A DNA vaccine candidate provides protection against Rift Valley Fever virus in sheep under natural field conditions
Bibliographic record
Abstract
Rift Valley Fever virus (RVFV) is a mosquito-borne zoonotic pathogen, that causes significant morbidity and mortality in livestock, including high abortion rates in pregnant animals and elevated case fatality in neonates, representing a major threat to both animal and human health. Vaccination is the most effective countermeasure to reduce RVFV's impact. In this study, we designed a veterinary DNA vaccine encoding a consensus RVFV glycoprotein precursor (GPC), optimized for expression in sheep. The construct was evaluated for immunogenicity in mice and sheep and for protective efficacy in sheep raised under natural field conditions in Senegal, West Africa. The vaccine induced robust humoral responses characterized by high neutralizing antibody titers in both mice and sheep. Under natural exposure, vaccinated sheep showed reduced infection rates (3.2%) compared with controls (14.3%), and neutralizing antibody responses persisted for more than one year. Importantly, the vaccine was well tolerated, including in pregnant animals, with no adverse outcomes such as abortions or fetal abnormalities. These findings demonstrate that a DNA-based RVFV vaccine can elicit durable immunity and provide protection in livestock under real-world conditions. This study highlights the potential of DNA vaccines as a safe, effective, and affordable alternative to existing veterinary vaccines and supports their further development as a key strategy to reduce RVFV transmission and improve animal and human health outcomes in endemic regions.
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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) | 0.001 | 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".