A novel pre-clinical strategy to deliver antimicrobial doses of inhaled nitric oxide
Bibliographic record
Abstract
Effective treatment of respiratory infections continues to be a major challenge. In high doses (≥160 ppm), inhaled Nitric Oxide (iNO) has been shown to act as a broad-spectrum antimicrobial agent, including its efficacy in vitro for coronavirus family. However, the safety of prolonged in vivo implementation of high-dose iNO therapy has not been studied. Herein we aim to explore the feasibility and safety of delivering continuous high-dose iNO over an extended period of time using an in vivo animal model. Yorkshire pigs were randomized to one of the following two groups: group 1, standard ventilation; and group 2, standard ventilation + continuous iNO 160 ppm + methylene blue (MB) as intravenous bolus, whenever required, to maintain metHb <6%. Both groups were ventilated continuously for 6 hours, then the animals were weaned from sedation, mechanical ventilation and followed for 3 days. During treatment, and on the third post-operative day, physiologic assessments were performed to monitor lung function and other significative markers were assessed for potential pulmonary or systemic injury. No significant change in lung function, or inflammatory markers were observed during the study period. Both gas exchange function, lung tissue cytokine analysis and histology were similar between treated and control animals. During treatment, levels of metHb were maintained <6% by administration of MB, and NO2 remained <5 ppm. Additionally, considering extrapulmonary effects, no significant changes were observed in biochemistry markers. Our findings showed that high-dose iNO delivered continuously over 6 hours with adjuvant MB is clinically feasible and safe. These findings support the development of investigations of continuous high-dose iNO treatment of respiratory tract infections, including SARS-CoV-2.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".