Differences in oxidative metabolism modulation induced by ischemia/reperfusion between trained and untrained individuals assessed by NIRS
Bibliographic record
Abstract
Endurance training is associated with skeletal muscle adaptations that regulate the oxidative metabolism during ischemia/reperfusion. The aim of this study was to noninvasively assess in vivo differences in the oxidative metabolism activity during ischemia/reperfusion between trained and untrained individuals, using near infrared spectroscopy (NIRS) combined with a vascular occlusion test (VOT) technique (NIRS-VOT). Sixteen untrained (26.3 ± 5.1 year) and seventeen trained (29.4 ± 4.9 year) healthy young adult men were submitted to a VOT (2 min baseline, 5 min occlusion, and 8 min reperfusion). Oxygen utilization was estimated from the area under the curve of the NIRS-derived deoxyhemoglobin [HHb] signal during occlusion (AUCocc). Muscle reperfusion was derived from the area above the curve (AACrep) of the [HHb] signal after cuff release. The AUCocc of the untrained participants (21010 ± 9553 % · s) was significantly larger than the AUCocc of their trained counterparts (12320 ± 3283 % · s); P = 0.001). The AACrep of the untrained participants (5928 ± 3769 % · s) was significantly larger than the AACrep of the trained participants (3745 ± 1900 % · s; P = 0.042). There was a significant correlation between AUCocc and AACrep (r = 0.840; P = 0.001). NIRS assessment of oxidative metabolism showed that trained individuals are more efficient in shifting between oxidative and anaerobic metabolism in response to ischemia and reperfusion.
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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".