Coadministration of the AMPAKINE CX717 with Propofol Reduces Respiratory Depression and Fatal Apneas
Bibliographic record
Abstract
BACKGROUND: Propofol (2,6-diisopropylphenol) is used for the induction and maintenance of anesthesia in human and veterinary medicine. Propofol's disadvantages include the induction of respiratory depression and apnea. Here, the authors report a clinically feasible pharmacological solution for reducing propofol-induced respiratory depression via a mechanism that does not interfere with anesthesia. Specifically, they test the hypothesis that the AMPAKINE CX717, which has been proven metabolically stable and safe for human use, can prevent and rescue from propofol-induced severe apnea. METHODS: The actions of propofol and the AMPAKINE CX717 were measured via (1) ventral root recordings from newborn rat brainstem-spinal cord preparations, (2) phrenic nerve recordings from an adult mouse in situ working heart-brainstem preparation, and (3) plethysmographic recordings from unrestrained newborn and adult rats. RESULTS: In vitro, respiratory depression caused by propofol (2 μM, n = 11, mean ± SEM, 41 ± 5% of control frequency, 63 ± 5% of control duration) was alleviated by CX717 (n = 4, 50-150 μM). In situ, a decrease in respiratory frequency (44 ± 9% of control), phrenic burst duration (66 ± 7% of control), and amplitude (78 ± 5% of control) caused by propofol (2 μM, n = 5) was alleviated by coadministration of CX717 (50 μM, n = 5). In vivo, pre- or coadministration of CX717 (20-25mg/kg) with propofol markedly reduced propofol-induced respiratory depression (n = 7; 20mg/kg) and propofol-induced lethal apnea (n = 6; 30 mg/kg). CONCLUSIONS: Administration of CX717 before or in conjunction with propofol provides an increased safety margin against profound apnea and death.
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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) | 0.001 | 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".