Ex vivo lung perfusion moderates gene expression differences between cardiac death and brain death donor lungs
Bibliographic record
Abstract
Donation after cardiac death (DCD) donor lungs have been shown to express less pro-inflammatory genes than donation after brain death (DBD) lungs, likely due to the absence of brain-death related inflammatory physiology. However, it is unclear whether this difference is clinically significant following reperfusion. To avoid confounding by the recipient immune system and activation state, we utilized ex vivo lung perfusion (EVLP) as a reperfusion-like event and examined the effect of EVLP on the transcriptome of DCD (n=39) and DBD (n=49) lungs. To validate our RNA results, banked EVLP perfusates from a separate cohort of DCD (n=24) and DBD (n=24) cases were assayed for IL-6, IL-8, IL-10, IL-1β, sTNFR1, and sTREM1 protein levels at 15 min intervals for three hours. While DCD lungs demonstrated lower levels of pro-inflammatory transcripts and perfusate cytokine protein levels than DBD lungs prior to EVLP, after EVLP there were no significant gene expression differences or cytokine protein levels between groups. Therefore, while DCD and DBD lungs differ by the amounts of pro-inflammatory cytokines following procurement, the propagation of inflammation becomes limited during EVLP, and DBD and DCD lungs reach a similar plateau of transcript expression, including pro-inflammatory cytokines at the end of perfusion. EVLP may therefore play a pre-conditioning role by dampening the pro-inflammatory state prior to transplant reperfusion.
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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 | 0.000 |
| 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".