Measuring Human Immunodeficiency Virus Reservoirs: Do We Need to Choose Between Quantity and Quality?
Bibliographic record
Abstract
The persistence of latent viral genomes in people receiving antiretroviral therapy (ART) is the main obstacle to a cure for human immunodeficiency virus (HIV) infection. Viral reservoirs can be defined as cells harboring HIV genomes that have the ability to produce infectious virions. Precise quantification of the cellular reservoirs of HIV is challenging because these cells are rare, heterogeneous, and outnumbered by a larger number of cells carrying defective genomes. In addition, measuring the inducibility of these proviruses requires functional assays and remains technically difficult. The recent development of single-cell and single-viral genome approaches revealed additional layers of complexity: the cell subsets that harbor proviruses are heterogeneous and their ability to be induced is variable. A substantial fraction of intact HIV genomes may be permanently silenced after years of ART, revealing the underappreciated importance of induction assays. As such, a simple approach that would assess simultaneously the genetic intactness and the inducibility of the reservoir is still lacking. In this study, we review recent advances in the development of methods to quantify and characterize persistently infected cells, and we discuss how these findings can inform the design of future assays aimed at measuring the size of the intact and inducible HIV reservoir.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".