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Intestinal Colonization of Campylobacter jejuni and Its Hepatic Dissemination Are Associated with Local and Systemic Immune Responses in Broiler Chickens

2023· article· en· W4382401975 on OpenAlexafffund
Sophie Chagneau, Marie‐Lou Gaucher, Philippe Fravalo, William Thériault, Alexandre Thibodeau

Bibliographic record

VenueMicroorganisms · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCampylobacter jejuniImmune systemCampylobacterBiologyImmunityColonizationContext (archaeology)ImmunologyBroilerMicrobiologyInnate immune systemBacteria

Abstract

fetched live from OpenAlex

Campylobacter jejuni is an important foodborne pathogen. Despite the lack of clinical signs associated with its colonization in poultry, it has been reported to interact with the intestinal immune system. However, little is known about the interaction between C. jejuni and the chicken immune system, especially in the context of hepatic dissemination. Therefore, to follow up on our previous study showing intestinal colonization and hepatic spread of C. jejuni, cecal tonsils and liver samples were collected from these birds to determine the mRNA levels of chemokines and cytokines. Serum samples were also collected to determine serum amyloid A (SAA) concentrations and specific IgY titers. Lack of Th17 induction was observed in the cecal tonsils of only the liver-contaminated groups. This hepatic dissemination was accompanied by innate, Th1 and Th2 immune responses in livers, as well as an increase in SAA concentrations and specific IgY levels in sera. Campylobacter appears to be able to restrain the induction of the chicken gut immunity in particular conditions, possibly enhancing its hepatic dissemination and thus eliciting systemic immune responses. Although Campylobacter is often recognized as a commensal-like bacterium in chickens, it seems to modulate the gut immune system and induce systemic immunity.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 designObservational
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

Citations8
Published2023
Admission routes2
Has abstractyes

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