Electrical Stimulation Alters FMD and Arterial Compliance in Extremely Inactive Legs
Bibliographic record
Abstract
PURPOSE: The main aim of the study was to assess the effect and time course of 4 wk of electrically induced leg training on arterial compliance and endothelial function. METHODS: Six spinal cord-injured (SCI) individuals participated in 4 wk of daily one-leg functional electrical stimulation (FES) training for 30 min per session. Eight able-bodied individuals served as a control group (C) and were tested on one occasion. Echo Doppler measurements were performed before the FES training and after 1, 2, and 4 wk of training to measure vascular characteristics of femoral artery (FA), brachial artery (BA), and carotid artery (CA). RESULTS: Baseline arterial compliance of FA (SCI: 0.0185 +/- 0.063 mm2 x mm Hg(-1); C: 0.066 +/- 0.017 mm2 x mm Hg(-1), P = 0.001) and CA (SCI: 0.073 +/- 0.02 mm2 x mm Hg(-1); C: 0.102 +/- 0.02 mm2 x mm Hg(-1), P = 0.02) was significantly decreased in SCI. Baseline endothelial function in the leg was significantly enhanced in SCI compared with C (SCI: 11 +/- 1.3%; C: 7.9 +/- 0.9%, P = 0.001). No differences between the groups were found for arterial compliance and endothelial function in the arm. Vascular changes after FES training showed an increase in arterial compliance (significant at week 4, P < 0.05) and a decrease in FMD response (significant at weeks 2 and 4, P < 0.05) in the FA of the trained leg only, with no changes evident in the other arteries examined. CONCLUSION: Daily electrically induced training of an extremely deconditioned leg appears to enhance arterial compliance in the femoral artery and may normalize endothelial function.
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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".