Correlation between systemic inflammatory response and quality of life in patients with chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Local sinonasal inflammation resulting from altered T-cell immune signaling is a contributor to the pathogenesis of chronic rhinosinusitis (CRS). CRS patients experience negative impacts on quality of life (QOL) and suffer from comorbidities linked to systemic inflammation. However, systemic inflammatory profiling to evaluate the association between systemic inflammation and QOL in CRS has not been performed. Our objectives were to compare local and systemic inflammatory gene expression in patients with CRS to determine if systemic markers of inflammation associate with disease severity and disease-specific QOL. METHODS: A prospective observational study was conducted comparing 16 patients with CRS to 10 controls. Inflammatory gene expression in the anterior ethmoid tissues and peripheral blood of patients was measured using multiplex gene expression analysis and correlated to disease severity (computed tomography and nasal endoscopy) and disease-specific QOL (22-item Sino-Nasal Outcome Test [SNOT-22] and Rhinosinusitis Disability Index) using linear regression analyses. RESULTS: Patients with CRS showed significant increases in the expression of ctla4 and jak1 in sinonasal tissue and blood (p < 0.05), whereas the gene expression of hla-dqa1, hla-dqb1, and dusp4 was significantly decreased in patients with CRS compared to controls (p < 0.05). Soluble and local ctla4 and jak1 showed a significant positive correlation with clinical markers of disease severity and disease-specific QOL (p < 0.05). CONCLUSION: Local and systemic gene expression involved in T-cell immune signaling was found to be significantly altered in the blood and sinonasal tissues of patients with CRS compared to controls and significantly correlated to disease severity and QOL in patients with CRS.
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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 | 0.001 |
| 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".