Nasal and pulmonary vaccine delivery using particulate carriers
Bibliographic record
Abstract
INTRODUCTION: Many human pathogens cause respiratory illness by colonizing and invading the respiratory mucosal surfaces. Preventing infection at local sites via mucosally active vaccines is a promising and rational approach for vaccine development. However, stimulating mucosal immunity is often challenging. Particulate adjuvants that can specifically target mucosal immune cells offer a promising opportunity to stimulate local immunity at the nasal and/or pulmonary mucosal surfaces. AREAS COVERED: This review analyzes the common causes of respiratory infections, the challenges in the induction of mucosal and systemic responses and current pulmonary and nasal mucosal vaccination strategies. The ability of various particulate adjuvant formulations, including lipid-based particles, polymers and other particulate systems, to be effectively utilized for mucosal vaccine delivery is discussed. EXPERT OPINION: Induction of antibody and cell-mediated mucosal immunity that can effectively combat respiratory pathogens remains a challenge. Particulate delivery systems can be developed to target mucosal immune cells and effectively present antigen to evoke a rapid and long-term local immunity in the respiratory mucosa. In particular, particulate delivery systems offer the versatility of being formulated with multiple adjuvants and antigenic cargo, and can be tailored to effectively prime immune responses across the mucosal barrier. The opportunity for rational design of novel subunit particulate vaccines is emerging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".