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Record W2118647217 · doi:10.1139/y2012-039

MK-626, a dipeptidyl peptidase-4 inhibitor, does not improve the hyperglycemia or hyperinsulinemia of nonobese diabetic MKR mice

2012· article· en· W2118647217 on OpenAlexafffundvenue
Alpana Bhattacharjee, Emma M. Allister, Michael B. Wheeler

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2012
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsCanada Research Chairs
FundersMerck Canada
KeywordsInternal medicineEndocrinologyHyperinsulinemiaDipeptidyl peptidase-4 inhibitorIncretinType 2 diabetesInsulinMedicineDipeptidyl peptidase-4Dipeptidyl peptidaseGlucagon-like peptide-1GlucagonDiabetes mellitusInsulin resistanceChemistryEnzymeBiochemistry

Abstract

fetched live from OpenAlex

Dipeptidyl peptidase-4 (DPP-4) inhibitors increase circulating levels of incretin hormones, which can enhance insulin secretion and β cell function. The aim of this study was to evaluate the effectiveness of MK-626 (a novel DPP-4 inhibitor) to reduce the hyperglycemia and hyperinsulinemia of nonobese type 2 diabetic MKR mice. Twelve to 14-week-old hyperglycemic MKR mice were gavaged daily with MK-626 (3 mg/kg body weight) or vehicle (0.5% methyl cellulose (MC)) for 2 weeks. MK-626-treated mice displayed no change in body weight or adverse reactions, suggesting good tolerance of the drug. Fed blood glucose was significantly reduced over the 2-week experiment; however, it was also reduced in the MC group, suggesting an effect of gavage alone. Fed plasma insulin and glucagon levels and glucose tolerance of MK-626-treated mice were similar to those of MC mice. Therefore, treatment with MK-626 did not correct the prolonged hyperglycemia and impaired glucose tolerance of MKR mice.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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

Citations7
Published2012
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Physiology and Pharmacology→Same topicDiabetes Treatment and Management→French-language works237,207→