State-dependent vs. central motor effects of ethanol on breathing
Bibliographic record
Abstract
Ethanol, one of the most widely used drugs in Western society, worsens obstructive sleep apnea in humans. No studies, however, have distinguished between two primary mechanisms that could mediate suppression of genioglossus (GG) activity with ethanol. We test the hypothesis that ethanol suppresses GG activity by effects at the hypoglossal motor pool and/or by state-dependent regulation of motor activity via independent influences on sleep/arousal processes. Intraperitoneal injections of ethanol (1.25 g/kg, n = 6 rats) resulted in maximum blood levels of 125.5 +/- 15.8 mg/dl, i.e., physiologically relevant levels for producing behavioral impairment in rats and humans. Ethanol decreased wakefulness, reduced sleep latency, and increased non-rapid eye movement sleep (P < 0.001, n = 10 rats) and significantly reduced postural muscle tone and electroencephalogram frequencies, consistent with sedation. Ethanol also caused a state-dependent (wakefulness only) decrease in respiratory-related GG activity (P = 0.018) but did not affect diaphragm amplitude or rate, with the magnitude of GG decrease related to baseline activity (P < 0.0002). Ethanol did not alter GG activity when applied to the hypoglossal motor pool (0.025-1 M, n = 16 isoflurane-anesthetized rats). In conclusion, ethanol promoted sleep and altered electroencephalogram and postural motor activities, indicative of sedation. The lack of effect on GG with ethanol at the hypoglossal motor pool indicates that the GG and postural motor suppression following systemic administration was mediated via effects on state-dependent/arousal-related processes. These data show that ethanol can suppress GG by primary influences on state-dependent aspects of central nervous system function independent of effects on the respiratory network per se, a distinction that has not previously been identified experimentally.
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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".