Vesicular Stomatitis Virus–Based Ebola Vaccines With Improved Cross-Protective Efficacy
Bibliographic record
Abstract
For Ebola virus (EBOV), 4 different species are known: Zaire, Sudan, Côte d'Ivoire, and Reston ebolavirus. The newly discovered Bundibugyo ebolavirus has been proposed as a 5th species. So far, no cross-neutralization among EBOV species has been described, aggravating progress toward cross-species protective vaccines. With the use of recombinant vesicular stomatitis virus (rVSV)-based vaccines, guinea pigs could be protected against Zaire ebolavirus (ZEBOV) infection only when immunized with a vector expressing the homologous, but not a heterologous, EBOV glycoprotein (GP). However, infection of guinea pigs with nonadapted wild-type strains of the different species resulted in full protection of all animals against subsequent challenge with guinea pig-adapted ZEBOV, showing that cross-species protection is possible. New vectors were generated that contain EBOV viral protein 40 (VP40) or EBOV nucleoprotein (NP) as a second antigen expressed by the same rVSV vector that encodes the heterologous GP. After applying a 2-dose immunization approach, we observed an improved cross-protection rate, with 5 of 6 guinea pigs surviving the lethal ZEBOV challenge if vaccinated with rVSV-expressing SEBOV-GP and -VP40. Our data demonstrate that cross-protection between the EBOV species can be achieved, although EBOV-GP alone cannot induce the required immune response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".