The impact of baseline artery diameter on flow-mediated vasodilation: a comparison of brachial and radial artery responses to matched levels of shear stress
Bibliographic record
Abstract
An inverse relationship between baseline artery diameter (BAD) and flow-mediated vasodilation (FMD) has been identified using reactive hyperemia (RH) to create a shear stress (SS) stimulus in human conduit arteries. However, RH creates a SS stimulus that is inversely related to BAD. The purpose of this study was to compare FMD in response to matched levels of SS in two differently sized upper limb arteries [brachial (BA) and radial (RA) artery]. With the use of exercise, three distinct, shear rate (SR) stimuli were created (SR = blood velocity/vessel diameter; estimate of SS) in the RA and BA. Artery diameter and mean blood velocity were assessed with echo and Doppler ultrasound in 15 healthy male subjects (19-25 yr). Data are means ± SE. Subjects performed 6 min of adductor pollicis and handgrip exercise to increase SR in the RA and BA, respectively. Exercise intensity was modulated to achieve uniformity in SR between arteries. The three distinct SR levels were as follows: steady-state exercise 39.8 ± 0.6, 57.3 ± 0.7, and 72.4 ± 1.2 s(-1) (P < 0.001). %FMD and AbsFMD (mm) at the end of exercise were greater in the RA vs. the BA at each shear level [at the highest level: RA = 15.7 ± 1.5%, BA = 5.4 ± 0.8% (P < 0.001)]. The mean slope of the within-subject SR-%FMD regression line was greater in the RA (RA = 0.33 ± 0.04, BA = 0.13 ± 0.02, P < 0.001), and a strong within-subjects relationship between %FMD and SR was observed in both arteries (RA: r(2) = 0.92 ± 0.02; BA: r(2) = 0.90 ± 0.03). Within the RA, there was a significant relationship between baseline diameter and %FMD; however, this relationship was not present in the BA (RA: r(2) = 0.76, P < 0.001; BA: r(2) = 0.03, P = 0.541). These findings suggest that the response to SS is not uniform across differently sized vessels, which is in agreement with previous studies.
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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 | 0.001 | 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".