Urban particulate matter increases human airway epithelial cell IL-1β secretion following scratch wounding and H1N1 influenza A exposure<i>in vitro</i>
Bibliographic record
Abstract
PURPOSE: The airway epithelium represents the first line of defense against inhaled environmental insults including air pollution, allergens, and viruses. Epidemiological and experimental evidence has suggested a link between air pollution exposure and the symptoms associated with respiratory viral infections. We hypothesized that multiple insults integrated by the airway epithelium NLRP3 inflammasome would result in augmented IL-1β release and downstream cytokine production following respiratory virus exposure. MATERIALS AND METHODS: We performed in vitro experiments with a human airway epithelial cell line (HBEC-6KT) that involved isolated or combination exposure to mechanical wounding, PM10, house dust mite, influenza A virus, and respiratory syncytial virus. We performed confocal microscopy to image the localization of PM10 within HBEC-6KT and ELISAs to measure soluble mediator production. RESULTS: Airway epithelial cells secrete IL-1β in a time-dependent fashion that is associated with internalization of PM10 particles. PM10 exposure primes human airway epithelial cells to subsequent models of cell damage and influenza A virus exposure. Prior PM10 exposure had no effect on IL-1β responses to RSV exposure. Finally we demonstrate that PM10-priming of human airway epithelial cell IL-1β and GM-CSF responses to influenza A exposure are sensitive to NLRP3 inflammasome inhibition. CONCLUSIONS: Our results suggest the NLRP3 inflammasome may contribute to exaggerated immune responses to influenza A virus following periods of poor air quality. Intervention strategies targeting the NLRP3 inflammasome in at risk individuals may restrict poor air quality priming of mucosal immune responses that result from subsequent viral exposures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".