Role of proteinase-activated receptor-2 in allergic sensitization to house dust mite allergens
Bibliographic record
Abstract
A number of common aeroallergens have serine proteinase activity, which is important for allergic sensitization. House dust mite (HDM), and other allergens with serine proteinase activity activate Protease-Activated Receptor-2 (PAR-2). We have shown that PAR-2 activation in the airways leads to allergic sensitization to concomitantly inhaled antigens, implicating PAR-2 in the pathogenesis of asthma. We hypothesized that PAR-2 activation in the airways by HDM allergens is important for the development of allergic sensitization. HDM extract was administered to mice intranasally for 5 consecutive days to induce allergic sensitization. One group of mice received a blocking anti-PAR-2 antibody intranasally before each HDM administration. Administration of the PAR-2 blocking antibody decreased IL-4, IL13 and IL-33 mRNA as well as IL-4, IL-5 and MIP1A protein levels in the lung tissue, suggesting decreased allergic airway sensitization. Mice sensitized in the presence of the PAR-2 blocking antibody or isotype control were then challenged intranasally with HDM extract for 4 consecutive days. Mucosal exposure to HDM extract induced AHR and airway eosinophilic inflammation. Administration of the anti-PAR-2 blocking antibody during the sensitization phase completely inhibited the development of AHR and airway inflammation in response to HDM challenge. These results indicate that HDM extract induces PAR-2-dependent allergic sensitization in mice and lead to PAR-2-dependet allergic airway inflammation. These results will allow us to better define the mechanisms of allergic sensitization to allergens with serine proteinase activity.
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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) | 0.001 | 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".