A Tat/Rev Induced Limiting Dilution Assay to Measure Viral Reservoirs in Non-Human Primate Models of HIV Infection
Bibliographic record
Abstract
Abstract The establishment of latent infection and poorly characterized viral reservoirs in tissues represent major obstacles to a definitive cure for HIV. Non-human primate (NHP) models of HIV infection are critical to elucidate pathogenic processes and an essential tool to test novel therapeutic strategies. Thus, the availability of novel assays to measure residual viral replication and reservoirs in NHP models may increase their utility in the search for an HIV cure. We developed a tat/rev induced limiting dilution assay to measure the frequency of CD4+ T cells that express multiply-spliced(ms)_SIV RNA in presence and absence of stimulation. We validated the assay using cell lines and cells from blood and lymph nodes of SIV infected macaques. In vitro, SIV/SHIV TILDA detects only cells expressing viral proteins. In SIV/SHIV-infected macaques, CD4+ T cells that express msSIV/SHIV RNA (TILDA data) were detected also in the setting of very low/undetectable viremia. TILDA data were significantly higher after stimulation and correlated with plasma viral load (pVL). Interestingly, TILDA data from early cART initiation correlated with peak and AUC pVL post-cART interruption. In summary, we developed an assay that may be useful in characterizing viral reservoirs and determining the effect of HIV interventions in NHP models.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".