MétaCan
Menu
Back to cohort
Record W4294920542 · doi:10.1128/jvi.01337-22

A Recombinant VSV-Based Bivalent Vaccine Effectively Protects against Both SARS-CoV-2 and Influenza A Virus Infection

2022· article· en· W4294920542 on OpenAlexafffund
Zhujun Ao, Maggie Jing Ouyang, Titus Abiola Olukitibi, Bryce M. Warner, Robert Vendramelli, Thang Truong, Courtney Meilleur, Manli Zhang, Sam Kung, Keith R. Fowke, Darwyn Kobasa, Xiaojian Yao

Bibliographic record

VenueJournal of Virology · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsPublic Health Agency of CanadaUniversity of Manitoba
FundersCanadian Institutes of Health ResearchGovernment of CanadaResearch Manitoba
KeywordsVirologyBiologyBivalent (engine)VirusEctodomainVesicular stomatitis virusInfluenza vaccineEbola vaccineRecombinant DNAAttenuated vaccineEbola virusGeneticsVirulenceGeneReceptor

Abstract

fetched live from OpenAlex

Given that both COVID-19 and influenza are preferably transmitted through respiratory droplets during the same seasons, it is highly advantageous to develop a bivalent vaccine that could simultaneously protect against both COVID-19 and influenza. In this study, we generated the attenuated replicating recombinant vesicular stomatitis virus (rVSV)-based vaccine candidates that target both spike protein of SARS-Cov-2 Delta variant and the conserved influenza M2 domain. Importantly, these vaccine candidates effectively protected hamsters or mice against the challenges of SARS-CoV-2 Delta variant and lethal H1N1 and H3N2 influenza viruses and significantly reduced respiratory viral loads.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.352
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of VirologySame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207