Dendritic Cell Targeted Chitosan Nanoparticles for Nasal DNA Immunization against SARS CoV Nucleocapsid Protein
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Bibliographic record
Abstract
This work investigates the formulation and in vivo efficacy of dendritic cell (DC) targeted plasmid DNA loaded biotinylated chitosan nanoparticles for nasal immunization against nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus (SARS-CoV) as antigen. The induction of antigen-specific mucosal and systemic immune response at the site of virus entry is a major challenge for vaccine design. Here, we designed a strategy for noninvasive receptor mediated gene delivery to nasal resident DCs. The pDNA loaded biotinylated chitosan nanoparticles were prepared using a complex coacervation process and characterized for size, shape, surface charge, plasmid DNA loading and protection against nuclease digestion. The pDNA loaded biotinylated chitosan nanoparticles were targeted with bifunctional fusion protein (bfFp) vector for achieving DC selective targeting. The bfFp is a recombinant fusion protein consisting of truncated core-streptavidin fused with anti-DEC-205 single chain antibody (scFv). The core-streptavidin arm of fusion protein binds with biotinylated nanoparticles, while anti-DEC-205 scFv imparts targeting specificity to DC DEC-205 receptor. We demonstrate that intranasal administration of bfFp targeted formulations along with anti-CD40 DC maturation stimuli enhanced magnitude of mucosal IgA as well as systemic IgG against N protein. The strategy led to the detection of augmented levels of N protein specific systemic IgG and nasal IgA antibodies. However, following intranasal delivery of naked pDNA no mucosal and systemic immune responses were detected. A parallel comparison of targeted formulations using intramuscular and intranasal routes showed that the intramuscular route is superior for induction of systemic IgG responses compared with the intranasal route. Our results suggest that targeted pDNA delivery through a noninvasive intranasal route can be a strategy for designing low-dose vaccines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it