Effects of melatonin on low-dose lipopolysaccharide-induced oxidative stress in mouse liver, muscle, and kidney
Bibliographic record
Abstract
Lipopolysaccharide (LPS) administration in an in vivo experimental mice model causes oxidative damage in the liver, muscle, and kidney. We aimed to determine specific mechanisms underlying melatonin's antioxidant protective role. Assays were carried out in quadruplicate in the control, melatonin (10 mg/kg, 10 days), acute LPS administration (once 150 μg), and LPS + melatonin groups. LPS stimulated lipid peroxidation processes (dienes and malondialdehyde) and antioxidant enzyme concentrations (superoxide dismutase, catalase, glutathione peroxidase, and glutathione reductase) were assessed in all investigated tissues. Protein oxidation processes (measured as aldehyde and kenotic carbonyl protein derivatives) were enhanced by LPS in the kidney and liver but not in muscle. Melatonin reversed LPS-induced changes, with the exception of muscle protein oxidation. LPS-induced oxidative stress resulted in augmented early-stage diene conjugated and end-stage malondialdehyde lipid peroxidation processes and affected antioxidant activity in liver, kidney, and muscle tissues. LPS activated protein oxidation processes in the kidney and liver. Melatonin ameliorated oxidative damage in the liver, kidney, and partially in the muscle. Melatonin modulates oxidative stress-induced states. Potential synergism between melatonin and systemic inflammation in terms of oxidative modification of muscle proteins needs to be clarified in further studies.
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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".