Immunogenicity and Protection Against Influenza H7N3 in Mice by Modified Vaccinia Virus Ankara Vectors Expressing Influenza Virus Hemagglutinin or Neuraminidase
Bibliographic record
Abstract
Influenza subtypes such as H7 have pandemic potential since they are able to infect humans with severe consequences, as evidenced by the ongoing H7N9 infections in China that began in 2013. The diversity of H7 viruses calls for a broadly cross-protective vaccine for protection. We describe the construction of recombinant modified vaccinia virus Ankara (MVA) vectors expressing the hemagglutinin (HA) or neuraminidase (NA) from three H7 viruses representing both Eurasian and North American H7 lineages - A/mallard/Netherlands/12/2000 (H7N3), A/Canada/rv444/2004 (H7N3), and A/Shanghai/02/2013 (H7N9). These vectors were evaluated for immunogenicity and protective efficacy against H7N3 virus in a murine model of intranasal challenge. High levels of H7-, N3-, and N9-specific antibodies, including neutralizing antibodies, were induced by the MVA-HA and MVA-NA vectors. Mice vaccinated with MVA vectors expressing any of the H7 antigens were protected, suggesting cross-protection among H7 viruses. In addition, MVA vectors expressing N3 but not N9 elicited protection against H7N3 virus challenge. Similar outcomes were obtained when immune sera from MVA vector-immunized mice were passively transferred to naïve mice prior to challenge with the H7N3 virus. The results support the further development of an MVA vector platform as a candidate vaccine for influenza strains with pandemic potential.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".