Plasma Extracellular Vesicle Subtypes May be Useful as Potential Biomarkers of Immune Activation in People With HIV
Bibliographic record
Abstract
BACKGROUND: Extracellular vesicles (EVs) are intercellular messengers with epigenetic potential since they can shuttle microRNA (miRNA). EVs and miRNA play a role in human immunodeficiency virus (HIV) infection immunopathogenesis. Chronic immune activation and systemic inflammation during HIV infection despite effective antiretroviral therapy (ART) are associated with non-acquired immunodeficiency syndrome (AIDS) comorbidities in people living with HIV (PLWH). Analysis of plasma EVs and their miRNA content may be useful as immune activation or inflammatory biomarkers in PLWH receiving ART. In this study, we hypothesized that the number, size, and miRNA of large and small EVs could reflect immune activation associated with an elevated CD8 T-cell count or a low CD4/CD8 ratio in PLWH. METHODS: Plasma EVs subtype purified from PLWH and uninfected controls were sized using dynamic light scattering and quantified using flow cytometry and acetylcholine esterase (AChE) activity. Expression of mature miRNAs miR-92, miR-155, miR-223 was measured by quantitative reverse-transcriptase polymerase chain reaction in EVs and leucocytes. RESULTS: HIV infection induces increased production of small EVs in plasma. EV subtypes were differentially enriched in miR-92, miR-155, and miR-223. Positive correlations between CD8 T-cell count and large EVs abundance and small EVs AChE activity were observed. CD4/CD8 ratio was negatively correlated with small EV AChE activity, and miRNA-155 level per small EV was negatively correlated with CD8 T-cell count. CONCLUSIONS: These findings suggest that quantifying large or small EVs and profiling miRNA content per EV might provide new functional biomarkers of immune activation and inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".