Pretransplantation Recipient Regulatory T cell Suppressive Function Predicts Delayed and Slow Graft Function after Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Delayed graft function (DGF) and slow graft function (SGF) are a continuous spectrum of ischemia-reperfusion-related acute kidney injury (AKI) that increases the risk for acute rejection and graft loss after kidney transplantation. Regulatory T cells (Tregs) are critical in transplant tolerance and attenuate murine AKI. In this prospective observational cohort study, we evaluated whether pretransplantation peripheral blood recipient Treg frequency and suppressive function are predictors of DGF and SGF after kidney transplantation. METHODS: Deceased donor kidney transplant recipients (n=53) were divided into AKI (n=37; DGF, n=10; SGF, n=27) and immediate graft function (n=16) groups. Pretransplantation peripheral blood CD4CD25FoxP3 Treg frequency was quantified by flow cytometry. Regulatory T-cell suppressive function was measured by suppression of autologous effector T-cell proliferation by Treg in co-culture. RESULTS: Pretransplantation Treg suppressive function, but not frequency, was decreased in AKI recipients (P<0.01). In univariate and multivariate analyses accounting for the effects of cold ischemic time and donor age, Treg suppressive function discriminated DGF from immediate graft function recipients in multinomial logistic regression (odds ratio, 0.77; P<0.01), accurately predicted AKI in receiver operating characteristic curve (area under the curve, 0.82; P<0.01), and predicted 14-day estimated glomerular filtration rate in linear regression (P<0.01). CONCLUSION: Our results indicate that recipient peripheral blood Treg suppressive function is a potential independent pretransplantation predictor of DGF and SGF.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".