Epstein‐Barr virus latent gene EBNA‐1 genetic diversity among transplant patients compared with patients with infectious mononucleosis
Bibliographic record
Abstract
INTRODUCTION: As a step toward evaluating the association between Epstein-Barr virus genetic diversity and post-transplant lymphoproliferative disorder (PTLD), we conducted a preliminary study to compare the genetic diversity of the EBNA-1 gene among transplant patients and patients with infectious mononucleosis (IM). METHODS: We sequenced the EBNA-1 gene in blood samples from study subjects using Sanger methodology. The sequences were aligned with a reference strain and compared with publicly available sequences. RESULTS: We analyzed 33 study samples and 25 publicly available sequences along with the reference strain B95-8. The evaluable samples were from sixteen patients with IM (median age 14.0 years, range 2-24) and 17 transplant patients. There were six children without PTLD (median age 1.93 years, range 0.79-7.46) and 11 who developed PTLD (median age 5.67 years, range 0.96-17.45). A predominant EBNA-1 variant (P-thr) was identified across the study groups. Differences were observed between the samples from the IM patients compared with the transplant samples. CONCLUSION: The predominant EBNA-1 strain is in contrast to reports of the predominant strain in North America. The results suggest differences between the EBNA-1 strains among the study groups. Further studies will examine the relationship between EBNA-1 strains and PTLD occurrence and outcomes.
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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 | 0.001 | 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 | 0.000 |
| 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".