Dating reservoir formation in virologically suppressed people living with HIV-1 in Rakai, Uganda
Bibliographic record
Abstract
Abstract The timing of the establishment of the HIV latent viral reservoir (LVR) is of particular interest, as there is evidence that proviruses are preferentially archived at the time of antiretroviral therapy (ART) initiation. Quantitative viral outgrowth assays (QVOAs) were performed using Peripheral Blood Mononuclear Cells (PBMC) collected from Ugandans living with HIV who were virally suppressed on ART for >1 year, had known seroconversion windows, and at least two archived ART-naïve plasma samples. QVOA outgrowth populations and pre-ART plasma samples were deep sequenced for the pol and gp41 genes. The bayroot program was used to estimate the date that each outgrowth virus was incorporated into the reservoir. Bayroot was also applied to previously published data from a South African cohort. In the Ugandan cohort (n = 11), 87.9 per cent pre-ART and 56.3 per cent viral outgrowth sequences were unique. Integration dates were estimated to be relatively evenly distributed throughout viremia in 9/11 participants. In contrast, sequences from the South African cohort (n = 9) were more commonly estimated to have entered the LVR close to ART initiation, as previously reported. Timing of LVR establishment is variable between populations and potentially viral subtypes, which could limit the effectiveness of interventions that target the LVR only at ART initiation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".