Age‐ and sex‐mediated differences in T lymphocyte populations of kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: Graft failure rates increase through childhood and adolescence, decline in adulthood, and are higher in female than male kidney transplant recipients (KTR) until middle age. We aimed to describe age- and sex-related differences in T-cell subsets among KTR to determine which differences may help to explain the differences in kidney graft failure rates. METHODS: Effector T (Teff)-cell and regulatory T (Treg)-cell phenotypes in PBMCs from healthy controls and KTR, who were at least 1 year post-transplant with stable graft function under immunosuppression, were analyzed by flow cytometry. The effects of age, sex, and status (KTR or control) were analyzed using linear regressions. RESULTS: We enrolled 20 male and 21 female KTR and 20 male and 20 female controls between 3 and 29 years of age. CD3+ T-cell frequencies were not associated with age or sex but were higher in KTR than controls. There were no differences in CD4+ and CD8+ frequencies. Th1 (IFNγ+ IL-4- IL-17A-) and Th17 (IL-17A+) frequencies within the CD4+ T-cell population were higher at older ages. The frequencies of FOXP3 + Helios + Treg cells in CD4+ CD25+ CD127- T cells were lower in females than males and in KTR than controls. CONCLUSIONS: Increasing frequencies of Th1 and Th17 cells with increasing age mirrors the increasing graft failure rates from childhood to young adulthood. Importantly, sex differences in frequencies of circulating Treg cells may suggest a role in the sex differences in graft failure rates.
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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 | 0.001 |
| 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) | 0.002 | 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".