Serum cytokine and chemokine levels in patients with eosinophilic granulomatosis with polyangiitis, hypereosinophilic syndrome, or eosinophilic asthma.
Bibliographic record
Abstract
OBJECTIVES: The pathogenesis of eosinophilic granulomatosis with polyangiitis (EGPA) remains poorly understood, and may overlap with eosinophilic asthma and primary hypereosinophilic syndrome (HES). The aim of this study was to analyse a panel of serum cytokines and chemokines as markers of disease activity in patients with these conditions. METHODS: The levels of 54 cytokines and chemokines were measured in the sera of 40 patients with active EGPA, 10 of these patients during inactive disease, 6 patients with HES, 8 with asthma, and 10 healthy controls. Serum cytokine/chemokines measured included interleukin (IL)-1α, 1β, 3, 4, 5, 6, 8, 13, 15, 17A, 17E(25), 18, 23 and 33, soluble IL-2 receptor alpha, eotaxin-1 (CCL11), -2 (CCL24) and -3 (CCL26), macrophage-derived chemokine (MDC/CCL22), macrophage inflammatory protein (MIP)-1a and -1b, and tumour necrosis factor (TNF)-α. Results were compared between disease and control groups using regression analysis, with Bonferroni correction for multiple comparisons (significant p value ≤0.00093). RESULTS: Significant differences were observed only in serum levels of MDC, IL-8, MIP-1a and -1b, TNF-α, each of which were lower in patients with active EGPA than in healthy controls (p<0.0001). Differences between patients with active disease and other disease groups did not reach significance. Paired comparisons between sera from patients with active or inactive EGPA showed no significant difference for any of the studied cytokines or chemokines. CONCLUSIONS: No clear difference in the serum levels of measured cytokines and chemokines helped distinguish between active EGPA or inactive EGPA, or other disease or control groups.
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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".