ArticleJournal of neurovirology2020
Using neuronal extracellular vesicles and machine learning to predict cognitive deficits in HIV.
Article in Journal of neurovirology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review.JMIR aging · 2025Pooled it
- Implications of virus-induced stress granules in tauopathies.Translational neurodegeneration · 2026Review
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- SHIV.D Infection Alters Production and Protein Composition of Myeloid-Derived Extracellular Vesicles.International journal of molecular sciences · 2026Article
- Machine Learning Application in Enhancing HIV Management and Treatment Outcomes: Revolutionizing HIV Infection.Current HIV research · 2026Review
- Advanced Multipurpose Spectroscopic Nanobio-Device for Concurrent Lab-on-a-Chip Label-Free Separation and Detection of Extracellular Vesicles as Key-Biomarkers for Point-of-Care Cardiovascular Disease Diagnostics.Advanced healthcare materials · 2025Article
- Detection and Isolation of Tissue-Specific Extracellular Vesicles From the Blood.Journal of extracellular biology · 2025Review
- Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models.BMC public health · 2025Article
- α-Synuclein species in plasma neuron-derived extracellular vesicles as biomarkers for iRBD.Annals of clinical and translational neurology · 2024Article
- Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Single-extracellular vesicle (EV) analyses validate the use of L1 Cell Adhesion Molecule (L1CAM) as a reliable biomarker of neuron-derived EVs.Journal of extracellular vesicles · 2024Article
- Bridging brain and blood: a prospective view on neuroimaging-exosome correlations in HIV-associated neurocognitive disorders.Frontiers in neurology · 2024Review
- Inhibiting tau-induced elevated nSMase2 activity and ceramides is therapeutic in an Alzheimer's disease mouse model.Translational neurodegeneration · 2023Article
- Biomarkers for parkinsonian disorders in CNS-originating EVs: promise and challenges.Acta neuropathologica · 2023Review
- Small Extracellular Vesicles' miRNAs: Biomarkers and Therapeutics for Neurodegenerative Diseases.Pharmaceutics · 2023Review
- Progressive Degeneration and Adaptive Excitability in Dopamine D1 and D2 Receptor-Expressing Striatal Neurons Exposed to HIV-1 Tat and Morphine.Cellular and molecular neurobiology · 2023Article
- Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals.International journal of general medicine · 2023Article
- Article
- L1CAM-associated extracellular vesicles: A systematic review of nomenclature, sources, separation, and characterization.Journal of extracellular biology · 2022Article
- Alterations in arthropod and neuronal exosomes reduce virus transmission and replication in recipient cells.Extracellular vesicles and circulating nucleic acids · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Our objective was to predict HIV-associated neurocognitive disorder (HAND) in HIV-infected people using plasma neuronal extracellular vesicle (nEV) proteins, clinical data, and machine learning. We obtained 60 plasma samples from 38 women and 22 men, all with HIV infection and 40 with HAND. All underwent neuropsychological testing. nEVs were isolated by immunoadsorption with neuron-specific L1CAM antibody. High-mobility group box 1 (HMGB1), neurofilament light (NFL), and phosphorylated tau-181 (p-T181-tau) proteins were quantified by ELISA. Three different computational algorithms were performed to predict cognitive impairment using clinical data and nEV proteins. Of the 3 different algorithms, support vector machines performed the best. Applying 4 different models of clinical data with 3 nEV proteins, we showed that selected clinical data and HMGB1 plus NFL best predicted cognitive impairment with an area under the curve value of 0.82. The most important features included CD4 count, HMGB1, and NFL. Previous published data showed nEV p-T181-tau was elevated in Alzheimer's disease (AD), and in this study, p-T181-tau had no importance in assessing HAND but may actually differentiate it from AD. Machine learning can access data without programming bias. Identifying a few nEV proteins plus key clinical variables can better predict neuronal damage. This approach may differentiate other neurodegenerative diseases and determine recovery after therapies are identified.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.