Targeting the OXE receptor with a selective antagonist inhibits allergen‐induced pulmonary inflammation in non‐human primates
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The 5-lipoxygenase product, 5-oxo-ETE (5-oxo-6,8,11,14-eicosatetraenoic acid), is a potent chemoattractant for eosinophils and neutrophils. However, little is known about its pathophysiological role because of the lack of a rodent ortholog of the oxoeicosanoid (OXE) receptor. The present study aimed to determine whether the selective OXE receptor antagonist S-Y048 can inhibit allergen-induced pulmonary inflammation in a monkey model of asthma. EXPERIMENTAL APPROACH: Monkeys sensitized to house dust mite antigen (HDM) were treated with either vehicle or S-Y048 prior to challenge with aerosolized HDM, and bronchoalveolar (BAL) fluid was collected 24 h later. After 6 weeks, animals that had initially been treated with vehicle received S-Y048 and vice versa for animals initially treated with S-Y048. Eosinophils and neutrophils in BAL and lung tissue samples were evaluated, as well as mucus-containing cells in bronchi. KEY RESULTS: HDM significantly increased the numbers of eosinophils, neutrophils, and macrophages in BAL fluid 24 h after challenge. These responses were all significantly inhibited by S-Y048, which also reduced the numbers of eosinophils and neutrophils in lung tissue 24 h after challenge with HDM. S-Y048 also significantly reduced the numbers of bronchial epithelial cells staining for mucin and MUC5AC after antigen challenge. CONCLUSION AND IMPLICATIONS: This study provides the first evidence that 5-oxo-ETE may play an important role in inducing allergen-induced pulmonary inflammation and could also be involved in regulating MUC5AC in goblet cells. OXE receptor antagonists such as S-Y048 may useful therapeutic agents in asthma and other eosinophilic as well as neutrophilic diseases.
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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) | 0.001 | 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) | 0.003 | 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".