Protective Efficacy of a Bivalent Recombinant Vesicular Stomatitis Virus Vaccine in the Syrian Hamster Model of Lethal Ebola Virus Infection
Bibliographic record
Abstract
BACKGROUND: Outbreaks of filoviral hemorrhagic fever occur sporadically and unpredictably across wide regions in central Africa and overlap with the occurrence of other infectious diseases of public health importance. METHODS: As a proof of concept we developed a bivalent recombinant vaccine based on vesicular stomatitis virus (VSV) expressing the Zaire ebolavirus (ZEBOV) and Andes virus (ANDV) glycoproteins (VSVΔG/Dual) and evaluated its protective efficacy in the common lethal Syrian hamster model. Hamsters were vaccinated with VSVΔG/Dual and were lethally challenged with ZEBOV or ANDV. Time to immunity and postexposure treatment were evaluated by immunizing hamsters at different times prior to and post ZEBOV challenge. RESULTS: A single immunization with VSVΔG/Dual conferred complete and sterile protection against lethal ZEBOV and ANDV challenge. Complete protection was achieved with an immunization as close as 3 days prior to ZEBOV challenge, and 40% of the animals were even protected when treated with VSVΔG/Dual one day postchallenge. In comparison to the monovalent VSV vaccine, the bivalent vaccine has slightly reduced postexposure efficacy most likely due to its restricted lymphoid organ replication. CONCLUSIONS: Bivalent VSV vectors are a feasible approach to vaccination against multiple pathogens.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".