Low-pressure sequential compression of lower limbs enhances forearm skin blood flow
Bibliographic record
Abstract
Purpose We investigated whether forearm skin blood flow could be improved when a multilayer pulsatile inflatable suit was applied at a low pressure to the lower limbs and abdomen. We hypothesized that a non-invasive purely mechanical stimulation of the lower limbs could induce remote forearm blood flow modifications. Methods The pulsatile suit induced a sequential compartmentalized low compression (65 mmHg), which was synchronized with each diastole of the cardiac cycle with each phase evolving centripetally (lower limbs to abdomen). Modifications of the forearm skin blood flow were continuously recorded by laser Doppler flowmetry (LDF) at baseline and during the pulsatile suit application. Endothelium-dependent and endothelium-independent vasodilations of the forearm skin microcirculation were measured by LDF in response to a local transdermal iontophoretic application of acetylcholine (ACh-test) and to hyperthermia (hyperT- test). Results Twenty-four healthy volunteers, 12 men and 12 women (43±14 years) were included in the study. LDF responses increased 1) under pulsatile suit (97±106%, p<0.01), 2) during ACh-test (338±336%, p<0.001) and 3) during hyperT-test (587±383%, p<0.001). No significant gender differences were seen. There was a significant linear positive regression between LDF response under pulsatile suit and during ACh-test (R= 0.79, p<0.001) and during hyperT-test (R=0.62, p=0.004). No adverse effects were reported. Conclusions This proof-of-concept study shows that a sequential low-pressure compression applied to the lower limbs and abdomen induces a significant increase of the forearm skin blood flow, suggestive a systemic effect. Mechanisms still need to be investigated.
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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".