Delivery of CO<sub>2</sub>by displacement ventilation for the treatment of sleep apnea
Bibliographic record
Abstract
Introduction: In this study, we explore a therapeutic intervention using respiratory gases and a surfactant in a Brown Norway (BN) rat model of central sleep apnea. The BN rat exhibits a high spontaneous sleep apnea index, which is greatly increased following allergen-induced bronchoconstriction. Aims and objectives: To determine if carbon dioxide gas and/or a synthetic nebulized surfactant, Perflubron (PFOB) can treat central sleep apnea in the BN rat model and to determine if they can be delivered non-invasively using displacement ventilation. Methods: A late phase allergic airway response was elicited in ovalbumin-sensitized and challenged BN rats. Apneas were monitored using a non-invasive whole-body barometric plethysmograph. During the late-phase plateau, rats were treated for 10 minutes with one of the following: 8% CO2, 8% CO2 with nebulized perfluorooctylbromide (PFOB), nebulized PFOB, or medical air. Results: PFOB and CO2 inhalation resulted in immediate and sustained decreases in apnea duration. The combination of gases had an additive effect Conclusion: This BN rat model appears to have aspects of both CSA and OSA. CO2 alone and in combination with PFOB, are potent formulations for reducing apneic events following allergen challenge. A novel non-invasive delivery device (figure1B), under development for the treatment of SA, is being tested in this animal model. Funded by AIHS/Pfizer.
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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".