Improvement of Cardiac Function and Subcellular Defects Due to Chronic Diabetes upon Treatment with Sarpogrelate
Bibliographic record
Abstract
In order to investigate the subcellular mechanisms underlying the beneficial effects of sarpogrelate—a 5-HT2A receptor antagonist—on diabetic cardiomyopathy, diabetes was induced in rats by injecting streptozotocin (65 mg/kg). Diabetic animals were treated with or without sarpogrelate (5 mg/kg daily) for 6 weeks; diabetic animals were also treated with insulin (10 units/kg daily) for comparison. Elevated plasma levels of glucose and lipids, depressed insulin levels, hemodynamic alterations and cardiac dysfunction in diabetic animals were partially or fully attenuated by sarpogrelate or insulin treatment. Diabetes-induced changes in myocardial high-energy phosphate stores, as well as depressed mitochondrial oxidative phosphorylation and Ca2+-uptake activities, were significantly prevented by these treatments. Reductions in sarcolemma Na+-K+ ATPase, Na+-Ca2+ exchange, Ca2+-channel density and Ca2+-uptake activities were also attenuated by treatments with sarpogrelate and insulin. In addition, decreases in diabetes-induced sarcoplasmic reticulum Ca2+-uptake, Ca2+-release and Ca2+-stimulated ATPase activities, myofibrillar Mg2+-ATPase and Ca2+-stimulated ATPase activities, and myosin Mg2+-ATPase and Ca2+-ATPase activities were fully or partially prevented by sarpogrelate and insulin treatments. Marked alterations in different biomarkers of oxidative stress, such as malondialdehyde, superoxide dismutase and glutathione peroxidase, in diabetic hearts were also attenuated by treating the animals with sarpogrelate or insulin. These observations suggest that therapy with sarpogrelate, like that with insulin, may improve cardiac function by preventing subcellular and metabolic defects as a consequence of a reduction in oxidative stress.
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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) | 0.001 | 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".