Negative Pressure Ventilation Ex‐Situ Lung Perfusion Preserves Porcine and Human Lungs for 36‐Hours
Bibliographic record
Abstract
INTRODUCTION: Preclinically, 24-hour continuous Ex-Situ Lung Perfusion (ESLP) is the longest duration achieved in large animal models and rejected human lungs. Here, we present our 36-hour Negative Pressure Ventilation (NPV)-ESLP protocol applied to porcine and rejected human lungs. METHODS: Five sets of donor domestic pig lungs (45-55 kg) underwent 36-hour NPV-ESLP. Two sets of clinically rejected human lungs were preserved on 36-hour NPV-ESLP. Graft function was assessed via physiologic parameters, edema formation, and cytokine profiles. RESULTS: Porcine and human lung function was stable with mean partial pressure of oxygen divided by the fraction of inspired oxygen (PaO2/FiO2; PF) ratios throughout preservation of 473±11.79 and 554.7±13.26, respectively (mean±standard error of the mean). In porcine lungs, mean compliance (Cdyn) during ESLP was 33.96±2.18, pulmonary artery pressure (PAP) 13.03±0.53, and pulmonary vascular resistance (PVR) 481.20 ±21.86. In human lungs, mean Cdyn was 82.68±3.54, PAP 6.00±0.33, and PVR 184.00±9.71. Average percentage weight-gain was 34.47±13.22 in porcine lungs and 116.3±6.65 in rejected human lungs. CONCLUSION: NPV-ESLP can preserve porcine lungs and human lungs for 36-hours with acceptable physiologic function. Greater weight-gain in the human lungs is likely due to prolonged ischemic time prior to ESLP and use of an acellular perfusate. Continuous 36-hour NPV-ESLP could support therapies for endothelial protection and mitigate fluid accumulation.
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 | 0.001 | 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) | 0.000 | 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".