Expiratory Resistive Loading Increases in Sympathetic Vasomotor Outflow and BP During Dynamic Leg Exercise
Bibliographic record
Abstract
High intensity voluntary contraction of the inspiratory muscles against resistive loads augments an increase in muscle sympathetic nerve activity (MSNA) with a corresponding increase in arterial blood pressure (BP) through an inspiratory muscle fatigue-induced metaboreflex. We hypothesized that expiratory muscle fatigue would also elicit increases in MSNA and BP during dynamic leg exercise. PURPOSE: The purpose of this study was to clarify the influence of an enhancement of expiratory muscle activity on sympathetic vasoconstrictor outflow and BP during submaximal exercise. METHODS: The subjects performed two 10-min exercise bouts at 40% peak oxygen uptake using a cycle ergometer in a semirecumbent position [spontaneous breathing for 5 min and with or without expiratory resistive breathing for 5 min (breathing frequency: 60 breaths/min, inspiratory and expiratory times were each constant at 0.5 s)]. MSNA was recorded via microneurography of the right median nerve at the elbow. RESULTS: A large increase in MSNA burst frequency (BF) occurred during exercise with expiratory resistance, accompanied by an augmentation of mean BP (MBP) (with resistance: MSNA BF +39.0%, MBP +42.5%, without resistance: MSNA BF: +12.8%, MBP: +22.9%, from resting values). CONCLUSIONS: These results suggest that an enhancement of expiratory muscle activity leads to large increases in muscle sympathetic vasomotor outflow and BP during dynamic leg exercise.
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 | 0.001 |
| 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".