Microbial Superantigens Induce Glucocorticoid Receptor β and Steroid Resistance in a Nasal Explant Model
Bibliographic record
Abstract
OBJECTIVE: To study the role of superantigen (SAg) in inducing glucocorticoid (GC) receptor beta and steroid resistance in an explant model of nasal tissue. METHODS: Nasal tissue was obtained from inferior turbinates of controls and ragweed (RW)-sensitive patients. Tissue samples were incubated with SAg of staphylococcal enterotoxin B. In addition, tissue samples from RW-sensitive patients were incubated with RW allergen in the presence and absence of both SAg and dexamethasone (DEX). The expression of GC receptor beta was assessed by immunocytochemistry. The expression of interleukin (IL)-2 and IL-4 mRNA was assessed by in situ hybridization. RESULTS: SAg induced an increase in the expression of GC receptor beta in atopic tissue and to a lesser extent in nonatopic tissue. The most significant induction of GC receptor beta was observed in response to SAg and RW in atopic tissue. Stimulation of atopic tissue with RW alone and SAg alone induced IL-4 and IL-2 mRNA, respectively. Incubation of atopic tissue with both SAg and RW induced both IL-2 and IL-4 mRNA. The increase in IL-4 mRNA expression was blunted by the addition of DEX to atopic tissue stimulated with RW alone but not to tissue stimulated by both RW and SAg. CONCLUSION: Our results demonstrate that SAgs induce steroid resistance in atopic nasal explant tissue by up-regulating the expression of GC receptor beta. Furthermore, we have shown that the up-regulation of GC receptor beta is a local event that is associated with the coexpression of IL-2 and IL-4 mRNA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".