A Multi-Targeting, Nucleoside-Modified mRNA Influenza Virus Vaccine Provides Broad Protection in Mice
Bibliographic record
Abstract
Influenza viruses are respiratory pathogens of public health concern worldwide with up to 650,000 deaths occurring each year. Seasonal influenza virus vaccines are employed to prevent disease, but with limited effectiveness. Development of a universal influenza virus vaccine with the potential to elicit long-lasting, broadly cross-reactive immune responses is necessary for reducing influenza virus prevalence. In this study, we have utilized lipid nanoparticle-encapsulated, nucleoside-modified mRNA vaccines to intradermally deliver a combination of conserved influenza virus antigens (hemagglutinin stalk, neuraminidase, matrix-2 ion channel, and nucleoprotein) and induce strong immune responses with substantial breadth and potency in a murine model. The immunity conferred by nucleoside-modified mRNA-lipid nanoparticle vaccines provided protection from challenge with pandemic H1N1 virus at 500 times the median lethal dose after administration of a single immunization, and the combination vaccine protected from morbidity at a dose of 50 ng per antigen. The broad protective potential of a single dose of combination vaccine was confirmed by challenge with a panel of group 1 influenza A viruses. These findings support the advancement of nucleoside-modified mRNA-lipid nanoparticle vaccines expressing multiple conserved antigens as universal influenza virus vaccine candidates.
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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 | 0.001 | 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".