Noninvasive and in vivo assessment of upper and lower limb skeletal muscle oxidative metabolism activity and microvascular responses to glucose ingestion in humans
Bibliographic record
Abstract
This study investigated changes in muscle oxidative metabolism and microvascular responsiveness induced by glucose ingestion in the upper and lower limbs using near-infrared spectroscopy (NIRS). Fourteen individuals (aged 27 ± 1.4 years) underwent 5 vascular occlusion tests (VOT) (pre-intervention (Pre), 30 min, 60 min, 90 min, and 120 min after glucose challenge). NIRS-derived oxygen saturation (StO2) was measured on the forearm and leg muscle at each VOT. Muscle oxidative metabolism was determined by the StO2 downslope during cuff inflation (deoxygenation slope); microvascular responsiveness was estimated by the StO2 upslope (reperfusion slope) following cuff deflation. There was a significant increase in arm (p < 0.05; 1-β = 0.860) and leg (p < 0.05; 1-β = 1.000) oxidative metabolism activity as represented by the faster deoxygenation slope at 60, 90, and 120 min (0.08 ± 0.03, 0.08 ± 0.03, 0.08 ± 0.02%·s–1, respectively) (leg) and at 90 min (0.16 ± 0.08%·s−1) (arm) observed after glucose ingestion when compared with their respective Pre values (leg = 0.06 ± 0.02; arm = 0.11 ± 0.04%·s−1). There was a significant increase in arm (p < 0.05; 1-β = 0.880) and leg (p < 0.05; 1-β = 0.983) reperfusion slope at 60 min (arm = 3.63 ± 2.1%·s−1; leg = 1.56 ± 0.6%·s−1), 90 min (arm = 3.91 ± 2.1%·s−1; leg = 1.60 ± 0.6%·s−1), and 120 min (arm = 3.91 ± 1.6%·s−1; leg = 1.54 ± 0.6%·s−1) when compared with their Pre values (arm = 2.79 ± 1.7%·s−1; leg = 1.26 ± 0.5%·s−1). Our findings showed that NIRS–VOT technique is capable of detecting postprandial changes in muscle oxidative metabolism activity and microvascular reactivity in the upper and lower limb. Novelty NIRS-VOT is a promising noninvasive clinical approach that may help in the early, limb-specific detection of impairments in glucose oxidation and microvascular function.
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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".