Melatonin ameliorates oxidative damage in hyperglycemia-induced liver injury
Bibliographic record
Abstract
PURPOSE: Melatonin (N-acetyl-5-methoxy-tryptamine) is synthesized mainly by the pineal gland and its antioxidant properties have been demonstrated both in short and long term studies. Our aim was to clarify the effects of hyperglycemia and to administer melatonin on lipid peroxidation, protein oxidation and oxidative DNA damage in rat. METHODS: Malondialdehyde (MDA), protein carbonyl (PCO) and total thiol (T-SH) levels were determined in plasma and liver tissue, glutathione (GSH) levels in erythrocyte and liver tissue, and 8-hydroxy-2-deoxyguanosine (8-OHdG) levels in plasma and liver. Thirty-eight male Wistar rats were divided into four groups: 1--injected with saline (n = 8), 2--injected with melatonin (n = 10), 3--injected with STZ (65 mg/kg, i.p.) (diabetic group) (n = 10) and 4--injected with melatonin (10 mg/kg/day, i.p.) and STZ (65 mg/kg, i.p.) (n = 10) for 8 weeks (diabetic+ melatonin group). Colorimetric methods were used to determine the level of the oxidative stress markers. 8-OhdGwas measured using ELISA. RESULTS: MDA, PCO and 8-OHdG levels in the plasma and the liver homogenates of diabetic rats were higher than controls and were significantly reduced after melatonin treatment. T-SH and GSH levels in samples were markedly reduced in untreated diabetic rats compared with control rats; however, these parameters were increased in diabetic rats following melatonin treatment. CONCLUSION: Our findings showed that melatonin administration partially ameliorated oxidative damage in liver injury in STZ-induced diabetic rats. The present study suggests that melatonin functions as a potent antioxidant agent in diabetes. Melatonin, a nutritional supplement, may be a good therapeutic option for diabetic patients.
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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 | 0.000 | 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".