Font Size: Effect of Escitalopram on White Blood Cells in Patients With Major Depression
Bibliographic record
Abstract
BACKGROUND: Immunological dysfunctions in the course of depression are recently intensively investigated. Pharmacotherapy of depression is speculated to affect immune response. In this study, our objective was to investigate whether escitalopram treatment would affect white blood cells in patients with major depression. METHODS: Fifteen patients (11 women and 4 men), meeting the criteria for a current episode of major depressive disorder, were participated. White blood cell (WBC), neutrophil (NEUT), lymphocyte (LYMPH), monocyte (MONO), eosinophyl (EO), and basophyl (BASO) levels were measured at the entry to the study. After 8 weeks of open-label treatment with the selective serotonin reuptake inhibitor escitalopram (10-20 mg/d), the patients were readmitted and the measurements were repeated. RESULTS: At the end of the study, LYMPH was found to be significantly decreased compared to the baseline value after 8 weeks treatment with escitalopram (p < 0.001). There was not a significant change in WBC, NEUT, MONO, EO, and BASO parameters. CONCLUSIONS: The present study has shown that escitalopram increased LYMPH in patients with major depression according to these results, the possible treatment of depression with escitalopram must be carried out with caution, in patients with immunological disturbances. KEYWORDS: Escitalopram; Major depression; White blood cells.
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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 | 0.002 |
| 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) | 0.005 | 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".