Abstract

Neuronal aging is a complex process characterized by cognitive decline and susceptibility to neurodegenerative diseases, including AD, MS, and PD. As individuals age, brain undergoes structural and functional alterations, leading to impaired synaptic plasticity, neuroinflammation, and oxidative stress. Additionally, aging weakens immune response, ultimately CNS more vulnerable to latent viral infections and reactivations. HSV-1 and EBV have been strongly implicated in neurodegenerative pathologies. HSV-1, a neurotropic virus, has been found in postmortem brains of AD patients, suggesting a role in amyloidbeta accumulation and chronic uroinflammation. Similarly, EBV has been linked to MS, with viral reactivation triggering inflammatory cascades that contribute to demyelination and neurodegeneration. Other viruses, including CMV, HIV, and SARS-CoV-2, have also been associated with long-term neurological complications, worsening cognitive decline in aging individuals. By integrating deep learning and machine learning approaches, AI can predict disease progression, assess individual susceptibility to viral-induced neurodegeneration, and aid in early diagnosis. Brain transcriptomics, which involves RNA sequencing and gene expression profiling, provides insights into molecular pathways disrupted by viral infections. These technologies enhance our ability to identify biomarkers for neurodegenerative diseases, paving the way for targeted interventions. This study highlights the intersection of viral neuropathology, aging, and AI-based transcriptomics, emphasizing the need for computational tools to unravel the complexities of neurodegeneration. Understanding viral contributions to neuronal aging can lead to innovative therapeutic strategies, potentially mitigating the impact of chronic viral infections on cognitive health. As AI continues to evolve, its integration with

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Data Sharing Statement

Funding