Improvement of platelet preservation by inhibition of TRPC6.

BACKGROUND: The preservation of platelets (PLTs) by room temperature (RT) oscillation limits their shelf life to between 4 and 7 days because of the decrease in PLT function. TRPC6 is a non-selective mechanically sensitive cation channel that has been shown to mediate Ca2+ signalling, implying a role in PLT activation during preservation by RT oscillation. OBJECTIVES: This study was designed to investigate whether inhibition of TRPC6 can improve the RT preservation of PLTs and the possible underlying mechanism. METHODS: Human PLTs from whole blood were stored at 22 ± 2°C with oscillation in plasma or M-sol (mixture of solutions). BI-749327, a specific TRPC6 inhibitor, was administered throughout the preservation period. PLT distribution width (PDW), mean platelet volume (MPV), maximum platelet aggregation rate (MAR) and average aggregation rate (AAR) were measured. The MTT method was used to assess the relative viability of PLTs. Flow cytometry was used to measure the changes of Ca2+ concentration in PLTs and phosphatidylserine (PS) exposure on the PLT surface, and western blotting was used to assess the expression changes of platelet TRPC6 and CD62P proteins. RESULTS: Compared with the control group, inhibition of TRPC6 with BI-749327 significantly reduced the PDW, MPV and Ca2+ concentration, the MAR and AAR were significantly increased, the expression of TRPC6 and CD62P protein was significantly down-regulated in PLTs, and the PS exposure was significantly reduced on the PLT surface. However, these effects were all reversed by activation of TRPC6. CONCLUSION: Inhibition of TRPC6 improves the quality of PLT preservation by inhibiting the Ca2+ signal mediated by TRPC6.

Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5.

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model's accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species.

Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning.

Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have shortcomings such as low segmentation accuracy and manual intervention. To achieve accurate segmentation of standing tree images effectively, SEMD, a lightweight network segmentation model based on deep learning, is proposed in this article. DeepLabV3+ is chosen as the base framework to perform multi-scale fusion of the convolutional features of the standing trees in images, so as to reduce the loss of image edge details during the standing tree segmentation and reduce the loss of feature information. MobileNet, a lightweight network, is integrated into the backbone network to reduce the computational complexity. Furthermore, SENet, an attention mechanism, is added to obtain the feature information efficiently and suppress the generation of useless feature information. The extensive experimental results show that using the SEMD model the MIoU of the semantic segmentation of standing tree images of different varieties and categories under simple and complex backgrounds reaches 91.78% and 86.90%, respectively. The lightweight network segmentation model SEMD based on deep learning proposed in this paper can solve the problem of multiple standing trees segmentation with high accuracy.

Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3.

Ground-object classification using remote-sensing images of high resolution is widely used in land planning, ecological monitoring, and resource protection. Traditional image segmentation technology has poor effect on complex scenes in high-resolution remote-sensing images. In the field of deep learning, some deep neural networks are being applied to high-resolution remote-sensing image segmentation. The DeeplabV3+ network is a deep neural network based on encoder-decoder architecture, which is commonly used to segment images with high precision. However, the segmentation accuracy of high-resolution remote-sensing images is poor, the number of network parameters is large, and the cost of training network is high. Therefore, this paper improves the DeeplabV3+ network. Firstly, MobileNetV2 network was used as the backbone feature-extraction network, and an attention-mechanism module was added after the feature-extraction module and the ASPP module to introduce focal loss balance. Our design has the following advantages: it enhances the ability of network to extract image features; it reduces network training costs; and it achieves better semantic segmentation accuracy. Experiments on high-resolution remote-sensing image datasets show that the mIou of the proposed method on WHDLD datasets is 64.76%, 4.24% higher than traditional DeeplabV3+ network mIou, and the mIou on CCF BDCI datasets is 64.58%. This is 5.35% higher than traditional DeeplabV3+ network mIou and outperforms traditional DeeplabV3+, U-NET, PSP-NET and MACU-net networks.

Ribokinase screened from T7 phage displayed Mycobacterium tuberculosis genomic DNA library had good potential for the serodiagnosis of tuberculosis.

Tuberculosis caused by Mycobacterium tuberculosis (M. tuberculosis) is the leading cause of death among infectious diseases in the worldwide. Lack of more sensitive and effective diagnostic reagents has increased the awareness of rapid diagnosis for tuberculosis. In this study, T7 phage displayed genomic DNA library of M. tuberculosis was constructed to screen the antigens that specially bind with TB-positive serum from the whole genome of M. tuberculosis and to improve the sensitivity and specificity of tuberculosis serological diagnosis. After three rounds of biopanning, results of DNA sequencing and BLAST analysis showed that 19 positive phages displayed four different proteins and the occurrence frequency of the phage which displayed ribokinase was the highest. The results of indirect ELISA and dot immunoblotting indicated that representative phages could specifically bind to tuberculosis-positive serum. The prokaryotic expression vector containing the DNA sequence of ribokinase gene was then constructed and the recombinant protein was expressed and purified to evaluate the serodiagnosis value of ribokinase. The reactivity of the recombinant ribokinase with different clinical serum was detected and the sensitivities and specificities in tuberculosis serodiagnosis were 90% and 86%, respectively by screening serum from tuberculosis patients (n = 90) and uninfected individuals (n = 90) based on ELISA. Therefore, this study demonstrated that ribokinase had good potential for the serodiagnosis of tuberculosis.

Three-Source Partitioning of Methane Emissions from Paddy Soil: Linkage to Methanogenic Community Structure.

Identification of the carbon (C) sources of methane (CH4) and methanogenic community structures after organic fertilization may provide a better understanding of the mechanism that regulate CH4 emissions from paddy soils. Based on our previous field study, a pot experiment with isotopic 13C labelling was designed in this study. The objective was to investigate the main C sources for CH4 emissions and the key environmental factor with the application of organic fertilizer in paddies. Results indicated that 28.6%, 64.5%, 0.4%, and 6.5% of 13C was respectively distributed in CO2, the plants, soil, and CH4 at the rice tillering stage. In total, organically fertilized paddy soil emitted 3.51 kg·CH4 ha-1 vs. 2.00 kg·CH4 ha-1 for the no fertilizer treatment. Maximum CH4 fluxes from organically fertilized (0.46 mg·m-2·h-1) and non-fertilized (0.16 mg·m-2·h-1) soils occurred on day 30 (tillering stage). The total percentage of CH4 emissions derived from rice photosynthesis C was 49%, organic fertilizer C < 0.34%, and native soil C > 51%. Therefore, the increased CH4 emissions from paddy soil after organic fertilization were mainly derived from native soil and photosynthesis. The 16S rRNA sequencing showed Methanosarcina (64%) was the dominant methanogen in paddy soil. Organic fertilization increased the relative abundance of Methanosarcina, especially in rhizosphere. Additionally, Methanosarcina sp. 795 and Methanosarcina sp. 1H1 co-occurred with Methanobrevibacter sp. AbM23, Methanoculleus sp. 25XMc2, Methanosaeta sp. HA, and Methanobacterium sp. MB1. The increased CH4 fluxes and labile methanogenic community structure in organically fertilized rice soil were primarily due to the increased soil C, nitrogen, potassium, phosphate, and acetate. These results highlight the contributions of native soil- and photosynthesis-derived C in paddy soil CH4 emissions, and provide basis for more complex investigations of the pathways involved in ecosystem CH4 processes.

Comparison of the Abundance and Community Structure of N-Cycling Bacteria in Paddy Rhizosphere Soil under Different Rice Cultivation Patterns.

Eco-agricultural systems aim to reduce the use of chemical fertilizers in order to improve sustainable production and maintain a healthy ecosystem. The aim of this study was to explore the effects of rice-frog farming on the bacterial community and N-cycling microbes in paddy rhizosphere soil. This experiment involved three rice cultivation patterns: Conventionally cultivated rice (CR), green rice-frog farming (GR), and organic rice-frog farming (OR). The rice yield, paddy soil enzyme activities, physicochemical variables and bacterial and N-cycling bacterial abundances were quantitatively analyzed. Rice-frog cultivations significantly increased soil protease, nitrate and reductase activity. Additionally, the nirS gene copy number and the relative abundance of denitrifying bacteria also increased, however urease activity and the relative abundance of nitrifying bacteria significantly decreased. The bacterial community richness and diversity of OR soil was significantly higher than that of the GR or CR soil. Nitrogen use efficiency (NUE) of GR was highest. The N-cycling bacterial community was positively correlated with the total carbon (TC), total nitrogren (TN) and carbon to nitrogen (C:N) ratio. The present work strengthens our current understanding of the soil bacterial community structure and its functions under rice-frog farming. The present work also provides certain theoretical support for the selection of rational rice cultivation patterns.

DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention.

Blurry scenarios, such as light reflections and water ripples, often affect the clarity and signal-to-noise ratio of fish images, posing significant challenges for traditional deep learning models in accurately recognizing fish species. Firstly, deep learning models rely on a large amount of labeled data. However, it is often difficult to label data in blurry scenarios. Secondly, existing deep learning models need to be more effective for the processing of bad, blurry, and otherwise inadequate images, which is an essential reason for their low recognition rate. A method based on the diffusion model and attention mechanism for fish image recognition in blurry scenarios, DiffusionFR, is proposed to solve these problems and improve the performance of species recognition of fish images in blurry scenarios. This paper presents the selection and application of this correcting technique. In the method, DiffusionFR, a two-stage diffusion network model, TSD, is designed to deblur bad, blurry, and otherwise inadequate fish scene pictures to restore clarity, and a learnable attention module, LAM, is intended to improve the accuracy of fish recognition. In addition, a new dataset of fish images in blurry scenarios, BlurryFish, was constructed and used to validate the effectiveness of DiffusionFR, combining bad, blurry, and otherwise inadequate images from the publicly available dataset Fish4Knowledge. The experimental results demonstrate that DiffusionFR achieves outstanding performance on various datasets. On the original dataset, DiffusionFR achieved the highest training accuracy of 97.55%, as well as a Top-1 accuracy test score of 92.02% and a Top-5 accuracy test score of 95.17%. Furthermore, on nine datasets with light reflection noise, the mean values of training accuracy reached a peak at 96.50%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 90.96% and 94.12%, respectively. Similarly, on three datasets with water ripple noise, the mean values of training accuracy reached a peak at 95.00%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 89.54% and 92.73%, respectively. These results demonstrate that the method showcases superior accuracy and enhanced robustness in handling original datasets and datasets with light reflection and water ripple noise.

Small extracellular vesicles promote the formation of the pre-metastatic niche through multiple mechanisms in colorectal cancer.

Colorectal cancer (CRC) ranks among the most prevalent global malignancies, posing significant threats to human life and health due to its high recurrence and metastatic potential. Small extracellular vesicles (sEVs) released by CRC play a pivotal role in the formation of the pre-metastatic niche (PMN) through various mechanisms, preparing the groundwork for accelerated metastatic invasion. This review systematically describes how sEVs promote CRC metastasis by upregulating inflammatory factors, promoting immunosuppression, enhancing angiogenesis and vascular permeability, promoting lymphangiogenesis and lymphatic network remodeling, determining organophilicity, promoting stromal cell activation and remodeling and inducing the epithelial-to-mesenchymal transition (EMT). Furthermore, we explore potential mechanisms by which sEVs contribute to PMN formation in CRC and propose novel insights for CRC diagnosis, treatment, and prognosis.

Combining bioinformatics and machine learning to identify diagnostic biomarkers of TB associated with immune cell infiltration.

OBJECTIVE: The asymptomatic nature of tuberculosis (TB) during its latent phase, combined with limitations in current diagnostic methods, makes accurate diagnosis challenging. This study aims to identify TB diagnostic biomarkers by integrating gene expression screening with machine learning, evaluating their diagnostic potential and correlation with immune cell infiltration. METHODS: We analyzed GSE19435, GSE19444, and GSE54992 datasets to identify differentially expressed genes (DEGs). GO and KEGG enrichment characterized gene functions. Three machine learning algorithms identified potential biomarkers, validated with GSE83456, GSE62525, and RT-qPCR on clinical samples. Immune cell infiltration was analyzed and verified with blood data. RESULTS: 249 DEGs were identified, with PDE7A and DOK3 emerging as potential biomarkers. RT-qPCR confirmed their expression, showing AUCs above 0.75 and a combined AUC of 0.926 for TB diagnosis. Immune infiltration analysis revealed strong correlations between PDE7A, DOK3, and immune cells. CONCLUSION: PDE7A and DOK3 show strong diagnostic potential for TB, closely linked to immune cell infiltration, and may serve as promising biomarkers and therapeutic targets.

Prognostic risk model of LIHC T-cells based on scRNA-seq and RNA-seq and the regulation of the tumor immune microenvironment.

BACKGROUND: T-cell-related genes play a crucial role in LIHC development. However, a reliable prognostic profile based on risk models of these genes has yet to be identified. METHODS: Single-cell datasets from both tumor and normal tissue samples were obtained from the GEO database. We identified T-cell marker genes and developed a genetic risk model using the TCGA-LIHC dataset, which was subsequently validated with an independent GEO dataset. We also explored the relationship between risk model predictions and immune responses. RESULTS: We constructed a prognostic risk model using eight gene features identified through screening 860 T-cell marker genes via scRNA-seq and RNA-seq, which was subsequently integrated with the TCGA dataset. Its validity was independently confirmed using GEO and ICGC datasets. The TCGA dataset was stratified into high-risk and low-risk groups based on the risk model. Multivariate Cox regression analysis confirmed the risk score as an independent prognostic factor. GSEA indicated ribosomal transporter metabolism enrichment in the high-risk group and significant transcriptional activation in the low-risk group. ESTIMATE analysis showed higher ESTIMATE, immune, and stromal scores in the low-risk group, which also exhibited lower tumor purity than the high-risk group. Immunophenotyping revealed distinct patterns of immune cell infiltration and an immunosuppressive environment in the high-risk group. CONCLUSIONS: This study introduces a T-cell marker-based prognostic risk model for LIHC patients. This model effectively predicted survival outcomes and immunotherapy effectiveness in LIHC patients, aligning with diverse immune responses and the distinct immunological profiles observed in the high-risk group.

Research on WSN reliable ranging and positioning algorithm for forest environment.

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

An Enrichment Method for Small Extracellular Vesicles Derived from Liver Cancer Tissue.

Small extracellular vesicles (sEVs) derived from tissue can reflect the functional status of the source cells and the characteristics of the tissue's interstitial space. The efficient enrichment of these sEVs is an important prerequisite to the study of their biological function and a key to the development of clinical detection techniques and therapeutic carrier technology. It is difficult to isolate sEVs from tissue because they are usually heavily contaminated. This study provides a method for the rapid enrichment of high-quality sEVs from liver cancer tissue. The method involves a four-step process: the incubation of digestive enzymes (collagenase D and DNase Ι) with tissue, filtration through a 70 µm cell strainer, differential ultracentrifugation, and filtration through a 0.22 µm membrane filter. Owing to the optimization of the differential ultracentrifugation step and the addition of a filtration step, the purity of the sEVs obtained by this method is higher than that achieved by classic differential ultracentrifugation. It provides an important methodology and supporting data for the study of tissue-derived sEVs.

AC-UNet: an improved UNet-based method for stem and leaf segmentation in Betula luminifera.

Plant phenotypic traits play an important role in understanding plant growth dynamics and complex genetic traits. In phenotyping, the segmentation of plant organs, such as leaves and stems, helps in automatically monitoring growth and improving screening efficiency for large-scale genetic breeding. In this paper, we propose an AC-UNet stem and leaf segmentation algorithm based on an improved UNet. This algorithm aims to address the issues of feature edge information loss and sample breakage in the segmentation of plant organs, specifically in Betula luminifera. The method replaces the backbone feature extraction network of UNet with VGG16 to reduce the redundancy of network information. It adds a multi-scale mechanism in the splicing part, an optimized hollow space pyramid pooling module, and a cross-attention mechanism in the expanding network part at the output end to obtain deeper feature information. Additionally, Dice_Boundary is introduced as a loss function in the back-end of the algorithm to circumvent the sample distribution imbalance problem. The PSPNet model achieves mIoU of 58.76%, mPA of 73.24%, and Precision of 66.90%, the DeepLabV3 model achieves mIoU of 82.13%, mPA of 91.47%, and Precision of 87.73%, on the data set. The traditional UNet model achieves mIoU of 84.45%, mPA of 91.11%, and Precision of 90.63%, and the Swin-UNet model achieves . The mIoU is 79.02%, mPA is 85.99%, and Precision is 88.73%. The AC-UNet proposed in this article achieved excellent performance on the Swin-UNet dataset, with mIoU, mPA, and Precision of 87.50%, 92.71%, and 93.69% respectively, which are better than the selected PSPNet, DeepLabV3, traditional UNet, and Swin-UNet. Commonly used semantic segmentation algorithms. Experiments show that the algorithm in this paper can not only achieve efficient segmentation of the stem and leaves of Betula luminifera but also outperforms the existing state-of-the-art algorithms in terms of both speed. This can provide more accurate auxiliary support for the subsequent acquisition of plant phenotypic traits.

Research progress on the role of extracellular vesicles in neurodegenerative diseases.

Neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease, affect millions of people worldwide. Tremendous efforts have been put into disease-related research, but few breakthroughs have been made in diagnostic and therapeutic approaches. Extracellular vesicles (EVs) are heterogeneous cell-derived membrane structures that arise from the endosomal system or are directly separated from the plasma membrane. EVs contain many biomolecules, including proteins, nucleic acids, and lipids, which can be transferred between different cells, tissues, or organs, thereby regulating cross-organ communication between cells during normal and pathological processes. Recently, EVs have been shown to participate in various aspects of neurodegenerative diseases. Abnormal secretion and levels of EVs are closely related to the pathogenesis of neurodegenerative diseases and contribute to disease progression. Numerous studies have proposed EVs as therapeutic targets or biomarkers for neurodegenerative diseases. In this review, we summarize and discuss the advanced research progress on EVs in the pathological processes of several neurodegenerative diseases. Moreover, we outline the latest research on the roles of EVs in neurodegenerative diseases and their therapeutic potential for the diseases.

The Biological Effect of Small Extracellular Vesicles on Colorectal Cancer Metastasis.

Colorectal cancer (CRC) is a malignancy that seriously threatens human health, and metastasis from CRC is a major cause of death and poor prognosis for patients. Studying the potential mechanisms of small extracellular vesicles (sEVs) in tumor development may provide new options for early and effective diagnosis and treatment of CRC metastasis. In this review, we systematically describe how sEVs mediate epithelial mesenchymal transition (EMT), reconfigure the tumor microenvironment (TME), modulate the immune system, and alter vascular permeability and angiogenesis to promote CRC metastasis. We also discuss the current difficulties in studying sEVs and propose new ideas.

The role and application of small extracellular vesicles in breast cancer.

Breast cancer (BC) is the most common malignancy and the leading cause of cancer-related deaths in women worldwide. Currently, patients' survival remains a challenge in BC due to the lack of effective targeted therapies and the difficult condition of patients with higher aggressiveness, metastasis and drug resistance. Small extracellular vesicles (sEVs), which are nanoscale vesicles with lipid bilayer envelopes released by various cell types in physiological and pathological conditions, play an important role in biological information transfer between cells. There is growing evidence that BC cell-derived sEVs may contribute to the establishment of a favorable microenvironment that supports cancer cells proliferation, invasion and metastasis. Moreover, sEVs provide a versatile platform not only for the diagnosis but also as a delivery vehicle for drugs. This review provides an overview of current new developments regarding the involvement of sEVs in BC pathogenesis, including tumor proliferation, invasion, metastasis, immune evasion, and drug resistance. In addition, sEVs act as messenger carriers carrying a variety of biomolecules such as proteins, nucleic acids, lipids and metabolites, making them as potential liquid biopsy biomarkers for BC diagnosis and prognosis. We also described the clinical applications of BC derived sEVs associated MiRs in the diagnosis and treatment of BC along with ongoing clinical trials which will assist future scientific endeavors in a more organized direction.

DeepMDSCBA: An Improved Semantic Segmentation Model Based on DeepLabV3+ for Apple Images.

The semantic segmentation of apples from images plays an important role in the automation of the apple industry. However, existing semantic segmentation methods such as FCN and UNet have the disadvantages of a low speed and accuracy for the segmentation of apple images with complex backgrounds or rotten parts. In view of these problems, a network segmentation model based on deep learning, DeepMDSCBA, is proposed in this paper. The model is based on the DeepLabV3+ structure, and a lightweight MobileNet module is used in the encoder for the extraction of features, which can reduce the amount of parameter calculations and the memory requirements. Instead of ordinary convolution, depthwise separable convolution is used in DeepMDSCBA to reduce the number of parameters to improve the calculation speed. In the feature extraction module and the cavity space pyramid pooling module of DeepMDSCBA, a Convolutional Block Attention module is added to filter background information in order to reduce the loss of the edge detail information of apples in images, improve the accuracy of feature extraction, and effectively reduce the loss of feature details and deep information. This paper also explored the effects of rot degree, rot position, apple variety, and background complexity on the semantic segmentation performance of apple images, and then it verified the robustness of the method. The experimental results showed that the PA of this model could reach 95.3% and the MIoU could reach 87.1%, which were improved by 3.4% and 3.1% compared with DeepLabV3+, respectively, and superior to those of other semantic segmentation networks such as UNet and PSPNet. In addition, the DeepMDSCBA model proposed in this paper was shown to have a better performance than the other considered methods under different factors such as the degree or position of rotten parts, apple varieties, and complex backgrounds.

Current perspectives on clinical use of exosomes as novel biomarkers for cancer diagnosis.

Exosomes are a heterogeneous subset of extracellular vesicles (EVs) that biogenesis from endosomes. Besides, exosomes contain a variety of molecular cargoes including proteins, lipids and nucleic acids, which play a key role in the mechanism of exosome formation. Meanwhile, exosomes are involved with physiological and pathological conditions. The molecular profile of exosomes reflects the type and pathophysiological status of the originating cells so could potentially be exploited for diagnostic of cancer. This review aims to describe important molecular cargoes involved in exosome biogenesis. In addition, we highlight exogenous factors, especially autophagy, hypoxia and pharmacology, that regulate the release of exosomes and their corresponding cargoes. Particularly, we also emphasize exosome molecular cargoes as potential biomarkers in liquid biopsy for diagnosis of cancer.

Comparison of the Variability of Small Extracellular Vesicles Derived from Human Liver Cancer Tissues and Cultured from the Tissue Explants Based on a Simple Enrichment Method.

A potential use of small extracellular vesicles (sEVs) for diagnostic and therapeutic purposes has recently generated a great interest. sEVs, when purified directly from various tissues with proper procedures, can reflect the physiological and pathological state of the organism. However, the quality of sEV is affected by many factors during isolation, including separation of sEV from cell and tissues debris, the use of enzymes for tissue digestion, and the storage state of tissues. In the present study, we established an assay for the isolation and purification of liver cancer tissues-derived sEVs (tdsEVs) and cultured explants-derived sEVs (cedsEVs) by comparing the quality of sEVs derived from different concentration of digestion enzyme and incubation time. The nano-flow cytometry (NanoFCM) showed that the isolated tdsEVs by our method are purer than those obtained from differential ultracentrifugation. Our study thus establishes a simple and effective approach for isolation of high-quality sEVs that can be used for analysis of their constituents.