Exploring the Ability of Meningococcal Vaccines to Elicit Mucosal Immunity: Insights from Humans and Mice
Bibliographic record
Abstract
causes a devastating invasive disease but is also a normal colonizer of the human nasopharynx. Due to the rapid progression of disease, the best tool to protect individuals against meningococcal infections is immunization. Clinical experience with polysaccharide conjugate vaccines has revealed that an ideal meningococcal vaccine must prevent both invasive disease and nasal colonization, which confers herd immunity. However, not all meningococcal vaccines are equal in their ability to prevent nasal colonization, for unknown reasons. Herein, we describe recent efforts to utilize humanized mouse models to understand the impact of different meningococcal vaccines on nasal colonization. These mice are susceptible to nasal colonization, and they become immune following live nasal infection or immunization with matched capsule-conjugate or protein-based vaccines, replicating findings from human work. We bring together insights regarding meningococcal colonization and immunity from clinical work with findings using humanized mouse models, providing new perspective into the different determinants of mucosal versus systemic immunity. Then, we use this as a framework to help focus future studies toward understanding key mechanistic aspects left unresolved, including the bacterial factors required for colonization and immune evasion, determinants of nasal mucosal protection, and characteristics of an ideal meningococcal vaccine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".