Evaluation of a conjugate vaccine platform against enterotoxigenic Escherichia coli (ETEC), Campylobacter jejuni and Shigella
Bibliographic record
Abstract
Enterotoxigenic Escherichia coli (ETEC), Campylobacter jejuni (CJ), and Shigella sp. are major causes of bacterial diarrhea worldwide, but there are no licensed vaccines against any of these pathogens. Most current approaches to ETEC vaccines are based on recombinant proteins that are involved in virulence, particularly adhesins. In contrast, approaches to Shigella and CJ vaccines have included conjugate vaccines in which Shigella lipopolysaccharides (LPS) or CJ capsule polysaccharides are chemically conjugated to proteins. We have explored the feasibility of developing a multi-pathogen vaccine by using ETEC proteins as conjugating partners for CJ and Shigella polysaccharides. We synthesized three vaccines in which two CJ polysaccharides were conjugated to two recombinant ETEC adhesins based on CFA/I (CfaEB) and CS6 (CssBA), and LPS from Shigella flexneri was also conjugated to CfaEB. The vaccines were immunogenic in mice as monovalent, bivalent and trivalent formulations. Importantly, functional antibodies capable of inducing hemaglutination inhibition (HAI) of a CFA/I expressing ETEC strain were induced in all vaccines containing CfaEB. These data suggest that conjugate vaccines could be a platform for a multi-pathogen, multi-serotype vaccine against the three major causes of diarrheal disease worldwide.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".