A Novel M2e Based Flu Vaccine Formulation for Dogs
Bibliographic record
Abstract
BACKGROUND: The USA 2004 influenza virus outbreak H3N8 in dogs heralded the emergence of a new disease in this species. A new inactivated H3N8 vaccine was developed to control the spread of the disease but, as in humans and swine, it is anticipated that the virus will mutate shift and drift in the dog population. Therefore, there is a need for a vaccine that can trigger a broad protection to prevent the spread of the virus and the emergence of new strains. METHODOLOGY AND PRINCIPAL FINDINGS: The universal M2e peptide is identical in almost all the H3N8 influenza strains sequenced to date and known to infect dogs. This epitope is therefore a good choice for development of a vaccine to provide broad protection. Malva mosaic virus (MaMV) nanoparticles were chosen as a vaccine platform to improve the stability of the M2e peptide and increase its immunogenicity in animals. The addition of an adjuvant (OmpC) purified from Salmonella typhi membrane in the vaccine formulation increased the immune response directed to the M2e peptide significantly and enlarged the protection to include the heterosubtypic strain of influenza in a mouse model. An optimal vaccine formulation was also shown to be immunogenic in dogs. CONCLUSIONS AND SIGNIFICANCE: The MaMV vaccine platform triggered an improved immune response directed towards the universal M2e peptide. The adjuvant OmpC increased the immune response to the M2e peptide and protection to a heterosubtypic influenza strain that harbors a different M2e peptide in a mouse model. Antibodies generated by the vaccine formulation showed cross-reactivity with M2e peptides derived from influenza strains H9N2, H5N1 and H1N1. The vaccine formulation shows a potential for commercialization of a new M2e based vaccine in dogs.
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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".