Innate and Adaptive Immune Correlates of Chronic and Self-limiting EBV DNAemia in Solid-organ Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: Epstein-Barr virus (EBV) DNAemia is a major risk factor for posttransplant lymphoproliferative disorder; however, immune correlates of EBV DNAemia in the transplant setting are limited. METHODS: Peripheral blood mononuclear cells were collected from 30 transplant recipients with self-limiting EBV DNAemia (SLD; n = 11) or chronic EBV DNAemia (CD; n = 19) at enrollment and 4-8 weeks later. Mass cytometry was used to characterize innate and T-cell immune correlates of EBV DNAemia. Furthermore, flow cytometry was used to measure the frequency of EBV-specific T-cell responses between groups following stimulation with an EBV-infected cell lysate. RESULTS: Unsupervised analysis of the innate compartment (CD3CD19 cells) identified 5 CD11c clusters at higher abundance in the SLD group (false discovery rate ≤ 1%). These clusters expressed CD11b, CD45RO, CD14, CD123, CD127, and CD38, among others. Unsupervised profiling of the T-cell compartment (CD3CD19) revealed 2 CD4 T-cell clusters at higher frequency among those with SLD (false discovery rate ≤ 1%), which expressed CD45RA, CCR7, CD27, CD28, and CD40L-suggestive of a naive T cell (TN). Manual biaxial gating confirmed increased frequencies of conventional dendritic cells (3.1% versus 2.1%; P = 0.023) and CD4 TN (4.4% versus 1.9%; P = 0.018) among those with SLD. Last, frequencies of interferon-γ-producing EBV-specific CD4 T cells were significantly lower in the CD group relative to those with SLD (4243 versus 250 cells/10 cells; P = 0.015). CONCLUSIONS: CD is associated with a reduction of CD11c cells, CD4 TN, and interferon-γ-producing EBV-specific CD4 T cells, suggesting an interplay between innate and adaptive immune compartments may be important for regulating EBV DNAemia.
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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".