MétaCan
Menu
Back to cohort
Record W2397714095 · doi:10.1038/srep26511

Engineering the Campylobacter jejuni N-glycan to create an effective chicken vaccine

2016· article· en· W2397714095 on OpenAlexafffund
Harald Nothaft, Brandi Davis, Yee Ying Lock, María Elisa Pérez-Muñoz, Evgeny Vinogradov, Jens Walter, Colin Coros, Christine M. Szymanski

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsNational Research Council CanadaAlberta Glycomics CentreUniversity of Alberta
FundersUniversity of AlbertaDalhousie UniversityKeio UniversityAlberta Innovates - Technology Futures
KeywordsCampylobacter jejuniMicrobiologyBiologyGlycoconjugateGlycanCampylobacterEscherichia coliVaccinationVirologyBacteriaGeneGlycoproteinBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Campylobacter jejuni is a predominant cause of human gastroenteritis worldwide. Source-attribution studies indicate that chickens are the main reservoir for infection, thus elimination of C. jejuni from poultry would significantly reduce the burden of human disease. We constructed glycoconjugate vaccines combining the conserved C. jejuni N-glycan with a protein carrier, GlycoTag, or fused to the Escherichia coli lipopolysaccharide-core. Vaccination of chickens with the protein-based or E. coli-displayed glycoconjugate showed up to 10-log reduction in C. jejuni colonization and induced N-glycan-specific IgY responses. Moreover, the live E. coli vaccine was cleared prior to C. jejuni challenge and no selection for resistant campylobacter variants was observed. Analyses of the chicken gut communities revealed that the live vaccine did not alter the composition or complexity of the microbiome, thus representing an effective and low-cost strategy to reduce C. jejuni in chickens and its subsequent entry into the food chain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.216
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations87
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicSalmonella and Campylobacter epidemiologyFrench-language works237,207