Increased rhinovirus replication following corticosteroid and air pollution exposures
Bibliographic record
Abstract
Respiratory tract infections have been linked to air pollution exposure, and both are known exogenous risk factors of asthma exacerbations. The current frontline therapy used to minimize exacerbations are inhaled corticosteroids (ICS), but these medications increase the risk of respiratory tract infections in patients due to immunosuppressive side-effects. Virally-triggered exacerbations are most commonly associated with rhinovirus, and the ICS fluticasone propionate (FP) has been demonstrated to increase rhinovirus viral load by 2.5-fold in infected epithelial cells. We hypothesized that air pollution exposure combined with ICS would magnify effects on host-defence antiviral responses and viral replication. Using rhinovirus (RV16), we infected human bronchial epithelial (BEAS-2B) cells that were pre-treated with FP (250 nM) and exposed to diesel exhaust particles (DEP) SRM2975 (50 μg/cm 2 ). We show that DEP exposure significantly increased RV16 viral RNA by 12-fold over untreated cells 24 hours post-infection, while FP alone induced a 2-fold rise. However, when combined, FP and DEP induced a significant supra-additive 17-fold increase in RV16 RNA. In addition, we demonstrate that DEP induces the expression of the RV16 entry receptor ICAM-1 through the canonical NF-κB pathway and that suppression of this pathway results in attenuation of RV16 viral expression. Our findings suggest a mechanism through which combined epithelial exposure to DEP and ICS increases RV16 infectivity, through the suppression of innate antiviral immune responses and induction of the RV16 entry receptor ICAM-1. These findings suggest a need for caution during periods of high air pollution by those managing chronic lung diseases with corticosteroids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".