Baicalin Nasal Irrigation Mitigates Allergic Rhinitis in Mice by Inhibiting Eosinophil Chemotaxis
Bibliographic record
Abstract
INTRODUCTION: Allergic rhinitis (AR) significantly impairs quality of life. Although baicalin is known for its anti-inflammatory properties, its therapeutic potential via localized nasal delivery remains unexplored. This study evaluated the efficacy and potential mechanisms of baicalin nasal irrigation in an AR mouse model. METHODS: Ovalbumin (OVA)-induced BALB/c mice (n = 17 per group) were randomized into six groups: control, AR model, baicalin, loratadine, combination of baicalin and loratadine, and excipient. Nasal symptom scores were recorded pre- and post-treatment. Serum and nasal lavage fluid (NALF) levels of total immunoglobulin E (IgE), OVA-specific IgE, OVA-specific IgG1, and eosinophil cationic protein (ECP) were quantified by ELISA. Nasal mucosal pathology was assessed through hematoxylin and eosin staining (eosinophil counts) and MUC5AC immunofluorescence (goblet cell hyperplasia). Tight junction proteins (ZO-1, occludin) were visualized via immunofluorescence. Transcriptomic profiling of nasal tissues identified baicalin-modulated pathways, with key chemokines validated by immunohistochemistry. RESULTS: Baicalin significantly improved nasal symptom scores, reduced eosinophil and goblet cell infiltration, and decreased total IgE, OVA-specific IgE, OVA-specific IgG1, and ECP in serum and NALF. It was more effective than loratadine in reducing tissue eosinophils and suppressing local inflammatory markers. Baicalin also restored mucosal barrier proteins ZO-1 and occludin. Transcriptomic analysis revealed modulation of chemokines (CCL6, CCL8, and CCL24) involved in eosinophil chemotaxis, a finding corroborated by protein-protein interaction analysis and immunohistochemistry. CONCLUSION: Baicalin nasal irrigation inhibits eosinophil chemotaxis and alleviates AR symptoms, highlighting its potential as a novel local therapeutic strategy for AR.
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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 | 0.000 | 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".