Understanding Leukocyte Recruitment in Murine Ozone‐Induced Lung Inflammation
Bibliographic record
Abstract
Ozone, a highly reactive air pollutant, has been linked to a variety of acute and chronic respiratory diseases in humans. Health Canada guidelines suggest acute (1 h) ozone exposures should not exceed 120 ppb. Studies exposing mice to much higher levels of ozone (2 ppm) suggest significant recruitment of lung neutrophils and macrophages at 24 h. However, to our knowledge, no studies have been conducted at ambient ozone concentrations. Daytime “ground” ozone levels can easily reach 50 ppb. We report that ambient ozone, at 50 ppb, induces alveolar cell death, leukocyte recruitment and has the ability to cause lung damage and thus is of relevance to public health. We hypothesized that CX3CR1 macrophages are protective in murine ozone‐induced lung inflammation and mediate lung neutrophil recruitment. We exposed wild‐type and CX3CR1‐null mice to 50 ppb ozone or filtered air for 2 hours, and collected peripheral blood, lung vascular perfusate and bronchoalveolar lavage at 0 h, 6 h and 22 h after exposure. Our preliminary data suggest alveolar macrophage, epithelial and endothelial cell toxicity, alveolar and systemic nuclear chromatin and actin deposits, with vascular neutrophil recruitment being observed as early as 0 h, 6 h and 22 h after ozone exposure. Moreover, absence of CX3CR1 leads to an exaggerated inflammatory response marked by enhanced neutrophil recruitment. We aim to further characterize the cellular metabolic and cytokine responses in our model, which will allow us to better understand how and why leukocytes are recruited in ozone‐induced lung inflammation, and will guide development of protective health strategies and environmental standards. Support or Funding Information Fedoruk Centre and Innovation Saskatchewan This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".