Serotonin modulates the cytokine network in the lung: involvement of prostaglandin E2
Bibliographic record
Abstract
Serotonin, well known for its role in depression, has been shown to modulate immune responses. Interestingly, the plasma level of serotonin is increased in symptomatic asthmatic patients and the use of anti-depressants, known to reduce serotonin levels, provokes a decrease in asthma symptoms and an increase in pulmonary function. Thus, we tested the hypothesis that serotonin affects alveolar macrophage (AM) cytokine production, altering the cytokine network in the lung and contributing to asthma pathogenesis. AMs were treated with different concentrations of serotonin (10(-11)-10(-9) M) or 5-HT(1) and 5-HT(2) receptor agonists for 2 h prior stimulation. T helper 1 (Th1) and Th2 cytokines, prostaglandin-E(2) (PGE(2)) and nitric oxide (NO) were measured in cell-free supernatants. Serotonin significantly inhibited the production of tumour necrosis factor (TNF) and interleukin (IL)-12, whereas IL-10, NO and PGE(2) production were increased. These immunomodulatory effects of serotonin were mimicked by 5-HT(2) receptor agonist but were not abrogated by 5-HT(2) receptor antagonist, suggesting the implication of other 5-HT receptors. Inhibitors of cyclooxygenase and antibody to PGE(2) abrogated the inhibitory and stimulatory effect of serotonin on TNF and IL-10 production, respectively, whereas NO synthase inhibitor eliminated serotonin-stimulated IL-10 increase. Furthermore, PGE(2) significantly increased AM IL-10 and NO production. These results suggest that serotonin alters the cytokine network in the lung through the production of PGE(2). The reduction of Th1-type cytokine by serotonin may contribute to asthma pathogenesis.
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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 | 0.000 |
| 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) | 0.001 | 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".