Pressure‐Induced Microvascular Reactivity With Whole Foot Loading Is Unique Across the Human Foot Sole
Bibliographic record
Abstract
BACKGROUND: Foot sole plantar pressure generates transient but habitual cutaneous ischemia, which is even more exacerbated in atypical gait patterns. Thus, adequate post-occlusive reactive hyperaemia (PORH) is necessary to maintain skin health. Plantar pressure regional variance during daily tasks potentially generates region-specific PORH, crucial for ischemic defence. AIMS: The current work investigated regional PORH across the human foot sole resulting from stance-like loading. MATERIALS & METHODS: A loading device equipped with an in-line laser speckle contrast imager measured blood flux before, during, and after whole-foot loading for 2 and 10 min durations at 15% and 50% body weight. Flux was compared between six regions: the heel, lateral arch, medial arch, and fifth, third, and first metatarsals (MT). RESULTS: Baseline flux was significantly greater in the 1MT and 3MT than all other regions. Loading occluded the heel, 5MT and 3MT more than all other regions. Regional PORH peak, time to peak, area under the curve, and recovery rate were ranked between regions. DISCUSSION: The 3MT, followed by 5MT, overall had the strongest PORH response, suggesting a heightened protection against ischemia compared to other regions. CONCLUSION: This work highlights regional variations within a healthy foot, providing a framework for future ulcer risk assessments and interventions to preserve foot health.
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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) | 0.002 | 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".