Antiviral action of interferon‐α against hepatitis C virus replicon and its modulation by interferon‐γ and interleukin‐8
Bibliographic record
Abstract
BACKGROUND AND AIM: Interferon-alpha (IFN-alpha) based therapy is the main treatment used to control hepatitis C virus (HCV) infection. The aim of this study was to understand the mechanisms of IFN-alpha inhibition of HCV replication and the resistance of HCV to IFN-alpha therapy, and improve the efficiency of HCV treatment. METHODS: The inhibitory effects of IFN-alpha on a HCV replicon system were examined and the potential regulatory effects of interferon-gamma (IFN-gamma) and interleukin-8 (IL-8) on the antiviral actions of IFN-alpha were also investigated in this report. RESULTS: The results showed that IFN-alpha can effectively inhibit the replication of HCV replicon. Pretreatment of HCV replicon cells with IFN-gamma could significantly potentiate the inhibitory effects of IFN-alpha on the HCV replicon. Direct addition of IL-8 to the culture medium of HCV replicon cells could partially rescue the HCV replicon from the inhibition of IFN-alpha, which may be the result of IL-8 down-regulation of interferon-stimulated genes. CONCLUSION: Our study demonstrated that IFN-gamma has synergistic antiviral effects with IFN-alpha; whereas IL-8 can attenuate the anti-HCV actions of IFN-alpha and is associated with HCV resistance to interferon-alpha therapy.
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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".