Short-term exercise training augments sympathetic vasoconstrictor responsiveness and endothelium-dependent vasodilation in resting skeletal muscle
Bibliographic record
Abstract
We tested the hypotheses that 4 wk of exercise training would diminish the magnitude of vasoconstriction in response to sympathetic nerve stimulation and augment endothelium-dependent vasodilation (EDD) in resting skeletal muscle in a training intensity-dependent manner. Sprague-Dawley rats were randomly assigned to sedentary time-control (S), mild- (M; 20 m/min, 5% grade), or heavy-intensity (H; 40 m/min, 5% grade) treadmill exercise groups. Animals trained 5 days/wk for 4 wk with training volume matched between groups. Rats were anesthetized and instrumented for study 24 h after the last training session. Arterial pressure and femoral artery blood flow were measured, and femoral vascular conductance (FVC) was calculated. Lumbar sympathetic chain stimulation was delivered continuously at 2 Hz and in patterns at 20 and 40 Hz. EDD was assessed by the vascular response to intra-arterial bolus injections of ACh. The response (% change FVC) to sympathetic stimulation increased (P < 0.05) in a training intensity-dependent manner at 2 Hz (S: -20.2 ± 9.8%, M: -34.0 ± 6.7%, and H: -44.9 ± 2.0%), 20 Hz (S: -22.0 ± 10.6%, M: -31.2 ± 8.4%, and H: -42.8 ± 5.9%), and 40 Hz (S: H -24.5 ± 8.5%, M: -35.1 ± 8.9%, H: -44.9 ± 6.5%). The magnitude of EDD also increased in a training intensity-dependent manner (P < 0.05). These data demonstrate that short-term exercise training augments the magnitude of vasoconstriction in response to sympathetic stimulation and EDD in resting skeletal muscle in a training intensity-dependent manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".