CpG-mediated augmentation of CD8+ T-cell responses in mice is attenuated by a water-in-oil emulsion (Montanide ISA-51) but enhanced by an oil-in-water emulsion (IDRI SE)
Bibliographic record
Abstract
Adjuvants are a key component in enhancing immunogenicity of vaccines and play a vital role in facilitating the induction of the correct type of immunity required for each vaccine to be optimally efficacious. Several different adjuvants are found in licensed vaccines, and many others are in pre-clinical or clinical testing. Agonists for TLRs are potent activators of the innate immune system and some, such as CpG (TLR9 agonist), are particularly good for promoting cellular immunity because of the induction of Th1 cytokines. Emulsions that have both delivery and adjuvant properties are classified as water-in-oil (W/O) or oil-in-water (O/W) formulations. The W/O emulsion Montanide ISA-51, often combined with CpG, has been widely tested in cancer vaccine clinical trials. Squalene-based O/W emulsions are in licensed influenza vaccines, and T-cell responses have been assessed pre-clinically. No clinical study has compared the two types of emulsions, and the continued use of W/O with CpG in cancer vaccines may be because the lack of single adjuvant controls has masked the interference issue. These findings may have important implications for the development of vaccines where T-cell immunity is considered essential, such as those for cancer and chronic infections. Using particulate (hepatitis B surface antigen) and soluble protein (ovalbumin) antigen, we show in mice that a W/O emulsion (ISA-51) abrogates CpG-mediated augmentation of CD8(+) T-cell responses, whereas a squalene-based O/W emulsion significantly enhanced them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".