Biosensing Extracellular Vesicle Subpopulations in Neurodegenerative Disease Conditions

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

Smart Nanoscale Extracellular Vesicles in the Brain: Unveiling their Biology, Diagnostic Potential, and Therapeutic Applications

Information exchange is essential for the brain, where it communicates the physiological and pathological signals to the periphery and vice versa. Extracellular vesicles (EVs) are a heterogeneous group of membrane-bound cellular informants actively transferring informative calls to and from the brain via lipids, proteins, and nucleic acid cargos. In recent years, EVs have also been widely used to understand brain function, given their "cell-like" properties. On the one hand, the presence of neuron and astrocyte-derived EVs in biological fluids have been exploited as biomarkers to understand the mechanisms and progression of multiple neurological disorders; on the other, EVs have been used in designing targeted therapies due to their potential to cross the blood-brain-barrier (BBB). Despite the expanding literature on EVs in the context of central nervous system (CNS) physiology and related disorders, a comprehensive compilation of the existing knowledge still needs to be made available. In the current review, we provide a detailed insight into the multifaceted role of brain-derived extracellular vesicles (BDEVs) in the intricate regulation of brain physiology. Our focus extends to the significance of these EVs in a spectrum of disorders, including brain tumors, neurodegenerative conditions, neuropsychiatric diseases, autoimmune disorders, and others. Throughout the review, parallels are drawn for using EVs as biomarkers for various disorders, evaluating their utility in early detection and monitoring. Additionally, we discuss the promising prospects of utilizing EVs in targeted therapy while acknowledging the existing limitations and challenges associated with their applications in clinical scenarios. A foundational comprehension of the current state-of-the-art in EV research is essential for informing the design of future studies.

Challenges associated with using extracellular vesicles as biomarkers in neurodegenerative disease

INTRODUCTION

The hunt for new biomarkers - for the diagnosis of subcategories of disease, or for the monitoring of the efficacy of novel therapeutics - is an increasingly relevant challenge in the current era of precision medicine. In neurodegenerative research, the aim is to look for simple tools which can predict cognitive or motor decline early, and to determine whether these can also be used to test the efficacy of new interventions. Extracellular vesicles (EVs) are thought to play an important role in intercellular communication and have been shown to play a vital role in a number of diseases.

AREAS COVERED

The aim of this review is to examine what we know about EVs in neurodegeneration and to discuss their potential to be diagnostic and prognostic biomarkers in the future. It will cover the techniques used to isolate and study EVs and what is currently known about their presence in neurodegenerative diseases. In particular, we will discuss what is required for standardization in biomarker research, and the challenges associated with using EVs within this framework.

EXPERT OPINION

The technical challenges associated with isolating EVs consistently, combined with the complex techniques required for their efficient analysis, might preclude 'pure' EV populations from being used as effective biomarkers. Whilst biomarker discovery is important for more effective diagnosis, monitoring, prediction and prognosis in neurodegenerative disease, reproducibility and ease-of-use should be the priorities.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.