Impaired IL-6-induced JAK-STAT signaling in CD4+ T cells associates with longer treatment duration in giant cell arteritis
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: The IL-12-IFNγ-Th1 and the IL-6-IL-23-Th17 axes are considered the dominant pathogenic pathways in Giant Cell Arteritis (GCA). Both pathways signal via activation of the downstream JAK/STAT proteins. We hypothesized that phosphorylated STAT (pSTAT) signatures in circulating immune cells may aid to stratify GCA-patients for personalized treatment. METHODS: To investigate pSTAT expression, PBMCs from treatment-naive GCA-patients (n = 18), infection controls (INF, n = 11) and age-matched healthy controls (HC, n = 15) were stimulated in vitro with IL-6, IL-2, IL-10, IFN-γ, M-CSF or GM-CSF, and stained with CD3, CD4, CD19, CD45RO, pSTAT1, pSTAT3, pSTAT5 antibodies, and analyzed by flow cytometry. Serum IL-6, sIL-6-receptor and gp130 were measured by Luminex. The change in percentages of pSTAT3+CD4+T-cells was evaluated at diagnosis and at 3 months and 1-year of follow-up. Kaplan-Meier analyses was used to asses prognostic accuracy. RESULTS: Analysis of IL-6 stimulated immune cell subsets revealed a significant decrease in percentages of pSTAT3+CD4+T-cells of GCA-patients and INF-controls compared to HCs. Following patient stratification according to high (median>1.5 pg/mL) and low (median<1.5 pg/mL) IL-6 levels, we observed a reduction in the pSTAT3 response in GCA-patients with high serum IL-6. Percentages of pSTAT3+CD4+T-cells in patients with high serum IL-6 levels at diagnosis normalized after glucocorticoid (GC) treatment. Importantly, we found that patients with low percentages of pSTAT3+CD4+T-cells at baseline require longer GC-treatment. CONCLUSION: Overall, in GCA, the percentages of in vitro IL-6-induced pSTAT3+CD4+T-cells likely reflect prior in vivo exposure to high IL-6 and may serve as a prognostic marker for GC-treatment duration and may assist improving personalized treatment options in the future.
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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 | 0.001 |
| 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 it