MicroRNAs (miRNAs) are a class of short, single-stranded, noncoding RNAs, with a length of about 18–22 nucleotides. Extracellular vesicles (EVs) are derived from cells and play a vital role in the development of diseases and can be used as biomarkers for liquid biopsy, as they are the carriers of miRNA. Existing studies have found that most of the functions of miRNA are mainly realized through intercellular transmission of EVs, which can protect and sort miRNAs. Meanwhile, detection sensitivity and specificity of EV-derived miRNA are higher than those of conventional serum biomarkers. In recent years, EVs have been expected to become a new marker for liquid biopsy. This review summarizes recent progress in several aspects of EVs, including sorting mechanisms, diagnostic value, and technology for isolation of EVs and detection of EV-derived miRNAs. In addition, the study reviews challenges and future research avenues in the field of EVs, providing a basis for the application of EV-derived miRNAs as a disease marker to be used in clinical diagnosis and even for the development of point-of-care testing (POCT) platforms.
MicroRNA (miRNA) in extracellular vesicles (EVs) has great potential to be a promising marker in liquid biopsy. However, the present EV isolation methods, such as ultracentrifugation, have complicated and long-time operation, which impedes research on EV miRNA. The downstream complex miRNA extraction process will also significantly increase the detection cycle and loss. We first established a simple automated technique to efficiently extract target miRNAs in EVs from plasma based on Fe3O4@TiO2 beads with high affinity and capture efficiency. We combined a heat-lysis method for quick and simple EV miRNA extraction and detection. The results indicated that our method has more RNA yield than TRIzol or a commercial kit and could complete EV enrichment and miRNA extraction in 30 min. Through the detection of miRNA-21, healthy people and lung cancer patients were distinguished, which verified the possibility of the application in clinical detection. The automated isolation technology for EV miRNA has good repeatability and high throughput, with great application potential in clinical diagnosis.
Central nervous system (CNS) diseases are difficult to treat and harmful. Many CNS diseases are secondary to peripheral diseases, such as tumor brain metastases (BMS), viral infections and inflammation of the brain, and their pathogenic factors travel through the circulatory system
to the brain, eventually leading to lesions. Extracellular vesicles (EVs) play an important role in this process. Recent studies have shown that, extracellular EVs can effectively cross the blood– brain barrier (BBB) through endocytosis and they transmit molecular signals in cell-to-cell
communication. Abnormal EVs produced in the lesion portion transport pathogenic factors, including miRNAs, proteins, and virions into the CNS. These pathogenic factors participate in cellular pathways to interfere with homeostasis or are themselves pathogens that directly damage CNS. In addition,
different or specific pathological molecules in EVs are potential disease markers. We herein reviewed pathways through which the abnormal EVs cross BBB and adverse effects of abnormal exosomes. We also and summarized their existing detection techniques, so as to provide basis for prevention
and early diagnosis of secondary diseases.
Few-shot learning is to recognize novel classes with a few labeled samples per class. Although numerous meta-learning methods have made significant progress, they struggle to directly address the heterogeneity of training and evaluating task distributions, resulting in the domain shift problem when transitioning to new tasks with disjoint spaces. In this paper, we propose a novel method to deal with the heterogeneity. Specifically, by simulating class-difference domain shift during the metatrain phase, a bilevel optimization procedure is applied to learn a transferable representation space that can rapidly adapt to heterogeneous tasks. Experiments demonstrate the effectiveness of our proposed method.
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