Multilayer Adjuvanted Influenza Protein Nanoparticles Improve Intranasal Delivery and Antigen-Specific Immunity
Bibliographic record
Abstract
Intranasal vaccination is a desired route for protection against influenza viruses by mucosal and systemic immunity. However, the nasal mucosa impedes the intranasal delivery of vaccines. Here, we formulated layer-by-layer (LBL) influenza vaccine nanoparticles for effective intranasal delivery by coating them with alternating mucoadhesive cationic chitosan and muco-inert anionic CpG adjuvants. The nanoparticle cores were formed by desolvating influenza M2e antigen and coating it with hemagglutinin (HA) antigen via biotin-streptavidin conjugation. LBL modification promoted nasal delivery and interaction with the resident immune cells. Intranasal administration with LBL nanoparticles significantly improved cellular and humoral immune responses against HA and M2e including high IgA titers, a hallmark of potent mucosal immunity and persistence of immune responses. Distinct trends for antigen-specific immune responses were observed for different routes of vaccination. The enhanced immune responses conferred mice protection against the influenza challenge and prominently reduced viral titers, demonstrating the effectiveness of intranasal LBL vaccine nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".