Inhibitory effect of p38 mitogen‐activated protein kinase inhibitors on cytokine release from human macrophages
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Macrophages release cytokines that may contribute to pulmonary inflammation in conditions such as chronic obstructive pulmonary disease. Thus, inhibition of macrophage cytokine production may have therapeutic benefit. p38 MAPK may regulate cytokine production, therefore, the effect of two p38 MAPK inhibitors, SB239063 and SD-282, on the release of TNF-alpha, GM-CSF and IL-8 from human macrophages was investigated. EXPERIMENTAL APPROACH: Cytokine release was measured by ELISA. Immunoblots and mRNA expression studies were performed to confirm p38 MAPK isoform expression and activity. Macrophages were isolated from lung tissue of current smokers, ex-smokers and emphysema patients and exposed to lipopolysaccharide. These cells then released cytokines in a concentration-dependent manner. KEY RESULTS: SB239063 only inhibited TNF-alpha release (EC50 0.3 +/- 0.1 microM). Disease status had no effect on the efficacy of SB239063. SD-282 inhibited both TNF-alpha and GM-CSF release from macrophages (EC50 6.1 +/- 1.4 nM and 1.8 +/- 0.6 microM respectively) but had no effect on IL-8 release. In contrast, both inhibitors suppressed cytokine production in monocytes. CONCLUSIONS AND IMPLICATIONS: The differential effects of p38 MAPK inhibitors between macrophages and monocytes could not be explained by differences in p38 MAPK isoform expression or activity. However, the stability of TNF-alpha mRNA was significantly increased in macrophages compared to monocytes. These data suggest a differential involvement for p38 MAPK in macrophage cytokine production compared with monocytes. These effects are not due to lack of p38 activation or p38alpha expression in macrophages but may reflect differential effects on the stability of cytokine mRNA.
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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) | 0.001 | 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".