Evaluating Activated Regulatory T Cells as a Biomarker of Chronic Allograft Inflammation in Pediatric Kidney Transplant Recipients
Bibliographic record
Abstract
ABSTRACT Background There is a need for noninvasive immunological biomarkers that can identify stable kidney allograft immune quiescence to inform individualized immunosuppression. Methods We conducted a cross‐sectional, pilot cohort study evaluating the relative abundance of regulatory T cells (Tregs) to effector T‐cell (Teff) populations as a surrogate marker of long‐term graft tolerance. We obtained fresh peripheral blood mononuclear cell samples from stable pediatric kidney transplant recipients, most with recent surveillance biopsies to identify the presence or absence of chronic inflammation. Tregs were sub‐phenotyped as naïve, memory, and activated Tregs (aTreg). Treg/Teff ratios were modeled for association with chronic inflammation and in the context of potential clinical features. Results Twenty‐seven patient samples were included on standard immunosuppression (tacrolimus, mycophenolate, and prednisone) with a mean age of 9.2 ± 5.0 years, at 30.2 ± 21.7 months posttransplant. The ratio of aTreg (FOXP3++CD45RA−) to Th17 cells (CD4+IL‐17+) was significantly greater in patients without inflammation than in patients with graft inflammation (p < 0.01). Similarly, there was a trend toward greater aTreg/CD4+ T cells and aTreg/CD8+ Teff in patients without inflammation (p = 0.05 and 0.09, respectively). There was no significant association for inflammation with naïve or memory Treg/Teff ratios. Multiple logistic regression with all three aTreg/Teff ratios modeled allograft inflammation with high sensitivity and specificity (AUC = 0.83, 95% CI 0.67–0.98). Conclusions The proportion of peripheral blood aTregs/Teff cells in this pilot cohort of stable pediatric kidney transplant recipients was associated with immune quiescence. These data support further investigation into aTreg/Teff monitoring to inform precision immunosuppressive treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".