Immunological dynamics after subcutaneous immunization with a squalene‐based oil‐in‐water adjuvant
Bibliographic record
Abstract
Abstract The clinically successful adjuvant MF59 is used in seasonal influenza vaccines, which is proposed to enhance immunity by creating an immune‐competent microenvironment in the muscle that allows recruitment of immune cells that drive adaptive immune responses. Here, we examined whether the clinically successful adjuvants MF59/AddaVax could be used for subcutaneous use and how antigen delivery can be synergized with cellular dynamics at the vaccination site. Subcutaneous injection of AddaVax leads to thickening of the skin, characterized by a neutrophil‐monocyte recruitment sequence. Skin‐infiltrating CCR2 + Ly6C high monocytes showed differentiation to CD11b + Ly6C + MHCII + CD11c + CD64 + monocyte‐derived DCs over time in the hypodermal layers of the skin, expressing high levels of CD209a/mDC‐SIGN. Surprisingly, skin thickening was accompanied with increased white adipose tissue highly enriched with monocytes. Analysis of the skin‐draining lymph nodes revealed early increases in neutrophils and moDCs at 12 hours after injection and later increases in migratory cDC2s. Subcutaneous vaccination with AddaVax enhanced antigen‐specific CD8 + and CD4 + T cell responses, while moDC targeting using antigen‐coupled CD209a antibody additionally boosted humoral responses. Hence, oil‐in‐water emulsions provide an attractive immune modulatory adjuvants aimed at increasing cellular responses, as well as antibody responses when combined with moDC targeting.
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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) | 0.001 | 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".