Role of Microbial Toxins in the Induction of Glucocorticoid Receptor β Expression in an Explant Model of Rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Glucocorticoids (GCs) are the most potent agents currently available for relieving the symptoms of chronic rhinosinusitis. The pathogenesis and molecular basis of GC insensitivity in allergic rhinosinusitis are unknown. Studies done on patients with GC-insensitive asthma demonstrated an overexpression of GC receptor beta (GRbeta), an abnormal splice variant and an endogenous inhibitor of the classic GC receptor alpha. The mechanisms that induce the overexpression of GRbeta remain poorly understood. OBJECTIVE: To study the role of Staphylococcus-derived enterotoxin in inducing GRbeta in a human explant model of rhinosinusitis. METHODS: Nasal tissue was obtained from inferior turbinates of nonatopic and ragweed-sensitive patients. Tissue samples from nonatopic patients were incubated in the presence and absence of superantigen (SAg) of staphylococcal enterotoxin. In addition, tissue samples from ragweed-sensitive patients were incubated with and without ragweed allergen in the presence or absence of SAg. The expression of GRbeta was assessed by immunocytochemistry using a specific polyclonal antibody to GRbeta. RESULTS: SAg increased the expression of GRbeta in both atopic and nonatopic tissue. The highest increase in the expression of GRbeta occurred when atopic nasal tissue was incubated with both ragweed and SAg. CONCLUSION: SAg-induced GRbeta is an important modulator of steroid sensitivity in chronic rhinosinusitis.
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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) | 0.001 | 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".