Ketamine and epigenetic processes in depression, intersection between serotonergic and glutamatergic pathways
Bibliographic record
Abstract
Abstract Background Hypotheses surrounding the etiology of depressive disorders encompass a wide range of biological changes that can occur in a depressed individual, from gene variations to epigenetic modifications and not only serotonergic mechanisms. Once again, the therapy response of the patient to antidepressants is connected to modifications in the epigenetic regulation of genes within the serotonergic system. The persistence of depressive symptoms points to the possibility that stable molecular adaptations in the brain, particularly at the epigenetic level, may be involved. Methods Narrative review to first, discuss the historical evidence behind how serotonin (5-hydroxytryptamine, 5-HT) signaling and its associated actors are involved in various biological processes and second, examine the role of ketamine as one of the newer treatments for depression. Results There is increasing evidence that responses to psychotherapy for mood disorders are correlated with epigenetic alterations. Although therapy response appears to be associated with epigenetic changes in genes regulating the serotonergic system, there are multiple lines of research that provide additional data implicating epigenetic alterations in the glutamatergic system. Also, the epigenetic regulation of target genes along the HPA axis are becoming more intriguing in linking mood disorders with environmental stressors, and warrant a closer look. Recent research suggests that ketamine's antidepressant effects may be linked to epigenetic alterations. Considering the multiple studies linking BDNF with depression, further exploration of its relation with ketamine in the context of epigenetic signaling is warranted. Conclusion Understanding how and to what extent epigenetic mechanisms change gene expression and how these changes are influenced by environmental stressors may eventually allow mental health professionals to better understand the biological basis of depression as well as to gauge the efficacy, onset, durability and duration of therapies to treat mood disorders. Moreover, understanding the relation between serotonergic neurotransmission and epigenetic mechanisms of how these may be modified by ketamine should lead us to a greater knowledge of their therapeutic potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".