Epithelial expression of TLR4 is modulated in COPD and by steroids, salmeterol and cigarette smoke
Bibliographic record
Abstract
The toll-like receptors (TLRs) are a key component of host defense in the respiratory epithelium. Cigarette smoking is associated with increased susceptibility to infection, while COPD is characterised by bacterial colonisation and infective exacerbations. We found reduced TLR4 gene expression in the nasal epithelium of smokers compared with non-smoking controls, while TLR2 expression was unchanged. Severe COPD was associated with reduced TLR4 expression compared to less severe disease, with good correlation between nasal and tracheal expression. We went on to examine the effect of potential modulators of TLR4 expression in respiratory epithelium pertinent to airways disease. Using an airway epithelial cell line, we found a dose-dependent downregulation in TLR4 mRNA and protein expression by stimulation with cigarette smoke extracts. Treatment with the corticosteroids fluticasone and dexamethasone resulted in a dose-dependent reduction in TLR4 mRNA and protein. The functional significance of this effect was demonstrated by impaired IL-8 and HBD2 induction in response to LPS. Stimulation with salmeterol (10-6 M) caused upregulation of TLR4 membrane protein presentation with no upregulation of mRNA, suggesting a post-translational effect. The effect of dexamethasone and salmeterol in combination was additive, with downregulation of TLR4 gene expression, and no change in membrane receptor expression. Modulation of TLR4 in respiratory epithelium may have important implications for airway inflammation and infection in response to inhaled pathogens.
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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 | 0.001 | 0.000 |
| 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".