Synthetic Antigens Representing the Antigenic Variation of Human Hepatitis C Virus
Bibliographic record
Abstract
Immune responses against hepatitis C virus (HCV) have been studied by numerous groups. However, details concerning the production of antibodies to antigenically variable epitopes remain to be elucidated. Since the sequences of the variable regions of several HCV proteins are different among the virus strains infecting patients, we decided to design peptide combinations that represent the theoretical maximum antigenic variation of each epitope to be used as capture antigens. We prepared six peptide mixtures (hypervariable epitope constructs; HECs) representing six different epitopes from structural and non-structural proteins of HCV from genotypes 1-6. Plasma from 300 HCV patients was tested to determine if their antibodies recognize the synthetic constructs. All the patients were chronically infected with diverse HCV genotypes and did not receive antiviral treatment. Antibodies to one or more of the HECs were detected in all of the HCV-infected individuals. Immunogenicity of the HCV HECs was also evaluated in outbred and inbred mice. Strong HEC-specific antibodies were produced, and cellular responses were also induced that were Th-1 rather than Th-2. Our results show that HCV HECs are both antigens that can be used to detect the broad cross-reactivity of antibodies from HCV-infected patients, and strong immunogens that can induce antigen-specific humoral and cellular immune responses in mice.
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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".