Delineating the Metabolic Phenotype of Biopsy-Derived Kidney Cells
Bibliographic record
Abstract
Kidney transplantation is the optimal treatment for end-stage kidney disease; however, transplanted kidneys are often lost prematurely, with up to 50% graft loss at 10 years post-transplant. One of the major causes of premature graft loss is the injury sustained by the graft at the time of transplantation, known as ischemia-reperfusion injury (IRI). Delayed graft function (DGF), defined as the need for dialysis in the first week post-transplant, is a manifestation of severe IRI that shows functional and histologic features of acute kidney injury (AKI). While the mechanisms driving AKI remain unclear, accumulating evidence suggests that altered metabolic function in the allograft mediates AKI and may be the reason for DGF. Thus, deciphering and monitoring the metabolic underpinnings of IRI will improve our capacity to diagnose and prevent AKI. This article describes a unique method to assess mitochondrial respiration (by means of oxygen consumption rate), glycolysis (extracellular acidification rate), and intracellular ATP levels in needle biopsy-derived kidney cell suspensions. The methodology has been optimized in healthy adult male pigs and validated in a porcine model of auto-transplantation. The approach presented has the potential to enhance the real-time assessment of kidney allograft viability in the clinic. Profiling metabolism in patient-derived biopsies may also uncover new biology in other metabolism-based kidney diseases.
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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".