Trivalent nucleoside-modified mRNA vaccine yields durable memory B cell protection against genital herpes in preclinical models
Bibliographic record
Abstract
Nucleoside-modified mRNA vaccines have gained global attention because of COVID-19. We evaluated a similar vaccine approach for preventing a chronic, latent genital infection rather than an acute respiratory infection. We used animal models to compare an HSV-2 trivalent nucleoside-modified mRNA vaccine with the same antigens prepared as proteins, with an emphasis on antigen-specific memory B cell responses and immune correlates of protection. In guinea pigs, serum neutralizing-antibody titers were higher at 1 month and declined far less by 8 months in mRNA- compared with protein-immunized animals. Both vaccines protected against death and genital lesions when infected 1 month after immunization; however, protection was more durable in the mRNA group compared with the protein group when infected after 8 months, an interval representing greater than 15% of the animal's lifespan. Serum and vaginal neutralizing-antibody titers correlated with protection against infection, as measured by genital lesions and vaginal virus titers 2 days after infection. In mice, the mRNA vaccine generated more antigen-specific memory B cells than the protein vaccine at early times after immunization that persisted for up to 1 year. High neutralizing titers and robust B cell immune memory likely explain the more durable protection by the HSV-2 mRNA vaccine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".