HSV-2 Infection as a Potential Cofactor for HIV Disease Progression and Selection of Drug Resistance Mutations in Adults under WHO-Recommended First-Line Antiretroviral Therapy: A Multicentric, Cross-Sectional Study in Cameroon, Central African Republic, Chad, and Gabon
Bibliographic record
Abstract
Although herpes simplex virus-2 (HSV-2) infection is a known cofactor for HIV transmission in Central Africa, its role in HIV disease progression is unclear. The aim of this study was to examine the potential link between HSV-2 infection and HIV disease progression, in addition to identifying the presence of genes conferring HIV antiretroviral resistance mutations. This was a cross-sectional study involving 302 HIV-infected adults in Central Africa with virological failure (viral load >1000 copies/mL) on first-line antiretroviral therapy from four different countries. The seroprevalence of HSV-2 was 32% (96/302). Amongst the HIV-infected individuals who were HSV-2 seropositive, the mean HIV viral load and CD4 count were 4.82 ± 0.83 log copies/mL and 243 ± 144 cells/microliter, respectively. Among the HIV-infected individuals who were HSV-2-seronegative, the mean HIV viral load and CD4 count were 3.48 ± 0.44 log copies/mL and 646 ± 212 cells/microliter, respectively (p < 0.001). There was a statistically significant relationship (p < 0.001) between HSV-2 seropositivity and the presence of resistance mutations to antiretrovirals (ARV), non-nucleoside reverse transcriptase inhibitors (NNRTI), and nucleoside reverse transcriptase inhibitors (NRTI) with odds ratios of 9.7, 10, and 11.9, respectively. There was no link between HSV-2 serostatus and protease inhibitor (PI) resistance mutations. There was a substantial accumulation of resistance mutations in HSV-2-seropositive compared to -seronegative patients. These findings support the link between HIV disease progression and HSV-2 infection. An association was observed between the presence of NNRTI and NRTI resistance mutations and HSV-2 seropositivity.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".