Normothermic Perfusion is Superior to Cold Perfusion in Porcine Ex Situ Lung Perfusion
Bibliographic record
Abstract
BACKGROUND: Cold ex situ lung perfusion (ESLP) has demonstrated improved preservation in small animal ESLP compared to normothermic ESLP and cold static preservation. We hypothesized that cold negative pressure ventilation (NPV)-ESLP would improve graft function in a porcine transplantation model. METHODS: Four perfusate temperatures were examined with 12 hours NPV-ESLP in a large animal transplantation model. Pig lungs were allotted to four groups: (1) Normothermia (38°C, n = 6); (2) profound hypothermia (10°C, n = 6); (3) moderate hypothermia (20°C, n = 3); (4) subnormothermia (32°C, n = 3). A fifth group subnormothermic low-flow (SNLF) perfusion was examined to assess the effect of reduced cardiac output with cold perfusion (32°C, 10% cardiac output, n = 6). RESULTS: Only Normothermic and SNLF groups demonstrated acceptable oxygenation after 12 hours NPV-ESLP and were transplanted. All other groups failed prematurely. After 12 hours of ESLP, Normothermic lungs demonstrated significantly greater dynamic compliance compared to SNLF lungs (P = .03). Edema formation post-ESLP was significantly worse in the SNLF group (P = .01). There was no significant difference in pulmonary artery pressures after ESLP (P = .10); however, pulmonary vascular resistance was significantly greater in the SNLF (P = .04). Isolated left lung oxygenation 4-hours post-transplant and left lung edema formation was not significantly different between Normothermic and SNLF post-transplant (P = .09). Proinflammatory cytokines were significantly greater during SNLF-ESLP (tumor necrosis factor alpha, P < .05). CONCLUSIONS: Prolonged normothermic (38°C) NPV-ESLP is superior to 10, 20, and 32°C perfusion. Normothermic ESLP of porcine lungs results in superior graft function and reduced inflammation versus SNLF-ESLP.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".