Acute effects of normocapnic hyperpnoea exercise on intercostal and locomotor muscle oxygenation in COPD
Bibliographic record
Abstract
Introduction: During whole body exercise, respiratory muscle work may affect locomotor muscle oxygenation and consequently reduce exercise capacity1. Respiratory muscle training using normocapnic hyperpnoea exercise (NHE) is a promising strategy to improve exercise capacity in COPD. Aims: To investigate the acute effects of NHE on intercostal and locomotor muscle oxygenation in COPD. Methods: Eight patients with COPD (FEV1 = 55±8% of pred) performed a NHE at 50-60% of peak ventilation (VEpeak) assessed during maximal cycling test. The NHE was conducted to exhaustion, using a respiratory device (SpiroTiger®) connected to a gas analyzer to monitor VE, VO2 and PETCO2. Cardiac output (CO) and intercostal and vastus lateralis muscle oxygenation were continuously measured during NHE by means of finger photoplethysmography and NIRS, respectively. Arterial blood gases (PaCO2) and inspiratory capacities (IC) were obtained at rest and at end-exercise. Results: The NHE was maintained for 653±397s, at a VE averaging 25±10 l/min or 52±5% of VEpeak and with a VO2 corresponded to 52±6% of VO2peak. Intercostal muscle deoxyhemoglobin and total hemoglobin increased by 14±6% and 13±8% respectively (p<0.05) while they remained unchanged in the vastus lateralis (p=NS). Compared to resting values, there was no change in PETCO2, PaCO2, IC and CO during NHE (p=NS). Conclusions: NHE promotes muscle deoxygenation in the intercostals but not in the vastus lateralis but it was not accompanied by changes in CO or dynamic hyperinflation in COPD. Our results suggest that NHE stimulates the intercostal muscles, which could potentially improve exercise tolerance following a NHE training. 1 Harms et al JAP 1997
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".