Topical CpG adjuvant enhances immune response to subcutaneous antigen by modulating the cutaneous lymph node environment (P4267)
Bibliographic record
Abstract
Abstract Current vaccines are mainly administered by intramuscular route but the skin harbors many immune cells and can be used as a site of immunization. Topical CpG oligodeoxynucleotide (ODN), a Toll-like receptor 9 (TLR9) agonist, improves humoral and cell-mediated immune responses to locally injected protein-based vaccines in mice but the mechanisms are still unclear. Topical CpG ODN is internalized by CD11c+ cells that are detected within the skin draining lymph nodes (SLN) at 48 hours post treatment. Ablation of TLR9 in the hematopoietic compartment of bone marrow chimeric mice abrogates antigen-specific CD8+ T cells production. TLR9 ablation in the stromal compartment also decreases topical CpG adjuvant effect. Topical CpG ODN differentially modulates the environment of SLN compared to subcutaneous administration within 24 hours of treatment (with 4-7 fold increased expression of 4 genes: Ccl4, Cxcl3, Ifng and Il11). A higher proportion of antigen-specific CD4+ T cells in the SLN express tissue-homing molecules (P- and E-selectin ligand) when adjuvant is administered topically compared to subcutaneously. Egress of lymphocytes from SLN is necessary for optimal CpG adjuvant effect using a contact hypersensitivity model. We propose that topical CpG ODN activates stromal cells as well as hematopoietic-derived cells in the skin, to subsequently modulate the environment of SLN. These changes enhance protective humoral and cell-mediated immune responses.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".