Modulation of Cardiac Metabolism by Beta‐Blockers During Diabetes: A Role in Apoptosis Signaling
Bibliographic record
Abstract
Diabetic Cardiomyopathy is characterized by problems during diastole, this is due to loss of contractile tissue after apoptosis. Apoptosis may be caused by increases in oxidative stress associated with metabolic modifications. Beta‐adrenergic receptor antagonists, (β‐blockers) improve heart function. Metoprolol (met) and carvedilol (car) are clinically important β‐blockers that modulate metabolism and reduce apoptosis, car, also has antioxidant properties. We tested whether β‐blockers will reduce apoptosis and improve heart function via oxidative stress dependant and or independent pathways. We employed a Streptozotocin (STZ) induced rat model of type 1 diabetes. This model has been shown to develop diastolic dysfunction. STZ was delivered at 60mg/kg body weight. Met and car were delivered at a rate of 15 and 10mg/kg/day respectively. In order to assess the significance of car's antioxidant abilities, we included groups where met treatment was supplemented with vitamin C at 1000mg/kg/day. Analysis indicates induction of diabetes in STZ animals. β‐blocker treatment caused a significant reduction in heart rates. Remaining analysis includes assessment of in vivo and ex vivo heart function, metabolic flux, apoptosis, expression analysis of effecter proteins involved in apoptosis, and measurement of oxidative stress. Funding for this research was provided by the Canadian Institutes of Health Research
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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".