MétaCan
Menu
Back to cohort
Record W4310251723 · doi:10.1126/science.abm0271

A multivalent nucleoside-modified mRNA vaccine against all known influenza virus subtypes

2022· article· en· W4310251723 on OpenAlexaff
Claudia P. Arevalo, Marcus J. Bolton, Valerie Le Sage, Naiqing Ye, Colleen Furey, Hiromi Muramatsu, Mohamad‐Gabriel Alameh, Norbert Pardi, Elizabeth M. Drapeau, Kaela Parkhouse, Tyler A. Garretson, Jeffrey S. Morris, Louise H. Moncla, Ying K. Tam, Steven H.Y. Fan, Seema S. Lakdawala, Drew Weissman, Scott E. Hensley

Bibliographic record

VenueScience · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsAcuitas Therapeutics (Canada)
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsVirologyNucleosideVirusMessenger RNAInfluenza A virusChemistryBiologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Seasonal influenza vaccines offer little protection against pandemic influenza virus strains. It is difficult to create effective prepandemic vaccines because it is uncertain which influenza virus subtype will cause the next pandemic. In this work, we developed a nucleoside-modified messenger RNA (mRNA)-lipid nanoparticle vaccine encoding hemagglutinin antigens from all 20 known influenza A virus subtypes and influenza B virus lineages. This multivalent vaccine elicited high levels of cross-reactive and subtype-specific antibodies in mice and ferrets that reacted to all 20 encoded antigens. Vaccination protected mice and ferrets challenged with matched and mismatched viral strains, and this protection was at least partially dependent on antibodies. Our studies indicate that mRNA vaccines can provide protection against antigenically variable viruses by simultaneously inducing antibodies against multiple antigens.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.528

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 science0.0010.001
Research integrity0.0000.000
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 head0.061
GPT teacher head0.376
Teacher spread0.315 · 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 designBench or experimental
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

Citations348
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueScienceSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207