Superior effect of MP-AzeFlu than azelastine or fluticasone propionate alone on reducing inflammatory markers
Bibliographic record
Abstract
MP-AzeFlu, intranasal formulation of azelastine hydrochloride (AZE) and fluticasone propionate (FP), is superior to AZE or FP alone for treatment of allergic rhinitis (AR). However, the precise anti-inflammatory mechanism of action of MP-AzeFlu has not been characterized. To investigate the anti-inflammatory effects of MP-AzeFlu compared with AZE or FP alone in an established in vitro model of eosinophilic inflammation. Nasal mucosal epithelial cells and peripheral blood eosinophils were obtained from human volunteers. Epithelial cells were stimulated with 10% fetal bovine serum (FBS) in the presence of MP-AzeFlu, AZE, or FP (1:10 2 to 1:10 5 dilution). Concentrations of interleukin (IL)-6, IL-8, and granulocyte–macrophage colony-stimulating factor (GM-CSF) were measured by ELISA. Eosinophils were incubated in 10% human epithelial cell–conditioned medium (HECM) and survival assessed by trypan blue dye exclusion. Results are expressed as mean ± SEM percentage secretion/survival compared with FBS/HECM (respectively). FP and MP-AzeFlu (all dilutions) and AZE (1:10 2 ) significantly reduced IL-6 secretion and eosinophil survival compared with positive controls. At 1:10 2 dilution, IL-6 secretion was significantly lower with MP-AzeFlu (38.3 ± 4.2%, compared with FBS = 100%) than with AZE (76.1 ± 4.9%) or FP (53.0 ± 4.9%). At 1:10 2 dilution, eosinophil survival was significantly lower with MP-AzeFlu at day 3 (17.5 ± 3.0%) and day 4 (2.4 ± 1.4%, compared with HECM = 100%) than with AZE (day 3: 75.2 ± 7.2%; day 4: 44.0 ± 9.7%) or FP (day 3: 38.5 ± 3.5%; day 4: 14.6 ± 4.0%). Greater reductions in cytokine secretion and eosinophil survival observed with MP-AzeFlu in vitro may underlie MP-AzeFlu’s superior clinical efficacy vs. AZE or FP alone observed in AR patients.
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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) | 0.001 | 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 | 0.001 | 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".