Metabolomic Insight into Donation After Circulatory-Death Kidney Grafts in Porcine Autotransplant Model: Normothermic Ex Vivo Kidney Perfusion Compared with Hypothermic Machine Perfusion and Static Cold Storage
Bibliographic record
Abstract
Organ shortage is a major challenge in transplantation, prompting the use of extended criteria donor grafts. These require improved preservation techniques and reliable methods to assess graft function. This study aimed to evaluate changes in the kidney metabolome following three preservation methods: normothermic ex vivo kidney perfusion (NEVKP), hypothermic machine perfusion (HMP) and static cold storage (SCS) in porcine autotransplant models. A chemical biopsy allowed minimally invasive sampling of metabolites, which were analyzed using liquid chromatography coupled with high-resolution mass spectrometry. The results highlighted metabolites affected by ischemia and oxidative stress in donor kidneys, as well as changes specific to each preservation method. Differences were observed immediately after transplantation and reperfusion and several days post-surgery. NEVKP was associated with the activation of physiological anti-oxidative and anti-inflammatory mechanisms, suggesting potential protective effects. However, some metabolites had dual roles, which may influence future graft treatment designs. HMP and SCS, while reducing energy demand in cells, also limit physiological repair mechanisms. These findings provide a basis for improving graft assessment and organ preservation, with chemical biopsy serving as both a tool for discovery and a potential diagnostic method for monitoring graft quality.
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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) | 0.001 | 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".