Near-infrared diffuse correlation spectroscopy tracks changes in oxygen delivery and utilization during exercise with and without isolated arterial compression
Bibliographic record
Abstract
Near-infrared diffuse correlation spectroscopy (NIR-DCS) is an emerging technology for simultaneous measurement of skeletal muscle microvascular oxygen delivery and utilization during exercise. The extent to which NIR-DCS can track acute changes in oxygen delivery and utilization has not yet been fully established. To address this knowledge gap, 14 healthy men performed rhythmic handgrip exercise at 30% maximal voluntary contraction, with and without isolated brachial artery compression, designed to acutely reduce convective oxygen delivery to the exercising muscle. Radial artery blood flow (Duplex Ultrasound) and NIR-DCS derived variables [blood flow index (BFI), tissue oxygen saturation ([Formula: see text]), and metabolic rate of oxygen ([Formula: see text])] were simultaneously measured. During exercise, both radial artery blood flow (+51.6 ± 20.3 mL/min) and DCS-derived BFI (+155.0 ± 82.2%) increased significantly ( P < 0.001), whereas [Formula: see text] decreased −7.9 ± 6.2% ( P = 0.002) from rest. Brachial artery compression during exercise caused a significant reduction in both radial artery blood flow (−32.0 ± 19.5 mL/min, P = 0.001) and DCS-derived BFI (−57.3 ± 51.1%, P = 0.01) and a further reduction of [Formula: see text] (−5.6 ± 3.8%, P = 0.001) compared with exercise without compression. [Formula: see text] was not significantly reduced during arterial compression ( P = 0.83) due to compensatory reductions in [Formula: see text], driven by increases in deoxyhemoglobin/myoglobin (+7.1 ± 6.1 μM, P = 0.01; an index of oxygen extraction). Together, these proof-of-concept data help to further validate NIR-DCS as an effective tool to assess the determinants of skeletal muscle oxygen consumption at the level of the microvasculature during exercise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".