Melatonin Modulation of Sirtuin-1 Attenuates Liver Injury in a Hypercholesterolemic Mouse Model
Bibliographic record
Abstract
Hypercholesterolemia increases and exacerbates stress signals leading also to liver damage (LD) and failure. Sirtuin1 (SIRT1) is involved in lifespan extension and it plays an essential role in hepatic lipid metabolism. However, its involvement in liver hypercholesterolemic damage is not yet completely defined. This in vivo study evaluated the role of SIRT1 in the hypercholesterolemic-related LD and, then, investigated how oral supplementation of melatonin, pleiotropic indoleamine, may be protective. Control mice and apolipoprotein E-deficient mice (ApoE−/−) of 6 and 15 weeks of age were treated or not treated with melatonin at the dose of 10 mg/kg/day for 9 weeks. In this study, we evaluated serum biochemical markers, liver SIRT1 expression, and oxidative stress markers. We observed that hypercholesterolemia increased significantly serum cholesterol and triglycerides, reduced significantly liver SIRT1, and, in turn, induced hepatic oxidative stress in untreated ApoE−/− mice with respect to control mice. Interestingly, melatonin treatment improved serum biochemical markers and hepatic morphological impairment and inhibited oxidative stress through its antioxidant properties and also by SIRT1 upregulation. In summary, melatonin oral supplementation may represent a new protective approach to block hypercholesterolemic liver alterations involving also a SIRT1-dependent mechanism.
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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) | 0.001 | 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 | 0.001 |
| 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".