Polyvalent DNA Vaccines Expressing HA Antigens of H5N1 Influenza Viruses with an Optimized Leader Sequence Elicit Cross-Protective Antibody Responses
Bibliographic record
Abstract
Highly pathogenic avian influenza A (HPAI) H5N1 viruses are circulating among poultry populations in parts of Asia, Africa, and the Middle East, and have caused human infections with a high mortality rate. H5 subtype hemagglutinin (HA) has evolved into phylogenetically distinct clades and subclades based on viruses isolated from various avian species. Since 1997, humans have been infected by HPAI H5N1 viruses from several clades. It is, therefore, important to develop strategies to produce protective antibody responses against H5N1 viruses from multiple clades or antigenic groups. In the current study, we optimized the signal peptide design of DNA vaccines expressing HA antigens from H5N1 viruses. Cross reactivity analysis using sera from immunized rabbits showed that antibody responses elicited by a polyvalent formulation, including HA antigens from different clades, was able to elicit broad protective antibody responses against multiple key representative H5N1 viruses across different clades. Data presented in this report support the development of a polyvalent DNA vaccine strategy against the threat of a potential H5N1 influenza pandemic.
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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) | 0.000 | 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 | 0.001 |
| 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".