Methanolic Extract of Rhizoma Coptidis Inhibits the Early Viral Entry Steps of Hepatitis C Virus Infection
Bibliographic record
Abstract
Hepatitis C Virus (HCV) remains an important public health threat with approximately 170 million carriers worldwide who are at risk of developing hepatitis C-associated end-stage liver diseases. Despite improvement of HCV treatment using the novel direct-acting antivirals (DAAs) targeting viral replication, there is a lack of prophylactic measures for protection against HCV infection. Identifying novel antivirals such as those that target viral entry could help broaden the therapeutic arsenal against HCV. Herein, we investigated the anti-HCV activity of the methanolic extract from Rhizoma coptidis (RC), a widely used traditional Chinese medicine documented by the WHO and experimentally reported to possess several pharmacological functions including antiviral effects. Using the cell culture-derived HCV system, we demonstrated that RC dose-dependently inhibited HCV infection of Huh-7.5 cells at non-cytotoxic concentrations. In particular, RC blocked HCV attachment and entry/fusion into the host cells without exerting any significant effect on the cell-free viral particles or modulating key host cell entry factors to HCV. Moreover, RC robustly suppressed HCV pseudoparticles infection of Huh-7.5 cells and impeded infection by several HCV genotypes. Collectively, our results identified RC as a potent antagonist to HCV entry with potential pan-genotypic properties, which deserves further evaluation for use as an anti-HCV agent.
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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".