Assessing the Suitability of Next-Generation Viral Outgrowth Assays to Measure Human Immunodeficiency Virus 1 Latent Reservoir Size
Bibliographic record
Abstract
BACKGROUND: Evaluations of human immunodeficiency virus (HIV) curative interventions require reliable and efficient quantification of replication-competent latent reservoirs. The "classic" quantitative viral outgrowth assay (QVOA) has been regarded as the reference standard, although prohibitively resource and labor intensive. We compared 6 "next-generation" viral outgrowth assays, using polymerase chain reaction or ultrasensitive p24 to assess their suitability as scalable proxies for QVOA. METHODS: Next-generation QVOAs were compared with classic QVOA using single leukapheresis-derived samples from 5 antiretroviral therapy-suppressed HIV-infected participants and 1 HIV-uninfected control; each laboratory tested blinded batches of 3 frozen and 1 fresh sample. Markov chain Monte Carlo methods estimated extra-Poisson variation at aliquot, batch, and laboratory levels. Models also estimated the effect of testing frozen versus fresh samples. RESULTS: Next-generation QVOAs had similar estimates of variation to QVOA. Assays with ultrasensitive readout reported higher infectious units per million values than classic QVOA. Within-batch testing had 2.5-fold extra-Poisson variation (95% credible interval [CI], 2.1-3.5-fold) for next-generation assays. Between-laboratory variation increased extra-Poisson variation to 3.4-fold (95% CI, 2.6-5.4-fold). Frozen storage did not substantially alter infectious units per million values (-18%; 95% CI, -52% to 39%). CONCLUSIONS: The data offer cautious support for use of next-generation QVOAs as proxies for more laborious QVOA, while providing greater sensitivities and dynamic ranges. Measurement of latent reservoirs in eradication strategies would benefit from high throughput and scalable assays.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".