Roll of Toll‐like Receptor 9 in Mouse Lung Inflammation in Response to Chicken Barn Air
Bibliographic record
Abstract
Exposure to barn air is an occupational hazard that results in lung dysfunction in barn workers. These respiratory symptoms are typically associated with endotoxin, but within these environments gram negative bacteria may constitute only a small portion of the species and quantity of microorganisms. In contrast, un‐methylated DNA by can be found in all bacteria, most viruses and, and mould. Therefore, we investigated the role of TLR9, which binds to un‐methylated DNA, in barn‐air induced lung inflammation. First we used immunohistology, immuno‐electron microscopy and in situ hybridization to show expression of TLR9 on bronchial epithelium, alveolar septal cells, and alveolar macrophage of mouse and human lungs. Using a TLR9‐deficient mouse model, these animals were exposed to barn air for 8 hours/day for 1, 5, or 20 days. Examination of bronchiolar lavage and serum against a panel of six common cytokines (IL‐1β, IL‐6, IL‐10, IL‐12, TNF‐α, and IFN‐γ) showed no significant differences after a single day exposure. While TNF‐α (p=0.06) levels in TLR9‐deficient mice were reduced in blood and lavage fluids after 5 and somewhat reduced at 20 days of exposure (p=0.14), IFN‐γ at 5 days(p=0.06) remained reduced after 20 days (p=0.05). Taken together, our data shows expression of TLR9 in mouse and human lungs and that TLR9 may partially contributes to inflammation induced following exposure to chicken barn air. Grant Funding Source : NSERC Discovery Grant
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".