Intranasal co-delivery of measles virus and mumps virus expressing nucleocapsid and prefusion spike proteins enhances protection against SARS-CoV-2 infection 2659
Bibliographic record
Abstract
Abstract Description While current SARS-CoV-2 vaccines have lowered the risk of death associated with COVID-19 disease, SARS-CoV-2 continues to mutate leading to new Variants of Concern (VoC). These mutations, focused in the S protein, can often lead to immune evasiveness, leaving current vaccines ineffective and leading to more investigation of more conserved proteins as potential antigens such as the nucleocapsid (N). In this study, we generated a bivalent live attenuated virus using mumps (MuV) Jeryl Lynn 2 (JL2) strain as a viral vector to express the N protein in combination with MuV-JL2 expressing WA1, the ancestral strain of SARS-CoV-2, or MuV-JL1 expressing XBB 1.5 spike protein stabilized by 6 prolines. The MuV-JL2-N co-delivered with MuV-JL2-WA1 was not only able to induce high anti-N and anti-WA1 S-specific antibodies but also resident memory T cells in the lungs of immunized IFNAR-/- mice. When MuV-JL2-N was co-delivered intranasally with MuV-JL1-XBB 1.5 in golden Syrian hamsters, hamsters could also induce high anti-N and anti-S XBB 1.5 IgG antibodies. When challenged against the Omicron JN.1 variant, hamsters immunized with the bivalent vaccine exhibited no viral titer in the lungs as opposed to hamsters immunized with either MuV-JL2-N and MuV-JL1-XBB 1.5 monovalent vaccines, which had an average viral titer of log 4.7 and log 3.5, respectively. Therefore, co-delivery of MuV-JL2-N and MuV-JL1-XBB 1.5 intranasally enhances protection against SARS-CoV-2 variants such as JN.1. Funding Sources NIH GR135608 Topic Categories Vaccines and Immunotherapy (VAC)
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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 | 0.001 |
| 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".