Lower limb conduit artery endothelial responses to acute upper limb exercise in spinal cord injured and able-bodied men
Bibliographic record
Abstract
Vascular improvements in the nonactive regions during exercise are likely primarily mediated by increased shear rate (SR). Individuals with spinal cord injury (SCI) experience sublesional vascular deconditioning and could potentially benefit from upper body exercise-induced increases in lower body SR. The present study utilized a single bout of incremental arm-crank exercise to generate exercise-induced SR changes in the superficial femoral artery in an effort to evaluate the acute postexercise impact on superficial femoral artery endothelial function via flow-mediated dilation (FMD), and determine regulatory factors in the nonactive legs of individuals with and without SCI. Eight individuals with SCI and eight age, sex, and waist-circumference-matched able-bodied (AB) controls participated. Nine minutes of incremental arm-crank exercise increased superficial femoral artery anterograde SR (P = 0.02 and P < 0.01), retrograde SR (P < 0.01 and P < 0.01), and oscillatory shear index (OSI) (P < 0.001 and P < 0.001) in both SCI and AB, respectively. However, these SR alterations resulted in acute postexercise increases in FMD in the AB group only (SCI 6.0 ± 1.2% to 6.3 ± 2.7%, P = 0.74; AB 7.5 ± 1.4% to 11.2 ± 1.4%, P = 0.03). While arm exercise has many cardiovascular benefits and results in changes in SR patterns in the nonactive legs, these changes are not sufficient to induce acute changes in FMD among individuals with SCI, and therefore are less likely to stimulate exercise training-associated improvements in nonactive limb endothelial function. Understanding the role of SR patterns on FMD brings us closer to designing effective strategies to combat impaired vascular function in both healthy and clinical populations.
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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".