PapMV nanoparticles improve mucosal immune responses to the trivalent inactivated flu vaccine
Bibliographic record
Abstract
BACKGROUND: Trivalent inactivated flu vaccines (TIV) are currently the best means to prevent influenza infections. However, the protection provided by TIV is partial (about 50%) and it is needed to improve the efficacy of protection. Since the respiratory tract is the main site of influenza replications, a vaccine that triggers mucosal immunity in this region can potentially improve protection against this disease. Recently, PapMV nanoparticles used as an adjuvant in a formulation with TIV administered by the subcutaneous route have shown improving the immune response directed to the TIV and protection against an influenza challenge. FINDINGS: In the present study, we showed that intranasal instillation with a formulation containing TIV and PapMV nanoparticles significantly increase the amount of IgG, IgG2a and IgA in lungs of vaccinated mice as compared to mice that received TIV only. Instillation with the adjuvanted formulation leads to a more robust protection against an influenza infection with a strain that is lethal to mice vaccinated with the TIV. CONCLUSIONS: We demonstrate for the first time that PapMV nanoparticles are an effective and potent mucosal adjuvant for 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 | 0.000 |
| 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 | 0.000 |
| 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".