Pretransplant Transcriptome Profiles Identify among Kidneys with Delayed Graft Function Those with Poorer Quality and Outcome
Bibliographic record
Abstract
Robust biomarkers are needed to identify donor kidneys with poor quality associated with inferior early and longer-term outcome. The occurrence of delayed graft function (DGF) is most often used as a clinical outcome marker to capture poor kidney quality. Gene expression profiles of 92 preimplantation biopsies were evaluated in relation to DGF and estimated glomerular filtration rate (eGFR) to identify preoperative gene transcript changes associated with short-term function. Patients were stratified into those who required dialysis during the first week (DGF group) versus those without (noDGF group) and subclassified according to 1-month eGFR of >45 mL/min (eGFR(hi)) versus eGFR of ≤45 mL/min (eGFR(lo)). The groups and subgroups were compared in relation to clinical donor and recipient variables and transcriptome-associated biological pathways. A validation set was used to confirm target genes. Donor and recipient characteristics were similar between the DGF versus noDGF groups. A total of 206 probe sets were significant between groups (P < 0.01), but the gene functional analyses failed to identify any significantly affected pathways. However, the subclassification of the DGF and noDGF groups identified 283 probe sets to be significant among groups and associated with biological pathways. Kidneys that developed postoperative DGF and sustained an impaired 1-month function (DGF(lo) group) showed a transcriptome profile of significant immune activation already preimplant. In addition, these kidneys maintained a poorer transplant function throughout the first-year posttransplant. In conclusion, DGF is a poor marker for organ quality and transplant outcome. In contrast, preimplant gene expression profiles identify "poor quality" grafts and may eventually improve organ allocation.
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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".