Ampakines Stimulate Diaphragm Activity after Spinal Cord Injury
Bibliographic record
Abstract
Respiratory compromise after cervical spinal cord injury (SCI) is a leading cause of mortality and morbidity. Most SCIs are incomplete, and spinal respiratory motoneurons as well as proprio- and bulbospinal synaptic pathways provide a neurological substrate to enhance respiratory output. Ampakines are allosteric modulators of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, which are prevalent on respiratory neurons. We hypothesized that low dose ampakine treatment could safely and effectively increase diaphragm electromyography (EMG) activity that has been impaired as a result of acute- or sub-acute cervical SCI. Diaphragm EMG was recorded using chronic indwelling electrodes in unanesthetized, freely moving rats. A spinal hemi-lesion was induced at C2 (C2Hx), and rats were studied at 4 and 14 days post-injury during room air breathing and acute respiratory challenge accomplished by inspiring a 10% O2, 7% CO2 gas mixture. Once a stable baseline recording was established, one of two different ampakines (CX717 or CX1739, 5 mg/kg, intravenous) or a vehicle (2-hydroxypropyl-beta-cyclodextrin [HPCD]) was delivered. At 4 days post-injury, both ampakines increased diaphragm EMG output ipsilateral to C2Hx during both baseline breathing and acute respiratory challenge. Only CX1739 treatment also led to a sustained (15 min) increase in ipsilateral EMG output. At 14 days post-injury, both ampakines produced sustained increases in ipsilateral diaphragm EMG output and enabled increased output during the respiratory challenge. We conclude that low dose ampakine treatment can increase diaphragm EMG activity after cervical SCI, and therefore may provide a pharmacological strategy that could be useful in the context of respiratory rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".