Lung and Blood Gene Expression Profiles from Cynomolgus Monkeys Exposed to Ozone
Bibliographic record
Abstract
Exposure to ozone has been implicated in the pathology of COPD and evokes a neutrophilic inflammation in airways. It has been used for assessing anti‐inflammatory drugs in early clinical development. There is interest in using primates as a preclinical mechanistic model of ozone‐evoked lung inflammation. Here we characterize gene expression profiles of lung and blood from monkeys exposed to ozone to validate this model. METHODS: 12 monkeys were exposed by to 1 ppm ozone or filtered air for 6 hours. Lung and blood samples were run on Affymetrix Rhesus microarrays. Analysis of covariance and a list of genes with p <= 0.05 was used for pathway analysis in Ingenuity & MetaCore. RESULTS: The major pathways activated by ozone challenge were oxidative phosphorylation, ubiquinone biosynthesis, protein ubiquitination, TGF signaling, IL2, IL4, cell adhesion, integrin & chemokine signaling and T cell receptor signaling, consistent with an inflammatory response. MMP9, p65, AP‐1, HDAC8, FOSL2, MAPKK5, MAPK11, MAPK12, SYK, IL8, IL10RA, SERPINE1, SERPINE8 & Cytochrome C oxidases were significantly modified by ozone challenge as reported in human ozone responses and COPD. CONCLUSIONS: These genomic data demonstrate that ozone challenge induces pulmonary inflammation via activation of ozone‐ & COPD‐associated inflammatory pathways. These data support the preclinical use of the primate ozone‐evoked lung inflammation model.
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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) | 0.002 | 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".