Imbalance between Pro- and Anti-inflammatory Cytokines, and between Th1 and Th2 Cytokines in Depressed Patients: The Effect of Electroacupuncture or Fluoxetine Treatment
Bibliographic record
Abstract
BACKGROUND: An increase in inflammatory response and an imbalance between T-helper (Th) 1 and 2 functions have been implicated in major depression. The aims of the present study were to 1) study the relationship between pro- and anti-inflammatory cytokines and between Th1 and Th2 produced cytokines in depressed patients and 2) evaluate and compare the effect of treatments with electroacupuncture (EA) and fluoxetine on these cytokines. METHODS: 95 outpatients with major depressive disorder were treated for 6 weeks with EA, fluoxetine or placebo. Hamilton Depression Rating Scale (HDRS) and Clinical Global Impression (CGI) were used to assess severity and therapeutic effects. 30 volunteers served as controls. Serum cytokine concentrations were measured by ELISA. RESULTS: Increased proinflammatory cytokine interleukin (IL)-1beta and decreased anti-inflammatory cytokine IL-10 were found in the depressed patients. By contract, Th1 produced proinflammatory cytokines, tumor necrosis factor (TNF)-alpha and interferon (IFN)-gamma were decreased, and Th2 produced cytokine IL-4 was significantly increased in depressed patients. The ratio of IFN/IL-4 was also increased. Both acupuncture and fluoxetine treatments, but not the placebo, reduced IL-1beta concentrations in responders. However, only acupuncture attenuated TNF-alpha concentration and INF-gamma/IL-4 ratio towards the control level. DISCUSSION: These results suggest that an imbalance between the pro- and anti-inflammatory cytokines (IL-1 and IL-10), and between Th1 and Th2 cytokines (INF-gamma or TNF-alpha and IL-4) occurred in untreated depressed patients. Both EA and fluoxetine had an anti-inflammatory effect by reducing IL-1beta. EA treatment also restored the balance between Th1 and Th2 systems by increasing TNF-alpha and decreasing IL-4.
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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".