Lung responses to secondary endotoxin challenge in rats exposed to pig barn air
Bibliographic record
Abstract
BACKGROUND: Swine barn air contains endotoxin and many other noxious agents. Single or multiple exposures to pig barn air induces lung inflammation and loss of lung function. However, we do not know the effect of exposure to pig barn air on inflammatory response in the lungs following a secondary infection. Therefore, we tested a hypothesis that single or multiple exposures to barn air will result in exaggerated lung inflammation in response to a secondary insult with Escherichia coli LPS (E. coli LPS). METHODS: We exposed Sprague-Dawley rats to ambient (N = 12) or swine barn air (N = 24) for one or five days and then half (N = 6/group) of these rats received intravenous E. coli LPS challenge, observed for six hours and then euthanized to collect lung tissues for histology, immunohistochemistry and ELISA to assess lung inflammation. RESULTS: Compared to controls, histological signs of lung inflammation were evident in barn exposed rat lungs. Rats exposed to barn air for one or five days and challenged with E. coli LPS showed increased recruitment of granulocytes compared to those exposed only to the barn. Control, one and five day barn exposed rats that were challenged with E. coli LPS showed higher levels of IL-1beta in the lungs compared to respective groups not challenged with E. coli LPS. The levels of TNF-alpha in the lungs did not differ among any of the groups. Control rats without E. coli LPS challenge showed higher levels of TGF-beta2 compared to controls challenged with E. coli LPS. CONCLUSION: These results show that lungs of rats exposed to pig barn air retain the ability to respond to E. coli LPS challenge.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".