Mucosal Vaccination with a Newcastle Disease Virus-Vectored Vaccine Reduces Viral Loads in SARS-CoV-2-Infected Cynomolgus Macaques
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged following an outbreak of unexplained viral illness in China in late 2019. Since then, it has spread globally causing a pandemic that has resulted in millions of deaths and has had enormous economic and social consequences. The emergence of SARS-CoV-2 saw the rapid and widespread development of a number of vaccine candidates worldwide, and this never-before-seen pace of vaccine development led to several candidates progressing immediately through clinical trials. Many countries have now approved vaccines for emergency use, with large-scale vaccination programs ongoing. Despite these successes, there remains a need for ongoing pre-clinical and clinical development of vaccine candidates against SARS-CoV-2, as well as vaccines that can elicit strong mucosal immune responses. Here, we report on the efficacy of a Newcastle disease virus-vectored vaccine candidate expressing SARS-CoV-2 spike protein (NDV-FLS) administered to cynomolgus macaques. Macaques given two doses of the vaccine via respiratory immunization developed robust immune responses and had reduced viral RNA levels in nasal swabs and in the lower airway. Our data indicate that NDV-FLS administered mucosally provides significant protection against SARS-CoV-2 infection, resulting in reduced viral burden and disease manifestation, and should be considered as a viable candidate for clinical development.
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) | 0.000 | 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".