Pre-Operative Regulatory T Cell Suppressive Function Correlates with Short-Term Graft Outcome after Kidney Transplantation
Bibliographic record
Abstract
Introduction: Regulatory T cells (Treg) are involved in preventing rejection and inducing tolerance after kidney transplantation by suppressing the action of effector T cells (Teff). A higher Treg frequency and suppressive function or a lower Teff frequency and proliferation prior to kidney transplantation could limit initial immunologic injury and be associated with better graft outcome post-transplant. We investigated whether Treg frequency and function or Teff frequency and proliferation correlated with estimated glomerular filtration rate (eGFR) following kidney transplantation. Methods: Peripheral blood was collected from 49 de novo kidney transplant recipients (44 deceased donors, 5 living donors) pre-operatively prior to induction immunosuppression (34 anti-thymocyte globulin, 12 alemtuzumab, 3 basiliximab). All recipients received tacrolimus and mycophenolate mofetil for maintenance immunosuppression, with corticosteroids used in all but the alemtuzumab group. Mononuclear cells were isolated by density centrifugation. CD4+CD25hiFoxP3+ Treg and CD4+CD25- Teff frequencies were measured by flow cytometry. For assessment of Treg suppressive function and Teff proliferation, CD4+CD25+ Treg (purity: 89 ± 1%) and CD4+CD25- Teff (purity: 85 ± 2%) were magnetically isolated. CFSE-labelled Teff were co-cultured in the presence of anti-CD3/CD28-coated beads with Treg at a 1:0 and 1:1 ratio for 5 days. Treg suppressive function was measured by percentage suppression of CFSE-labelled Teff proliferation. Graft function at 7, 14, 30, 90, and 180 days post-transplant was assessed by eGFR calculated by the MDRD formula. Correlations were made using Spearman's rank order correlation. Results: Pre-operative Treg frequency did not correlate with post-transplant eGFR. However, significant positive correlations were found between pre-operative Treg suppressive function and eGFR on post-transplant day 7, 14, 30, 90, and 180 (Fig 1A–E). Significant inverse correlations were also found between pre-operative Teff frequency and eGFR on post-transplant day 7, 14, and 30 (rs=-0.44 p< 0.01, rs=-0.35 p=0.02, and rs=-0.36 p=0.02 respectively). There were no correlations between pre-operative Teff proliferation and eGFR.[Figure 1]Conclusion: Pre-operative Treg suppressive function but not frequency correlated with graft function up to 180 days after kidney transplantation, while a weaker inverse correlation existed between pre-operative Teff frequency and graft function up to 30 days after transplantation. These results suggest that pre-operative Treg suppressive function measurement could be a tool to identify patients at risk for short-term graft dysfunction. Additionally, development of clinical strategies to enhance Treg suppressive function pre-emptively could improve outcomes in kidney transplantation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".