Molecular Phenotypes of Acute Kidney Injury in Kidney Transplants
Bibliographic record
Abstract
Little is known regarding the molecular phenotype of kidneys with AKI because biopsies are performed infrequently. However, all kidney transplants experience acute injury, making early kidney transplants an excellent model of acute injury, provided the absence of rejection, because donor kidneys should not have CKD, post-transplant biopsies occur relatively frequently, and follow-up is excellent typically. Here, we used histopathology and microarrays to compare indication biopsies from 26 transplants with acute injury with 11 pristine protocol biopsies of stable transplants. Kidneys with acute injury showed increased expression of 394 transcripts associated with the repair response to injury, including many epithelium-like injury molecules tissue, remodeling molecules, and inflammation molecules. Many other genes also predicted the phenotype, including the acute injury biomarkers HAVCR1 and IL18. Pathway analysis of the injury-repair transcripts revealed similarities to cancer, development, and cell movement. The injury-repair transcript score in kidneys with acute injury correlated with reduced graft function, future renal recovery, brain death, and need for dialysis, but not with future graft loss. In contrast, histologic features of acute tubular injury did not correlate with function or with the molecular changes. Thus, the transcripts associated with repair of injury suggest a massive coordinated response of the kidney parenchyma to acute injury, providing both an objective measure for assessing the severity of injury in kidney biopsies and validation for many biomarkers of AKI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".