Ampakine CX717 potentiates intermittent hypoxia-induced hypoglossal long-term facilitation
Bibliographic record
Abstract
Glutamatergic currents play a fundamental role in regulating respiratory motor output and are partially mediated by α-amino-3-hydroxy-5-methyl-isoxazole-propionic acid (AMPA) receptors throughout the premotor and motor respiratory circuitry. Ampakines are pharmacological compounds that enhance glutamatergic transmission by altering AMPA receptor channel kinetics. Here, we examined if ampakines alter the expression of respiratory long-term facilitation (LTF), a form of neuroplasticity manifested as a persistent increase in inspiratory activity following brief periods of reduced O2 [intermittent hypoxia (IH)]. Current synaptic models indicate enhanced effectiveness of glutamatergic synapses after IH, and we hypothesized that ampakine pretreatment would potentiate IH-induced LTF of respiratory activity. Inspiratory bursting was recorded from the hypoglossal nerve of anesthetized and mechanically ventilated mice. During baseline (BL) recording conditions, burst amplitude was stable for at least 90 min (98 ± 5% BL). Exposure to IH (3 × 1 min, 15% O2) resulted in a sustained increase in burst amplitude (218 ± 44% BL at 90 min following final bout of hypoxia). Mice given an intraperitoneal injection of ampakine CX717 (15 mg/kg) 10 min before IH showed enhanced LTF (500 ± 110% BL at 90 min). Post hoc analyses indicated that CX717 potentiated LTF only when initial baseline burst amplitude was low. We conclude that under appropriate conditions ampakine pretreatment can potentiate IH-induced respiratory LTF. These data suggest that ampakines may have therapeutic value in the context of hypoxia-based neurorehabilitation strategies, particularly in disorders with blunted respiratory motor output such as spinal cord injury.
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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 | 0.001 |
| 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".