Article ; Online: Machine Learning Prediction and Phyloanatomic Modeling of Viral Neuroadaptive Signatures in the Macaque Model of HIV-Mediated Neuropathology.
2023 , Page(s) e0308622
Abstract: In human immunodeficiency virus (HIV) infection, virus replication in and adaptation to the central nervous system (CNS) can result in neurocognitive deficits in approximately 25% of patients with unsuppressed viremia. While no single viral mutation can ... ...
Abstract | In human immunodeficiency virus (HIV) infection, virus replication in and adaptation to the central nervous system (CNS) can result in neurocognitive deficits in approximately 25% of patients with unsuppressed viremia. While no single viral mutation can be agreed upon as distinguishing the neuroadapted population, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the virus envelope glycoprotein (Gp120) predictive of disease. The S[imian]IV-infected macaque is a widely used animal model of HIV neuropathology, allowing in-depth tissue sampling infeasible for human patients. Yet, translational impact of the ML approach within the context of the macaque model has not been tested, much less the capacity for early prediction in other, noninvasive tissues. We applied the previously described ML approach to prediction of SIV-mediated encephalitis (SIVE) using |
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Language | English |
Publishing date | 2023-02-27 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2807133-5 |
ISSN | 2165-0497 ; 2165-0497 |
ISSN (online) | 2165-0497 |
ISSN | 2165-0497 |
DOI | 10.1128/spectrum.03086-22 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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