Determinants of skeletal muscle oxygen consumption assessed by near-infrared diffuse correlation spectroscopy during incremental handgrip exercise
Bibliographic record
Abstract
Near-infrared diffuse correlation spectroscopy (DCS) is a rapidly evolving optical imaging technique for the assessment of skeletal muscle O2 utilization (mVO2). We compared DCS-derived determinants of mVO2 with conventional measures [blood flow by brachial artery Doppler ultrasound and venous O2 saturation ([Formula: see text])] in eight volunteers at rest and during incremental handgrip exercise. Brachial artery blood flow and DCS-derived blood flow index (BFI) were linearly related (R2 = 0.57) and increased with each workload, whereas [Formula: see text] decreased from 65.3 ± 2.5% (rest) to 39.9 ± 3.0% (light exercise; P < 0.01) with no change thereafter. In contrast, DCS-derived tissue O2 saturation decreased progressively with each incremental stage ( P < 0.01), driven almost entirely by an initial steep rise in deoxyhemoglobin/myoglobin, followed by a linear increase thereafter. Whereas seemingly disparate at first glance, we believe these two approaches provide similar information. Indeed, by plotting the mean convective O2 delivery and diffusive O2 conductance, we show that the initial increase in mVO2 during the transition from rest to exercise was achieved by a greater increase in diffusive O2 conductance versus convective O2 delivery (10-fold vs. 4-fold increase, respectively), explaining the initial decline in [Formula: see text]. In contrast, the increase in mVO2 from light to heavy exercise was achieved by equal increases (1.8-fold) in convective O2 delivery and diffusive O2 conductance, explaining the plateau in [Formula: see text]. That DCS-derived BFI and deoxyhemoglobin/myoglobin (surrogate measure of O2 extraction) share the same general biphasic pattern suggests that both DCS and conventional approaches provide complementary information regarding the determinants of mVO2. NEW & NOTEWORTHY Near-infrared diffuse correlation spectroscopy (DCS) is an emerging optical imaging technique for quantifying skeletal muscle O2 delivery and utilization at the microvascular level. Here, we show that DCS provides complementary insight into the determinants of muscle O2 consumption across a wide range of exercise intensities, further establishing the utility of DCS.
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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".