AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The EV miRNAs, specifically, hsa-let-7e-5p, hsa-miR-19b-3p, hsa-miR-24-3p, and hsa-miR-331-5p are promising candidates for PD diagnosis as they are associated with regulatory pathways involved in PD pathogenesis.
Accurate diagnosis of Alzheimer's disease (AD) remains challenging due to its multifactorial nature and the limitations of current diagnostic methods. Exosome-derived microRNAs (miRNAs) have emerged as promising minimally invasive biomarkers, owing to their stability in peripheral biofluids and their ability to reflect...
Despite significant scientific progress in recent years, a notable gap remains in the availability of reliable non-invasive biomarkers for the early diagnosis of Parkinson’s disease (PD). This study aimed to identify and validate novel plasma biomarkers for PD using integrated bioinformatics and machine learning. Trans...
OBJECTIVE Parkinson's disease (PD) lacks reliable, minimally invasive biomarkers for diagnosis. This study aimed to identify and validate PD-specific plasma extracellular vesicle (EV)-associated proteins. METHODS Plasma EVs were isolated by ultracentrifugation and characterized. Proteomic profiling of total plasma EV...
Background Tuberculosis (TB) remains a leading global cause of infectious disease mortality, while current diagnostic methods face substantial limitations. Extracellular vesicle (EV)–encapsulated microRNAs (miRNAs) demonstrate significant potential as diagnostic biomarkers. This study aimed to evaluate their diagnostic...
Background Parkinson’s disease (PD) is characterized by progressive loss of dopaminergic neurons in the substantia nigra and pathological aggregation of α-synuclein, while early diagnosis and disease stratification remain constrained by the lack of sensitive and specific biomarkers. Exosomes carry proteins, lipids, and...
An integrated multi-omics approach provides a robust framework for stage-specific PD monitoring and potential clinical deployment using multi-omics machine learning to identify biofluid-specific signatures and evaluated predictive performance.
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