovinol could be a potential functional ingredient for liver health.
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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".