Effect of Interferon-Alpha, Ribavirin, Pentoxifylline, and Interleukin-18 Antibody on Hepatitis C Sera-Stimulated Hepatic Stellate Cell Proliferation
Bibliographic record
Abstract
Chronic hepatitis C virus (HCV) infection is a major cause of liver fibrosis ultimately leading to cirrhosis. Hepatic stellate cell (HSC) proliferation is crucial in fibrosis development. Current antiviral treatment for HCV involves interferon-alpha (IFN-alpha) and Ribavirin combination therapy. IL-18, a novel cytokine of the IL-1 family of cytokines, is involved in inflammation and may be important in HCV-related inflammation. We hypothesize that block of one of the crucial events will block fibrosis due to HCV. The effect of HCV patient sera with and without IFN-alpha, ribavirin, and IL-18 antibody on HSC proliferation was assessed by [(3)H]-thymidine incorporation assays. Western analysis was used to assess the effect of pentoxifylline (PTX) on c-Jun immediate early gene phosphorylation (p-c-Jun formation). We demonstrate that HCV patient sera-stimulated HSC proliferation. Ribavirin with or without IFN-alpha significantly decreased HCV sera-stimulated HSC proliferation by 50%. Western analysis revealed that HCV serum increased p-c-Jun levels, which were decreased with Ribavirin and PTX. ELISA results showed an elevation of IL-18 levels in HCV sera when compared to normal sera. IL-18 did not stimulate HSC proliferation. However, IL-18 antibody significantly decreased patient sera-stimulated HSC proliferation. In conclusion, Ribavirin decreased HSC proliferation and may act by decreasing p-c-Jun levels in HSCs. IL-18 alone did not stimulate HSC proliferation but IL-18 antibody decreased stimulation, suggesting that IL-18 may work in conjunction with some other factor to increase HSC proliferation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".