Acellular ex vivo lung perfusate silences pro-inflammatory signaling in human lung endothelial and epithelial cells
Bibliographic record
Abstract
BACKGROUND: Ischemia-reperfusion injury is a key complication following lung transplantation. The clinical application of ex vivo lung perfusion (EVLP) to assess donor lung function has significantly increased the utilization of "marginal" donor lungs with good clinical outcomes. The potential of EVLP on improving organ quality and ameliorating ischemia-reperfusion injury has been suggested. METHODS: To determine the effects of ischemia-reperfusion and EVLP on gene expression in human pulmonary microvascular endothelial cells and epithelial cells, cell culture models were used to simulate cold ischemia (4 °C for 18 h) followed by either warm reperfusion (DMEM + 10% FBS) or EVLP (acellular Steen solution) at 37 °C for 4 h. RNA samples were extracted for bulk RNA sequencing, and data were analyzed for significant differentially expressed genes and pathways. RESULTS: Endothelial and epithelial cells showed significant changes in gene expressions after ischemia-reperfusion or EVLP. Ischemia-reperfusion models of both cell types showed upregulated pro-inflammatory and downregulated cell metabolism pathways. EVLP models, on the other hand, exhibited downregulation of cell metabolism, without any inflammatory signals. CONCLUSION: The commonly used acellular EVLP perfusate, Steen solution, silenced the activation of pro-inflammatory signaling in both human lung endothelial and epithelial cells, potentially through the lack of serum components. This finding could establish the basic groundwork of studying the benefits of EVLP perfusate as seen from current clinical practice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".