Metabolomic and lipidomic landscape of porcine kidney associated with kidney perfusion in heart beating donors and donors after cardiac death
Bibliographic record
Abstract
Abstract With the ever-increasing shortage of kidney donors, transplant centers are faced with the challenge of finding ways to maximize their use of all available organ resources and extend the donor pool, including the use of expanded criteria donors. To address the need for a new analytical solution for graft quality assessments, we present a novel biochemical analysis method based on solid-phase microextraction (SPME) – a chemical biopsy. In this study, renal autotransplantation was performed in porcine models to simulate two types of donor scenarios: heart beating donors (HBD) and donors after cardiac death (DCD). All renal grafts were perfused using continuous normothermic ex vivo kidney perfusion. The small diameter of SPME probes enables minimally invasive and repeated sampling of the same tissue, thus allowing changes occurring in the organ to be tracked throughout the entire transplantation procedure. Samples were subjected to metabolomic and lipidomic profiling using high-performance liquid chromatography coupled with a mass spectrometer. As a result, we observed differences in the profiles of HBD and DCD kidneys. The most pronounced alterations were reflected in the levels of essential amino acids, purine nucleosides, lysophosphocholines, phosphoethanolamines, and triacylglycerols. Our findings demonstrate the potential of chemical biopsy in donor graft quality assessment and monitoring kidney function during perfusion.
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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".