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 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".