Impact of CMV Therapy With Valganciclovir on Immune Activation and the HIV Viral Load in Semen and Blood
Bibliographic record
Abstract
BACKGROUND: The HIV RNA viral load (VL) in vaginal secretions and semen is an independent predictor of HIV transmission. Blood VL is associated with semen VL, and local mucosal factors, such as semen cytomegalovirus (CMV) reactivation, may play an important role. METHODS: Twenty-one HIV-CMV-coinfected, antiretroviral-naive men received 900 mg of oral valganciclovir once daily for 2 weeks in an open-label study. Blood and semen were collected at baseline, after 2 weeks of valganciclovir, and 2 months after therapy completion. The primary end point was change in semen HIV levels at 2 weeks, and the secondary end points were change in semen HIV VL at 2 months and change in semen CMV levels. RESULTS: The HIV VLs fell significantly at 2 weeks in semen (median 3.44-3.02 log10 copies/mL, P = 0.02) and blood (median 3.61-3.10 log10 copies/mL, P < 0.01) and returned to baseline after therapy completion (median 3.24 and 3.71 log10 copies/mL in semen and blood, respectively). Semen CMV levels also fell on treatment (median 2.13-1.62 log10 copies/mL, P < 0.01) and continued to fall after therapy completion (median 0.91 log10 copies/mL at week 8, P < 0.001 vs. baseline). The reduced semen CMV VL was associated with decreased semen T-cell activation and enhanced CMV-specific T-cell responses in blood; changes in the semen HIV VL were not associated with immune parameters. CONCLUSIONS: Although valganciclovir therapy was associated with reduced HIV and semen CMV levels, these results suggest that the reduced HIV VL was a direct drug effect rather than a CMV antiviral effect or CMV-associated immune alterations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".