Changing the threshold—Signals and mechanisms of mast cell priming
Bibliographic record
Abstract
Summary Mast cells play a key role in allergy and other inflammatory diseases involving engagement of multivalent antigen with IgE bound to high‐affinity IgE receptors (FcεRIs). Aggregation of FcεRIs on mast cells initiates a cascade of signaling events that eventually lead to degranulation, secretion of leukotrienes and prostaglandins, and cytokine and chemokine production contributing to the inflammatory response. Exposure to pro‐inflammatory cytokines, chemokines, bacterial and viral products, as well as some other biological products and drugs, induces mast cell transition from the basal state into a primed one, which leads to enhanced response to IgE‐antigen complexes. Mast cell priming changes the threshold for antigen‐mediated activation by various mechanisms, depending on the priming agent used, which alone usually do not induce mast cell degranulation. In this review, we describe the priming processes induced in mast cells by various cytokines (stem cell factor, interleukins‐4, ‐6 and ‐33), chemokines, other agents acting through G protein‐coupled receptors (adenosine, prostaglandin E 2 , sphingosine‐1‐phosphate, and β‐2‐adrenergic receptor agonists), toll‐like receptors, and various drugs affecting the cytoskeleton. We will review the current knowledge about the molecular mechanisms behind priming of mast cells leading to degranulation and cytokine production and discuss the biological effects of mast cell priming induced by several cytokines.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".