Pre-Transplant Circulating CD4+CD25hiCD127lo Tregs Predict Delayed and Slow Graft Function After Kidney Transplantation.
Bibliographic record
Abstract
Background: Regulatory T cells (Tregs) dampen acute kidney injury (AKI) in murine renal ischemia-reperfusion injury (IRI). In kidney transplantation (KTx), delayed (DGF) and slow (SGF) graft function are the results of IRI and negatively impact long-term graft outcomes. We investigated if measuring circulating CD4+CD25hiCD127lo Tregs in KTx recipients pre-transplant predicts DGF/SGF. Methods: 74 sequential deceased donor KTx recipients were divided into DGF (n=17), SGF (n=35), and immediate graft function (IGF; n=22) based on post-transplant dialysis and 24-h SCr. Since DGF and SGF are a continuous spectrum of IRI, those outcomes were combined to form an AKI group (n=52). Donor, organ procurement, and recipient variables were similar between groups except for donor age and cold ischemic time (CIT). CD4+CD25hiCD127lo Tregs were quantified in recipient peripheral blood by flow cytometry pre-transplant. Results: Pre-transplant circulating CD4+CD25hiCD127lo Tregs were decreased in DGF (0.56±0.08%) and SGF (0.57±0.05%) in comparison to IGF (0.81±0.08%) recipients (p<0.03, Figure 1). Using IGF as reference group in multinomial logistic regression, each 0.1% increase in Tregs decreased the odds of having DGF or SGF by 9% (p<0.03). Tregs also accurately predicted AKI in ROC curve (AUC=0.69, p<0.01, Figure 2). When accounting for donor age and CIT in multivariate analysis, Tregs remained a significant predictor of DGF (p<0.03) and AKI (p=0.02) in multinomial and binary logistic regressions.Figure: No Caption available.Figure: No Caption available.Conclusion: Measurement of pre-transplant recipient circulating CD4+CD25hiCD127lo Tregs is a novel immune marker to identify patients at risk for DGF/SGF, and has the potential to guide Treg immunotherapy to prevent graft injury. DISCLOSURES:Cantarovich, M.: Grant/Research Support, Astellas, Novartis, Other, Astellas, Advisory board.
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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) | 0.002 | 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".