De Novo Design and Development of a Nutrient-Rich Perfusate for Ex Vivo Lung Perfusion with Cell Culture Models
Bibliographic record
Abstract
Ex vivo lung perfusion (EVLP) has increased donor lung utilization through assessment of "marginal" lungs prior to transplantation. To develop it as a donor lung reconditioning platform, prolonged EVLP is necessary, and new perfusates are required to provide sufficient nutritional support. Human pulmonary microvascular endothelial cells and epithelial cells were used to test different formulas for basic cellular function. A selected formula was further tested on an EVLP cell culture model, and cell confluence, apoptosis, and GSH and HSP70 levels were measured. When a cell culture medium (DMEM) was mixed with a current EVLP perfusate-Steen solution, DMEM enhanced cell confluence and migration and reduced apoptosis in a dose-dependent manner. A new EVLP perfusate was designed and tested based on DMEM. The final formula contains 5 g/L Dextran-40 and 7% albumin and is named as D05D7A solution. It inhibited cold static storage and warm reperfusion-induced cell apoptosis, improved cell confluence, and enhanced GSH and HSP70 levels in human lung cells compared to Steen solution. DMEM-based nutrient-rich EVLP perfusate could be a promising formula to prolong EVLP and support donor lung repair, reconditioning and further improve donor lung quality and quantity for transplantation with better clinical outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".