HIV‐1 RNA in Large and Small Plasma Extracellular Vesicles: A Novel Parameter for Monitoring Immune Activation and Virological Failure
Bibliographic record
Abstract
Antiretroviral therapy (ART) suppresses viral replication in most people living with HIV-1 (PLWH). However, PLWH remain at risk of viral rebound. HIV-1 infection modifies the content of extracellular vesicles (EVs). The changes in microRNA content in EVs are biomarkers of immune activation and viral replication in PLWH. Moreover, viral molecules are enclosed in EVs produced from infected cells. Our objective was to assess the value of EV-associated HIV-1 RNA as a biomarker of immune activation and viral replication in PLWH. Plasma samples were obtained from a cohort of 53 PLWH with a detectable viremia. Large and small EVs were respectively purified by plasma centrifugation at 17 000g and by precipitation with ExoQuick. HIV-1 RNA and microRNAs were quantified in the EV subtypes by RT-qPCR. HIV-1 RNA content was higher in large EVs of ART-naive PLWH. Small EVs HIV-1 RNA was equivalent in ART-naive and ART-treated PLWH and positively correlated with the CD4/CD8 T cell ratio. In ART-naive PLWH, HIV-1 RNA content of large EVs correlated with small EV-associated miR-29a, miR-146a, and miR-155, biomarkers of viral replication and immune activation. A receiver operating characteristic analysis showed that HIV-1 RNA in large EVs discriminated PLWH with a high CD8 T cell count. HIV-1 RNA in large EVs was associated with viral replication and immune activation biomarkers. Inversely, HIV-1 RNA in small EVs was related to immune restoration. Overall, these results suggest that HIV-1 RNA quantification in purified EVs could be a useful parameter to monitor HIV-1 infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".