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Record W2255782172 · doi:10.1096/fj.15-277905

Impact of keratin intermediate filaments on insulin‐mediated glucose metabolism regulation in the liver and disease association

2015· review· en· W2255782172 on OpenAlexafffund
Alexandra Roux, Stéphane Gilbert, Anne Loranger, Normand Marceau

Bibliographic record

VenueThe FASEB Journal · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsCentre hospitalier de l'Université LavalCentre hospitalier universitaire de Québec
FundersCanadian Institutes of Health Research
KeywordsInsulinCarbohydrate metabolismKeratinMetabolismDiseaseEndocrinologyInternal medicineChemistryMedicinePathology

Abstract

fetched live from OpenAlex

In all cells, a tight regulation exists between glucose uptake and utilization to prevent diseases related to its perturbed metabolism. In insulin-targeted cells, such as hepatocytes, proper glucose utilization requires an elaborate interplay between the insulin receptor, the glucose transporter, and mitochondria that involves the participation of actin microfilaments and microtubules. In addition, there is increasing evidence of an involvement of the third cytoskeletal network provided by intermediate filaments (IFs). Keratins belong to the multigene family of IF proteins, coordinately expressed as distinct pairs within the context of epithelial cell differentiation. Hepatocyte IFs are made up of the [keratin (K)8/K18] pair only, whereas pancreatic β-cell IFs additionally include small amounts of K7. There are accumulating examples of K8/K18 involvement in the glucose-insulin cross-talk, including the modulation of plasma glucose levels, insulin release from pancreatic β-cells, and insulin-mediated glucose uptake and glycogen production in hepatocytes after a K8/K18 loss. This review integrates the mechanistic features that support such an impact of K8/K18 IFs on insulin-dependent glucose metabolism regulation in liver and its implication in glucose- or insulin-associated diseases.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.032
GPT teacher head0.335
Teacher spread0.303 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations20
Published2015
Admission routes2
Has abstractyes

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