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Record W2156788906 · doi:10.1517/14728222.12.10.1265

Targeting glycogen synthase kinase-3 (GSK-3) in the treatment of Type 2 diabetes

2008· review· en· W2156788906 on OpenAlexafffund
Katrina MacAulay, James R. Woodgett

Bibliographic record

VenueExpert Opinion on Therapeutic Targets · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsGSK-3Glycogen synthaseRegulatorDiabetes mellitusEnzymeType 2 diabetesBiologyGlycogenInsulin resistanceGSK3BGlucose homeostasisKinaseCell biologyPharmacologyBiochemistryChemistryEndocrinologyGene

Abstract

fetched live from OpenAlex

BACKGROUND: In spite of its rather specific name, glycogen synthase kinase-3 (GSK-3) is an eclectic cellular regulator that modulates an array of processes from nuclear transcription, to neurological functions and metabolism. The enzyme is also a focal point for diverse signaling pathways that act to suppress its activity. OBJECTIVES: To review recent evidence supporting the important role GSK-3 plays in glucose homeostasis and discuss the therapeutic potential of inhibiting this enzyme in the treatment of diabetes and insulin resistance. RESULTS/CONCLUSION: Despite its pleiotropic nature, GSK-3 has significant promise as a target for diabetes due to functional partitioning of the enzyme, tissue-selectivity and acute dosage-dependency of effects of inhibition, suggesting useful therapeutic windows.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.338
Teacher spread0.286 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations131
Published2008
Admission routes2
Has abstractyes

Explore more

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