Influence of PapMV nanoparticles on the kinetics of the antibody response to flu vaccine
Bibliographic record
Abstract
BACKGROUND: The addition of an adjuvant to a vaccine is a promising approach to increasing strength and immunogenicity towards antigens. Despite the fact that adjuvants have been used in vaccines for decades, their mechanisms of action and their influence on the kinetics of the immune response are still not very well understood. The use of papaya mosaic virus (PapMV) nanoparticles-a novel TLR7 agonist-was recently shown to improve and broaden the immune response directed to trivalent inactivated flu vaccine (TIV) in mice and ferrets. RESULTS: We investigated the capacity of PapMV nanoparticles to increase the speed of the immune response toward TIV. PapMV nanoparticles induced a faster and stronger humoral response to TIV that was measured as early as 5 days post-immunization. The addition of PapMV nanoparticles was shown to speed up the differentiation of B-cells into early plasma cells, and increased the growth of germinal centers in a CD4+ dependent manner. TIV vaccination with PapMV nanoparticles as an adjuvant protected mice against a lethal infection as early as 10 days post-immunization. CONCLUSION: In conclusion, PapMV nanoparticles are able to accelerate a broad humoral response to TIV. This property is of the utmost importance in the field of vaccination, especially in the case of pandemics, where populations need to be protected as soon as possible after vaccination.
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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 | 0.001 |
| 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 | 0.000 |
| 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".