Epigenetic Landscapes of Pain: DNA Methylation Dynamics in Chronic Pain
Bibliographic record
Abstract
Chronic pain is a prevalent condition with a multifaceted pathogenesis, where epigenetic modifications, particularly DNA methylation, might play an important role. This review delves into the intricate mechanisms by which DNA methylation and demethylation regulate genes associated with nociception and pain perception in nociceptive pathways. We explore the dynamic nature of these epigenetic processes, mediated by DNA methyltransferases (DNMTs) and ten-eleven translocation (TET) enzymes, which modulate the expression of pro- and anti-nociceptive genes. Aberrant DNA methylation profiles have been observed in patients with various chronic pain syndromes, correlating with hypersensitivity to painful stimuli, neuronal hyperexcitability, and inflammatory responses. Genome-wide analyses shed light on differentially methylated regions and genes that could serve as potential biomarkers for chronic pain in the epigenetic landscape. The transition from acute to chronic pain is marked by rapid DNA methylation reprogramming, suggesting its potential role in pain chronicity. This review highlights the importance of understanding the temporal dynamics of DNA methylation during this transition to develop targeted therapeutic interventions. Reversing pathological DNA methylation patterns through epigenetic therapies emerges as a promising strategy for pain management.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".