Normothermic Ex Vivo Perfusion Prevents Lung Injury Compared to Extended Cold Preservation for Transplantation
Bibliographic record
Abstract
Treatment of injured donor lungs ex vivo to accelerate organ recovery and ameliorate reperfusion injury could have a major impact in lung transplantation. We have recently demonstrated a feasible technique for prolonged (12 h) normothermic ex vivo lung perfusion (EVLP). This study was performed to examine the impact of prolonged EVLP on ischemic injury. Pig donor lungs were cold preserved in Perfadex for 12 h and subsequently divided into two groups: cold static preservation (CSP) or EVLP at 37 degrees C with Steen solution for a further 12 h (total 24 h preservation). Lungs were then transplanted and reperfused for 4 h. EVLP preservation resulted in significantly better lung oxygenation (PaO(2) 531 +/- 43 vs. 244 +/- 49 mmHg, p < 0.01) and lower edema formation rates after transplantation. Alveolar epithelial cell tight junction integrity, evaluated by zona occludens-1 protein staining, was disrupted in the cell membranes after prolonged CSP but not after EVLP. The maintenance of integrity of barrier function during EVLP translates into significant attenuation of reperfusion injury and improved graft performance after transplantation. Integrity of functional metabolic pathways during normothermic perfusion was confirmed by effective gene transfer and GFP protein synthesis by lung alveolar cells. In conclusion, EVLP prevents ongoing injury associated with prolonged ischemia and accelerates lung recovery.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".