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Nanotechnologies promise to improve disease diagnosis and treatment, overcoming the limitations of conventional administrations. In particular, extracellular vesicles (EVs) and artificial vesicles (AVs) are strongly emerging tools in nanomedicine (Leggio et al., 2020). EVs are cell-derived membrane structures secreted after the fusion of endosomes with the plasma membrane (exosomes) or shed from the plasma membrane (microvesicles). EVs are released by different brain cells (neurons, oligodendrocytes, astrocytes, and microglia) and constitute a physiological intercellular communication system. Indeed, EVs can deliver different types of molecules (nucleic acids and proteins), which often influence the phenotype of the recipient cells. They play a physiological role in the central nervous system (CNS), such as development, myelination, regeneration, and synaptic activity (Lai et al., 2012). Due to their content, EVs could therefore constitute an important biomarker for neurodegenerative diseases, represent candidates for therapeutic use, enclosing regulatory molecules, or be considered as vectors for brain drug delivery (Croese et al.,2018)
nanovesicles, Nanotechnologies, Perspective, Neurology. Diseases of the nervous system, RC346-429, brain disease
nanovesicles, Nanotechnologies, Perspective, Neurology. Diseases of the nervous system, RC346-429, brain disease
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |