Normothermic Ex Situ Liver Perfusion Enhances Mitochondrial Function of DCD Grafts as Evidenced by High-throughput Metabolomics
Bibliographic record
Abstract
BACKGROUND: Normothermic ex situ liver perfusion (NEsLP) reduces reperfusion injury of donation after circulatory death (DCD) grafts and optimizes graft function. The goal of our study was to elucidate how NEsLP impacts global metabolism in DCD grafts using high-throughput metabolomics. METHODS: Pig livers were preserved by 2 different techniques: static cold storage and NEsLP. Grafts obtained from heart-beating donors were compared with donation after circulatory death (DCD) grafts with either 30 minutes (DCD30) or 60 minutes (DCD60) ischemia time. Liver tissues were collected at the end of preservation period (T0) with either cold storage or NEsLP (n = 5 per group). Grafts were transplanted into recipient pigs and a second liver biopsy was collected 2 hours following liver transplantation (T1). Snap-frozen tissue was processed and analyzed by Sciex 6600 Q-TOF high-resolution mass spectrometer. Data analysis was performed using MetaboAnalyst 4.0 software. RESULTS: Prolonged ischemia resulted in 38 out of 81 metabolites being differentially abundant over time. Mitochondrial metabolism was significantly affected, with disruption in oxidative phosphorylation capacity i.e the Warburg effect (P = 3.62E-03) and urea cycle (P = 7.95E-0.4). NEsLP resulted in improved mitochondrial metabolism and glycolysis (4.20E-02) oxidation of branched chain fatty acids (P = 4.07E-02). CONCLUSIONS: This unbiased, high-throughput metabolomics study reveals that mitochondrial function is globally rescued with NEsLP, associated with improvement in DCD graft function. NEsLP is able to rescue DCD grafts, improving their metabolic function to that of livers not exposed to DCD procurement.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".