Metabolic changes during cold ischemic preservation and reperfusion in porcine lung transplants
Bibliographic record
Abstract
Lung transplantation is a cornerstone in treating patients with end-stage lung disease, yet ischemia-reperfusion injury poses significant complications in posttransplant recovery. This study aimed to understand the effects of donor type, cold ischemic time (CIT), and reperfusion on metabolic changes in lung grafts. Porcine donor lungs underwent different CITs on ice: minimal time (control), 6 hours (CIT-6H), and 30 hours (CIT-30H). Additionally, lungs recovered from animals after brain death (BD) underwent 24-hour CIT (BD-CIT-24H). Both CIT-30H and BD-CIT-24H lungs underwent ex vivo lung perfusion for 12 hours, followed by left lung transplantation and reperfusion for 2 hours. Lung tissue samples were subjected to metabolomic analysis. Cold preservation induced time-dependent changes of certain metabolites. In the BD-CIT-24H group, while most trends in metabolite levels were similar to those in the CIT-30H group, some were markedly different. In CIT-30H lungs, reperfusion induced significant changes in the carbohydrate and amino acid pathways, along with consumption of energy substrates and reduction in antioxidants. BD donor lungs exhibited significantly reduction in lysophospholipids after reperfusion. Understanding these metabolic changes in the lung grafts shed lights on the mechanism of ischemia-reperfusion injury, offering valuable insights for future development of targeted strategies to improve donor lung preservation and 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".