Recent Trends on the Role of Epigenomics, Metabolomics and Noncoding RNAs in Rationalizing Mood Stabilizing Treatment
Bibliographic record
Abstract
Mood stabilizers are the cornerstone in treatment of mood disorders, but their use is characterized by high interindividual variability. This feature has stimulated intensive research to identify predictive biomarkers of response and disentangle the molecular bases of their clinical efficacy. Nevertheless, findings from studies conducted so far have only explained a small proportion of the observed variability, suggesting that factors other than DNA variants could be involved. A growing body of research has been focusing on the role of epigenetics and metabolomics in response to mood stabilizers, especially lithium salts. Studies from these approaches have provided new insights into the molecular networks and processes involved in the mechanism of action of mood stabilizers, promoting a systems-level multiomics synergy. In this article, we reviewed the literature investigating the involvement of epigenetic mechanisms, noncoding RNAs and metabolomic modifications in bipolar disorder and the mechanism of action and clinical efficacy of mood stabilizers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".