Vesicular Stomatitis Virus-Based Vaccines Provide Cross-Protection against Andes and Sin Nombre Viruses
Bibliographic record
Abstract
Andes virus (ANDV) and Sin Nombre virus (SNV) are the main causative agents responsible for hantavirus cardiopulmonary syndrome (HCPS) in the Americas. HCPS is a severe respiratory disease with a high fatality rate for which there are no approved therapeutics or vaccines available. Some vaccine approaches for HCPS have been tested in preclinical models, but none have been tested in infectious models in regard to their ability to protect against multiple species of HCPS-causing viruses. Here, we utilize recombinant vesicular stomatitis virus-based (VSV) vaccines for Andes virus (ANDV) and Sin Nombre virus (SNV) and assess their ability to provide cross-protection in infectious challenge models. We show that, while both rVSVΔG/ANDVGPC and rVSVΔG/SNVGPC display attenuated growth as compared to wild type VSV, each vaccine is able to induce a cross-reactive antibody response. Both vaccines protected against both homologous and heterologous challenge with ANDV and SNV and prevented HCPS in a lethal ANDV challenge model. This study provides evidence that the development of a single vaccine against HCPS-causing hantaviruses could provide protection against multiple agents.
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 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) | 0.000 | 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".