The Effect of Nutritional Supplementation in Ex Vivo Lung Perfusion Perfusate on Human Lung Endothelial Cell Function
Bibliographic record
Abstract
Clinical application of ex vivo lung perfusion (EVLP) has increased marginal donor lung utilization. It has been developed as a platform for donor lung reconditioning. However, many of the current repair strategies are limited by a maximum reliable EVLP circuit duration of 12 h. Past studies have successfully extended EVLP through nutrient supplementation, but the exact components and respective mechanisms by which EVLP is extended remains unknown. As such, the focus of this study was to systematically evaluate the effects of nutritional supplements in EVLP perfusates on cell apoptosis, viability, confluence, and migration. To test this, we developed a high-throughput human lung endothelial cell culture platform where experimental perfusates with various combinations of GlutaMAX (a glutamine dipeptide), Travasol (amino acids), Intralipid (lipids), Multi-12 (vitamins), cysteine, and glycine were tested using the Incucyte Live imaging system. GlutaMAX supplementation alone significantly reduced apoptosis, improved viability and cell migration beyond all other supplements and further outperformed standard endothelial cell culture medium. Travasol offered short-term benefits, while Intralipid offered minimal functional support. Multi-12 improved viability and apoptosis independently and in combination with other supplements. The best experimental perfusate targeted the glutathione synthesis pathway, combining GlutaMAX, cysteine and glycine and further reduced apoptosis compared with GlutaMAX alone. Collectively, these results suggest that nutrient selection during EVLP is critical and highlights the need to systematically evaluate perfusate modifications as opposed to broad-spectrum nutrient delivery. This in vitro model provides a cost-effective platform for preclinical screening of perfusate modifications to enhance organ viability during EVLP.
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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 | 0.000 | 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) | 0.000 | 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".