Effectiveness of VSV vectored SARS-CoV-2 spike when administered through intranasal, intramuscular or a combination of both
Bibliographic record
Abstract
A critical feature of the VSV vector platform is the ability to pseudotype the virus with different glycoproteins from other viruses, thus altering cellular tropism of the recombinant virus. The route of administration is critical in triggering local and systemic immune response and protection. Most of the vaccine platforms used at the forefront are administered by intramuscular injection. However, it is not known at what level ACE2 is expressed on the surface of skeletal muscle cells, which will have a significant impact on the efficiency of a VSV-SARS-CoV-2 spike vaccine to mount a protective immune response when administered intramuscularly. In this study, we investigate the immunogenicity and efficacy of a prime-boost immunization regimen administered intranasally (IN), intramuscularly (IM), or combinations of the two. We determined that the prime-boost combinations of IM followed by IN immunization (IM + IN) or IN followed by IN immunization (IN + IN) exhibited strong spike-specific IgG, IgA and T cell response in vaccinated K18 knock-in mice. Hamsters vaccinated with two doses of VSV expressing SARS-CoV-2 spike, both delivered by IN or IM + IN, showed strong protection against SARS-CoV-2 variants of concern Alpha and Delta. This protection was also observed in aged hamsters. Our study underscores the highly crucial role immunization routes have with the VSV vector platform to elicit a strong and protective 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) | 0.000 | 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".