Modulation of Genioglossus Muscle Activity Across Sleep-Wake States by Histamine at the Hypoglossal Motor Pool
Bibliographic record
Abstract
STUDY OBJECTIVES: Histamine neurons comprise a major component of the aminergic arousal system and significantly influence sleep-wake states, with antihistamines widely used as sedative hypnotics. Unlike the serotonergic and noradrenergic components of this arousal system, however, the role of histamine in the central control of respiratory motor activity has not been determined. The aims of this study were to characterize the effects of histamine receptor agonists and antagonists at the hypoglossal motor pool on genioglossus muscle activity across sleep and awake states, and also determine if histamine contributes an endogenous excitatory drive to modulate hypoglossal motor outflow to genioglossus muscle. DESIGN, PARTICIPANTS, AND INTERVENTIONS: Thirty-three rats were implanted with electroencephalogram and neck electrodes to record sleep-wake states, and genioglossus and diaphragm electrodes for respiratory muscle recordings. Microdialysis probes were inserted into the hypoglossal motor nucleus. MEASUREMENTS AND RESULTS: Histamine at the hypoglossal motor nucleus significantly increased tonic genioglossus muscle activity in wakefulness, non-REM sleep and REM sleep. The activating effects of histamine on genioglossus muscle activity also occurred with a histamine type-1 (H1) but not H2 receptor agonist. However, H1 receptor antagonism at the hypoglossal motor nucleus did not decrease genioglossus muscle activity in wakefulness or sleep. CONCLUSIONS: The results suggest that histamine at the hypoglossal motor pool increases genioglossus muscle activity in freely behaving rats in wakefulness, non-REM, and REM sleep via an H1 receptor mechanism.
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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".