Evaluation of Extension of Blood Vessels during Static Stretching Using Ultrasound 2D Speckle Tracking Imaging
Bibliographic record
Abstract
BACKGROUND: During static stretching, a muscle extends longitudinally, and blood vessels seem to extend simultaneously. However, it is difficult to visualize, and few findings have seen. The recent progress with ultrasonography enables measurements of movement in vivo using 2D speckle tracking imaging, as well as detailed evaluation of extension in tissues at the same site. The aim of this study is to evaluate longitudinal extension of blood vessels during static stretching using this methodology.METHODS: Participants were 10 healthy female volunteers (age of 39.4±11.6). They extended their right wrist with elbow extended. Then the ulnar artery was measured by using 2D speckle tracking imaging with a general-purpose ultrasound instrument. Tissue extension per unit time at the stretching site was calculated from before stretching to maximum of stretching. Simultaneous changes in the caliber of blood vessels during stretching were measured using ultrasound M-mode.RESULTS: The maximum angle of wrist extension was 0 to 83.6±12.5°. The muscle extended by 3.80±1.65% per unit time during stretching, and blood vessels simultaneously extended by 3.20±1.96%. These changes were significant compared to measurements before stretching (p<0.01) and shows the correlation between muscles and blood vessels (r=0.56, p=0.1). The calibers of blood vessels per unit time before and during stretching were 2.24±0.27 and 1.64±0.53 mm with a significant decrease during stretching (p<0.01).CONCLUSIONS: Imaging of static stretching showed extension of both the muscle/skeletal system and blood vessels longitudinally. The finding suggests that endothelial function might be activated by mechanical stress on vascular endothelial cells.
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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 | 0.001 |
| 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".