Differential Regulation of the Lung Endothelin System by Urban Particulate Matter and Ozone
Bibliographic record
Abstract
Periodic elevation of ambient particulate matter and ozone levels is linked to acute cardiac morbidity and mortality. Increased plasma levels of the potent vasoconstrictor endothelin (ET)-1, a prognostic indicator of cardiac mortality, have been detected in both animal models and humans after exposure to air pollutants. The lungs are the primary source of circulating ET-1, but the direct effects of individual air pollutants and their interaction in modulating the pulmonary endothelin system are unknown. Fischer-344 rats were exposed to particles (0, 5, 50 mg/m3 EHC-93), ozone (0, 0.4, 0.8 ppm), or combinations of particles and ozone for 4 h. Changes in gene expression were measured using real-time reverse transcription polymerase chain reaction immediately after exposure and following 24 h recovery in clean air. Both pollutants individually increased preproET-1, endothelin converting enzyme-1, and endothelial nitric oxide synthase mRNA levels in the lungs shortly after exposure, consistent with the concomitant increase in plasma of the 21 amino acid ET-1[1-21] peptide measured by HPLC-fluorescence. PreproET-1 mRNA remained elevated 24 h after exposure to particles but not after ozone, in line with previously documented changes of the peptide in plasma. Both pollutants transiently increased endothelin-B receptor mRNA expression, while ozone decreased endothelin-A receptor mRNA levels. Coexposure to particles plus ozone increased lung preproET-1 mRNA but not plasma ET-1[1-21], suggesting alternative processing or degradation of endothelins. This coincided with an increase in the lungs of matrix metalloproteinase-2 (MMP-2), an enzyme that cleaves bigET-1 to ET-1[1-32]. Taken together, our data indicate that ozone and particulate matter independently regulate the expression of lung endothelin system genes, but show complex toxicological interaction with respect to plasma ET-1.
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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".