A T cell-targeted multi-antigen vaccine generates robust cellular and humoral immunity against SARS-CoV-2 infection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it