Importance of Histamine in the Cytokine Network in the Lung Through H2 and H3 Receptors: Stimulation of IL-10 Production
Bibliographic record
Abstract
Histamine, a well-known inflammatory mediator, has been implicated in various immunoregulatory effects that are poorly understood. Thus, we tested the hypothesis that histamine inhibits the release of a proinflammatory cytokine, namely TNF, by stimulating the release of an anti-inflammatory cytokine, IL-10. Alveolar macrophages (AMs) from humans, Sprague Dawley rats, and the AM cell line, NR8383, were treated with different concentrations of histamine (10-5-10-7 M) for 2 h prior to their stimulation with suboptimal concentration of LPS (1 ng/ml) for 4 h. Histamine inhibited TNF release in a dose-dependent manner. This inhibition was mimicked by H2 and H3 receptor agonists, but not by H1 receptor agonist. Furthermore, we demonstrated the expression of H3 receptor mRNA in human AMs. Interestingly, treatment of AMs with anti-IL-10, anti-PGE2, or a NO synthase inhibitor (Nomega-nitro-l -arginine methyl ester) before the addition of histamine abrogated the inhibitory effect of the latter on TNF release. Histamine treatment (10-5 M) increased the release of IL-10 from unstimulated (2.2-fold) and LPS-stimulated (1. 7-fold) AMs. Unstimulated AMs, NR8383, express few copies of IL-10 mRNA, as tested by quantitative PCR, but expression of IL-10 was increased by 1.5-fold with histamine treatment. Moreover, the stimulation of IL-10 release by histamine was abrogated by pretreatment with anti-PGE2 or the NO synthase inhibitor, Nomega-nitro-l -arginine methyl ester. Thus, histamine increases the synthesis and release of IL-10 from AMs through PGE2 and NO production. These results suggest that histamine may play an important role in the modulation of the cytokine network.
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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".