Development and Characterization of Recombinant Vesicular Stomatitis Virus (rVSV)-based Bivalent Vaccine Against COVID-19 Delta Variant and Influenza Virus
Bibliographic record
Abstract
Abstract COVID-19 and influenza are both highly contagious respiratory diseases with a wide range of severe symptoms and cause great disease burdens globally. It has become very urgent and important to develop a bivalent vaccine that is able to target these two infectious diseases simultaneously. In this study, we generated three attenuated replicating recombinant VSV (rVSV) vaccine candidates. These rVSV-based vaccines co-express SARS-CoV-2 Delta variant spike protein (SP) or the receptor binding domain (RBD) and four copies of the highly conserved M2 ectodomain (M2e) of influenza A fused with the Ebola glycoprotein DC-targeting/activation domain. Animal studies have shown that immunization with these bivalent rVSV vaccines induced efficient but variable levels of humoral and cell-mediated immune responses against both SARS-CoV-2 and influenza M2e protein. Significantly, our vaccine candidates induced production of high levels of neutralizing antibodies that protected cells against SARS-CoV-2 Delta and other SP-pseudovirus infections in culture. Furthermore, vaccination with the bivalent VSV vaccine via either intramuscular or intranasal route efficiently protected mice from the lethal challenge of H1N1 and H3N2 influenza viruses and significantly reduced viral load in the lungs. These studies provide convincing evidence for the high efficacy of this bivalent vaccine to prevent influenza replication and initiate robust immune responses against SARS-CoV-2 Delta variants. Further investigation of its efficacy to protect against SARS-CoV-2 Delta variants will provide substantial evidence for new avenues to control two contagious respiratory infections, COVID-19 and influenza.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".