An Analysis of Regulatory T-Cell and Th-17 Cell Dynamics during Cytomegalovirus Replication in Solid Organ Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: CMV-specific T-cells are crucial to control CMV-replication post-transplant. Regulatory T-cells (T-regs) are associated with a tolerant immune state and may contribute to CMV-replication. However, T-cell subsets such as T-regs and IL-17 producing T-cells (Th-17) are not well studied in this context. We explored T-regs and Th-17 frequencies during CMV-replication after transplantation. METHODS: We prospectively evaluated 30 transplant patients with CMV-viremia. We quantified CMV-specific CD4(+) and CD8(+) T-cells, T-regs (CD4(+)CD25(+)FoxP3(+)) and Th-17 frequencies using flow-cytometry and followed patients requiring anti-viral treatment. Two subsets were compared: anti-viral treatment requirement (n = 20) vs. spontaneous clearance of viremia (n = 10). RESULTS: Higher initial CMV-specific CD4(+) T-cells and lower T-regs were observed in patients with spontaneous clearance (p = 0.043; p = 0.021 respectively). Using a ratio of CMV-specific CD4(+) T-cells to T-regs allowed prediction of viral clearance with 80% sensitivity and 90% specificity (p = 0.001). One month after stop of treatment, the same correlation was observed in patients protected from CMV-relapse. The ratio of CMV-specific CD4(+) T-cells to T-regs allowed prediction of relapse with 85% sensitivity and 86% specificity (p = 0.004). Th-17 responses were not correlated with virologic outcomes. CONCLUSIONS: This study provides novel insights into T-regs and Th-17 subpopulations during CMV-replication after transplantation. These preliminary data suggest that measurement of CMV-specific CD4(+) T-cells together with T-regs has value in predicting spontaneous clearance of viremia and relapse.
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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) | 0.001 | 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".