Effects of mometasone and formoterol on the innate immunity of human gingival epithelial cells
Bibliographic record
Abstract
Background: Corticosteroids (CS) and long acting b2-agonists (LABA) are mainstay of therapy for persistent asthma. Inhaled asthma medications are associated with oropharyngeal disorders and low salivary flow rate which may affect oral epithelial cell innate immunity. Objectives: To evaluate the effect of formoterol and mometasone on Candida albicans growth and transition, and on the expression of Toll-like receptors (TLR) and b-defensin expression by gingival epithelial cells. Methods: C. albicans (103 cells) were incubated with formoterol (10 or 50 ng/ml) or mometasone (10 or 50 ng/ml) separately or in combination, then the yeast growth and transition from blastospore to hyphe forms were evaluated. The effects of the mometasne or formoterol on the expression of TLR-2 and -4 as well as human b-defensin-2 expression by gingival epithelial cells were investigated. Results: Formoterol at 10 ng/ml promoted the growth of C. albicans as compared to the control or to the mometasone. Used separately or in combination, formoterol and mometasone increased the transition of C. albicans to hyphe form which may increase C. albicans pathogenesis. C. albicans growth and form changing may be due to the deregulation of the epithelial cell defense. Our results showed that the expression of TLR2 and 4 were down-regulated by formoterol as compared to mometasone. We also showed that single or combined molecules induced b-defensin-2 increase, but higher effect was obtained with mometasone. In conclusion: Our data show that CS and LABA promote in vitro C. albicans out growth and affect oral epithelial cell innate immunity (Study funded by the Fonds Émile-Beaulieu).
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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) | 0.000 | 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) | 0.002 | 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".