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Record W4291019342 · doi:10.1038/s41467-022-32149-8

Development of a pentavalent broadly protective nucleoside-modified mRNA vaccine against influenza B viruses

2022· article· en· W4291019342 on OpenAlexaff
Norbert Pardi, Juan Manuel Carreño, George O’Dell, Jessica Tan, Csaba Bajusz, Hiromi Muramatsu, Willemijn F. Rijnink, Shirin Strohmeier, Madhumathi Loganathan, Dominika Bielak, Molly M. H. Sung, Ying K. Tam, Florian Krammer, Meagan McMahon

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsAcuitas Therapeutics (Canada)
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthDivision of Intramural Research, National Institute of Allergy and Infectious DiseasesU.S. Department of Health and Human Services
KeywordsVirologyVaccinationVirusAntigenInfluenza vaccineBiologyMessenger RNANucleosideAntigenic variationImmune systemAntigenic driftInfluenza A virusImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Messenger RNA (mRNA) vaccines represent a new, effective vaccine platform with high capacity for rapid development. Generation of a universal influenza virus vaccine with the potential to elicit long-lasting, broadly cross-reactive immune responses is a necessity for reducing influenza-associated morbidity and mortality. Here we focus on the development of a universal influenza B virus vaccine based on the lipid nanoparticle-encapsulated nucleoside-modified mRNA (mRNA-LNP) platform. We evaluate vaccine candidates based on different target antigens that afford protection against challenge with ancestral and recent influenza B viruses from both antigenic lineages. A pentavalent vaccine combining all tested antigens protects mice from morbidity at a very low dose of 50 ng per antigen after a single vaccination. These findings support the further advancement of nucleoside-modified mRNA-LNPs expressing multiple conserved antigens as universal influenza virus vaccine candidates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.119
GPT teacher head0.417
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations85
Published2022
Admission routes1
Has abstractyes

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