Therapeutic potential of the topical recombinant human interleukin-1 receptor antagonist in guinea pigs with allergic rhinitis
Bibliographic record
Abstract
BACKGROUND: Recombinant human Interleukin receptor antagonist (rhIL-Ra) can bind to the IL-1 receptor on the cell membrane and reversibly blocks the proinflammatory signaling pathway. However, its effect on allergic rhinitis (AR) and the underlying mechanism remains unknown. This study aims to investigate the efficacy of recombinant human interleukin-1 receptor antagonist (rhIL-1Ra) on AR guinea pigs. METHODS: Guinea pigs were systemically sensitized by intraperitoneal injection and topical intranasal instillation with ovalbumin within 21 days. Animals administrated with saline served as the normal control. The AR animals were randomly divided into the model group and distinct concentrations of rhIL-1Ra and budesonide treatment groups. IL-1β and ovalbumin specific IgE levels were detected by ELISA kits. Nasal mucosa tissues were stained with hematoxylin & eosin (HE) for histological examination. RESULTS: It was found that the numbers of sneezing and nose rubbing were remarkably reduced in rhIL-1Ra and budesonide-treated guinea pigs. Besides, rhIL-1Ra distinctly alleviated IgE levels in serum and IL-1β levels in nasal mucus, together with decreased exfoliation of epithelial cells, eosinophilic infiltration, tissue edema and vascular dilatation. CONCLUSIONS: rhIL-1Ra is effective in AR guinea pigs and may provide a novel potential choice for AR treatments.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".