An Assessment of Interactions Between Hepatitis C Virus and Herpesvirus Reactivation in Liver Transplant Recipients Using Molecular Surveillance
Bibliographic record
Abstract
Hepatitis C virus (HCV) has been proposed to have immunomodulatory effects in transplant recipients and may promote herpesvirus reactivation. To assess this, we compared the incidence of herpesvirus reactivation in HCV-positive and HCV-negative liver transplant recipients. Quantitative viral load testing was performed at regular intervals posttransplantation for cytomegalovirus (CMV), Epstein-Barr virus (EBV), human herpesviruses (HHV) 6, 7, and 8, and varicella zoster virus (VZV) in 177 liver transplant patients who were HCV-positive (n=60) or HCV-negative (n=117). The incidence of CMV disease, CMV viremia, and the peak CMV viral load was not significantly different in HCV-positive vs. HCV-negative patients. Similarly, no differences in HHV-6 or EBV reactivation were observed. HHV-8 or VZV viremia was not detected in any patient in the study. A lower incidence of HHV-7 infection occurred in HCV-positive patients vs. HCV-negative patients (47.6% vs. 72.7%; P=0.006). In conclusion, these results suggest that HCV infection does not appear to promote herpesvirus reactivation after liver transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".