Fractionation of mRNA vaccines improves immune responses
Bibliographic record
Abstract
Abstract mRNA vaccines are used to prevent SARS-CoV-2 infection and are being explored for other infectious diseases as well as cancer. While they have shown high efficacy at preventing COVID-19, waning immunity has motivated the development of improved mRNA vaccine formulations. Vaccine fractionation has been explored previously in the context of viral vector and protein immunogens, but its effect on mRNA vaccines is still unclear. We interrogated whether fractionating an mRNA-SARS-CoV-2 spike vaccine over three days, as opposed to a single dose, would enhance immune responses in C57BL/6 mice. We compared the immune responses induced by three consecutive 1 μg doses over three days (fractionated vaccine) with a single 3 μg dose. Despite the equivalent overall dosage, the fractionated vaccine demonstrated superior CD8 T cell responses. This pattern of enhanced immune responses with the fractionated vaccine regimen was also observed with an mRNA-HIV vaccine, suggesting generalizability. Taken together, these findings suggest that prolonging the duration of antigen expression through mRNA vaccination could enhance immune responses, supporting the rationale for developing slow-release formulations for mRNA vaccines.
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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".