Effects of bupropion augmentation on pro-inflammatory cytokines in escitalopram-resistant patients with major depressive disorder
Bibliographic record
Abstract
Studies so far have provided contradictory results on immune system markers during use of antidepressants. There are no data on changes in immune parameters after treatment augmentation. The present study aimed to clarify whether the addition of bupropion in escitalopram-resistant patients with major depression causes changes in the immune system and whether treatment response could be predicted by baseline levels of cytokines. We recruited 28 depressive patients (11 men and 17 women) who did not respond to 12-week treatment with escitalopram (20 mg/d) for an augmentation trial with bupropion (150-300 mg/day). The levels of soluble interleukin-2 receptor, interleukin-8 (IL-8) and tumor-necrosis factor-alpha were measured before and 6 weeks after addition of bupropion. For a control group, we recruited 45 healthy volunteers (19 men and 26 women). The results indicated that the baseline levels of studied cytokines did not predict treatment response to bupropion augmentation. Concentration of IL-8 increased during the treatment similarly in both responder and non-responder groups. Although bupropion augmentation had increased the response rate in escitalopram-resistant patients, this clinical improvement was not accompanied by specific changes in studied cytokine levels.
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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) | 0.001 | 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) | 0.001 | 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".