Melatonin Act as an Antidepressant via Attenuation of Neuroinflammation by Targeting Sirt1/Nrf2/HO-1 Signaling
Bibliographic record
Abstract
Physical or psychological stress can cause an immunologic imbalance that disturbs the central nervous system followed by neuroinflammation. The association between inflammation and depression has been widely studied in recent years, though the molecular mechanism is still largely unknown. Thus, targeting the signaling pathways that link stress to neuroinflammation might be a useful strategy against depression. The current study investigated the protective effect of melatonin against lipopolysaccharide (LPS)-induced neuroinflammation and depression. Our results showed that LPS treatment significantly induced depressive-like behavior in mice. Moreover, LPS-treatment enhanced oxidative stress, pro-inflammatory cytokines including TNFα, IL-6, and IL-1β, NF-κB phosphorylation, and glial cell activation markers including GFAP and Iba-1 in the brain of mice. Melatonin treatment significantly abolished the effect of LPS, as indicated by improved depressive-like behaviors, reduced cytokines level, reduced oxidative stress, and normalized LPS-altered Sirt1, Nrf2, and HO-1 expression. However, the melatonin protective effects were reduced after luzindole administration. Collectively, it is concluded that melatonin receptor-dependently protects against LPS-induced depressive-like behaviors via counteracting LPS-induced neuroinflammation.
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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".