Matrix metalloproteinases and tissue inhibitors of metalloproteinases in nasal lavage after an inhalation challenge with flour
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The existence of nasal mucosa remodeling in allergic rhinitis is controversial. Few data are available on the dynamics of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) in nasal fluid after an allergen challenge. We examined whether an immediate allergic reaction that induces nasal congestion and inflammation is able to also induce changes in remodeling parameters in nasal fluid. STUDY DESIGN: Controlled experimental study. METHODS: Ten patients with allergic occupational rhinitis due to flour underwent a control and active inhalation challenge with serial monitoring of nasal congestion and nasal symptoms with acoustic rhinometry and a visual analogue scale. Levels of remodeling markers (MMP-2, MMP-7, MMP-9, MMP-13, TIMP-1, TIMP-2) and inflammatory cells in nasal fluid were measured before the challenge and at 30 minutes, 6 hours, and 24 hours following the challenge. RESULTS: In contrast to the control challenge, the flour challenge induced nasal symptoms and significant decreases in nasal volume in all subjects. After the flour challenge, a significant increase in nasal levels of TIMP-2 and a nonsignificant increase in TIMP-1 levels were observed, whereas no significant changes in nasal levels of MMPs were documented. CONCLUSIONS: This study showed that after an inhalation challenge with an occupational allergen, the nasal mucosa displayed an imbalance in favor of TIMPs enzymes activity as compared to MMPs enzymes activity, represented in an increase in nasal levels of TIMP-2 during the course of the early reaction following the allergen challenge.
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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".