A T cell-targeted multi-antigen vaccine generates robust cellular and humoral immunity against SARS-CoV-2 infection
Bibliographic record
Abstract
SARS-CoV-2, the etiological agent behind the coronavirus disease 2019 (COVID-19) pandemic, has continued to mutate and create new variants with increased resistance against the WHO-approved spike-based vaccines. With a significant portion of the worldwide population still unvaccinated and with waning immunity against newly emerging variants, there is a pressing need to develop novel vaccines that provide broader and longer-lasting protection. To generate broader protective immunity against COVID-19, we developed our second-generation vaccinia virus-based COVID-19 vaccine, TOH-VAC-2, encoded with modified versions of the spike (S) and nucleocapsid (N) proteins as well as a unique poly-epitope antigen that contains immunodominant T cell epitopes from seven different SARS-CoV-2 proteins. We show that the poly-epitope antigen restimulates T cells from the PBMCs of individuals formerly infected with SARS-CoV-2. In mice, TOH-VAC-2 vaccination produces high titers of S- and N-specific antibodies and generates robust T cell immunity against S, N, and poly-epitope antigens. The immunity generated from TOH-VAC-2 is also capable of protecting mice from heterologous challenge with recombinant VSV viruses that express the same SARS-CoV-2 antigens. Altogether, these findings demonstrate the effectiveness of our versatile vaccine platform as an alternative or complementary approach to current vaccines.
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".