Inflammatory cytokines and S-100b protein in patients with hepatitis C infection and cryoglobulinemia
Bibliographic record
Abstract
OBJECTIVE: To investigate a predictive role for the protein S-100b and serum circulating levels of Th1/Th2 cytokines in patients with chronic hepatitis C virus (HCV) infection with and without mixed cryoglobulinemia (MC). METHODS: Sixty chronically HCV-infected patients were divided into two groups: 30 with and 30 without MC. Patients with MC presented detectable mixed cryoglobulins and clinical weakness, purpura and arthralgias. HCV-RNA and genotype, serum levels of cryoglobulins, principal hepatic indexes and levels of IL-6, IL-18 and S-100b protein were evaluated. Twenty uninfected healthy subjects were a control group to evaluate serum levels of S-100b protein. RESULTS: IL-6 and IL-18 serum levels were higher in the MC+ group than the MC- group (8.7 +/- 4.5 pg/mL versus 4.6 +/- 2.3 pg/mL P < 0.0001 and 743.5 +/- 128.2 pg/mL versus 578.5 +/- 296.5 pg/mL P < 0.001 respectively). S-100b serum levels were higher in HCV+ with MC (0.23 +/- 0.07 microg/L) respect to HCV+ patients without MC (0.17 +/- 0.05 microg/L, P < 0.0001) and were statistically higher than in the control group (0.08 +/- 0.03 microg/L, P < 0.0001 and P < 0.0001, respectively). A positive correlation was shown between serum levels of S-100b protein and levels of cryoglobulins in the group of HCV+ patients MC+ (r=0.72 and P < 0.0001). CONCLUSION: HCV patients with MC have a worse inflammatory condition than those without MC. Moreover, S-100b protein seems to be a sensitive marker of endothelial and tissue damage in chronic HCV hepatitis with cryoglobulinemia.
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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".