Elicitation of strong immune responses by a DNA vaccine expressing a secreted form of hepatitis C virus envelope protein E2 in murine and porcine animal models
Bibliographic record
Abstract
AIM: To characterize the immunogenicity of a hepatitis C virus (HCV) E2 DNA vaccine alone or with a protein vaccine boost in murine and porcine animal models. METHODS: A DNA vaccine expressing a secreted form of HCV E2 protein was constructed and used to vaccinate mice and piglets with or without boosting with a recombinant E2 protein vaccine formulated with CpG ODN and 10% Emulsigen. The immunogenicity of HCV E2 vaccines was analyzed by ELISA for antibody responses, MTT assay for lymphocyte proliferation, ELISPOT for the number of interferon-gamma secreting cells, and cytotoxic T lymphocyte assays. RESULTS: Intradermal injection of E2 DNA vaccine induced strong Th1-like immune responses in mice. In piglets, E2 DNA vaccine elicited moderate and more balanced immune responses. A DNA vaccine prime and protein boost vaccination strategy induced significantly higher E2-specific antibody levels and shifted the immune response towards Th2-like ones in piglets. CONCLUSION: A DNA vaccine expressing a secreted form of HCV E2 protein elicited E2-specific immune responses in mice and piglets. Recombinant E2 protein vaccination following DNA immunization significantly increased the antibody response in piglets. These HCV E2 vaccines may represent promising hepatitis C vaccine candidates for further investigations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".