Murine model for chronic rhinosinusitis: An interventional study
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is a complex inflammatory disease of the sinonasal tract. To understand this disease entity and develop targeted treatments, a reproducible animal model is paramount. AIMS/OBJECTIVES: To optimize a murine model of eosinophilic CRS by establishing benchmark histological markers and validate its fidelity in evaluating intranasal treatments. MATERIAL AND METHODS: Forty-five Balb/c mice were included in the 7-week protocol. Experimental animals (n = 20) were induced a CRS disease state upon receiving intraperitoneal sensitization with ovalbumin (OVA), followed by intranasal OVA with Aspergillus oryzae protease. Analysis of complete blood count with differential, peripheral blood smear, and histological markers from the nasal cavity mucosa were performed. CRS mice were additionally treated with intranasal saline (n = 5) or mometasone (n = 10) and compared with control groups of untreated CRS (n = 5) and healthy (n = 5) mice after week 7. RESULTS: Histological analysis of experimental animal nasal mucosa revealed significantly higher levels of eosinophilic tissue infiltration/degranulation, hyaline droplets, Charcot-Leyden crystals, and respiratory epithelial thickness compared to healthy controls. Treatment with mometasone significantly reversed the histopathological changes observed in CRS mice. CONCLUSION AND SIGNIFICANCE: This murine model induced substantial local eosinophilic inflammation within sinonasal mucosa, that was reversible with mometasone. This model may be used to evaluate the efficacy of therapeutics designed to target CRS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".