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Record W2291480286 · doi:10.1186/s12917-016-0665-0

Assessment of yeast Saccharomyces cerevisiae component binding to Mycobacterium avium subspecies paratuberculosis using bovine epithelial cells

2016· article· en· W2291480286 on OpenAlexafffund
Ziwei Li, Qiumei You, Faisury Ossa, P. Mead, M. Quinton, Niel A. Karrow

Bibliographic record

VenueBMC Veterinary Research · 2016
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsLallemand (Canada)University of Guelph
FundersLallemandNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsMycobacterium avium subspecies paratuberculosisParatuberculosisSaccharomyces cerevisiaeMicrobiologyYeastBiologyMycobacteriumMycobacterium avium subsp. paratuberculosisSubspeciesVirologyGeneticsBacteriaZoology

Abstract

fetched live from OpenAlex

BACKGROUND: Since yeast Saccharomyces cerevisiae and its components are being used for the prevention and treatment of enteric diseases in different species, they may also be useful for preventing Johne's disease, a chronic inflammatory bowel disease of ruminants caused by Mycobacterium avium spp. paratuberculosis (MAP). This study aimed to identify potential yeast derivatives that may be used to help prevent MAP infection. The adherence of mCherry-labeled MAP to bovine mammary epithelial cell line (MAC-T cells) and bovine primary epithelial cells (BECs) co-cultured with yeast cell wall components (CWCs) from four different yeast strains (A, B, C and D) and two forms of dead yeast from strain A was investigated. RESULTS: The CWCs from all four yeast strains and the other two forms of dead yeast from strain A reduced MAP adhesion to MAC-T cells and BECs in a concentration-dependent manner after 6-h of exposure, with the dead yeast having the greatest effect. CONCLUSIONS: The following in vitro binding studies suggest that dead yeast and its' CWCs may be useful for reducing risk of MAP infection.

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 categoriesInsufficient payload (model declined to judge)
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 score0.595
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.179
GPT teacher head0.429
Teacher spread0.250 · 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.

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

Citations11
Published2016
Admission routes2
Has abstractyes

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