The effects of inhaled and oral corticosteroids on serum inflammatory biomarkers in COPD: an exploratory study
Bibliographic record
Abstract
BACKGROUND: Several studies suggest that inhaled and oral corticosteroids repress systemic inflammation in chronic obstructive pulmonary disease (COPD). However, the cytokines that may respond to these medications are unclear. METHOD: We used data from 41 patients with a history of stable moderate COPD (average age 64 years) who were randomised to inhaled fluticasone (500 microg twice daily from a Diskus inhaler), oral prednisone (30 mg daily) or placebo for 2 weeks. Using a multiplexed array system, different serum cytokines that have been implicated in COPD pathogenesis were measured. RESULTS: We found that compared with placebo, inhaled fluticasone significantly reduced levels of soluble tumour necrosis factor receptor-2 (sTNF-R2) by 24% (95% CI, 7-38%; p = 0.01), monocyte chemoattractant protein-1 by 20% (95% CI, 5-32%; p = 0.01), interferon gamma inducible CXCL10 (IP-10) by 43% (95% CI, 3-66%; p = 0.04), and soluble L-selectin levels by 15% (95% CI, 1-28%; p = 0.04). Compared with placebo, oral prednisone reduced levels of sTNF-R2 by 26% (95% CI, 15-36%; p < 0.001), L-selectin by 22% (95% CI, 8-34%; p = 0.004), intercellular adhesion molecule-1 by 31% (95% CI, 9-48%; p = 0.01), pulmonary and activation-regulated chemokine (PARC) by 18% (95% CI, 2-32%; p = 0.03) and IP-10 by 40% (95% CI, 0-64%; p = 0.05). sTNF-R2, L-selectin and IP-10 were significantly reduced by both oral and inhaled corticosteroids. The other cytokines were not significantly repressed by either oral or inhaled corticosteroids. CONCLUSIONS: In summary, inhaled and oral corticosteroids significantly repressed a selected number of systemic cytokines in patients with stable, moderate COPD; most of the steroid-responsive cytokines appear to be chemoattractants.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".