Air Pollution Particles Produce Airway Wall Remodeling in Rat Tracheal Explants
Bibliographic record
Abstract
There is evidence that chronic exposure to high levels of ambient particulate pollutants (PM) is associated with chronic airflow obstruction, but how this occurs is not known. We exposed rat tracheal explants to Ottawa urban air particles (ECH93) or diesel exhaust particles. After 7 d in air organ culture, both types of PM increased explant procollagen and transforming growth factor (TGF)-beta 1 gene expression, and markedly increased tissue hydroxyproline. For both types of particle, nuclear factor-kappa B inhibitor SN50 completely blocked increased gene expression. With EHC93, procollagen expression was inhibited by the oxidant scavenger, tetramethylthiourea, and by the iron chelator, deferoxamine, but TGF-beta1 expression was not inhibited by deferoxamine. Inhibitors of extracellular signal regulated kinase and p38 kinase did not affect EHC93-induced gene expression. With diesel exhaust particles, tetramethylthiourea and deferoxamine had no effect, but extracellular signal regulated kinase and p38 inhibitors completely blocked effects on procollagen and TGF-beta 1. Fetuin, an inhibitor of TGF-beta receptor binding, prevented increases in procollagen gene expression. We conclude that two common types of PM can directly induce expression of genes involved in fibrogenesis and actual airway wall fibrosis through nuclear factor-kappa B- and TGF-beta-mediated mechanisms. PM-induced airway wall remodeling may play an important role in producing airflow obstruction in individuals living in high PM regions.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".