Antiviral resistance in herpes simplex virus and varicella-zoster virus infections: diagnosis and management
Bibliographic record
Abstract
PURPOSE OF REVIEW: Aciclovir (ACV) is the first-line drug for the management of herpes simplex virus (HSV) and varicella-zoster virus (VZV) infections. Long-term administration of ACV for the treatment of severe infections in immunocompromised patients can lead to the development of drug resistance. Furthermore, the emergence of isolates resistant to ACV is increasingly recognized in immunocompetent individuals with herpetic keratitis. This review describes the mechanisms involved in drug resistance for HSV and VZV, the laboratory diagnosis and management of patients with infections refractory to ACV therapy. RECENT FINDINGS: Genotypic testing is more frequently performed for the diagnosis of infections caused by drug-resistant HSV or VZV isolates. Molecular biology-based systems for the generation of recombinant viruses have been developed to link unknown mutations with their drug phenotypes. Fast and sensitive methods based on next-generation sequencing will improve the detection of heterogeneous viral populations of drug-resistant viruses and their temporal changes during antiviral therapy, which could allow better patient management. Novel promising compounds acting on targets that differ from the viral DNA polymerase are under clinical development. SUMMARY: Antiviral drug resistance monitoring for HSV and VZV is required for a rational use of antiviral therapy in high-risk populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".