Double‐Layered M2e‐NA Protein Nanoparticle Immunization Induces Broad Cross‐Protection against Different Influenza Viruses in Mice
Bibliographic record
Abstract
The development of a universal influenza vaccine is an ideal strategy to eliminate public health threats from influenza epidemics and pandemics. This ultimate goal is restricted by the low immunogenicity of conserved influenza epitopes. Layered protein nanoparticles composed of well-designed conserved influenza structures have shown improved immunogenicity with new physical and biochemical features. Herein, structure-stabilized influenza matrix protein 2 ectodomain (M2e) and M2e-neuraminidase fusion (M2e-NA) recombinant proteins are generated and M2e protein nanoparticles and double-layered M2e-NA protein nanoparticles are produced by ethanol desolvation and chemical crosslinking. Immunizations with these protein nanoparticles induce immune protection against different viruses of homologous and heterosubtypic NA in mice. Double-layered M2e-NA protein nanoparticles induce higher levels of humoral and cellular responses compared with their comprising protein mixture or M2e nanoparticles. Strong cytotoxic T cell responses are induced in the layered M2e-NA protein nanoparticle groups. Antibody responses contribute to the heterosubtypic NA immune protection. The protective immunity is long lasting. These results demonstrate that double-layered protein nanoparticles containing structure-stabilized M2e and NA can be developed into a universal influenza vaccine or a synergistic component of such vaccines. Layered protein nanoparticles can be a general vaccine platform for different pathogens.
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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 | 0.001 | 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".