Quillaja brasiliensis nanoparticle adjuvant formulation improves the efficacy of an inactivated trivalent influenza vaccine in mice
Bibliographic record
Abstract
The threat of viral influenza infections has sparked research efforts to develop vaccines that can induce broadly protective immunity with safe adjuvants that trigger robust immune responses. Here, we demonstrate that subcutaneous or intranasal delivery of a seasonal trivalent influenza vaccine (TIV) adjuvanted with the Quillaja brasiliensis saponin-based nanoparticle (IMXQB) increases the potency of TIV. The adjuvanted vaccine (TIV-IMXQB) elicited high levels of IgG2a and IgG1 antibodies with virus-neutralizing capacity and improved serum hemagglutination inhibition titers. The cellular immune response induced by TIV-IMXQB suggests the presence of a mixed Th1/Th2 cytokine profile, antibody-secreting cells (ASCs) skewed toward an IgG2a phenotype, a positive delayed-type hypersensitivity (DTH) response, and effector CD4+ and CD8+ T cells. After challenge, viral titers in the lungs were significantly lower in animals receiving TIV-IMXQB than in those inoculated with TIV alone. Most notably, mice vaccinated intranasally with TIV-IMXQB and challenged with a lethal dose of influenza virus were fully protected against weight loss and lung virus replication, with no mortality, whereas, among animals vaccinated with TIV alone, the mortality rate was 75%. These findings demonstrate that TIV-IMXQB improved the immune responses to TIV, and, unlike the commercial vaccine, conferred full protection against influenza challenge.
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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".