Quantitative expression of osteopontin in nasal mucosa of patients with allergic rhinitis: effects of pollen exposure and nasal glucocorticoid treatment
Bibliographic record
Abstract
BACKGROUND: Osteopontin (OPN) is a multifunctional cytokine that has been primarily investigated in Th1 diseases. Recently, it has also been implicated in Th2-mediated allergic diseases, such as asthma. The expression of OPN in allergic rhinitis (AR) is currently unknown, as is the effect of intranasal glucocorticosteroids (GCs) on that expression. METHODS: Subjects with AR were randomised to receive treatment with fluticasone propionate (FP) (n = 12) or a placebo (n = 16) over the grass pollen season and nasal biopsies were taken prior to, and during the season. OPN expression in the nasal mucosa was examined with immunohistochemistry. Healthy non-AR controls (n = 5) were used as a comparator. RESULTS: OPN expression was detected in epithelial cells, subepithelial infiltrating/inflammatory cells and cells lining the vessels and glands of all subjects. Comparison of the pre- and peak-pollen season biopsy sections in placebo treated patients revealed no increase in OPN expression during the grass pollen season (5.7% vs 6.4%). Treatment with a local glucocorticosteroid did not alter the expression of OPN during pollen exposure (6.2% vs 6.7%). CONCLUSION: OPN has been increasingly associated with the pathogenesis of various Th2-mediated diseases. However, our finding that the OPN expression in the nasal mucosa of AR patients is not significantly affected by allergen exposure and is comparable to that of the healthy controls, suggests that intracellular OPN is not directly involved in the pathogenesis of allergic rhinitis.
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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 | 0.000 | 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 | 0.000 |
| 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".