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Record W2912326630 · doi:10.3389/fmicb.2018.03312

Wheat Fermentation With Enterococcus mundtii QAUSD01 and Wickerhamomyces anomalus QAUWA03 Consortia Induces Concurrent Gliadin and Phytic Acid Degradation and Inhibits Gliadin Toxicity in Caco-2 Monolayers

2019· article· en· W2912326630 on OpenAlexaff
Hafiz Arbab Sakandar, Stan Kubow, Behnam Azadi, Rani Faryal, Barkat Ali, Shakira Ghazanfar, Umar Masood Quraishi, Muhammad Imran

Bibliographic record

VenueFrontiers in Microbiology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsGliadinPhytic acidChemistryFood scienceFermentationCaco-2BiochemistryDegradation (telecommunications)GlutenIn vitro

Abstract

fetched live from OpenAlex

Foods containing high amounts of either phytic acid or gliadin can pose a risk for development of iron deficiency and celiac disease, respectively. Present study was conducted to evaluate the effects of preselected gliadin degrading strains, Enterococcus mundtii QAUSD01 and Wickerhamomyces anomalus QAUWA03, on phytic acid and gliadin degradation in six wheat cultivars (Lasani 2008, Seher 2006, Chakwal 97, Shafaq 2006, Bars 2009, Barani 83). In addition, tight junction proteins, trans-epithelial resistance (TER) and ruffle formation in Caco-2 cells was evaluated relative to S. cerevisie–mediated fermentation and unfermented controls. Phytic acid degradation as demonstrated was observed in all six cultivars fermented with E. mundtii QAUSD01 and W. anomalus QAUWA03 consortia. Among the six cultivars, Shafaq 2006 showed relatively higher degradation of gliadin. Relative to the other tested wheat varieties, Lasani 2006 showed minimal toxic effects on Caco-2 cells in terms of ruffle formation, tight junction proteins and TER values, which can be attributed to extensive degradation of toxic gliadin fragments.

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 score0.438
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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