Glycine at hypoglossal motor nucleus: genioglossus activity, CO<sub>2</sub> responses, and the additive effects of GABA
Bibliographic record
Abstract
There is evidence for glycine and GABA(A)-receptor-mediated inhibition of hypoglossal motoneurons in vitro. However, comparable studies have not been performed in vivo, and the interactions of such mechanisms with integrative reflex respiratory control have also not been determined. This study tests the hypotheses that glycine at the hypoglossal motor nucleus (HMN) will suppress genioglossus (GG) muscle activity, even in the presence of hypercapnic respiratory stimulation, and the effects of glycine will be blocked by strychnine. We also determined whether coapplication of glycine and muscimol (GABA(A)- receptor agonist) to the HMN is additive in suppressing GG activity. Twenty-four urethane-anesthetized, tracheotomized, and vagotomized rats were studied. Diaphragm and GG activities, the electroencephalogram, and blood pressure were recorded. Microdialysis probes were implanted into the HMN for delivery of artificial cerebrospinal fluid (control), glycine (0.0001-10 mM), or muscimol (0.1 microM). Increasing glycine at the HMN produced graded suppression of GG activity (P < 0.001), although the GG still responded to stimulation with 7% inspired CO(2) (P = 0.002). Strychnine (0.1 mM) reversed the glycine-mediated suppression of GG activity, whereas combined glycine and muscimol were additive in GG muscle suppression. It remains to be determined whether the recruitment of such glycine and GABA mechanisms explains the periods of major GG suppression in behaviors such as rapid eye movement sleep.
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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".