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Record W4403033006 · doi:10.1038/s41541-024-00976-z

Bivalent norovirus mRNA vaccine elicits cellular and humoral responses protecting human enteroids from GII.4 infection

2024· article· en· W4403033006 on OpenAlexaff
Elena N. Atochina‐Vasserman, Lisa C. Lindesmith, Carmen Mirabelli, Nathan Ona, Erin K. Reagan, Paul D. Brewer-Jensen, Xiomara Mercado-López, Hamna Shahnawaz, J. Meshanni, Ishana Baboo, Michael L. Mallory, Mark R. Zweigart, Samantha R. May, Barbara L. Mui, Ying K. Tam, Christiane E. Wobus, Ralph S. Baric, Drew Weissman

Bibliographic record

Venuenpj Vaccines · 2024
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsAcuitas Therapeutics (Canada)
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthEmory University
KeywordsBivalent (engine)NorovirusVirologyImmune systemMessenger RNABiologyImmunologyNeutralizationVirusGeneChemistryGenetics

Abstract

fetched live from OpenAlex

Nucleoside-modified mRNA-LNP vaccines have revolutionized vaccine development against infectious pathogens due to their ability to elicit potent humoral and cellular immune responses. In this article, we present the results of the first norovirus vaccine candidate employing mRNA-LNP platform technology. The mRNA-LNP bivalent vaccine encoding the major capsid protein VP1 from GI.1 and GII.4 of human norovirus, generated high levels of neutralizing antibodies, robust cellular responses, and effectively protected human enteroids from infection by the most prevalent genotype (GII.4). These results serve as a proof of concept, demonstrating that a modified-nucleoside mRNA-LNP vaccine based on norovirus VP1 sequences can stimulate an immunogenic response in vivo and generates neutralizing antibodies capable of preventing viral infection in models of human gastrointestinal tract infection.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.344
Teacher spread0.306 · 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 designObservational
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

Citations11
Published2024
Admission routes1
Has abstractyes

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