Low hemagglutinin antigen dose influenza vaccines adjuvanted with AS03 alter the long-term immune responses in BALB/c mice
Bibliographic record
Abstract
We investigated the long-term immune profiles of dose-sparing, AS03-adjuvanted vaccines compared to a traditional high-dose, unadjuvated influenza vaccine formulation. BALB/c mice received 2 IM injections of influenza A/Uruguay/716/2007 (H3N2) split vaccine antigen: high-dose (HD) (3 µg hemagglutinin (HA)/dose) or low-dose (LD) formulations (0.03 µg or 0.003 µg HA) with AS03 and were followed to 34 weeks post-boost (pb). We examined serologic responses, spleen and bone marrow (BM) HA-specific antibody-secreting cells (ASCs) by ELISpot, influenza-specific cytokine/chemokine production in re-stimulated splenocytes by multiplex ELISA, and antigen-specific CD4+ T cells that express cytokines (IL-2, IFNγ, TNFα and IL-5) by flow cytometry. All formulations elicited robust serum antibody titers that persisted for at least 34 weeks. The number of antigen-specific ASCs in the spleen and BM were higher in the 2 LD +AS03 groups, but despite having fewer ASCs, the average spot size in the HD-unadjuvanted group was larger at later time-points, suggesting greater antibody production per cell. Striking differences in the long-term profiles induced by the different vaccine formulations may contribute to these different ASC profiles. The HD-unadjuvanted vaccine elicited strong Th2 cytokines during the first 6 weeks pb but LD+AS03 groups generated broader, more durable responses at later timepoints. Finally, the 0.03 µg HA+AS03 group generated the greatest number of antigen-specific CD4+ T cells and the highest percentage of poly-functional cells that expressed 2 or more cytokines. Although all of the tested vaccines induced durable antibody responses, we show that different vaccine formulations (dose-sparing, adjuvant) generate distinct long-term immune profiles. Furthermore, our data suggest that the different profiles may be generated through unique mechanisms.
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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 | 0.001 | 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".