Antiviral Lectins from Red and Blue-Green Algae Show Potent In Vitro and In Vivo Activity against Hepatitis C Virus
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is a significant public health problem with over 170,000,000 chronic carriers and infection rates increasing worldwide. Chronic HCV infection is one of the leading causes of hepatocellular carcinoma which was estimated to result in ∼10,000 deaths in the United States in the year 2011. Current treatment options for HCV infection are limited to PEG-ylated interferon alpha (IFN-α), the nucleoside ribavirin and the recently approved HCV protease inhibitors telaprevir and boceprevir. Although showing significantly improved efficacy over the previous therapies, treatment with protease inhibitors has been shown to result in the rapid emergence of drug-resistant virus. Here we report the activity of two proteins, originally isolated from natural product extracts, which demonstrate low or sub-nanomolar in vitro activity against both genotype I and genotype II HCV. These proteins inhibit viral infectivity, binding to the HCV envelope glycoproteins E1 and E2 and block viral entry into human hepatocytes. In addition, we demonstrate that the most potent of these agents, the protein griffithsin, is readily bioavailable after subcutaneous injection and shows significant in vivo efficacy in reducing HCV viral titers in a mouse model system with engrafted human hepatocytes. These results indicate that HCV viral entry inhibitors can be an effective component of anti-HCV therapy and that these proteins should be studied further for their therapeutic potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".