A Recombinant Hepatitis C Virus Genotype 1a E1/E2 Envelope Glycoprotein Vaccine Elicits Antibodies That Differentially Neutralize Closely Related 2a Strains through Interactions of the N-Terminal Hypervariable Region 1 of E2 with Scavenger Receptor B1
Bibliographic record
Abstract
A vaccine is still urgently needed to overcome the hepatitis C virus (HCV) epidemic. It is estimated that 1.75 million new HCV infections occur each year, many of which will go undiagnosed and untreated. Untreated HCV can lead to continued spread of the disease, progressive liver fibrosis, cirrhosis, and eventually, end-stage liver disease and/or hepatocellular carcinoma (HCC). Previously, our 1a E1/E2 glycoprotein vaccine was shown to elicit broadly cross-neutralizing antibodies; however, there remains variation in the effectiveness of these antibodies against different HCV genotypes. In this study, we investigated determinants of differential neutralization sensitivity between two highly related genotype 2a isolates, J6 and JFH-1. Our data indicate that the HVR1 region determines neutralization sensitivity to vaccine antisera through modulation of sensitivity to antibodies and interactions with SR-B1. Our results provide additional insight into optimizing a broadly neutralizing HCV vaccine.
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".