Application of a <i>Campylobacter jejuni</i> mouse infection model to test efficacy of a <i>C. jejuni</i> capsule conjugate vaccine delivered with a potent liposome adjuvant containing monophosphoryl lipid A and QS-21
Bibliographic record
Abstract
Abstract Campylobacter jejuni is a major cause of infectious diarrhea worldwide. Increasing incidence of C. jejuni is attributed to new non-culture based detection methods and antibiotic resistance is unfortunately on the rise, necessitating development of interventions. Therapeutics development like vaccines have been hampered by lack of a small animal model that recapitulates campylobacteriosis symptoms. To better facilitate vaccine efficacy testing, we adapted a recently-published mouse C. jejuni infection model to adult mice fed a zinc-deficient diet and pre-treated with antibiotics prior to oral infection with C. jejuni strain 81–176. Non-vaccinated infected mice develop diarrhea, lose weight and show increased expression of fecal inflammatory markers indicating development of campylobacteriosis. We tested whether an 81–176 C. jejuni capsule conjugate vaccine delivered with a potent liposome adjuvant containing monophosphoryl lipid A and QS-21 known as ALFQ could protect mice against 81–176. Vaccinated mice developed high levels of anti-CPS IgG1 and IgG2b titers and serum bactericidal responses against 81–176. Vaccinated infected mice were protected against development of diarrhea, did not lose weight, and had significantly lower levels of fecal inflammatory marker expression. Importantly, vaccinated infected animals were protected against C. jejuni colonization indicating that parenteral vaccination with a conjugate vaccine plus the ALFQ adjuvant may provide protection against both C. jejuni disease and colonization. These promising results support further development of a multivalent C. jejuni conjugate vaccine platform delivered with potent adjuvant systems for use in human clinical studies.
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".