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Record W3205694228 · doi:10.1126/sciadv.abg3947

GLUT4-overexpressing engineered muscle constructs as a therapeutic platform to normalize glycemia in diabetic mice

2021· article· en· W3205694228 on OpenAlexaff
Margarita Beckerman, Chava Harel, Inbal Michael, Amira Klip, Philip J. Bilan, Emily J. Gallagher, Derek LeRoith, Eli C. Lewis, Eddy Karnieli, Shulamit Levenberg

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsHospital for Sick Children
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsGLUT4MedicineDiabetes mellitusInternal medicineEndocrinologyBioinformaticsInsulin resistanceBiology

Abstract

fetched live from OpenAlex

Skeletal muscle insulin resistance is a main defect in type 2 diabetes (T2D), which is associated with impaired function and content of glucose transporter type 4 (GLUT4). GLUT4 overexpression in skeletal muscle tissue can improve glucose homeostasis. Therefore, we created an engineered muscle construct (EMC) composed of GLUT4-overexpressing (OEG4) cells. The ability of the engineered implants to reduce fasting glucose levels was tested in diet-induced obesity mice. Decrease and stabilization of basal glucose levels were apparent up to 4 months after implantation. Analysis of the retrieved constructs showed elevated expression of myokines and proteins related to metabolic processes. In addition, we validated the efficiency of OEG4-EMCs in insulin-resistant mice. Following high glucose load administration, mice showed improved glucose tolerance. Our data indicate that OEG4-EMC implant is an efficient mode for restoring insulin sensitivity and improving glucose homeostasis in diabetic mice. Such procedure is a potential innovative modality 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.008
GPT teacher head0.266
Teacher spread0.259 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations27
Published2021
Admission routes1
Has abstractyes

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