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Alpha‐glucogenic activity of mammalian mucosal enzymes on different disaccharides

2011· article· en· W3163819607 on OpenAlexaff
Byung‐Hoo Lee, Roberto Quexada‐Calvillo, Buford L. Nichols, David R. Rose, Bruce R. Hamaker

Bibliographic record

VenueThe FASEB Journal · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDigestive system and related health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIsomaltoseMaltoseChemistrySucraseMaltaseBiochemistryEnzymeDisaccharidaseHydrolysisSucroseCarbohydrateAlpha-glucosidaseProtein subunit

Abstract

fetched live from OpenAlex

Small intestine mucosal maltase‐glucoamylase (MGAM) and sucrase‐isomaltase (SI) have two subunits each (N‐ and C‐ terminal) that are involved in the digestion of glycemic carbohydrates to glucose and fructose from diets. In this study, recombinant MGAM and SI enzymes were reacted with disaccharides, namely kojibiose, nigerose, maltose, and isomaltose each containing two glucose molecules as well as sucrose, but with different á‐linkages, to elucidate their digestion abilities. Reactions were carried at 37°C in 10 mM PBS and one unit (U) enzyme activity was defined as 1 μg of glucose released from maltose per 10 min. After enzyme reactions, released glucose was measured by the GOPOD method. Notably, each of the four subunits had á‐(1, 2), (1, 3) and (1, 4) hydrolysis activities, but N‐SI at 100 U was the only one to have á‐(1, 6) hydrolysis activity. Moreover, every subunit had á‐(1, 6) hydrolysis activity at higher enzyme concentration (above 2500 U of subunits except N‐SI). Each subunit had á‐glucosidase activity on different glycosidic linkages though maltase activity was at least 25 times greater than isomaltase activity. Also, only C‐SI and C‐MGAM showed sucrase activity with relatively lower than maltase activity. This is the first report of á‐glucogenic activity of disaccharides for each mucosal enzyme subunit and provides a new view of carbohydrate digestion model in the small intestine. Grant Funding Source : CNRC/ARS/USDA

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.291

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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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