Melatonin prevents neuroinflammation and relieves depression by attenuating autophagy impairment through FOXO3a regulation
Bibliographic record
Abstract
Major depressive disorder (MDD) is a life-threatening illness characterized by mood changes and high rates of suicide. Although the role of neuroinflammation in MMD has been studied, the mechanistic interplay between antidepressants, neuroinflammation, and autophagy is yet to be investigated. The present study investigated the effect of melatonin on LPS-induced neuroinflammation, depression, and autophagy impairment. Our results showed that in mice, lipopolysaccharide (LPS) treatment induced depressive-like behaviors and caused autophagy impairment by dysregulating ATG genes. Moreover, LPS treatment significantly increased the levels of cytokines (TNFα, IL-1β, IL-6), enhanced NF-ᴋB phosphorylation, caused glial (astrocytes and microglia) cell activation, dysregulated FOXO3a expression, increased the levels of redox signaling molecules such as ROS/TBARs, and altered expression of Nrf2, SOD2, and HO-1. Melatonin treatment significantly abolished the effects of LPS, as demonstrated by improved depressive-like behaviors, normalized autophagy-related gene expression, and reduced levels of cytokines. Further, we investigated the role of autophagy in LPS-induced depressive-like behavior and neuroinflammation using autophagy inhibitors 3-MA and Ly294002. Interestingly, inhibitor treatment significantly abolished and reversed the anti-depressive, pro-autophagy, and anti-inflammatory effects of melatonin. The present study concludes that the anti-depressive effects of melatonin in LPS-induced depression might be mediated via autophagy modulation through FOXO3a signaling.
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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".