Amelioration of Pathology by ELR-CXC Chemokine Antagonism in a Swine Model of Airway Endotoxin Exposure
Bibliographic record
Abstract
Airborne organic dusts in swine confinement facilities have detrimental effects on workers health. Bacterial endotoxins (i.e., lipopolysaccharides [LPS]) that contaminate these dusts have been implicated in their pro-inflammatory effects in the airways. Exposure to such dusts induces expression of ELR-CXC chemokines (e.g., interleukin [IL]-8), prototypical neutrophil chemoattractants and activators, and neutrophilic pathology. To confirm the roles of the ELR-CXC chemokines in LPS-driven airway pathology, the authors exposed swine to bacterial LPS and tested whether blocking ELR-CXC chemokines would have beneficial effects. Delivery of the ELR-CXC chemokine antagonist CXCL8(3-74)K11R/G31P (G31P) blocked reactive oxygen intermediate production and chemotactic responses by IL-8-challenged neutrophils in vitro. In vivo, one treatment with G31P (100 microg/kg) blocked neutrophil inflammatory responses to intradermal LPS challenge for > or =2 days. It also ameliorated pathology in piglets challenged via the airway with 1 mg of Eschericia coli LPS. On physical examination the saline-treated endotoxemic animals were depressed, pyrexic, and displayed labored breathing, whereas the G31P-treated animals were bright, active, and alert and had a low-grade fever and occasional cough. The lungs of the saline-treated animals displayed evidence of pleural surface hemorrhagic consolidation, and their airways contained large numbers of neutrophils (>80%) as well as substantial amounts of tumor necrosis factor (TNF) and IL-1. The G31P treatments of the LPS-challenged piglets reduced their airway neutrophilic inflammatory responses by approximately 86% and reduced the airway TNF (approximately 70%) and IL-1 (approximately 83%) levels. These data implicates the ELR-CXC chemokines in the neutrophilic inflammation observed after airways exposure to bacterial LPS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".