Attenuation of mast cell degranulation and pro-inflammatory cytokine release by rosemary extract and carnosic acid
Bibliographic record
Abstract
Abstract Mast cells are sentinel immune cells that drive normal and pathological inflammatory responses, most notably allergic inflammation. Allergen-induced mast cell activation results in rapid degranulation of preformed pro-inflammatory mediators in the early phase, and sustained release of newly synthesized pro-inflammatory mediators in the late phase. Plant compounds known as polyphenols, have been examined in models of pathology due to their inhibition of pro-inflammatory mediator release. Rosemary extract (RE) and three polyphenolic constituents: carnosic acid (CA), carnosol (CO) and rosmarinic acid (RA) have been found to inhibit signaling cascades required for pro-inflammatory mediator release. The objective of this study was to establish the inhibitory effects of RE in a mast cell model and dissect the polyphenolic mixture, focusing on CA, CO, and RA, evaluating their potential as novel therapeutics. Sensitized mast cells were stimulated with allergen and treated with RE (5–50 μg/ml), CA, CO or RA (1–100 μM). The β-hexosaminidase release assay was used to measure degranulation, and cytokine release was measured by ELISA. Degranulation was inhibited dose dependently to 10% when treated with RE, 14% CA and 17% CO (p<0.001), while RA had no effect. ELISA analysis showed RE and CA inhibit release of IL-6 (p<0.05, p<0.0001), TNF (p<0.05, p<0.0001), and IL-13 (p<0.05, p<0.0001) while CO and RA had no effect. These novel findings identify RE and CA as potent regulators of mast cell functional responses. Further dissecting the mechanism behind RE and CA mediated inhibition of pro-inflammatory mediator release, will support the establishment of these natural plant products as effective anti-inflammatory therapeutics.
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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".