Exosomes, which form a class of extracellular vesicles (EVs), are membrane-bound lipid nanovesicles with sizes typically in the Exosomes, a class of small extracellular vesicles (30-150 nm), are secreted by almost all types of cells into virtually all body fluids. These small vesicles are attracting increasing research attention owing to their potential for disease diagnosis and therapy. However, their inherent heterogeneity and the complexity of bio-fluids pose significant challenges for their isolation. Even the "gold standard," differential centrifugation, suffers from poor yields and is time-consuming. In this context, recent developments in microfluidic technologies have provided an ideal system for exosome extraction and these devices exhibit some fascinating properties such as high speeds, good portability, and low sample volumes. In this review, the focus is on the state-ofthe-art microfluidic technologies for exosome isolation and highlight potential directions for future research and development by analyzing the challenges faced by the current strategies. range of 30-150 nm. [1] They are secreted by almost all cells into diverse bio-fluids, including blood, urine, breast milk, saliva, lymph, and cerebrospinal fluid. [2] The discovery of exosomes dates back to the 1980s, but for many years after their discovery, they were regarded as "dust". [3,4] However, recent studies have shown that they play a crucial role in intercellular communication. [5,6] The biogenesis of exosomes includes double invagination of membranes, formation of intracellular multivesicular bodies (MVBs), and the release of exosomes (Figure 1). The first invagination process (plasma-membrane budding) generates early-sorting endosomes (ESEs) that can develop into late-sorting endosomes (LSEs). The LSEs are invaginated once again to form MVBs containing intraluminal vesicles (ILVs). Finally, the MVBs fuse with the plasma membrane to release ILVs (exosomes). [7] After release, these exosomes are taken up by recipient cells via multiple processes, such as macropinocytosis, fusion with the cell membrane, clathrin-dependent endocytosis, and phagocytosis. [8] The exosomes uptaken can act either at the surface of the recipient cells or deliver functional cargo in their bulk, thus affecting recipient-cell behavior. [9] Thus, exosomes play a key role in various physiological and pathological processes, including mammalian reproduction and development, immune responses, and disease progression. [10] Exosomes are reported to exhibit many excellent characteristics for clinical applications (Figure 2). 1) They can reflect the real-time state of the original cell, as they are actively secreted by living cells. 2) They are abundant in virtually all biological fluids (up to 10 10 vesicles per mL). [11] 3) Exosomes have enriched contents, including cell-surface substances and cytoplasmic constituents (including proteins, nucleic acids, lipids, and metabolites). Furthermore, exosomes can not only protect enzyme-sensitive cargos from degradation, but also ...

An in situ detection of plasma exosomal microRNA for lung cancer diagnosis using duplex-specific nuclease and molybdenum disulfide nanosheets.

In this study, an interesting phenomenon was found where cells (including tumor and normal cells) managed to significantly enhance chemiluminescence (CL) signals. The possible reaction mechanism may be that cells can be severely damaged by CL substrates, and the released contents, possibly proteins (such as cytochrome c), can remarkably magnify CL owing to the increased production of singlet oxygen. More importantly, based on the above phenomena, a novel cell-assisted enhanced CL strategy was proposed for the rapid and label-free detection of tumor cells. The complexes of aptamer sgc8c and streptavidin-modified magnetic beads were employed to recognize and isolate target tumor cells from whole blood. The enhanced CL intensity, which was triggered directly by the captured cells, was measured. The proposed strategy exhibited a good detection performance with a linear range from 200 to 10,000 cells/mL. The analysis can be finished in ∼30 min, and the limit of detection was down to 100 cells/mL. The recoveries and relative standard deviations were 97.81–102.71% and 3.46–12.71%, respectively. Moreover, the established method can successfully distinguish the leukemia patients from healthy people. Therefore, it provides a novel, rapid, and simple method for the determination of tumor cells, which can be used in further practice.

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ObjectivesThe early detection, early diagnosis, and early treatment of lung cancer are the best strategies to improve the 5-year survival rate. Logistic regression analysis can be a helpful tool in the early detection of high-risk groups of lung cancer. Convolutional neural network (CNN) could distinguish benign from malignant pulmonary nodules, which is critical for early precise diagnosis and treatment. Here, we developed a risk assessment model of lung cancer and a high-precision classification diagnostic model using these technologies so as to provide a basis for early screening of lung cancer and for intelligent differential diagnosis.Methods A total of 355 lung cancer patients, 444 patients with benign lung disease and 472 healthy people from The First Affiliated Hospital of Zhengzhou University were included in this study. Moreover, the dataset of 607 lung computed tomography images was collected from the above patients. The logistic regression method was employed to screen the high-risk groups of lung cancer, and the CNN model was designed to classify pulmonary nodules into benign or malignant nodules. ResultsThe area under the curve of the lung cancer risk assessment model in the training set and the testing set were 0.823 and 0.710, respectively. After finely optimizing the settings of the CNN, the area under the curve could reach 0.984. ConclusionsThis performance demonstrated that the lung cancer risk assessment model could be used to screen for high-risk individuals with lung cancer and the CNN framework was suitable for the differential diagnosis of pulmonary nodules.