Monovalent and trivalent VSV-based COVID-19 vaccines elicit neutralizing antibodies and CD8+ T cells against SARS-CoV-2 variants
Bibliographic record
Abstract
Recombinant vesicular stomatitis virus (rVSV) vaccines expressing spike proteins of Wuhan, Beta, and/or Delta variants of SARS-CoV-2 were generated and tested for induction of antibody and T cell immune responses following intramuscular delivery to mice. rVSV-Wuhan and rVSV-Delta vaccines and an rVSV-Trivalent (mixed rVSV-Wuhan, -Beta, -Delta) vaccine elicited potent neutralizing antibodies (nAbs) against live SARS-CoV-2 Wuhan (USAWA1), Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529) viruses. Prime-boost vaccination with rVSV-Beta was less effective in this capacity. Heterologous boosting of rVSV-Wuhan with rVSV-Delta induced strong nAb responses against Delta and Omicron viruses, with the rVSV-Trivalent vaccine consistently effective in inducing nAbs against all the SARS-CoV-2 variants tested. All vaccines, including rVSV-Beta, elicited a spike-specific immunodominant CD8 + T cell response. Collectively, rVSV vaccines targeting SARS-CoV-2 variants of concern may be considered in the global fight against COVID-19.
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 | 0.001 |
| 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".