Increased Protease-Activated Receptor-2 (PAR-2) Expression on CD14++CD16+ Peripheral Blood Monocytes of Patients with Severe Asthma
Bibliographic record
Abstract
BACKGROUND: Protease-Activated Receptor-2 (PAR-2), a G protein coupled receptor activated by serine proteases, is widely expressed in humans and is involved in inflammation. PAR-2 activation in the airways plays an important role in the development of allergic airway inflammation. PAR-2 expression is known to be upregulated in the epithelium of asthmatic subjects, but its expression on immune and inflammatory cells in patients with asthma has not been studied. METHODS: We recruited 12 severe and 24 mild/moderate asthmatics from the University of Alberta Hospital Asthma Clinics and collected baseline demographic information, medication use and parameters of asthma severity. PAR-2 expression on blood inflammatory cells was analyzed by flow cytometry. RESULTS: Subjects with severe asthma had higher PAR-2 expression on CD14++CD16+ monocytes (intermediate monocytes) and also higher percentage of CD14++CD16+PAR-2+ monocytes (intermediate monocytes expressing PAR-2) in blood compared to subjects with mild/moderate asthma. Receiver operating characteristics (ROC) curve analysis showed that the percent of CD14++CD16+PAR-2+ in peripheral blood was able to discriminate between patients with severe and those with mild/moderate asthma with high sensitivity and specificity. In addition, among the whole populations, subjects with a history of asthma exacerbations over the last year had higher percent of CD14++CD16+ PAR-2+ cells in peripheral blood compared to subjects without exacerbations. CONCLUSIONS: PAR-2 expression is increased on CD14++CD16+ monocytes in the peripheral blood of subjects with severe asthma and may be a biomarker of asthma severity. Our data suggest that PAR-2 -mediated activation of CD14++CD16+ monocytes may play a role in the pathogenesis of severe asthma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".