Assessment of a quadrivalent nucleoside-modified mRNA vaccine that protects against group 2 influenza viruses
Bibliographic record
Abstract
Combined vaccine formulations targeting not only hemagglutinin but also other influenza virus antigens could form the basis for a universal influenza virus vaccine that has the potential to elicit long-lasting, broadly cross-reactive immune responses. Lipid nanoparticle (LNP)-encapsulated messenger RNA (mRNA) vaccines can be utilized to efficiently target multiple antigens with a single vaccine. Here, we assessed the immunogenicity and protective efficacy of nucleoside-modified mRNA-LNP vaccines that contain four influenza A group 2 virus antigens (hemagglutinin stalk, neuraminidase, matrix protein 2, and nucleoprotein) in mice. We found that all vaccine components induced antigen-specific cellular and humoral immune responses after administration of a single dose. While the monovalent formulations were not exclusively protective, the combined quadrivalent formulation protected mice from all challenge viruses, including a relevant H1N1 influenza virus group 1 strain, with minimal weight loss. Importantly, the combined vaccine protected from morbidity at a dose of 125 ng per antigen after a single vaccination in mice. With these findings, we confidently conclude that the nucleoside-modified mRNA-LNP platform can be used to elicit protection against a large panel of influenza viruses.
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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 | 0.001 |
| 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".