High Serum Levels of CXCL11 in Mixed Cryoglobulinemia Are Associated with Increased Circulating Levels of Interferon-γ
Bibliographic record
Abstract
OBJECTIVE: No study has evaluated circulating chemokine C-X-C motif ligand (CXCL)11 in patients with "mixed cryoglobulinemia and chronic hepatitis C infection" (MC+HCV). We measured CXCL11, and correlated this measurement to the clinical phenotype. METHODS: Serum CXCL11, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) were assayed in 97 MC+HCV patients and in 97 sex- and age-matched controls. RESULTS: MC+HCV patients showed significantly higher mean CXCL11 serum levels than controls (254 ± 295, 68 ± 16 pg/ml, respectively; p = 0.0002; ANOVA). CXCL11 was significantly increased in 36 cryoglobulinemic patients with compared to those without active vasculitis (303 ± 208 vs 179 ± 62 pg/ml, respectively; p < 0.001; ANOVA). IFN-γ levels were significantly higher in MC+HCV than in controls [6.1 (range 0.8-114.5), 1.4 (range 0.7-2.4) pg/ml, respectively; p < 0.05; Mann-Whitney U test]. Serum TNF-α mean levels were significantly higher in MC+HCV than in controls [13.4 (range 1.8-369), 1.1 (range 0.7-3.2) pg/ml, respectively; p < 0.0001; Mann-Whitney U test]. A multiple regression analysis considering CXCL11 as a dependent variable, and age, alanine aminotransferase, IFN-γ, and TNF-α as independent variables, showed in MC+HCV patients a significant association only with IFN-γ (p < 0.0001). CONCLUSION: Our study demonstrates markedly high serum levels of CXCL11 in patients with MC+HCV compared to healthy controls overall in the presence of active vasculitis. A strong relationship between circulating IFN-γ and CXCL11 was shown, strongly supporting the role of a T helper 1 immune response in the pathogenesis of MC+HCV.
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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 | 0.001 | 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) | 0.002 | 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".