Cigarette smoke modulates rhinovirus-induced airway epithelial cell chemokine production
Bibliographic record
Abstract
Human rhinovirus (HRV) infections induce epithelial cell production of chemokines that may contribute to the pathogenesis of exacerbations of chronic obstructive pulmonary disease (COPD) and asthma. Cigarette smoking is the predominant risk factor for the development of COPD and also aggravates asthma symptoms. We examined whether cigarette smoke extract (CSE) modulates viral inflammation by altering the profile of HRV-induced epithelial chemokine production. Purified HRV-16, and CSE were used to examine the effects on CXC chemokine ligand (CXCL)8 and CXCL10 production from both primary human bronchial epithelial cells and the BEAS-2B epithelial cell line. Both CSE and HRV-16 induced CXCL8 production and, when used in combination, induced at least an additive production of CXCL8 compared with either stimulus alone. In contrast, CSE did not induce CXCL10 and markedly inhibited HRV-16-induced CXCL10 production. Inhibition of HRV-16-induced CXCL10 by CSE was mediated, at least in part, via transcriptional regulation. The increased CXCL8 production seen with the combination of CSE and HRV-16 was not due to transcriptional regulation but was associated with CXCL8 mRNA stabilisation. Thus, CSE differentially modulates HRV-16-induced chemokine production from human airway epithelial cells in a manner that might be expected to alter inflammatory cell profiles.
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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".