Evidence of a Shear Stress Stimulus Profile Dependent Impact of Cycling Training on Flow‐Mediated Dilation
Bibliographic record
Abstract
Lower limb endurance training can improve conduit artery flow‐mediated dilation (FMD) in response to transient increases in shear stress (reactive hyperemia; RH‐FMD) in both the upper and lower limbs. Sustained increases in shear stress recruit a partially distinct transduction pathway and elicit a physiologically relevant FMD response that appears to provide distinct information regarding endothelial function (sustained shear stress‐FMD, SS‐FMD). However, the impact of training on SS‐FMD is not well understood. PURPOSE To determine the impact of cycling training on handgrip exercise induced brachial artery (BA) FMD (BA SS‐FMD) and calf plantar‐flexion induced superficial femoral artery (SFA) FMD (SFA SS‐FMD). METHODS 28 young males were randomized to control (n=12) or training (n=16) groups. Participants in the training group cycled 30 min/day, 3 days/week for 4 weeks at 80 % heart rate reserve. RH‐FMD and SS‐FMD was assessed in the BA and SFA pre and post‐intervention via Duplex ultrasound. RESULTS Data are mean ±SD. Training did not impact SS‐FMD in either artery, and RH‐FMD in the SFA was also unchanged (p>0.05). When controlling for the shear rate stimulus via covariate analysis, BA RH‐FMD improved in the training group (p=0.05) (Control: pre‐ 5.7 ± 2.4%, post‐ 5.3 ± 2.4%; training: pre‐ 5.4 ± 2.5%, post‐ 7.2 ± 2.4%). CONCLUSIONS Endurance training resulted in non‐uniform adaptations to endothelial function, with an isolated impact on the brachial artery, and on the ability to transduce a transient increase in shear stress. Support or Funding Information Funded by NSERC This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.004 | 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".