Objective To explore the effects and mechanisms of bone marrow mesenchymal stem cell(BMMSC)-derived exosomes(BMMSC-Exo)on hypoxia/reoxygenation(H/R)-induced injury in rat cardiomyocyte cell line(H9c2).Methods BMMSC-Exosomes were isolated by ultracentrifugation.The cells were divided into three groups:control,H/R,and H/R+BMMSC-Exo(H/R+Exo).A hypoxia/reoxygenation(H/R)injury model was es-tablished by exposing cells to 12 hours of hypoxia followed by 6 hours of reoxygenation.Flow cytometry was used to detect cell apoptosis,DHE staining was used to assess cellular ROS levels,JC-1 immunofluorescence staining was used to evaluate mitochondrial membrane potential,and Western blot was used to detect mitochondrial autophagy-re-lated proteins.Results BMMSC-Exo treatment significantly alleviated oxidative stress,restored mitochondrial mem-brane potential,reduced mitochondrial autophagy levels,and effectively decreased cardiomyocyte apoptosis.Conclu-sions Bone marrow mesenchymal stem cell-derived exosomes alleviate H/R-induced cardiomyocyte injury.

Accurate identification of the glomerular ultrastructure is critical for the diagnosis of chronic kidney diseases,but the high cost of acquiring high-quality annotated data limits the application of fully-supervised learning.Therefore,a multi-class semi-supervised semantic segmentation framework based on segment anything model(MC4S-SAM)is proposed.After improving the mask decoder of segment anything model to enable multi-class semantic segmentation without requiring prompt information,the improved model is used to generate and refine pseudo-labels through a self-training strategy,and multi-level consistency regularization constraints are incorporated to enhance the model's performance.Experimental results show that,in the task of segmenting the glomerular mesangial ultrastructure,MC4S-SAM outperformes the fully-supervised model by 11.72%in mean intersection over union(mIoU)and 11.45%in mean Dice similarity coefficient(mDSC)when the labeled data accountes for 1/16 of the total.When the labeled data proportion is 1/4,the mIoU and mDSC reach 68.91%and 78.73%,respectively,demonstrating its significant potential for aiding the diagnosis of chronic kidney diseases.

Accurate identification of the glomerular ultrastructure is critical for the diagnosis of chronic kidney diseases,but the high cost of acquiring high-quality annotated data limits the application of fully-supervised learning.Therefore,a multi-class semi-supervised semantic segmentation framework based on segment anything model(MC4S-SAM)is proposed.After improving the mask decoder of segment anything model to enable multi-class semantic segmentation without requiring prompt information,the improved model is used to generate and refine pseudo-labels through a self-training strategy,and multi-level consistency regularization constraints are incorporated to enhance the model's performance.Experimental results show that,in the task of segmenting the glomerular mesangial ultrastructure,MC4S-SAM outperformes the fully-supervised model by 11.72%in mean intersection over union(mIoU)and 11.45%in mean Dice similarity coefficient(mDSC)when the labeled data accountes for 1/16 of the total.When the labeled data proportion is 1/4,the mIoU and mDSC reach 68.91%and 78.73%,respectively,demonstrating its significant potential for aiding the diagnosis of chronic kidney diseases.

A U-Net incorporating improved Transformer and convolutional channel attention module is designed for biventricular segmentation in MRI image.By replacing the high-level convolution of U-Net with the improved Transformer,the global feature information can be effectively extracted to cope with the challenge of poor segmentation performance due to the complex morphological variation of the right ventricle.The improved Transformer incorporates a fixed window attention for position localization in the self-attention module,and aggregates the output feature map for reducing the feature map size;and the network learning capability is improved by increasing network depth through the adjustment of multilayer perceptron.To solve the problem of unsatisfactory segmentation performance caused by blurred tissue edges,a feature aggregation module is used for the fusion of multi-level underlying features,and a convolutional channel attention module is adopted to rescale the underlying features to achieve adaptive learning of feature weights.In addition,a plug-and-play feature enhancement module is integrated to improve the segmentation performance which is affected by feature loss due to channel decay in the codec structure,which guarantees the spatial information while increasing the proportion of useful channel information.The test on the ACDC dataset shows that the proposed method has higher biventricular segmentation accuracy,especially for the right ventricle segmentation.Compared with other methods,the proposed method improves the DSC coefficient by at least 2.83%,proving its effectiveness in biventricular segmentation.

Objective To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy(OM),immunofluorescence microscopy(IM),and transmission electron microscopy(TEM).Methods We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi-instance model for classification of 3 immune-mediated glomerular diseases,namely immunoglobulin A nephropathy(IgAN),membranous nephropathy(MN),and lupus nephritis(LN).This model adopts an instance-level multi-instance learning(I-MIL)method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient.By comparing this model with unimodal and bimodal models,we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion.Results The multi-modal multi-instance model combining OM,IM,and TEM images had a disease classification accuracy of(88.34±2.12)%,superior to that of the optimal unimodal model[(87.08±4.25)%]and that of the optimal bimodal model[(87.92±3.06)%].Conclusion This multi-modal multi-instance model based on OM,IM,and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.

Objective To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy(OM),immunofluorescence microscopy(IM),and transmission electron microscopy(TEM).Methods We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi-instance model for classification of 3 immune-mediated glomerular diseases,namely immunoglobulin A nephropathy(IgAN),membranous nephropathy(MN),and lupus nephritis(LN).This model adopts an instance-level multi-instance learning(I-MIL)method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient.By comparing this model with unimodal and bimodal models,we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion.Results The multi-modal multi-instance model combining OM,IM,and TEM images had a disease classification accuracy of(88.34±2.12)%,superior to that of the optimal unimodal model[(87.08±4.25)%]and that of the optimal bimodal model[(87.92±3.06)%].Conclusion This multi-modal multi-instance model based on OM,IM,and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.

OBJECTIVE: We propose a novel region- level self-supervised contrastive learning method USRegCon (ultrastructural region contrast) based on the semantic similarity of ultrastructures to improve the performance of the model for glomerular ultrastructure segmentation on electron microscope images. METHODS: USRegCon used a large amount of unlabeled data for pre- training of the model in 3 steps: (1) The model encoded and decoded the ultrastructural information in the image and adaptively divided the image into multiple regions based on the semantic similarity of the ultrastructures; (2) Based on the divided regions, the first-order grayscale region representations and deep semantic region representations of each region were extracted by region pooling operation; (3) For the first-order grayscale region representations, a grayscale loss function was proposed to minimize the grayscale difference within regions and maximize the difference between regions. For deep semantic region representations, a semantic loss function was introduced to maximize the similarity of positive region pairs and the difference of negative region pairs in the representation space. These two loss functions were jointly used for pre-training of the model. RESULTS: In the segmentation task for 3 ultrastructures of the glomerular filtration barrier based on the private dataset GlomEM, USRegCon achieved promising segmentation results for basement membrane, endothelial cells, and podocytes, with Dice coefficients of (85.69 ± 0.13)%, (74.59 ± 0.13)%, and (78.57 ± 0.16)%, respectively, demonstrating a good performance of the model superior to many existing image-level, pixel-level, and region-level self-supervised contrastive learning methods and close to the fully- supervised pre-training method based on the large- scale labeled dataset ImageNet. CONCLUSION USRegCon facilitates the model to learn beneficial region representations from large amounts of unlabeled data to overcome the scarcity of labeled data and improves the deep model performance for glomerular ultrastructure recognition and boundary segmentation.
Humans ; Electrons ; Endothelial Cells ; Learning ; Podocytes ; Kidney Diseases

Methods:Cultured human alveolar epithelial A549 cells were assigned into LPS group and blank control group. LPS group was stimulated with LPS and adenosine triphosphate to induce pyroptosis and inflammation. A549 cells were divided into 4 groups: miR-20a mimics group, mimics-negative control (NC) group, inhibitor group and inhibitor-NC group. MiRNA-20a mimics, mimics-NC, inhibitor, and inhibitor-NC were transfected respectively into A549 cells, and after 24 h, the cells were collected to verify transfection efficiency by qPCR. MiRNA-20a mimics and the constructed TLR4-3'UTR double luciferase reporter plasmid were co-transfected into A549 cells, and luciferase activity was analyzed. MiRNA-20a mimics/inhibitors were transfected into A549 cells, and then the cells were stimulated by LPS for 8 h followed by adenosine triphosphate for 30 min. QPCR, Western Blot and ELISA were used to detect the expression of GSDMD, inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) and Signaling molecules (TLR4、NF-κB) in A549 cells at mRNA level and protein level. Immunofluorescence was used to detect the expression of TLR4 in the A549 cells and NF-κB in the nucleus of A549 cells after transfecting with miRNA-20a mimics/inhibitor.Results:The mRNA and protein expression of pyroptosis marker molecule (GSDMD) and inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) in A549 cells stimulated with LPS were significantly higher than those in the blank control group, and the differences were statistically significant ( P<0.05). The expression of miRNA-20 in the mimics group was significantly higher than that in the mimic-NC group ( P<0.05), while the expression of miRNA-20a in the inhibitor group was lower than that in the inhibitor-NC group ( P<0.01). The double luciferase reporter gene experiment showed that the relative fluorescence value of the co-transfection group for TLR4-3'UTR-WT and miRNA-20a mimics was significantly lower than the co-transfection group for TLR4-3'UTR-WT and miRNA-20a mimics-NC ( P<0.05). The mRNA and protein levels of pyroptosis marker molecule (GSDMD) , inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) and signaling molecules (TLR4, NF-κB) were decreased in the mimics group compared to the mimics-NC group, and increased in inhibitor group compared to inhibitor-NC group. Conclusions:miRNA-20a may inhibit LPS-induced pyroptosis and inflammation of A549 cells via TLR4/NF-κB signal pathway.Objetive:To explore the potential role of miRNA-20a in lipopolysaccharide (LPS) induced pyroptosis and inflamation of human alveolar epithelial A549 cells and its regulation mechanisim.

Allografts ; COVID-19 ; China/epidemiology* ; Disease Outbreaks ; Humans ; Kidney Transplantation/adverse effects* ; SARS-CoV-2

Objective:Differentially expressed circ_Dock6 was screened in vivo by applying circRNA high-throughput sequencing technology in lung tissue of newborn rats suffering from acute respiratory distress syndrome (ARDS). The corresponding target genes of microRNAs were predicted by bioinformatics, and their biological processes and signal pathways were analyzed as well. Methods:Real-time quantitative PCR was utilized to detect the expression of circ_Dock6 in the lung tissue of newborn rats in ARDS group (12 cases) and normal control group (12 cases). TargetScan, RNAhybrid and miRanda databases were adopted to predict the possible recruitment of miRNAs and their corresponding target genes by circ_Dock6.Functional enrichment analysis and signal pathway enrichment analysis were carried out on the target genes of each miRNA.Results:The expression of circ_Dock6 (0.44±0.29) in the lung tissue of ARDS group was significantly down-regulated ( t=2.060, P<0.05) compared with normal control group(1.63±1.33). The target gene intersections of miRNAs (miR-24-3p, miR-667-3p, miR-711, miR-203b-5p, miR-5132-5p, etc.) may be recruited by circ_Dock6 and were obtained from three databases.Its target gene aggregation function was enriched in various biological processes, including protein metabolism, protein amino acid phosphorylation, DNA-dependent transcriptional regulation, biological regulation, tissue and organ development, cell differentiation, signal regulation, gene expression, response to stimuli, almost all cellular components such as intracellular, organelle, cytoplasm, and nucleus, as well as molecular functions such as transferase activity, transcription factor activity, and phosphotransferase activity.The involved signaling pathways, including enrichment in mitogen-activated protein kinase(MAPK) signaling pathway, phosphatidylinositol-3-kinase-protein kinase B(PI3K-Akt)signaling pathway, and mammalian rapamycin target protein(mTOR)signaling pathway, were closely related to ARDS.Circ_Dock6 may play a significant role in the pathogenesis of ARDS. Conclusions:Circ_Dock6 may be closely correlated with the pathogenesis of neonatal ARDS.Through bioinformatics analysis, the prediction of its target genes and related signaling pathways laid the foundation for further explorations of its mechanism of action.