TNF-α Production by Monocytes Stimulated With Epstein-Barr Virus–Peptides as a Marker of Immunosuppression-Related Adverse Events in Kidney Transplant Recipients
Bibliographic record
Abstract
Introduction Infections and cancers now outnumber rejection as a cause of morbidity in transplant recipients, likely as a result of over-immunosuppression. Currently, there is no clinical tool to detect over-immunosuppression. We recently reported that tumor necrosis factor alpha (TNF-α) production by CD14 + CD16 + intermediate monocytes, following ex vivo stimulation by Epstein-Barr virus–peptides, could identify over-immunosuppressed patients. Methods We conducted a pilot study the assay using 142 peripheral blood mononuclear samples from a cohort of 71 kidney transplant recipients. Patients were classified as cases or controls according to the occurrence of opportunistic infection, recurring bacterial infections or de novo neoplasia in the 12 months following blood collection. We used both the classifier rule and a threshold of <73% of CD14 + CD16 + TNFα + cells developed in a previous training set. Results Cases were detected with 83% sensitivity and 68% specificity. The negative predictive value of the assay was 89%. The hazard ratio for the occurrence of the endpoint was 6.8 (95% confidence interval 2.0–23.9; P = 0.003) in patients with a positive test. Multivariable linear regression analysis revealed that the association was independent of baseline clinical characteristics, renal function, and immunosuppressive regimen. Conclusion These data validate this cell-based assay as a promising tool for personalizing immunotherapy. Studies are under way for a 2-step assay with improved specificity.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".