Modulation of TASK-1/3 channels at the hypoglossal motoneuron pool and effects on tongue motor output and responses to excitatory inputs in vivo: implications for strategies for obstructive sleep apnea pharmacotherapy
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) occurs exclusively during sleep due to reduced tongue motor activity. Withdrawal of excitatory inputs to the hypoglossal motor nucleus (HMN) from wake to sleep contributes to this reduced activity. Several awake-active neurotransmitters with inputs to the HMN (e.g. serotonin [5-HT]) inhibit K+ leak mediated by TASK-1/3 channels on hypoglossal motoneurons, leading to increased neuronal activity in vitro. We hypothesize that TASK channel inhibition at the HMN will increase tongue muscle activity in vivo and modulate responses to 5-HT. We first microperfused the HMN of anesthetized rats with TASK channel inhibitors: doxapram (75 μM, n = 9), A1899 (25 μM, n = 9), ML365 (25 μM, n = 9), acidified artificial cerebrospinal fluid (ACSF, pH = 6.25, n = 9); and a TASK channel activator terbinafine (50 μM, n = 9); all with and without co-applied 5-HT (10 mM). 5-HT alone at the HMN increased tongue motor activity (202.8% ± 45.9%, p < 0.001). However, neither the TASK channel inhibitors, nor activator, at the HMN changed baseline tongue activity (p > 0.716) or responses to 5-HT (p > 0.127). Tonic tongue motor responses to 5-HT at the HMN were also not different (p > 0.05) between ChAT-Cre:TASKf/f mice (n = 8) lacking TASK-1/3 channels on cholinergic neurons versus controls (n = 10). In freely behaving rats (n = 9), microperfusion of A1899 into the HMN increased within-breath phasic tongue motor activity in wakefulness only (p = 0.005) but not sleep, with no effects on tonic activity across all sleep-wake states. Together, the findings suggest robust maintenance of tongue motor activity despite various strategies for TASK channel manipulation targeting the HMN in vivo, and thus currently do not support this target and direction for potential OSA pharmacotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".