Treatment with carnosic acid attenuates the secretion of pro-inflammatory mast cell mediators following IL-33 activation
Bibliographic record
Abstract
Abstract IL-33 is an alarmin cytokine, released upon cellular damage, that has gained significant attention as a regulator of inflammation in several pathologies, including allergy. Once released, IL-33 binds to and activates its cognate receptor ST2, leading to the activation of the classical inflammatory Myddosome signalosome in an array of cells, including mast cells. Our group has recently identified the polyphenol carnosic acid (CA), as a potent regulator of mast cell activation in the context of allergic inflammation. Due to the key role IL-33 plays in the enhancement of allergies and other mast cell-associated diseases, we sought to determine the inhibitory potential of CA in a model of IL-33-mediated mast cell activation. Bone marrow-derived mast cells were stimulated with IL-33 under potentiation of SCF and treated with CA in the presence or absence of an allergen co-stimulation. Here, it was determined that treatment with CA led to a reduction in early ROS production, which translated into a significant impairment in the release of pro-inflammatory cytokines IL-6, IL-13, TNF and chemokines CCL1, CCL2 and CCL3. Surprisingly however, it was determined that CA treatment increased signaling through Akt and NFκB protein phosphorylation, leading to increased gene expression of IL6, IL13, and CCL3 as well as increased intracellular concentrations of IL-6 and CCL3. Taken together, our data suggests treatment with CA impairs the release mechanisms of pro-inflammatory mediators following IL-33 activation, warranting further investigation into the versatile biological activity of CA toward advance our understanding of CA as a potential anti-inflammatory therapeutic.
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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) | 0.001 | 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) | 0.002 | 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".