Persistent Low-Level Variants in a Subset of Viral Genes Are Highly Predictive of Poor Outcome in Immunocompromised Patients With Cytomegalovirus Infection
Bibliographic record
Abstract
BACKGROUND: Human cytomegalovirus (HCMV) is the most common and serious opportunistic infection after solid organ and hematopoietic stem cell transplantation. In this study, we used whole-genome HCMV data to investigate viral factors associated with the clinical outcome. METHODS: We sequenced HCMV samples from 16 immunocompromised pediatric patients with persistent viremia. Eight of the 16 patients died of complications due to HCMV infection. We also sequenced samples from 35 infected solid organ adult recipients, of whom 1 died with HCMV infection. RESULTS: We showed that samples from both groups have fixed variants at resistance sites and mixed infections. Next-generation sequencing also revealed nonfixed variants at resistance sites in most of the patients who died (6/9). A machine learning approach identified 10 genes with nonfixed variants in these patients. These genes formed a viral signature that discriminated patients with HCMV infection who died from those who survived with high accuracy (area under the curve = 0.96). Lymphocyte numbers for a subset of patients showed no recovery posttransplant in the patients who died. CONCLUSIONS: We hypothesize that the viral signature identified in this study may be a useful biomarker for poor response to antiviral drug treatment and indirectly for poor T-cell function, potentially identifying early those patients requiring nonpharmacological interventions.
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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 | 0.002 |
| 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) | 0.001 | 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".