Introduction: Diabetic nephropathy (DN) is a serious complication of diabetes mellitus and is considered to be a sterile inflammatory disease. Increasing evidence suggest that pyroptosis and subsequent inflammatory response play a key role in the pathogenesis of DN. However, the underlying cellular and molecular mechanisms responsible for pyroptosis in DN are largely unknown. Methods: The rat models of DN were successfully established by single 65 mg/kg streptozotocin treatment. Glomerular mesangial cells were exposed to 30 mmol/L high glucose media for 48 h to mimic the DN environment in vitro. Gene and protein expressions were determined by quantitative real-time PCR and Western blot. Cell viability and pyroptosis were measured by MTT assay and flow cytometry analysis, respectively. The relationship between lncRNA NEAT1, miR-34c, and Nod-like receptor protein-3 (NLRP3) was confirmed by luciferase reporter assay. Results: We found that upregulation of NEAT1 was associated with the increase of pyroptosis in DN models. miR-34c, as a target gene of NEAT1, mediated the effect of NEAT1 on pyroptosis in DN by regulating the expression of NLRP3 as well as the expressions of caspase-1 and interleukin-1β. Either miR-34c inhibition or NLRP3 overexpression could reverse the accentuation of pyroptosis and inflammation by sh-NEAT1 transfection in the in vitro model of DN. Conclusions: Our findings suggested NEAT1 and its target gene miR-34c regulated cell pyroptosis via mediating NLRP3 in DN, providing new insights into understanding the molecular mechanisms of pyroptosis in the pathogenesis of DN.
This paper presents an approach for the integrated process of classification and instance segmentation of leakage-area and scaling images from shield tunnel linings. For this purpose, the previously established dataset of leakage-area images by the authors is enlarged by means of adding scaling ones. Afterwards, data augmentation is implemented to enrich the database in the classification dataset, and the augmented classification dataset contains 5776 images. The instance segmentation dataset is subsequently enlarged through original images without any data augmentation, including 1496 images. Then a residual net with 101 layers (i.e., ResNet-101) is applied to the classification dataset to obtain a model that can identify leakage-area and scaling images from those of shield tunnel linings. The ResNet-101 classification model achieves an accuracy of 93.37% in terms of testing classification dataset. Moreover, a mask region-based convolutional neural network (Mask R-CNN) is utilized to perform instance segmentation of leakage areas and scaling in the images classified by the ResNet-101 model. The segmentation results of the Mask R-CNN model show 96.1% and 95.6% average precision (AP) with intersection over union (IoU) of 0.5 for bounding box and mask predication, respectively. By using the proposed approach, the leakage-area and scaling defects can be automatically classified and quantified with an overall accuracy of 89.3%, which is quite promising compared to the inherent uncertainty in geotechnical engineering. Image mosaicing is finally applied to provide inspectors with the intuitive observation of the location and distribution information of defects on the tunnel lining.
On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).
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