Ethyl-eicosapentaenoate and dexamethasone resistance in therapy-refractory depression
Bibliographic record
Abstract
Preliminary evidence shows that ethyl-eicosapentaenoate (E-EPA) has a marked clinical effect when used as an adjunct in therapy-refractory depression. EPA belongs to the class of polyunsaturated omega-3 fatty acids. The mechanism of its action in depression is not fully understood. There are two related fields where the pathophysiology of refractory depression meets the effect of EPA. First, a general immunosuppressive effect of EPA meets a general immunoactivation in severe depression, especially an increase in CD4/CD8 ratio, neutrophilia, and an increase in interleukins (IL)-6 and IL-12 and of prostaglandin E2 (PGE2). Secondly, a resistance to dexamethasone (Dex) suppression of the HPA axis meets the effects of EPA on multidrug resistance reversing and HPA axis suppression. The effects of EPA on the immune system, the HPA axis, and multidrug resistance are connected through the action of a transport protein called p-glycoprotein (p-gp). Physiological and synthetic steroids such as cortisol and Dex are substrates of p-gp, and so Dex resistance in depression may be related to dysfunction of this protein. In addition, expression of p-gp is induced by PGE2, and EPA inhibits the synthesis of PGE2. The reversal of drug resistance by EPA may be mediated via this immunological mechanism and lead to its antidepressive efficacy. In addition, antidepressants such as amitriptyline, which have special efficacy in severe depression, decrease p-gp function. EPA may, furthermore, enhance the action of antidepressants, like many SSRIs that are p-gp substrates, which are actively transported out of the intracerebral space at the level of the blood-brain barrier.
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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) | 0.000 | 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) | 0.002 | 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".