Pretransplant Recipient Circulating CD4+CD127lo/− Tumor Necrosis Factor Receptor 2+ Regulatory T Cells
Bibliographic record
Abstract
BACKGROUND: Delayed graft function (DGF) and slow graft function (SGF) are ischemia-reperfusion-associated acute kidney injuries (AKI) that decrease long-term graft survival after kidney transplantation. Regulatory T (Treg) cells are protective in murine AKI, and their suppressive function predictive of AKI in kidney transplantation. The conventional Treg cell function coculture assay is however time-consuming and labor intensive. We sought a simpler alternative to measure Treg cell function and predict AKI. METHODS: In this prospective observational cohort study, pretransplant recipient circulating CD4+CD25+CD127lo/- and CD4+CD127lo/- tumor necrosis factor receptor 2 (TNFR2)+ Treg cells were measured by flow cytometry in 76 deceased donor kidney transplant recipients (DGF, n = 18; SGF, n = 34; immediate graft function [IGF], n = 24). In a subset of 37 recipients, pretransplant circulating Treg cell-suppressive function was also quantified by measuring the suppression of autologous effector T-cell proliferation by Treg cell in coculture. RESULTS: The TNFR2+ expression on CD4+CD127lo/- T cells correlated with Treg cell-suppressive function (r = 0.63, P < 0.01). In receiver operating characteristic curves, percentage and absolute number of CD4+CD127lo/-TNFR2+ Treg cell predicted DGF from non-DGF (IGF + SGF) with area under the curves of 0.75 and 0.77, respectively, and also AKI (DGF + SGF) from IGF with area under the curves of 0.76 and 0.72, respectively (P < 0.01). Prediction of AKI (DGF + SGF) from IGF remained significant in multivariate logistic regression accounting for cold ischemic time, donor age, previous transplant, and pretransplant dialysis modality. CONCLUSIONS: Pretransplant recipient circulating CD4+CD127lo/-TNFR2+ Treg cell is potentially a simpler alternative to Treg cell function as a pretransplant recipient immune marker for AKI (DGF + SGF), independent from donor and organ procurement characteristics.
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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) | 0.000 | 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) | 0.000 | 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".