Unraveling HIV Reservoir Persistence and Dynamics in Peripheral Blood and the Central Nervous System during Antiretroviral Therapy
Bibliographic record
Abstract
Since the introduction of effective antiretroviral therapy (ART) in 1996, HIV has shifted from a fatal disease to a chronic infection. While ART suppresses viral replication, it does not remove the latent HIV reservoir from the cells, necessitating lifelong treatment. Most research focuses on the viral reservoir in the cells of the peripheral blood, but HIV also persists in cells of other tissues, including in the brain. This PhD thesis examines the dynamics of the HIV reservoir in both blood and brain tissue. We optimized a quantification technique capable of quantifying both HIV subtype B, prevalent in high-income countries, and subtype C, the most predominant subtype globally. Using this technique, we followed individuals on ART for 20 years and found that while the reservoir declines early in treatment, it stabilizes after a decade. In the brain, we identified the microglia cells as the primary HIV reservoir. This was studied using a newly developed microglia culture system and postmortem brain tissue from individuals with HIV. We demonstrated that viruses isolated from these could establish productive infection in these culture systems. Due to challenges in isolating intact cells from frozen brain tissue, we instead analyzed cell nuclei and found increased immune activation in individuals with HIV, even in those on ART. This persistent immune activation may contribute to cognitive impairments, known as HIV-associated neurocognitive disorder (HAND). These findings improve our understanding of the HIV reservoir in both blood and brain, as well as the potential harmful effects of HIV. Crucially, they highlight the need for innovative cure strategies and improvements in existing treatments to better address HAND.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".