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Extracellular Vesicles

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Demystifying the long noncoding RNA landscape of small EVs derived from human mesenchymal stromal cells.

Journal of advanced research
INTRODUCTION: The regenerative capacity of mesenchymal stromal cells or medicinal signaling cells (MSCs) is largely mediated by their secreted small extracellular vesicles (sEVs), and the therapeutic efficacy of sEVs can be enhanced by licensing appr...

Regulation of aged skeletal muscle regeneration by circulating extracellular vesicles.

Nature aging
Heterochronic blood exchange (HBE) has demonstrated that circulating factors restore youthful features to aged tissues. However, the systemic mediators of those rejuvenating effects remain poorly defined. We show here that the beneficial effect of yo...

Diagnosis of urogenital cancer combining deep learning algorithms and surface-enhanced Raman spectroscopy based on small extracellular vesicles.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PURPOSE: To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN).

Deep Learning-Enabled Raman Spectroscopic Identification of Pathogen-Derived Extracellular Vesicles and the Biogenesis Process.

Analytical chemistry
Pathogenic bacterial infections, exacerbated by increasing antimicrobial resistance, pose a major threat to human health worldwide. Extracellular vesicles (EVs), secreted by bacteria and acting as their "long-distance weapons", play an important role...

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and lat...

Deep Learning Promotes Profiling of Multiple miRNAs in Single Extracellular Vesicles for Cancer Diagnosis.

ACS sensors
Extracellular vesicle microRNAs (EV miRNAs) are critical noninvasive biomarkers for early cancer diagnosis. However, accurate cancer diagnosis based on bulk analysis is hindered by the heterogeneity among EVs. Herein, we report an approach for profil...

Advances in Extracellular Vesicle Research Over the Past Decade: Source and Isolation Method are Connected with Cargo and Function.

Advanced healthcare materials
The evolution of extracellular vesicle (EV) research has introduced nanotechnology into biomedical cell communication science while recognizing what is formerly considered cell "dust" as constituting an entirely new universe of cell signaling particl...

Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

International journal of molecular sciences
Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (E...

Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagnosis of Bladder Cancer.

Analytical chemistry
Extracellular vesicle (EV) molecular phenotyping offers enormous opportunities for cancer diagnostics. However, the majority of the associated studies adopted biomarker-based unimodal analysis to achieve cancer diagnosis, which has high false positiv...

Molecular profiling of blood plasma-derived extracellular vesicles derived from Duchenne muscular dystrophy patients through integration of FTIR spectroscopy and machine learning reveals disease signatures.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PURPOSE: To identify and monitor the FTIR spectral signatures of plasma extracellular vesicles (EVs) from Duchenne Muscular Dystrophy (DMD) patients at different stages with Healthy controls using machine learning models.