Persistent HIV transcription and variable antiretroviral drug penetration in lymph nodes during plasma viral suppression
Bibliographic record
Abstract
OBJECTIVE: The ability of antiretroviral drugs to penetrate and suppress viral replication in tissue reservoir sites is critical for HIV remission. We evaluated antiretroviral concentrations in lymph nodes and their impact on HIV transcription. METHODS: Participants of the RV254/SEARCH010 Acute HIV Infection Cohort in Thailand were enrolled. Group 1 (n = 6) initiated and continued antiretrovirals with two nucleoside reverse transcriptase inhibitors (NRTIs), dolutegravir (DTG) and mar- aviroc (MVC). Group 2 (n = 12) initiated antiretrovirals with two NRTIs as well as efavirenz and were switched to two NRTIs as well as DTG. Antiretroviral concentrations were measured by mass spectroscopy. HIV RNA+ and DNA+ cells were measured by in-situ hybridization. RESULTS: All participants were MSM. At lymph node biopsy, all had plasma HIV RNA less than 20 copies/ml. Group 2 had longer durations of antiretroviral and DTG use (medians of 135 and 63 weeks, respectively) compared with Group 1 (median 44 weeks for both). TFV-DP, 3TC-TP, DTG and MVC were quantifiable in all lymph node samples from participants receiving those drugs versus carbovir-triphosphate (CBV-TP) in four out of 14. Median ratios of lymph node to peripheral blood concentrations were DTG, 0.014; MVC, 6.9; CBV-TP, 0.38; 3TC-TP, 0.32; and TFV-DP, 3.78. Median inhibitory quotients [ratios of lymph node concentrations to in-vitro inhibitory levels (IC50-or-90)] were DTG, 0.8; MVC, 38.8; CBV-TP, 0.5; 3TC- TP, 4.1; and TFV-DP, 1.8. Ongoing viral transcription was detected in lymph node of all participants. Median lymph node RNA+ cells were 71 350 versus 99 750 cells/g for Groups 1 and 2, respectively (P = 0.111). CONCLUSION: MVC has enhanced lymph node penetration and thereby may contribute to more complete viral suppression in the lymph node.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".