Chitosan nanoparticle delivery of Influenza A Virus DNA vaccine enhances antibody class switching and abrogates weight loss post IAV challenge
Bibliographic record
Abstract
Abstract DNA vaccination presents a promising new approach for Influenza A Virus (IAV) vaccines, as they can be generated quickly in response to the viral antigenic shift and drift characteristic of IAV pandemic outbreaks. A DNA vaccination approach for IAV was examined using a plasmid encoding PR8 H1N1 hemagglutinin (HA) protein as a potential IAV vaccine. In addition to the HA antigen sequence the plasmid also encoded a sequence that when transcribed, activates the pattern recognition receptor RIG-I, improving innate immune activation. To increase the potential of this IAV DNA vaccine, our study focused on delivery of the plasmid to the respiratory tract using chitosan (CS) nanoparticles. CS, a mucoadhesive/mucopenetrating derivative of chitin, forms complexes with DNA to improve uptake and transfection of the plasmid by immune cells in the lung. In vitro, plasmid/CS complexes containing unbound CS induced bone marrow derived dendritic cell (BMDC) death. BMDC death was accompanied by a robust increase in inflammatory cytokine mRNA. Although BMDC death was abrogated by free CS removal, cytokine expression was also reduced. Alternative complexation methods are being explored to achieve an appropriate balance between cytokine production and cell death. In vivo intranasal prime-boost vaccination with low dose PR8 HA plasmid/CS nanoparticles resulted in PR8 specific IgM and total IgG antibody formation. Although weight loss was abrogated in plasmid/CS vaccinated mice, protection from lethal PR8 infection did not correlate with antibody level. By optimizing this vaccine approach, including dose, regime, and CS nanoparticle modification, critical insights into the development of rapidly deployable IAV vaccines could be gained.
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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".