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Record W2770441401 · doi:10.1111/ijfs.13605

Inhibitory activity towards human α‐amylase in wheat flour and gluten

2017· article· en· W2770441401 on OpenAlexaff
Pierre Gélinas, Fleur Gagnon

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2017
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsCegep de Saint HyacintheAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGlutenAcarboseAmylaseFood scienceWheat flourCultivarWheat glutenWheat breadWhole wheatStarchChemistryHuman healthBiologyAgronomyBiochemistryEnzymeMedicine

Abstract

fetched live from OpenAlex

Summary Wheat α‐amylase inhibitors (AI) have been targeted as potential triggers of noncoeliac gluten or wheat sensitivity (NCGS). The aim of this study was to determine AI activity towards α‐amylase from human source in wheat cultivars. Contrary to barley, buckwheat, or oats, high level of AI activity was found in wheat and, to a lesser extent, rye. AI activity (mean IC50 = 137 μg mL−1) did not vary with respect to ancient or recently developed wheat cultivars. Vital wheat gluten had very high and heat‐stable AI activity (mean IC50 = 23 μg mL−1), higher than wheat starch (˃10 000 μg mL−1) or acarbose (40 μg mL−1), a medication for the management of hyperglycaemia and potentially causing digestive disorders due to the accumulation of undigested carbohydrates in the intestine. Data suggest that eating raw wheat gluten, flour or dough could pose a health risk.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.029
GPT teacher head0.376
Teacher spread0.347 · 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 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

Citations19
Published2017
Admission routes1
Has abstractyes

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