Toll-like receptor 9 partially regulates lung inflammation induced following exposure to chicken barn air
Bibliographic record
Abstract
BACKGROUND: Exposure to animal barn air is an occupational hazard that causes lung dysfunction in barn workers. Respiratory symptoms experienced by workers are typically associated with endotoxin and TLR4 signalling, but within these environments gram negative bacteria constitute only a portion of the total microbial population. In contrast, unmethylated DNA can be found in all bacteria, some viruses, and mold. We hypothesized that in such environments TLR9, which binds unmethylated DNA, contributes to the overall immune responses in the lung. METHODS: Using a mouse model, wild-type and TLR9(-/-) mice were exposed to chicken barn air for 1, 5, or 20 days. Blood serum and bronchiolar lavage fluid was tested against a panel of six TLR9-induced cytokines (IL-1β, IL-6, IL-10, IL-12, TNFα, and IFNγ) for changes in expression. Bronchiolar lavage fluid (BAL) was also tested for macrophage as well as monocyte migration. RESULTS: There were significant decreases in serum TNFα after a single day exposure in TLR9(-/-) mice. BAL concentrations of TNFα and IFNγ, as well as TNFα in serum in TLR9(-/-) mice were also reduced after barn exposure for 5 days. After 20 days of exposure IFNγ was significantly reduced in lavage of TLR9(-/-) mice. Myeloperoxidase (MPO) accumulation in the lung was reduced at 20 days of exposure in TLR9(-/-) mice, as was total lavage cell counts. However, Masson's staining revealed no apparent lung histological differences between any of the treatment groups. CONCLUSIONS: Taken together our data show TLR9 plays a partial role in lung inflammation induced following exposure to chicken barn air potentially through binding of unmethylated DNA.
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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) | 0.002 | 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".