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Record W2060292828 · doi:10.2174/1568026033452276

Protein Tyrosine Phosphatase 1B: A Novel Target for Type 2 Diabetes and Obesity

2003· review· en· W2060292828 on OpenAlexaff
Chidambaram Ramachandran, Brian P. Kennedy

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsMerck Canada Inc. (Canada)
Fundersnot available
KeywordsAutophosphorylationInsulin receptorInsulinEndocrinologyInternal medicineProtein tyrosine phosphataseType 2 diabetesIRS2Glucose homeostasisPhosphorylationBiologyInsulin receptor substrateInsulin-like growth factor 1 receptorDiabetes mellitusReceptorMedicineInsulin resistanceCell biologyProtein kinase A

Abstract

fetched live from OpenAlex

The identification of autophosphorylation of the insulin receptor as a pivotal component in the signal transduction induced by insulin, initiated the hunt to identify the tyrosine phosphatase(s) that were responsible for regulating dephosphorylation, and thus inactivation of the receptor. Compelling evidence for the existence of an insulin receptor specific PTP has come from the remarkable phenotype of the PTP1B deficient mouse. PTP1B deficient mice display an insulin sensitive phenotype and are able to maintain glucose homeostasis with about half the level of circulating insulin. In response to insulin administration PTP1B deficient mice have a significant increase in insulin receptor phosphorylation in liver and muscle compared to wild type controls. Unexpectedly these animals were also resistant to diet induced obesity. These observations strongly support PTP1B as a negative regulator of insulin action, thereby making it an ideal therapeutic target for intervention in type 2 diabetes and obesity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.047
GPT teacher head0.342
Teacher spread0.295 · 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 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

Citations53
Published2003
Admission routes1
Has abstractyes

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