Novel platform technology for modular mucosal vaccine that protects against streptococcus
Bibliographic record
Abstract
The upper respiratory tract (URT) is the major entry site for human pathogens and strategies to activate this network could lead to new vaccines capable of preventing infection with many pathogens. Group A streptococcus (GAS) infections, causing rheumatic fever, rheumatic heart disease, and invasive disease, are responsible for substantial morbidity and mortality. We describe an innovative vaccine strategy to induce mucosal antibodies of significant magnitude against peptide antigens of GAS using a novel biocompatible liposomal platform technology. The approach is to encapsulate free diphtheria toxoid (DT), a standard vaccine antigen, within liposomes as a source of helper T-cell stimulation while lipidated peptide targets for B-cells are separately displayed on the liposome surface. As DT is not physically conjugated to the peptide, it is possible to develop modular epitopic constructs that simultaneously activate IgA-producing B-cells of different and complementary specificity and function that together neutralize distinct virulence factors. An inflammatory cellular immune response is also induced. The immune response provides profound protection against streptococcal infection in the URT. The study describes a new vaccine platform for humoral and cellular immunity applicable to the development of vaccines against multiple mucosal pathogens.
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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) | 0.001 | 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) | 0.002 | 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 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".