Inhibition of brain-type glycogen phosphorylase ameliorates high glucose-induced cardiomyocyte apoptosis via Akt–HIF-1α activation
Bibliographic record
Abstract
Brain-type glycogen phosphorylase (pygb) is one of the rate-limiting enzymes in glycogenolysis that plays a crucial role in the pathogenesis of type 2 diabetes mellitus. Here we investigated the role of pygb in high-glucose (HG)-induced cardiomyocyte apoptosis and explored the underlying mechanisms, by using the specific pygb inhibitors or pygb siRNA. Our results show that inhibition of pygb significantly attenuates cell apoptosis and oxidative stress induced by HG in H9c2 cardiomyocytes. Inhibition of pygb improved glucose metabolism in cardiacmyocytes, as evidenced by increased glycogen content, glucose consumption, and glucose transport. Mechanistically, pygb inhibition activates the Akt-GSK-3β signaling pathway and suppresses the activation of NF-κB in H9c2 cells exposed to HG. Additionally, pygb inhibition promotes the expression and the translocation of hypoxia-inducible factor-1α (HIF-1α) after HG stimulation. However, the changes in glucose metabolism and HIF-1α activation mediated by pygb inhibition are significantly reversed in the presence of the Akt inhibitor MK2206. In conclusion, this study found that inhibition of pygb prevents HG-induced cardiomyocyte apoptosis via activation of Akt-HIF-α.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".