Treadmill Exercise Training Improves Vascular Endothelial Growth Factor Expression in the Cardiac Muscle of Type I Diabetic Rats
Bibliographic record
Abstract
BACKGROUND: Vascular endothelial growth factor (VEGF) expression is a potent mitogen for endothelial cells that is involved in angiogenesis. Cardiac VEGF is decreased in many pathologic conditions, including diabetes mellitus and aging. Exercise training has improved VEGF expression in the aging heart. Thus, the aim of our study is to illustrate the impact of treadmill exercise training on the cardiac VEGF expression in type I diabetic rats. METHODS: Twenty normal Sprague-Dawley rats and Sprague-Dawley rats with streptozotocin-induced diabetes were divided into the following equal groups: sedentary control (SC), exercised control (EC), sedentary diabetic rats (SD) and exercised diabetic rats (ED). Immunohistochemistry was used to investigate VEGF expression in the cardiac tissue in each of the four different groups. RESULTS: Cardiac VEGF expression was significantly (P < 0.05) lower in SD compared with that in SC. However, exercise training significantly (P < 0.01) enhanced VEGF expression in the cardiac tissue in ED compared with that in SD. CONCLUSION: Our present data suggest that treadmill exercise training improved diabetes-induced downregulation in the cardiac VEGF expression.
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 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) | 0.001 | 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 | 0.000 |
| 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".