Activation of PANoptosis and ferroptosis during ex vivo lung perfusion in human lungs
Bibliographic record
Abstract
BACKGROUND: A recent study demonstrated upregulation of PANoptosis-related genes during reperfusion in human lung transplants. However, the impact of ex vivo lung perfusion (EVLP) on different cell death pathways and their relationship with inflammatory genes and clinical characteristics remains unknown. METHODS: We conducted transcriptomic analyses on pre- and post-EVLP biopsies from 49 donation after brain death (DBD) and 39 donation after circulatory death (DCD) lungs. Gene set enrichment analysis (GSEA) and single-sample GSEA were used to assess the enrichment of cell death and inflammatory pathways. We further explored the relationships between these pathways, donor characteristics, and clinical outcomes. RESULTS: DBD lungs showed significant enrichment of apoptosis and ferroptosis gene sets compared to DCD lungs. During EVLP, pyroptosis, apoptosis, necroptosis, and ferroptosis gene sets were significantly upregulated and strongly correlated with inflammatory pathways in both DBD and DCD donor lungs. Donor age, sex, and smoking history were associated with specific cell death pathways. In DCD lungs, the expression of ferroptosis-related genes was associated with recipient early outcomes. CONCLUSION: The expression of cell death gene sets is donor-type specific. The identification of multiple cell death and inflammatory pathways during EVLP provides potential therapeutic targets to improve donor lung quality and enhance clinical outcomes.
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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) | 0.000 | 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) | 0.003 | 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".