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Record W4405642678 · doi:10.3390/biom14121639

High Glucose Sensitizes Male and Female Rat Cardiomyocytes to Wnt/β-Catenin Signaling

2024· article· en· W4405642678 on OpenAlexafffund
Ruonan Gu, Jerry Wang, J. Morin, Aizhu Lu, Wenbin Liang

Bibliographic record

VenueBiomolecules · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchUniversity of OttawaHeart and Stroke Foundation of Canada
KeywordsWnt signaling pathwayCateninCell biologyInternal medicineEndocrinologySignal transductionBeta-cateninBiologyChemistryMedicine

Abstract

fetched live from OpenAlex

Wnt/β-catenin signaling has been shown to regulate gene expressions in cardiomyocytes. However, it is not known if this effect is dependent on the sex of cells or the glucose level in the culture medium. In the present study, ventricular myocytes were prepared from male and female neonatal rats and maintained in either a glucose-rich (25 mM) medium or a low-glucose (3 mM), lipid-rich medium. Real-time quantitative PCR was used to measure changes in target genes (Axin2, Scn5a, and Tbx3) after treatment with 1, 3, or 10 µM of CHIR-99021, an activator of Wnt/β-catenin signaling. CHIR induced similar changes in Axin2, Tbx3, and Scn5a transcripts in male and female NRVMs in both media, suggesting the absence of sex difference. However, cells in a high-glucose medium showed greater increases in Axin2 and Tbx3 transcripts than cells in a low-glucose medium. In addition, a low concentration of CHIR (1 µM) reduced the Scn5a transcript in cells in a high-glucose medium but not in a low-glucose medium, suggesting an increased sensitivity to Wnt signaling by high glucose. A non-linear relationship was identified between Axin2 transcript upregulation and Scn5a transcript downregulation in CIHR-treated NRVMs. These data suggest that high glucose sensitizes both male and female cardiomyocytes to Wnt/β-catenin signaling.

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 score0.025
Threshold uncertainty score0.992

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

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