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Record W3011302422 · doi:10.3390/cells9030706

Role of c-Jun N-terminal Kinase (JNK) in Obesity and Type 2 Diabetes

2020· review· en· W3011302422 on OpenAlexafffund
Justin Hou Ming Yung, Adria Giacca

Bibliographic record

VenueCells · 2020
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsInsulin resistanceType 2 diabetesDiabetes mellitusEndocrinologyKinaseMedicineInternal medicineProinflammatory cytokineObesityInflammationInsulinc-junProtein kinase ABiologyCell biologyBiochemistryGeneTranscription factor

Abstract

fetched live from OpenAlex

Obesity has been described as a global epidemic and is a low-grade chronic inflammatory disease that arises as a consequence of energy imbalance. Obesity increases the risk of type 2 diabetes (T2D), by mechanisms that are not entirely clarified. Elevated circulating pro-inflammatory cytokines and free fatty acids (FFA) during obesity cause insulin resistance and ß-cell dysfunction, the two main features of T2D, which are both aggravated with the progressive development of hyperglycemia. The inflammatory kinase c-jun N-terminal kinase (JNK) responds to various cellular stress signals activated by cytokines, free fatty acids and hyperglycemia, and is a key mediator in the transition between obesity and T2D. Specifically, JNK mediates both insulin resistance and ß-cell dysfunction, and is therefore a potential target for T2D therapy.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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

Citations256
Published2020
Admission routes2
Has abstractyes

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