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Record W4404041423 · doi:10.3389/fvets.2024.1474549

Predisposition factors and control strategies of avian pathogenic Escherichia coli in laying hens

2024· review· en· W4404041423 on OpenAlexafffund
Paul Kibaba Waliaula, Elijah G. Kiarie, Moussa S. Diarra

Bibliographic record

VenueFrontiers in Veterinary Science · 2024
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
FundersNatural Sciences and Engineering Research Council of CanadaOntario Agri-Food Innovation Alliance
KeywordsPathogenic Escherichia coliBiologyEscherichia coliGut microfloraImmune systemPoultry farmingAntibioticsGut floraMicrobiologyBacteriaImmunologyEcology

Abstract

fetched live from OpenAlex

Shift in laying hens housing from conventional cage-based systems to alternatives has impacted their health and performance. Microorganisms colonize young chick in the early stages of their physiological and immune development. These colonizing microbes originate from parent and the environment. Escherichia coli is among the normal gut colonizing bacteria however, some E. coli strains known as avian pathogenic E. coli (APEC), cause local or systemic infections (colibacillosis) responsible of significant economic losses to the poultry industry. Potential APEC strains and other poultry gut microbiota are influenced by several factors such as housing system, and the use of feed additives (prebiotics, probiotics, symbiotic, among others). This review will discuss the status of pullets and layers immunity, gut health, and predisposing factors of colibacillosis. Dietary interventions and some colibacillosis mitigation strategies in pullets and laying hens are reviewed and discussed. With the development of sequencing technologies and the use of feed additives as alternatives to antibiotics, future studies need to understand some of the complex associations between the feed additives, the rearing environment, and their selective pressure on gut microbiota, including E. coli , and their impacts on immune development in pullets and hens.

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: Review · Consensus signal: none
Teacher disagreement score0.637
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.040
GPT teacher head0.289
Teacher spread0.249 · 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
GenreReview

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

Citations11
Published2024
Admission routes2
Has abstractyes

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