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Record W2065131269 · doi:10.1042/bj20020393

Rapid insertion of GLUT2 into the rat jejunal brush-border membrane promoted by glucagon-like peptide 2

2002· article· en· W2065131269 on OpenAlexafffund
Anita G. Au, Alina GUPTA, Paul J Schembri, Chris I. Cheeseman

Bibliographic record

VenueBiochemical Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchFondation pour la Recherche Médicale
KeywordsGLUT2Brush borderJejunumChemistryInternal medicineGlucose transporterFructoseEndocrinologyIn vivoIntestinal villusBiologySmall intestineBiochemistryInsulinMembraneMedicine

Abstract

fetched live from OpenAlex

A possible role for GLUT2 transiently expressed in the rat jejunal brush-border membrane (BBM) under the influence of glucagon-like peptide 2 (GLP-2) was investigated using in vivo perfusion of the intestinal lumen as well as isolation of membrane proteins and immunohistochemistry. A 1 h vascular infusion of GLP-2 in vivo doubled the rate of fructose absorption and this increase could be blocked by luminal phloretin. Immunohistochemistry of frozen sections of rat jejunum showed the expression of GLUT2 in both the basolateral and BBMs of mature enterocytes. Perfusion of the intestinal lumen with 50 mM D-glucose or vascular infusion of 800 pM GLP-2 for 1 h increased the expression of GLUT2 in the BBM. Quantification of these changes using Western blotting of biotinylated surface-exposed protein showed a doubling of the expression of GLUT2 in the BBM, but the effects of glucose and GLP-2 were not additive. These results indicate that vascular GLP-2 can promote the insertion of GLUT2 into the rat jejunal BBM providing a low-affinity/high-capacity route of entry for dietary hexoses.

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 score0.003

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 head0.014
GPT teacher head0.249
Teacher spread0.236 · 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

Citations172
Published2002
Admission routes2
Has abstractyes

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