State-Dependent Modulation of Breathing in Urethane-Anesthetized Rats
Bibliographic record
Abstract
Respiratory activity is most fragile during sleep, in particular during paradoxical [or rapid eye movement (REM)] sleep and sleep state transitions. Rats are commonly used to study respiratory neuromodulation, but rodent sleep is characterized by a highly fragmented sleep pattern, thus making it very challenging to examine different sleep states and potential pharmacological manipulations within them. Sleep-like brain-state alternations occur in rats under urethane anesthesia and may be an effective and efficient model for sleep itself. The present study assessed state-dependent changes in breathing and respiratory muscle modulation under urethane anesthesia to determine their similarity to those occurring during natural sleep. Rats were anesthetized with urethane and respiratory airflow, as well as electromyographic activity in respiratory muscles were recorded in combination with local field potentials in neocortex and hippocampus to determine how breathing pattern and muscle activity are modulated with brain state. Measurements were made in normoxic, hypoxic, and hypercapnic conditions. Results were compared with recordings made from rats during natural sleep. Brain-state alternations under urethane anesthesia were closely correlated with changes in breathing rate and variability and with modulation of respiratory muscle tone. These changes closely mimicked those observed in natural sleep. Of great interest was that, during both REM and REM-like states, genioglossus muscle activity was strongly depressed and abdominal muscle activity showed potent expiratory modulation. We demonstrate that, in urethane-anesthetized rats, respiratory airflow and muscle activity are closely correlated with brain-state transitions and parallel those shown in natural sleep, providing a useful model to systematically study sleep-related changes in respiratory control.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".