Mucosally Delivered Dendritic Cells Activate T Cells Independently of IL-12 and Endogenous APCs
Bibliographic record
Abstract
In vitro manipulated dendritic cells (DC) have increasingly been used as a promising vaccine formulation against cancer and infectious disease. However, improved understanding of the immune mechanisms is needed for the development of safe and efficacious mucosal DC immunization. We have developed a murine model of respiratory mucosal immunization by using a genetically manipulated DC vaccine. Within 24 h of intranasal delivery, the majority of vaccine DCs migrated to the lung mucosa and draining lymph nodes and elicited a significant level of T cells capable of IFN-gamma secretion and CTL in the airway lumen as well as substantial T cell responses in the spleen. And such T cell responses were associated with enhanced protection against respiratory mucosal intracellular bacterial challenge. In comparison, parenteral i.m. DC immunization did not elicit marked airway luminal T cell responses and immune protection regardless of strong systemic T cell activation. Although repeated mucosal DC delivery boosted Ag-specific T cells in the airway lumen, added benefits to CD8 T cell activation and immune protection were not observed. By using MHC-deficient vaccine DCs, we further demonstrated that mucosal DC immunization-mediated CD8 and CD4 T cell activation does not require endogenous DCs. By using IL-12-deficient vaccine DCs, we also observed that IL-12(-/-) DCs failed to migrate to the lymph nodes but remained capable of T cell activation. Our observations indicate that mucosal delivery of vaccine DCs represents an effective approach to enhance mucosal T cell immunity, which may operate independent of vaccine IL-12 and endogenous DCs.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".