Mature Dendritic Cell-Derived Extracellular Vesicles are Potent Mucosal Adjuvants for Influenza Hemagglutinin Vaccines
Bibliographic record
Abstract
Immune cell-derived extracellular vesicles (EVs) possess intrinsic immunomodulatory properties, making them potential vaccine adjuvants. Here, we show that EVs from mature bone marrow-derived dendritic cells (mDC-EVs), rather than those from immature dendritic cells (imDC-EVs), are potent mucosal adjuvants for influenza hemagglutinin (HA) vaccines. In vitro, mDC-EVs exhibited intriguing immune-stimulating effects on various antigen-presenting cells, including DCs, macrophages, and B cells. Furthermore, intranasal immunization with mDC-EVs-adjuvanted A/Aichi/2/1968 (H3N2) HA (H3+mDC-EVs) significantly enhanced and expanded both systemic and mucosal antibody and cellular immune responses in female Balb/c mice. These responses offered complete protection against bodyweight loss following homologous and heterologous virus challenges. Mechanistically, H3+mDC-EVs immunization promoted enhanced airway immune cell recruitment, distinct antigen cellular uptake, and rapid activation of B and T cells within 24 h. It also induced robust germinal center reactions and antigen-experienced memory T-cell responses in lung-draining mediastinal lymph nodes 14 days postimmunization. Given their biocompatibility and solid adjuvanticity, mDC-EVs represent a promising adjuvant candidate for mucosal vaccine development.
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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) | 0.000 | 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 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".