Towards donor lung recovery—gene expression changes during ex vivo lung perfusion of human lungs
Bibliographic record
Abstract
We and others have demonstrated that acellular normothermic ex vivo lung perfusion of high-risk donor lungs can result in posttransplant outcomes equivalent to that of contemporaneous lung transplantation using standard donor lungs. However, the mechanism of this effect remains unclear. Given the restoration of cellular metabolic activity during normothermic perfusion, one possibility is that of lung healing via natural innate recovery mechanisms. We explored this by examining the gene expression changes occurring in human lungs during ex vivo lung perfusion. Human lungs clinically rejected for transplantation were perfused for 12 hours of EVLP with biopsies taken at the start, at 1 hour, at 3 hours, and then every 3 hours thereafter to 12 hours. Temporal changes were identified in 2585 genes using the Short Time-series Expression Miner and used for pathway analysis. Despite increases in endothelial markers of inflammation, circulating leukocyte cell-specific gene expression fell over 12 hours of ex vivo lung perfusion (EVLP), suggesting an interrupted inflammation response secondary to washout of circulating leukocytes. Analysis of these gene changes suggests lung recovery follows specific stages: cellular death, cellular preservation, cellular reorganization, and cellular invasion. EVLP may improve posttransplant lung function by washout of leukocytes and facilitating innate mechanisms of repair.
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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 | 0.000 | 0.000 |
| 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".