Comparison of the inhibitory effects of resveratrol and tranilast on IgE, 48/80 and substance P dependent-mast cell activation
Bibliographic record
Abstract
Several health promoting effects have been attributed to the polyphenol resveratrol including anti cancer, anti-oxidant and anti-inflammatory activities. We investigated the effects of resveratrol on LAD2 and CD34 -derived mast cell activation in comparison to the known anti-allergy drug tranilast. Degranulation was quantified by β hexosaminidase assay, and cytokine, chemokine and cysteinyl leukotrienes (cysLT) expression was measured by real time PCR and ELISA. Fura-2 Ca imaging was employed to measure [Ca ] i . In LAD2 cells, both resveratrol and tranilast (10 ug/ml) inhibited degranulation induced by mast cell activators IgE/anti-IgE (39% and 19%, respectively; P<0.03), compound 48/80 (9% and 6%), and substance P (23% and 28%; P<0.03). This may be attributable to modulation of Ca levels, as resveratrol, and to a lesser extent tranilast, attenuated substance P-dependent increases in [Ca ] i . Resveratrol and tranilast blocked cytokine formation, reducing substance P-induced TNF production (65%; P=0.04 and 46%; P=0.09, respectively), but not MCP-1 production. Furthermore, resveratrol inhibited FcepsilonRI mediated production of cysLT by 31% compared to control, whereas tranilast had no effect. The effects of resveratrol on degranulation and release of cysLT were more marked in human primary mast cells (HuMC) (64% and 90% inhibition, respectively; P<0.05), and the polyphenol was found to be significantly more efficacious than tranilast in these cells. Resveratrol inhibited mast cell function at the level of degranulation, and cytokine and cysLT production, and was comparable, and in some cases, more potent than the anti-allergy drug tranilast. Thus resveratrol may be an effective therapeutic agent for the treatment of allergic disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".