Turmeric curcumin inhibits entry of all hepatitis C virus genotypes into human liver cells
Bibliographic record
Abstract
OBJECTIVE: Hepatitis C virus (HCV) infection causes severe liver disease and affects more than 160 million individuals worldwide. People undergoing liver organ transplantation face universal re-infection of the graft. Therefore, affordable antiviral strategies targeting the early stages of infection are urgently needed to prevent the recurrence of HCV infection. The aim of the study was to determine the potency of turmeric curcumin as an HCV entry inhibitor. DESIGN: The antiviral activity of curcumin and its derivatives was evaluated using HCV pseudo-particles (HCVpp) and cell-culture-derived HCV (HCVcc) in hepatoma cell lines and primary human hepatocytes. The mechanism of action was dissected using R18-labelled virions and a membrane fluidity assay. RESULTS: Curcumin treatment had no effect on HCV RNA replication or viral assembly/release. However, co-incubation of HCV with curcumin potently inhibited entry of all major HCV genotypes. Similar antiviral activities were also exerted by other curcumin derivatives but not by tetrahydrocurcumin, suggesting the importance of α,β-unsaturated ketone groups for the antiviral activity. Expression levels of known HCV receptors were unaltered, while pretreating the virus with the compound reduced viral infectivity without viral lysis. Membrane fluidity experiments indicated that curcumin affected the fluidity of the HCV envelope resulting in impairment of viral binding and fusion. Curcumin has also been found to inhibit cell-to-cell transmission and to be effective in combination with other antiviral agents. CONCLUSIONS: Turmeric curcumin inhibits HCV entry independently of the genotype and in primary human hepatocytes by affecting membrane fluidity thereby impairing virus binding and fusion.
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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) | 0.002 | 0.001 |
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".