Coordination of the Respiratory Muscles in Hypercapnic and Hypoxic Environments
Bibliographic record
Abstract
Breathing is dependent on the coordination of respiratory muscles. The timing and contribution of the respiratory muscles determine the balance between breathing frequency and tidal volume. At rest, the mammalian breathing cycle has three phases: active inspiration (I), expiratory braking (E1) and an expiratory pause (E2). With elevated respiratory drive, expiratory muscles may contract to produce active expiration. This study investigated how respiratory muscles are recruited under hypercapnic and hypoxic conditions. These stimuli modify the breathing response by primarily increasing tidal volume or breathing frequency, respectively. EMG electrodes were placed in the diaphragm, intercostal and abdominal muscles. Anesthetized and unanesthetized rats were then exposed to progressive hypercapnia and hypoxia. Progressive hypercapnia (0% to 10% CO 2 ) induced active expiration at the end of the breathing cycle (E3 phase) at 8% and 10% inspired CO 2 in unanesthetized rats. The placement in E3 correlated with an increase in tidal volume. Active expiration was not observed in anesthetized rats, even at the highest level of CO 2 . Progressive hypoxia (21% to 9% O 2 ) never induced active expiration, in either unanesthetized or anesthetized rats indicating that passive expiration was sufficient even at the highest breathing frequencies. This further suggests that the control mechanisms that coordinate the respiratory muscles during hypercapnia and hypoxia are different and dependent on higher brain centers. Supported by the NSERC of Canada.
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".