Regional muscle oxygenation differences in vastus lateralis during different modes of incremental exercise
Bibliographic record
Abstract
BACKGROUND: Near infrared spectroscopy (NIRS) is used to assess muscle oxygenation (MO) within skeletal muscle at rest and during aerobic exercise. Previous investigations have used a single probe placement to measure MO during various forms of exercise. However, regional MO differences have been shown to exist within the same muscle which suggests that different areas of the same muscle may have divergent MO. Thus, the aim of this study was to examine whether regional differences in MO exist within the same muscle during different types of incremental (rest, 25, 50, 75, 100 % of maximum) exercise (1 leg knee extension (KE), 2 leg KE, or cycling). METHODS: Nineteen healthy active males (Mean +/- SD: Age 27 +/- 4 yrs; VO2max: 55 +/- 11 mL/kg/min) performed incremental exercise to fatigue using each mode of exercise. NIRS probes were placed on the distal and proximal portion of right leg vastus lateralis (VL). Results were analyzed with a 3-way mixed model ANOVA (probe x intensity x mode). RESULTS: Differences in MO exist within the VL for each mode of exercise, however these differences were not consistent for each level of intensity. Comparison of MO revealed that the distal region of VL was significantly lower throughout KE exercise (1 leg KE proximal MO - distal MO = 9.9 %; 2 leg KE proximal MO - distal MO = 13 %). In contrast, the difference in MO between proximal and distal regions of VL was smaller in cycling and was not significantly different at heavy workloads (75 and 100 % of maximum). CONCLUSION: MO is different within the same muscle and the pattern of the difference will change depending on the mode and intensity of exercise. Future investigations should limit conclusions on MO to the area under assessment as well as the type and intensity of exercise employed.
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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".