Cloning, Expression, and Functional Analysis of Patient-Derived Hepatitis C Virus Glycoproteins
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is a major cause of severe chronic liver disease including cirrhosis and hepatocellular carcinoma. HCV has been classified into six major genotypes that exhibit extensive genetic variability, particularly in the envelope glycoproteins E1 and E2. Knowledge of genotypic and quasispecies variation on viral glycoprotein properties is important in understanding the structure-function relationship of the proteins. Through their perceived role as components of the virion and mediators of virus attachment and entry, HCV glycoproteins are primary targets for the development of antiviral agents. In this chapter, we describe methods optimized to extract E1E2-encoding sequences of all the major genotypes from HCV-infected patient sera, and their amplification, cloning, expression, and biochemical characterization. Furthermore, we describe a method to generate retroviral nucleocapsid pseudotyped with HCV E1E2 of diverse genotypes (HCVpp) whereby infectivity of the retroviral particle is conferred by HCV glycoproteins. Finally, we show how the HCVpp can be used in an infection assay to determine the viral glycoprotein function at the level of the host-pathogen interface and subsequent events leading to virus infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".