The extract of black cumin, licorice, anise, and black tea alleviates OVA-induced allergic rhinitis in mouse via balancing activity of helper T cells in lung
Bibliographic record
Abstract
BACKGROUND: A formulation of black cumin (Nigella sativa L.), licorice (Glycyrrhiza glabra L.), anise (Pimpinella anisum L.) and tea (Camellia sinensis (L.) Kuntze) (denoted BLAB tea) is traditionally used to relief allergy reaction including allergic rhinitis. However, little is known about its underlining mechanism of anti-allergic effects. METHODS: To investigate the anti-allergenic mechanism of BLAB tea, we treated ovalbumin (OVA)-induced allergic rhinitis (AR) model of mice with BLAB tea, and elucidated its possible mechanism of action. Mice in the control group were treated with phosphate-buffered saline only. Subsequently, the infiltration of different inflammatory cells was measured. In addition, histopathological changes in the nasal mucosa, and the levels of allergen-specific cytokines and OVA-specific immunoglobulins were measured. RESULTS: The aqueous extract of BLAB significantly alleviated the nasal symptoms and reduced the accumulation of inflammatory cells in the nasal mucosa and nasal lavage fluid of AR model of mice. CONCLUSION: The aqueous extract of BLAB induced the production of Th1 and Treg cytokines and inhibited the release of Th2 cytokines and histamine in nasal mucosa and serum of mice while decreasing the serum levels of OVA-specific IgE, IgG1, and IgG2a. These results suggest the potential of the aqueous extract of BLAB as a treatment option for allergic diseases.
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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) | 0.001 | 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".