Role of Endogenous Serotonin in Modulating Genioglossus Muscle Activity in Awake and Sleeping Rats
Bibliographic record
Abstract
RATIONALE: Exogenous serotonin at the hypoglossal motor nucleus (HMN) stimulates genioglossus (GG) muscle activity. However, whether endogenous serotonin contributes to GG activation across natural sleep-wake states has not been determined, but is relevant given that serotonergic neurons have decreased activity in sleep and project to pharyngeal motoneurons. OBJECTIVES: To determine the role of endogenous serotonin at the HMN in modulating GG activity across natural sleep-wake states. METHODS: Ten rats were implanted with electroencephalogram and neck muscle electrodes to record sleep-wake states, and GG and diaphragm wires for respiratory muscle recordings. Microdialysis probes were implanted into the HMN for perfusion of artificial cerebrospinal fluid and the serotonin receptor antagonist mianserin (100 microM). MEASUREMENTS AND MAIN RESULTS: In room air, there was no effect of mianserin on respiratory-related or tonic GG activities across sleep-wake states (p > 0.300). In hypercapnia, however, the normal declines in GG activity from non-REM to REM sleep, and wakefulness to REM sleep, were reduced with mianserin (p < 0.005). These data demonstrate a normally low endogenous serotonergic drive modulating GG activity unless augmented by reflex inputs. We also demonstrated a significant serotonergic drive modulating GG activity in vagotomized rats, but not in vagi-intact rats, under anesthesia, suggesting that previous results in reduced preparations may have been influenced by vagotomy. CONCLUSIONS: The results show a minimal endogenous serotonergic drive at the HMN modulating GG activity across sleep-wake states, unless augmented by reflex inputs. This result has implications for pharmacologic strategies aiming to increase GG activity by manipulating endogenous serotonin in patients with obstructive sleep apnea.
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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 | 0.001 |
| 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".