Serum Concentrations of Interleukin 1ß, CXCL10, and Interferon-γ in Mixed Cryoglobulinemia Associated with Hepatitis C Infection
Bibliographic record
Abstract
OBJECTIVE: Mixed cryoglobulinemia (MC) is a systemic vasculitis of small and medium-size vessels, often associated with the hepatitis C virus. Research has shown an emerging role for chemokines and type 1 cytokines in the pathophysiology of this vasculitis. Interleukin 1 (IL-1) plays a role in initiating the cascade of immunoinflammatory responses, and levels of the interferon-gamma (IFN-gamma) inducible chemokine CXCL10 have been shown to be significantly associated with the presence of active vasculitis in patients with MC. We evaluated serum levels of IL-1beta, IFN-gamma, and CXCL10 in a series of patients with hepatitis C-related MC (MC+HCV), and correlated these measurements with clinical disease features. METHODS: Serum IL-1beta, IFN-gamma, and CXCL10 were assayed in 54 patients with MC+HCV, in 54 sex- and age-matched patients with type C chronic hepatitis without cryoglobulinemia (HCV+), and in 54 controls. RESULTS: MC+HCV patients showed significantly higher mean IL-1beta and CXCL10 serum levels than controls (p < 0.01) or HCV+ patients (p < 0.01). CXCL10 was significantly increased in 14 cryoglobulinemic patients with active vasculitis (necrotizing vasculitis or vasculitic skin ulcers) compared to those without (p < 0.001); IL-1beta was increased in cryoglobulinemic patients with active vasculitis (p = 0.06). No differences were observed for serum IFN-gamma levels. CONCLUSION: Serum levels of IL-1beta and CXCL10 were high in patients with MC+HCV. Increased CXCL10 and IL-1beta levels were associated with the presence of active vasculitis in MC+HCV patients.
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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 | 0.001 |
| 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".