Preferential Signaling and Induction of Allergy-promoting Lymphokines Upon Weak Stimulation of the High Affinity IgE Receptor on Mast Cells
Bibliographic record
Abstract
Mast cell degranulation and de novo cytokine production is a consequence of antigen-aggregation of the immunoglobulin E (IgE)-occupied high affinity receptor for IgE (Fc epsilon RI). Herein, we report that lymphokines that promote allergic inflammation, like MCP-1, were potently induced at low antigen (Ag) concentrations or at low receptor occupancy with IgE whereas some that down-regulate this response, like interleukin (IL)-10, required high receptor occupancy. Weak stimulation of mast cells caused minimal degranulation whereas a half-maximal secretory response was observed for chemokines and, with the exception of TNF-alpha, a weaker cytokine secretory response was observed. The medium from weakly stimulated mast cells elicited a monocyte/macrophage chemotactic response similar to that observed at high receptor occupancy. Weak stimulation also favored the phosphorylation of Gab2 and p38MAPK, while LAT and ERK2 phosphorylation was induced by a stronger stimulus. Gab2-deficient mast cells were severely impaired in chemokine mRNA induction whereas LAT-deficient mast cells showed a more pronounced defect in cytokines. These findings demonstrate that perturbation of small numbers of IgE receptors on mast cells favors certain signals that contribute to a lymphokine response that can mediate allergic inflammation.
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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) | 0.000 | 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 | 0.000 |
| 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".