Stress axis variability is associated with differential ozone-induced lung inflammatory signaling and injury biomarker response
Bibliographic record
Abstract
Ozone (O3), a ubiquitous urban air pollutant, causes adverse pulmonary and extrapulmonary effects. A large variability in acute O3-induced effects has been observed; however, the basis for interindividual differences in susceptibility is unclear. We previously demonstrated a role for the hypothalamic-pituitary-adrenal (HPA) stress axis and glucocorticoid response in acute O3 toxicity. Glucocorticoids have important anti-inflammatory actions, and have been shown to regulate lung inflammatory responses. We hypothesised that a hyporesponsive HPA axis would be associated with greater O3-dependent lung inflammatory signaling. Two genetically-related rat strains with known differences in stress axis reactivity, highly-stress responsive Fischer (F344) and less responsive Lewis (LEW), were exposed for 4 h by nose-only inhalation to clean air or 0.8 ppm O3, and euthanized immediately after exposure. As expected, baseline (air-exposed) plasma corticosterone was significantly lower in the hypo-stress responsive LEW. Although O3 exposure increased plasma corticosterone in both strains, corticosterone remained significantly lower in LEW when compared to F334. LEW exhibited greater O3-induced inflammatory cytokine/chemokine signaling compared to F344, consistent with the lower corticosterone levels. Since we observed strain-specific differences in inflammatory signaling, we further investigated injury biomarkers (total protein, albumin and lactate dehydrogenase). Although the hyper-responsive F344 exhibited lower inflammatory signaling in response to O3 compared with LEW, they had greater levels of lung injury biomarkers. Our results indicate that stress axis variability is associated with differential O3-induced lung toxicity. Given the large variability in stress axis reactivity among humans, stress axis regulation could potentially be a determining factor underlying O3 sensitivity.
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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 | 0.001 |
| 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".