Scrodentoid A Inhibits Mast Cell–Mediated Allergic Response by Blocking the Lyn–FcεRIβ Interaction
Bibliographic record
Abstract
Background: Mast cells are considered an attractive therapeutic target for treating allergic diseases, and the Lyn–FcεRIβ interaction is essential for mast cell activation. This study investigated the antiallergic effect of scrodentoid A (SA) on mast cells and mast cell–mediated anaphylaxis. Methods: For in vitro experiments, mast cells were treated with SA. Cell proliferation was tested using the XTT assay. The mRNA expression of various cytokines and chemokines was measured using qPCR. The levels of histamine, eicosanoids (PGD2, LTC4), and cytokines were measured using enzyme immunoassay kits. Signaling was investigated using Western blotting and immunoprecipitation. For in vivo experiments, the antiallergic activity of SA was evaluated using two mouse models of passive anaphylaxis as passive cutaneous and systemic anaphylaxis. The mechanism was investigated through immunohistochemistry and immunofluorescence. Results: SA considerably inhibited immunoglobulin (Ig) E-mediated mast cell activation, including -hexosaminidase release, mRNA and protein expression of various cytokines, and PGD2 and LTC4 release. Oral administration of SA effectively and dose-dependently suppressed mast cell–mediated passive cutaneous and systemic anaphylaxis. SA significantly attenuated the activation of Lyn, Syk, LAT, PLCγ, JNK, Erk1/2, and Ca2+ mobilization without Fyn, Akt, and P38 activation by blocking the Lyn–FcεRIβ interaction. Conclusions: SA suppresses mast cell–mediated allergic response by blocking the Lyn–FcεRIβ interaction in vitro and in vivo. SA may be a promising therapeutic agent for allergic and other mast cell–related diseases.
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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 | 0.001 |
| 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".