Modulation of protection against <i>Mycobacterium tuberculosis</i> by adjuvants that elicit different T cell responses. (166.18)
Bibliographic record
Abstract
Abstract The use of an adjuvant within a vaccine can influence and direct the immune response to enable a desired outcome. A T helper 1 (Th1) response, including antigen-specific production of interferon-gamma (IFN-γ), is needed to protect against Mycobacterium tuberculosis. A successful subunit vaccine should include not only an appropriate antigen but also a proper adjuvant to ensure that a Th1 mediated cellular response is induced. Only a few adjuvants have been approved for use in human vaccines such as Alum and oil-in-water (o/w) based emulsions, including MF59 (Novartis), AS03 (GSK Biologics), AF03 (Sanofi) and liposomes (Crucell). These adjuvants primarily induce humoral responses. A new adjuvant in approved products is AS04, which combines the TLR-4 agonist monophosphoryl lipid A (MPL) with Alum. In this study we combine our candidate TB vaccine, ID93, with a synthetic TLR-4 agonist, glucopyranosyl lipid adjuvant (GLA) mixed with a stable o/w emulsion (SE). Both SE and GLA-SE induce potent cellular responses when combined with ID93 in mice. ID93/GLA-SE induced multifunctional CD4+ Th1 cell responses (IFN-γ, TNF-α, IL-2) in mice and protected both mice and guinea pigs against M. tuberculosis. In contrast, ID93/SE in the absence of GLA induced IL-5 and provided no protection, as assessed by bacterial burden, survival, and pathology. These results demonstrate the importance of properly formulating subunit vaccines with effective adjuvants for use against TB.
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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) | 0.003 | 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".