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Record W4296630994 · doi:10.1093/jas/skac247.239

244 Application of the Escherichia Coli Challenge Model in Developing Strategies to Improve gut Health and Function in Weaned Pigs

2022· article· en· W4296630994 on OpenAlexaff
Martin Nyachoti, Jin‐Young Lee, Chengbo Yang

Bibliographic record

VenueJournal of Animal Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEnterotoxigenic Escherichia coliAntibioticsDiseasePsychological interventionEscherichia coliDiarrheaBiotechnologyMedicineWeaningEnvironmental healthBiologyAnimal scienceEnterotoxinMicrobiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Post-weaning diarrhea in piglets, caused primarily by enterotoxigenic Escherichia coli is a major cause of productive and economic losses to the swine industry. Traditionally, the swine industry has managed this disease by utilizing starter diets containing highly digestible and specialized ingredients such as spray-dried porcine plasma and sub-therapeutic levels of antibiotics as growth promoters. However, there has intense public pressure to discontinue the use of animal products and antibiotics in pig diets for fear that these products represent a risk to human health. This has sparked great interest in swine nutrition is to identify effective, safe, and environmentally friendly nutritional interventions that could be used in place of in-feed antibiotics. This requires that any such intervention is rigorously tested under conditions that represent those that are encountered in production settings. To this end, the Escherichia coli K88 (or F4) disease challenge model has been used to test the potential of these interventions to support gut health and function in piglets and to allow investigations into the mode of action underlying any such effects. Although this model has been applied successfully to examine the efficacy of different nutritional interventions, including diet formulation strategies and addition of various feed additives, the degree of success achieved varies due to among other factors such as dose used, age of the pigs, and study duration. Being a disease challenge model, animal care oversight, regulatory requirements for Containment Level II facilities, and trial site managements to avoid cross-contamination among treatment groups add to the complexity of protocols for running trials involving this model. Nonetheless, the enterotoxigenic Escherichia coli model has been an effective tool in our hands to test the efficacy of various nutritional interventions in protecting piglets against post-weaning diarrhea disease and to elucidate their modes of action.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.271
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

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