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Record W4322487738 · doi:10.1016/j.isci.2023.106292

Monovalent and trivalent VSV-based COVID-19 vaccines elicit neutralizing antibodies and CD8+ T cells against SARS-CoV-2 variants

2023· article· en· W4322487738 on OpenAlexafffund
Kate A. Parham, Gyoung Nyoun Kim, Connor G. Richer, Marina Ninkov, Kunyu Wu, Nasrin Saeedian, Yue Li, Rasheduzzaman Rashu, Stephen D. Barr, Eric J. Arts, S. M. Mansour Haeryfar, C. Yong Kang, Ryan M. Troyer

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchWestern UniversityPublic Health AgencyPublic Health Agency of Canada
KeywordsVirologyBiologyNeutralizing antibodyEbola vaccineAttenuated vaccineVesicular stomatitis virusAntibodyHeterologousVirusImmunologyGeneticsGeneEbola virusVirulence

Abstract

fetched live from OpenAlex

Recombinant vesicular stomatitis virus (rVSV) vaccines expressing spike proteins of Wuhan, Beta, and/or Delta variants of SARS-CoV-2 were generated and tested for induction of antibody and T cell immune responses following intramuscular delivery to mice. rVSV-Wuhan and rVSV-Delta vaccines and an rVSV-Trivalent (mixed rVSV-Wuhan, -Beta, -Delta) vaccine elicited potent neutralizing antibodies (nAbs) against live SARS-CoV-2 Wuhan (USAWA1), Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529) viruses. Prime-boost vaccination with rVSV-Beta was less effective in this capacity. Heterologous boosting of rVSV-Wuhan with rVSV-Delta induced strong nAb responses against Delta and Omicron viruses, with the rVSV-Trivalent vaccine consistently effective in inducing nAbs against all the SARS-CoV-2 variants tested. All vaccines, including rVSV-Beta, elicited a spike-specific immunodominant CD8 + T cell response. Collectively, rVSV vaccines targeting SARS-CoV-2 variants of concern may be considered in the global fight against COVID-19.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

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

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 head0.092
GPT teacher head0.386
Teacher spread0.294 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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