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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".