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Background Diabetic nephropathy (DN) is a kidney damage caused by diabetes and the main cause of end-stage renal disease. However, the current treatment of DN has many limitations. Quercetin is a bioflavonoid compound with therapeutic benefits in metabolic diseases. This study aims to determine the therapeutic potentials and underlying mechanism of quercetin on DN. Methods We collected blood samples from DN patients and healthy controls and treated human mesangial cells (HMCs) with high glucose (HG) to establish an in vitro model of DN. Then we assessed the expression difference of miR-485-5p as well as YAP1 in serum of DN patients and healthy controls and between HG-induced HMCs and control cells. qRT-PCR and western blot were performed to assess miR-485-5p and YAP1 expression levels; CCK-8 and ELISAs were used to examine cell proliferation, inflammation, and oxidative stress. Dual luciferase reporter assay was implemented to detect the binding of miR-485-5p and YAP1 mRNA sequence. Results Quercetin suppressed proliferation, inflammation, and oxidative stress of HMCs induced by HG. As for mechanism, miR-485-5p directly bound to YAP1 and inhibited YAP1 expression. The downregulation of miR-485-5p and upregulation of YAP1 were also observed in the serum of DN patients. Quercetin modulated miR-485-5p/YAP1 axis to regulate HG-induced inflammation and oxidative stress. Conclusion: Quercetin inhibits the proliferation, inflammation, and oxidative stress of HMCs induced by HG through miR-485-5p/YAP1 axis, which might provide a novel treatment strategy for DN.

Purpose Pyroptosis plays an important role in tumor progression. However, there is no pyroptosis-associated long noncoding RNA (lncRNA) signature to predict the prognosis of patients with colorectal cancer (CRC). Materials and Methods The RNA sequencing data (RNA-seq) and corresponding clinical information relating to CRC patients were obtained from the Cancer Genome Atlas (TCGA) database and the GSE39582 dataset. Univariate Cox regression analysis was used to identify pyroptosis-associated lncRNAs linked to CRC prognosis. Subsequently, multivariate Cox regression analysis was performed to construct a pyroptosis-associated lncRNAs signature within the TCGA cohort, which was then validated using the GSE39582 dataset. We used Kaplan–Meier (K-M) analysis, principal component analysis (PCA), and receiver operating characteristic curve (ROC) analysis to evaluate our novel lncRNA signature. Finally, gene set enrichment analysis (GSEA) was performed to explore the potential function of the lncRNA signature. Results We constructed a pyroptosis-associated lncRNA signature comprising four lncRNAs (ELFN1-AS1, PCAT6, TNRC6C-AS1, and ZEB1-AS1). CRC patients were subdivided into high- and low-risk groups based on median risk scores. The results of the K-M, PCA, and ROC analyses showed that this signature could accurately predict the prognosis of CRC patients. Univariate and multivariate Cox regression analyses showed that the pyroptosis-associated signature was an independent prognostic factor. Functional analysis suggested that tumor-associated pathways were enriched for in the high-risk CRC patient group. Conclusion Our study established an effective prognostic signature for CRC patients that may represent a potential therapeutic target.

As the core equipment of transmission and distribution hubs, the operational status of gas‐insulated switchgear (GIS) is closely linked to the safety of the power system. Recently, X‐ray digital imaging technology has been extensively used in GIS equipment fault detection. However, the X‐ray image of GIS is blurred, which is not conducive to the detection of tiny defects. Thus, a super‐resolution method for GIS X‐ray images based on multi‐scale context transformers is proposed in this study, namely MCTSR. Firstly, a second‐order image degradation model is introduced to generate GIS X‐ray low‐resolution images that more closely resemble the real world. Secondly, a contextual transformer gate module is constructed to improve attention to tiny defects in GIS X‐ray images. Thirdly, a U‐Net discriminator network based on multi‐scale contextual transformers is intended to enrich the information of the generated images. Finally, the proposed discriminator is combined with the existing generator to compose a super‐resolution method applicable to GIS X‐ray images. The experimental results demonstrate that the method outperforms other methods in peak signal‐to‐noise ratio and structural similarity on the constructed GIS X‐ray image dataset. In addition, the output image of the proposed method facilitates the subsequent defect detection.

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In view of the problems of large volume, high energy consumption and difficult maintenance of electric field measurement sensors in existing power systems, non-contact miniature electric field sensors have become a hot topic in current research. In this paper, a MEMS miniature electric field measurement sensor model based on the principle of piezoelectric-piezoresistive coupling is constructed, and the sensor structure is optimized by analyzing the steady-state characteristics of the piezoelectric material and semiconductor membrane of the sensor. The input and output characteristics of the sensor were tested. The test results show that the sensor has excellent mechanical strain capacity, and the output voltage of the sensor has a linear relationship with the electric field strength, thus verifying the feasibility of the sensor measurement in the electric field. The research results will provide some reference for the development of contactless coupled sensors.