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Record W3211329272 · doi:10.1172/jci154699

Deprogram and reprogram to solve the riddle of insulin resistance

2021· letter· en· W3211329272 on OpenAlexafffund
Victoria L. Tokarz, Paul Delgado-Olguı́n, Amira Klip

Bibliographic record

VenueJournal of Clinical Investigation · 2021
Typeletter
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsHeart and Stroke FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsInduced pluripotent stem cellInsulin resistanceInsulinEpigeneticsBiologyPopulationBasal (medicine)Cell biologyBioinformaticsEndocrinologyInternal medicineGeneticsEmbryonic stem cellMedicineGene

Abstract

fetched live from OpenAlex

Skeletal muscle preeminently determines whole-body glycemia. However, the molecular basis and inheritable influence that drive the progression of insulin resistance to type 2 diabetes remain debated. In this issue of the JCI, Haider and Lebastchi report on their use of induced pluripotent stem cell-derived (iPSC-derived) myoblasts (iMyos) to uncover multiple phosphoproteomic changes that carried over from the human to the cell-culture system. In this system devoid of in vivo influences, the researchers annotated changes between the sexes and between the most and least insulin-sensitive quintiles of a healthy population (defined by steady-state blood glucose levels). Many phosphoproteomic differences were detected in the absence of insulin, revealing that changes in the basal landscape of cells determine the efficiency of insulin action. Basal and insulin-dependent deficiencies of iPSCs and iMyos likely involve genetic and epigenetic determinants that modulate insulin sensitivity.

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)0.002
metaresearch head score (Gemma)0.008
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.010
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.0030.005

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 head0.113
GPT teacher head0.376
Teacher spread0.264 · 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
GenreCommentary

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

Citations8
Published2021
Admission routes2
Has abstractyes

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