Central and peripheral stress-induced epigenetic mechanisms of resilience
Bibliographic record
Abstract
PURPOSE OF REVIEW: Resilience is an adaptation process presented by an individual despite facing adversities. Epigenetic changes, such as histone acetylation/methylation and DNA methylation, have been demonstrated to mediate stress response. In this review, we summarize recent findings on epigenetic mechanisms contributing to stress resilience. RECENT FINDINGS: Epigenetic modifications of genes involved in synaptic plasticity, endocrine, immune, and vascular systems are linked to resilience. For instance, increased DNA methylation of the nonneuronal growth factor Gdnf in specific brain regions promotes stress resilience. Additionally, high DNA methylation at the glucocorticoid receptor gene was associated with resilience in both rodents and humans. At the immune level, chronic stress induces increased DNA methylation at IL6 gene, a mediator of stress vulnerability. Moreover, epigenetic adaptations of the blood--brain barrier have been recently associated with stress resilience, which could lead to innovative therapeutic approaches to treat depression. SUMMARY: Identification of both central and peripheral epigenetic changes promoting stress resilience represent promising novel targets in the development of preventive and personalized medicine. Nevertheless, more research is needed to establish sex specific differences and to identify novel epigenetic mechanisms, such as serotonylation and dopaminylation, that hold great promises for the field of psychiatry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".