Redox balance in human skeletal muscle-derived endothelial cells - Effect of exercise training
Bibliographic record
Abstract
Aerobic training can improve vascular endothelial function in-vivo. The aim of this study was to elucidate the mechanisms underlying this improvement in isolated human microvascular endothelial cells. Sedentary males, aged 57 ± 6 years completed 8 weeks of intense aerobic training. Resting muscle biopsies were obtained from the thigh muscle and used for isolation of endothelial cells (pre n = 23, post n = 16). The cells were analyzed for mitochondrial respiration, H2O2 emission, glycolysis, protein levels of antioxidants, NADPH oxidase, endothelial nitric oxide (NO) synthase and prostacyclin synthase (PGI2S). In-vivo microvascular function, assessed by acetylcholine infusion and arterial blood pressure were also determined. Endothelial mitochondrial respiration and H2O2 formation were similar before and after training whereas the expression of superoxide dismutase and the expression of glutathione peroxidase were 2.4-fold (p = 0.012) and 2.3–fold (p = 0.006) higher, respectively, after training. In-vivo microvascular function was increased by 1.4-fold (p = 0.036) in parallel with a 2.1-fold increase in endothelial PGI2S expression (p = 0.041). Endothelial cell glycolysis was reduced after training, as indicated by a 65% lower basal production of lactate (p = 0.003) and a 30% lower expression of phosphofructokinase (p = 0.011). Subdivision of the participants according to blood pressure at base-line (n = 23), revealed a 2-fold higher (p = 0.049) rate of H2O2 production in endothelial cells from hypertensive participants. Our data show that exercise training increases skeletal muscle microvascular endothelial cell metabolism, antioxidant capacity and the capacity to form prostacyclin. Moreover, elevated blood pressure is associated with increased endothelial mitochondrial ROS formation.
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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 | 0.000 |
| 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".