A Pilot Study Investigating Clinical Responses and Biological Pathways of Azelastine/Fluticasone in Nonallergic Vasomotor Rhinitis before and after Cold Dry Air Provocation
Bibliographic record
Abstract
BACKGROUND: Nonallergic vasomotor rhinitis (NAVMR) has been considered a diagnosis by exclusion due to unknown mechanisms or lack of diagnostic biomarkers. METHODS: To determine clinical responses and biological pathways in NAVMR subjects challenged to cold dry air (CDA) in an environmental exposure chamber (EEC) pre- and posttreatment with azelastine/fluticasone (AzeFlu), 30 NAVMR subjects, prescreened for CDA-induced symptoms (approx. 14°C, <15% relative humidity, ×1 h) were randomized to treatment with AzeFlu (n = 20) or placebo (n = 10) for 2 weeks. Total nasal symptoms scores, minimum cross-sectional area, cough, and conjunctival redness were recorded at visit 1 (pretreatment) and visit 2 (posttreatment) before, during, and after CDA challenge. At both visits, nasal lavage fluid (NLF) and nasal scrapings (NS) were collected pre- and post-CDA challenge. Substance P, neurokinin-A, and calcitonin gene-related peptide concentrations in NLF were analyzed pre- and postchallenge at each visit. Their relationship with CDA-induced symptoms was determined by statistical analysis. MicroRNA sequencing from NS determined differentially expressed miRNA between the treatment groups post-CDA challenge at each visit. RESULTS: The minimum cross-sectional area (p < 0.05), cough count (p < 0.05), and substance P (p < 0.01) improved posttreatment with AzeFlu versus placebo. Gene targets for differentially expressed miRNAs at visit 1 were enriched for biological pathways regulating epithelial ciliogenesis and cell integrity that were modified in the AzeFlu-treated group versus placebo posttreatment. CONCLUSIONS: This study demonstrated the feasibility of an EEC model to investigate CDA-induced clinical responses and pathobiology in NAVMR subjects pre- and posttreatment with AzeFlu. NAVMR disease mechanisms for other nonallergic triggers can be investigated similarly.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".