Treatment with carnosic acid during mast cell differentiation modulates the mature mast cell phenotype and inflammatory response 3847
Bibliographic record
Abstract
Abstract Description Hematopoietic progenitors from the bone marrow home to and are conditioned by the local milieu in the periphery in the development of tissue resident mast cells. Due to their long-lived nature, strategies altering the inflammatory capacity of mast cells are attractive in therapeutic considerations for mast cell-driven inflammatory disorders, including allergy. Carnosic acid (CA) has been previously demonstrated by our group to attenuate inflammatory mast cell responses when applied during acute mast cell activation. We therefore sought to determine if CA influences the inflammatory capacity of mast cells when applied during differentiation. Bone marrow-derived mast cells (BMMCs) were generated from C57BL/6 mice and cultured twice weekly under the influence of IL-3, with or without CA. CA was found to profoundly reduce cell surface levels of key mast cell receptors c-kit and ST2. Allergen-induced early and late phase responses were assessed after stimulation of IgE-sensitized BMMCs with allergen (TNP-BSA) and SCF. CA was found to abrogate early phase degranulation and reduce cell surface levels of the exocytosis marker CD63. Late phase mediator release was differentially regulated, as CA impaired the release of IL-13 and CCL2, while enhancing the release of CCL1 and CCL3. CA was further found to exacerbate ST2-dependent mediator release induced by IL-33. These findings position CA as a potential modulator of mast cell activity when applied during the differentiation period. Funding Sources Supported by the NSERC discovery grant, Canadian Foundation for Innovation, the Ontario government (Ontario Graduate Scholarship) and Brock University. Topic Categories Immediate Hypersensitivity, Asthma, and Allergic Responses (HYP)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.003 | 0.001 |
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".