Search

The rapid development of industrial applications places severe demands on the mechanical properties of protective coatings. Unraveling the hardening mechanism and deformation behavior is essential for the design of advanced hard coatings. In this paper, a novel AlCrNbSiTiN high-entropy nitride (HEN) coating with an extremely low wear rate of 6.3 × 10−7 mm3 N−1 m−1 and super-hardness of 46 GPa was deposited. The variation of ion-bombardment energy induces the transformation of the nanocomposite structure and hardening mechanism. The plastic deformation behavior was revealed using Ex situ TEM observation and the MeN-Si3N4 nanocomposite structure is a critical contributor to the outstanding mechanical properties and resistance to deformation.

Children’s physical and mental health development is highly related to furniture and integrating the emotional design concept into children’s furniture design helps cultivate children's personalities and promote healthy growth. The model framework of children’s furniture emotional design was constructed from the instinctive level, behavioral level, and reflective level. The comprehensive weight of the design factors was calculated using the Analytic Hierarchy Process (AHP), and three schemes were designed according to the priority of the design factors. The evaluation matrix was constructed by combining the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the distances of each scheme from the positive and negative ideal solutions were calculated, and the optimal scheme was selected according to the relative proximity ranking of the three schemes. The experimental results verified the importance of the reflective level in the three levels of the emotional design theory and emphasized the key role of emotional design in children’s furniture. Therefore, the evaluation system based on AHP and TOPSIS is feasible, which can effectively reduce the influence of subjective factors, make the design decision more scientific, and provide a new reference approach for furniture design.

Willow wood furniture has become one of the most popular types of wood furniture in the Chinese furniture market. Research was undertaken to solve the pain points of product design and meet the requirements of green development. Firstly, the Kano model was used to classify user requirements. Secondly, the Analytic Hierarchy Process was used to calculate each design factors’ comprehensive weights, ensuring consistency between the design objectives and user requirements. Thirdly, the TRIZ contradiction matrix was used to identify and solve engineering difficulties in the design process. During the design process, the Kano-AHP-TRIZ was combined to realize a scientific design strategy that combines qualitative and quantitative approaches. The results showed that the design scheme of willow furniture based on user requirements had consistency, matching, and effectiveness with the furniture market development trend, which makes the furniture design process more scientific and rigorous. It also provided a new research perspective and design strategy for the innovative development of the furniture industry.

Abstract Invasion and migration are the key hallmarks of cancer, and aggressive growth is a major factor contributing to treatment failure and poor prognosis in glioblastoma. Protein arginine methyltransferase 6 (PRMT6), as an epigenetic regulator, has been confirmed to promote the malignant proliferation of glioblastoma cells in previous studies. However, the effects of PRMT6 on glioblastoma cell invasion and migration and its underlying mechanisms remain elusive. Here, we report that PRMT6 functions as a driver element for tumor cell invasion and migration in glioblastoma. Bioinformatics analysis and glioma sample detection results demonstrated that PRMT6 is highly expressed in mesenchymal subtype or invasive gliomas, and is significantly negatively correlated with their prognosis. Inhibition of PRMT6 (using PRMT6 shRNA or inhibitor EPZ020411) reduces glioblastoma cell invasion and migration in vitro, whereas overexpression of PRMT6 produces opposite effects. Then, we identified that PRMT6 maintains the protein stability of EZH2 by inhibiting the degradation of EZH2 protein, thereby mediating the invasion and migration of glioblastoma cells. Further mechanistic investigations found that PRMT6 inhibits the transcription of TRAF6 by activating the histone methylation mark (H3R2me2a), and reducing the interaction between TRAF6 and EZH2 to enhance the protein stability of EZH2 in glioblastoma cells. Xenograft tumor assay and HE staining results showed that the expression of PRMT6 could promote the invasion of glioblastoma cells in vivo, the immunohistochemical staining results of mouse brain tissue tumor sections also confirmed the regulatory relationship between PRMT6, TRAF6, and EZH2. Our findings illustrate that PRMT6 suppresses TRAF6 transcription via H3R2me2a to enhance the protein stability of EZH2 to facilitate glioblastoma cell invasion and migration. Blocking the PRMT6-TRAF6-EZH2 axis is a promising strategy for inhibiting glioblastoma cell invasion and migration.

ABSTRACTThe urban environment exhibits significant vertical variations, Light Detection and Ranging (LiDAR) point cloud classification can provide insights for the 3D morphology of the urban environment. Introducing the adjacency relationships between urban objects can enhance the accuracy of LiDAR point cloud classification. Graph Neural Network (GNN) is a popular architecture to infer the labels of urban objects by utilizing adjacency relationships. However, existing methods ignored the power of the known labels of urban objects, such as crowd-sourced tagged labels from OpenStreetMap (OSM) data, in the inferring process. Therefore, this study proposes a strategy introduces OSM data into GNN for LiDAR point cloud classification. First, we perform an over-segmentation of the LiDAR point cloud to obtain superpoints, which act as basic elements for constructing superpoint adjacency graphs. Second, PointNet is applied to embed superpoint features and edge features are generated using these superpoint features. Finally, OSM data is associated with some part of superpoints and incorporated into the GNN to update the embedded features of superpoints. The results demonstrate that the GNN with OSM data significantly improves the classification accuracy of original GNN. The improvement highlights taking advantage of crowd-sourced geoinformation in LiDAR point cloud classification for understanding 3D urban landscape.

Objective Primary spinal cord glioblastoma (PSCGBM) is a rare malignancy with a poor prognosis. To date, no prognostic nomogram for this rare disease was established. Hence, we aimed to develop a nomogram to predict overall survival (OS) of PSCGBM. Methods Clinical data of patients with PSCGBM was retrospectively collected from the neurosurgery department of Soochow University Affiliated Second Hospital and the Surveillance Epidemiology and End Results database. Information including age, sex, race, tumor extension, extent of resection, adjuvant treatment, marital status, income, year of diagnosis and months from diagnosis to treatment were recorded. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for PSCGBM. A nomogram was constructed to predict 1-year, 1.5-year, and 2-year OS of PSCGBM. Results A total of 132 patients were included. The 1-year, 1.5-year, and 2-year OS were 45.5%, 29.5%, and 18.9%, respectively. Four variables: age groups, tumor extension, extent of resection, and adjuvant therapy, were identified as independent prognostic factors. The nomogram showed robust discrimination with a C-index value for the prediction of 1-year OS, 1.5-year OS, and 2-year of 0.71 (95% confidence interval [CI], 0.61–0.70), 0.72 (95% CI, 0.62–0.70), and 0.70 (95% CI, 0.61–0.70), respectively. The calibration curves exhibited high consistencies between the predicted and observed survival probability in this cohort. Conclusion We have developed and internally validated a nomogram for predicting the survival outcome of PSCGBM for the first time. The nomogram has the potential to assist clinicians in making individualized predictions of survival outcome of PSCGBM.

Abstract The prevalent RNA alternative splicing (AS) contributes to molecular diversity, which has been demonstrated in cellular function regulation and disease pathogenesis. However, the contribution of AS in pancreatic islets during diabetes progression remains unclear. Here, we reanalyze the full-length single-cell RNA sequencing data from the deposited database to investigate AS regulation across human pancreatic endocrine cell types in non-diabetic (ND) and type 2 diabetic (T2D) individuals. Our analysis demonstrates the significant association between transcriptomic AS profiles and cell-type-specificity, which could be applied to distinguish the clustering of major endocrine cell types. Moreover, AS profiles are enabled to clearly define the mature subset of β-cells in healthy controls, which is completely lost in T2D. Further analysis reveals that RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs) and FXR1 family proteins are predicted to induce the functional impairment of β-cells through regulating AS profiles. Finally, trajectory analysis of endocrine cells suggests the β-cell identity shift through dedifferentiation and transdifferentiation of β-cells during the progression of T2D. Together, our study provides a mechanism for regulating β-cell functions and suggests the significant contribution of AS program during diabetes pathogenesis.

Abstract Introduction The Weight-Adjusted Waist Index (WWI) is a new indicator of obesity that is associated with all-cause mortality in Asian populations. Our study aimed to investigate the linear and non-linear associations between WWI and all-cause mortality in non-Asian populations in the United States, and whether WWI was superior to traditional obesity indicators as a predictor of all-cause mortality. Methods We conducted a cohort study using data from the 2011–2018 National Health and Nutrition Examination Survey (NHANES), involving 18,592 participants. We utilized Cox proportional hazard models to assess the association between WWI, BMI, WC, and the risk of all-cause mortality, and performed subgroup analyses and interaction tests. We also employed a receiver operating characteristics (ROC) curve study to evaluate the effectiveness of WWI, BMI, and WC in predicting all-cause mortality. Results After adjusting for confounders, WWI, BMI, and WC were positively associated with all-cause mortality. The performance of WWI, BMI, and WC in predicting all-cause mortality yielded AUCs of 0.697, 0.524, and 0.562, respectively. The data also revealed a U-shaped relationship between WWI and all-cause mortality. Race and cancer modified the relationship between WWI and all-cause mortality, with the relationship being negatively correlated in African Americans and cancer patients. Conclusions In non-Asian populations in the United States, there is a U-shaped relationship between WWI and all-cause mortality, and WWI outperforms BMI and WC as a predictor of all-cause mortality. These findings may contribute to a better understanding and prediction of the relationship between obesity and mortality, and provide support for effective obesity management strategies.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) protect against diabetic cardiovascular diseases and nephropathy. However, their activity in diabetic retinopathy (DR) remains unclear. Our retrospective cohort study involving 1626 T2DM patients revealed superior efficacy of GLP-1 RAs in controlling DR compared to other glucose-lowering medications, suggesting their advantage in DR treatment. By single-cell RNA-sequencing analysis and immunostaining, we observed a high expression of GLP-1R in retinal endothelial cells, which was down-regulated under diabetic conditions. Treatment of GLP-1 RAs significantly restored the receptor expression, resulting in an improvement in retinal degeneration, vascular tortuosity, avascular vessels, and vascular integrity in diabetic mice. GO and GSEA analyses further implicated enhanced mitochondrial gene translation and mitochondrial functions by GLP-1 RAs. Additionally, the treatment attenuated STING signaling activation in retinal endothelial cells, which is typically activated by leaked mitochondrial DNA. Expression of STING mRNA was positively correlated to the levels of angiogenic and inflammatory factors in the endothelial cells of human fibrovascular membranes. Further investigation revealed that the cAMP-responsive element binding protein played a role in the GLP-1R signaling pathway on suppression of STING signaling. This study demonstrates a novel role of GLP-1 RAs in the protection of diabetic retinal vasculature by inhibiting STING-elicited inflammatory signals.

Abstract Background Diabetic retinopathy (DR) is a major cause of blindness and is characterized by dysfunction of the retinal microvasculature. Neutrophil stasis, resulting in retinal inflammation and the occlusion of retinal microvessels, is a key mechanism driving DR. These plugging neutrophils subsequently release neutrophil extracellular traps (NETs), which further disrupts the retinal vasculature. Nevertheless, the primary catalyst for NETs extrusion in the retinal microenvironment under diabetic conditions remains unidentified. In recent studies, cellular communication network factor 1 (CCN1) has emerged as a central molecule modulating inflammation in pathological settings. Additionally, our previous research has shed light on the pathogenic role of CCN1 in maintaining endothelial integrity. However, the precise role of CCN1 in microvascular occlusion and its potential interaction with neutrophils in diabetic retinopathy have not yet been investigated. Methods We first examined the circulating level of CCN1 and NETs in our study cohort and analyzed related clinical parameters. To further evaluate the effects of CCN1 in vivo, we used recombinant CCN1 protein and CCN1 overexpression for gain-of-function, and CCN1 knockdown for loss-of-function by intravitreal injection in diabetic mice. The underlying mechanisms were further validated on human and mouse primary neutrophils and dHL60 cells. Results We detected increases in CCN1 and neutrophil elastase in the plasma of DR patients and the retinas of diabetic mice. CCN1 gain-of-function in the retina resulted in neutrophil stasis, NETs extrusion, capillary degeneration, and retinal leakage. Pre-treatment with DNase I to reduce NETs effectively eliminated CCN1-induced retinal leakage. Notably, both CCN1 knockdown and DNase I treatment rescued the retinal leakage in the context of diabetes. In vitro, CCN1 promoted adherence, migration, and NETs extrusion of neutrophils. Conclusion In this study, we uncover that CCN1 contributed to retinal inflammation, vessel occlusion and leakage by recruiting neutrophils and triggering NETs extrusion under diabetic conditions. Notably, manipulating CCN1 was able to hold therapeutic promise for the treatment of diabetic retinopathy.

Abstract Background Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies. Methods The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients. Conclusions The MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.

Filtering is a crucial step in synthetic aperture radar (SAR) interferometric phase processing. In the context of rapid development in deep learning, the interferometric phase filtering networks have demonstrated more powerful filtering capabilities than spatial and transform domain filtering models. Inspired by the deep learning-based and nonlocal filtering models, this article proposes a nonlocal SAR interferometric phase filtering model based on a partial siamese network. Within this model, the interferogram is decomposed into multiple patches, and each patch undergoes filtering using a set of structurally identical encoder–decoder networks. In order to make better use of the nonlocal characteristics of the interferogram to improve the filtering effect, the encoder–decoder networks corresponding to different patches share a portion of their weights. Finally, the filtered patches outputted from each decoder are concatenated through an aggregation block to obtain the complete filtered interferogram. The advantage of this framework lies in its ability to fully leverage the powerful feature learning capabilities of neural networks while maximizing the utilization of nonlocal characteristics in the interferogram. The filtering performance of the proposed method is tested through simulated interferograms and real-world interferograms obtained from the Sentinel-1 and Gaofen-3 mission. The experimental results demonstrate that compared to traditional spatial and transform domain filtering methods, as well as state-of-the-art deep learning-based and nonlocal filtering methods, the filtering approach proposed in this article achieves superior filtering performance.

Steel truss bridges are especially vulnerable in the event of a sudden loss of a load-carrying element, which can trigger a chain of failures. This paper describes a unique case study of a steel truss bridge under construction subjected to sudden member breakages with an extensive monitoring system. The failures occurred during the dismantlement of temporary members that had been used to transform a three-span simply supported steel truss bridge into a three-span continuous structure during incremental launching. These temporary members needed to be removed once the bridge reached its final position. The robustness of the bridge was assessed using computer simulations of various failure scenarios to evaluate its capacity to effectively activate alternative load paths (ALPs). The results demonstrated the structural redundancy of the steel truss bridge. However, the dynamic response resulting from the failure of the temporary upper chord, due to the initially high tension in the rods, should not be overlooked. To mitigate this issue, a structural retrofitting method was proposed, involving jacking the truss girder above the side pier to reduce the tension in the temporary upper chord above the middle pier. The effectiveness of this method was demonstrated through both simulated and formal experimental tests.

Abstract Unfolded protein response (UPR) maintains the endoplasmic reticulum (ER) homeostasis, survival, and physiological function of mammalian cells. However, how cells adapt to ER stress under physiological or disease settings remains largely unclear. Here by a genome-wide CRISPR screen, we identified that RBBP8, an endonuclease involved in DNA damage repair, is required for ATF4 activation under ER stress in vitro. RNA-seq analysis suggested that RBBP8 deletion led to impaired cell cycle progression, retarded proliferation, attenuated ATF4 activation, and reduced global protein synthesis under ER stress. Mouse tissue analysis revealed that RBBP8 was highly expressed in the liver, and its expression is responsive to ER stress by tunicamycin intraperitoneal injection. Hepatocytes with RBBP8 inhibition by adenovirus-mediated shRNA were resistant to tunicamycin (Tm)-induced liver damage, cell death, and ER stress response. To study the pathological role of RBBP8 in regulating ATF4 activity, we illustrated that both RBBP8 and ATF4 were highly expressed in liver cancer tissues compared with healthy controls and highly expressed in Ki67-positive proliferating cells within the tumors. Interestingly, overexpression of RBBP8 in vitro promoted ATF4 activation under ER stress, and RBBP8 expression showed a positive correlation with ATF4 expression in liver cancer tissues by co-immunostaining. Our findings provide new insights into the mechanism of how cells adapt to ER stress through the crosstalk between the nucleus and ER and how tumor cells survive under chemotherapy or other anticancer treatments, which suggests potential therapeutic strategies against liver disease by targeting DNA damage repair, UPR or protein synthesis.

Abstract Aims/Introduction The purpose of the present study was to evaluate the validation and application of the 2019 International Working Group on the Diabetic Foot (IWGDF) risk stratification system among Chinese patients with diabetes. Materials and Methods A retrospective cohort study was carried out with 254 patients with diabetes, but without an active diabetic foot (DF) ulcer. Patients hospitalized from January to May 2017 were enrolled, and the follow‐up period was from January to May 2020. Patients were stratified into four risk groups based on the 2019 IWGDF risk stratification system. Results Of the 254 patients, four of 31 patients at risk 1 were diagnosed with DF within 3 years, whereas 12 of 26 patients at risk 2 and 16 of 20 patients at risk 3 developed DF. The area under the curve was 0.919 (P

Multi-modal fusion of remote sensing images poses challenges because of the intricate imaging mechanisms and variations in radiation across different modalities. Specifically, the fusion of visible-light and vegetation-sensitive images encounters similar difficulties. Traditional methods have seldom considered the varied imaging mechanisms and radiation difference between modalities, resulting in discrepancies in the correspond features. To address the issue, we propose the VAM-Net (Vegetation-Attentive Multi-modal deep Network) combining a radiometric correction mechanism and a lightweight multi-modal adaptive feature selection method for fusing multi-modal images. First, the vegetation index (VDVI) is integrated into visible-light images to mitigate the radiometric differences between visible-light images and vegetation-sensitive images (e.g., infrared and red edge images). Then, a two-branch network incorporating attention mechanisms is designed to independently capture the texture features and select similar features cross two different modalities of images. Last, a new loss function is presented to ensure the learned features are suitable for multi-modal fusion. The VAM-Net is evaluated by visible-light and vegetation-sensitive images in three different areas, and the experimental results show that VAM-Net attains an average precision of 67.02%, and recall of 35.49%, and an average RMSE of 2.191px, demonstrating the accuracy and robustness of VAM-Net in multi-modal image fusion.

This paper introduces a framework for reconstructing fine-grained room-level models from indoor point clouds. The motivation behind our method stems from the consistent floorwise appearance of building shapes in urban buildings along the vertical direction. To this end, each floor’s points are horizontally sliced to obtain a representative cross-section, from which the linear primitives are detected and enhanced. These linear primitives help to divide the entire space into non-overlapping connected faces with shared edges. These faces are then classified as indoor or outdoor categories by solving a binary energy minimization formulation. The indoor faces are further grouped into each individual rooms with the support of the room semantic map. By propagating and tracing each room’s contour, 2D floor plan can be generated in a semantic-aware manner. These generated 2D floor plans are vertically stretched to match the heights of their respective rooms. Experimental results on six complex scenes from the S3DIS dataset, which encompass both linear and non-linear shapes, demonstrate that our created room models exhibit accurate geometry, correct topology, and rich semantics. The source code of our room-level modeling algorithm is available at https://github.com/indoor-modeling/indoor-modeling.

Background and aims: Type 2 diabetes mellitus remains a substantial medical problem with increasing global prevalence. Pharmacological research is becoming increasingly focused on personalized treatment strategies. Drug development based on glucokinase (GK) activation is an important strategy for lowering blood glucose. This study aimed to investigate the effect of GK activation on glucose and lipid metabolism in diet-induced obese mice. Materials and methods: Mice were fed with a high-fat diet (HFD) for 16 weeks to induce obesity, followed by a GK activator (GKA, AZD1656) or vehicle treatment by gavage for 4 weeks. The effect of GKA treatment on glucose metabolism was evaluated using glucose and insulin tolerance tests. Hepatic lipid accumulation was assessed by hematoxylin and eosin staining, Oil Red O staining, and transmission electron microscopy. The underlying mechanism of GK activation in glucose and lipid metabolism in the liver was studied using transcriptomic analysis, with a mechanistic study in mouse livers in vivo and AML12 cells in vitro. Results: GK activation by GKA treatment improved glucose tolerance in HFD-fed mice while increasing hepatic lipid accumulation. Transcriptomic analysis of liver tissues indicated the lipogenesis and protein kinase RNA-like endoplasmic reticulum kinase (PERK)-unfolded protein response (UPR) pathway activations in GKA-treated HFD-fed mice. Inhibition of the ACC activity, which is an important protein in lipogenesis, attenuated GKA treatment-induced lipid accumulation and PERK-UPR activation in vitro. Conclusions: GK activation improved glucose tolerance and insulin sensitivity while inducing hepatic lipid accumulation by increasing the lipogenic gene expression, which subsequently activated the hepatic PERK-UPR signaling pathway.

Given the constraints on the invariant distribution in chaotic systems, flexibly setting the probability distribution of chaotic signals poses a significant challenge. To tackle this issue, this paper proposes a strategy that transforms the task into solving and modifying the probability density function of the chaotic intrinsic signal. Initially, kernel density estimation algorithms are employed to address the issue of obtaining smooth probability density functions for high-dimensional chaotic signals. Any chaotic signal can serve as the intrinsic signal source, with its probability density function and distribution function being solvable using this algorithm. Subsequently, a graph-based transformation algorithm is introduced for the flexible adjustment of chaotic signal probability distribution. This algorithm can convert the intrinsic signal into a chaotic signal with the desired distribution type based on the characteristics of the target distribution, providing an analytical expression for the transformation relationship. Finally, the effectiveness of this strategy is validated by generating uniform distribution chaotic signals using a Chua chaotic signal as the intrinsic source. The outstanding performance of this signal in suppressing common-mode conducted electromagnetic interference in high-frequency converters is highlighted. The experimental results demonstrate this strategy’s ability to flexibly configure probability distribution types of chaotic signals. Additionally, chaotic signals with a uniform distribution can achieve uniform power spectrum shaping, with a suppression effect on maximum common-mode conducted electromagnetic interference reaching 16.56 dB.

When urban subway trains run in the depot, they can cause vibration and noise, which affects the safety and reliability of the structure under the track, and these transmits to the over-track buildings and often trouble passengers and staff. This paper established a coupling model of a track–metro depot–over-track building based on the structural finite element method and analyzed vibration response and then summarized the vibration transmission and distribution characteristics as the speed changes. The results show that, at train speeds of 20 km/h and 5 km/h, the Z-vibration level difference between the two at the rail is nearly 20 dB, and the vibration can be reduced by 17.9% at most. The difference between the two on the 9 m platform is 6–8 dB and 5–14 dB on the 16 m platform, and the vibration can be reduced by 17.7% at most. The difference between the two in the over-track building is 3–11 dB, and the vibration can be reduced by 13.0% at most. The vibration has the highest energy within a range of 2 m radiating from the center of the line, reaching a maximum of 118.5 dB. The vibration shows a ring-shaped distribution, and the ring-shaped distribution is more pronounced as the train speed increases. In the horizontal direction of the track line, the vibration energy distribution is within a range of −4 m to 11.5 m from the track line. In the longitudinal direction of the track line, the ring-shaped distribution of vibration energy exhibits a periodic pattern. The results provide a reference for the vibration control of the over-track buildings.

Abstract Background SPRY4‐IT1 (SPRY4 intronic transcript 1) is a long non‐coding RNA (lncRNA) that has been identified as a novel oncogene in various cancers, including glioma. However, its function and underlying mechanism in glioma remain largely unclear. Here, we investigated the role of SPRY4‐IT1 in the development of glioma and its underlying mechanism. Methods Bioinformatics analysis and RT‐qPCR assay were used to examine the expression of SPRY4‐IT1 in glioma tissues. The CCK‐8, EdU, and Xenograft tumor assays wereperformed to assess the proliferation effect of glioma cells. The tube forming assay and Chick Embryo Chorioallantoic Membrane (CAM) assay were conducted to detect the angiogenesis effect of HUVECs. RNA‐sequencing, western blotting, RT‐qPCR, ELISA, and IHC assays were employed to verify the regulatory mechanism of the SPRY4‐IT1/ miR‐101‐3p/EZH2/VEGFA axis. Results Analysis of the TCGA dataset and data from our own cohort demonstrated that SPRY4‐IT1 was overexpressed in patients with glioma, and high SPRY4‐IT1 expression correlated with poor prognosis. In vitro and in vivo experiments showed that SPRY4‐IT1 promoted the proliferation of glioma cells. RNA sequencing and Gene Ontology (GO) enrichment analysis indicated significant enrichment of angiogenesis. HUVEC tube forming assay and CAM assay confirmed that SPRY4‐IT1 could induce angiogenesis of glioma cells in vitro and in vivo. Mechanistically, SPRY4‐IT1 upregulated EZH2 expression by sponging miR‐101‐3p to induce VEGFA expression in glioma cells. Moreover, SPRY4‐IT1 activated the VEGFR2/AKT/ERK1/2 pathway in HUVECs mediated by glioma cells. Rescue experiments further confirmed that SPRY4‐IT1 promoted glioma cell proliferation and angiogenesis via the miR‐101‐3p/EZH2/VEGFA signaling axis. Conclusions Our findings provide compelling evidence showing that SPRY4‐IT1 upregulated EZH2 to induce VEGFA by sponging miR‐101‐3p, thereby achieving cell proliferation and angiogenesis in glioma. Therefore, targeting SPRY4‐IT1/miR‐101‐3p/EZH2/VEGFA axis may improve the outcomes of patients with glioma.

From the perspective of user needs, a design evaluation system for dining room chairs that can meet user needs was established. Based on the analytic hierarchy process (AHP), the user needs of a dining room chair were quantitatively analyzed by combining qualitative and quantitative methods. Moreover, the comprehensive weight ranking of 14 factors in the object hierarchy was obtained, which provided the design focus and quantitative indexes for designers in the early stage of dining room chair design. Then, in the later stage of dining room chair design, the fuzzy analytic hierarchy process (FAHP) method was used to quantitatively evaluate the three design schemes and obtain the optimal design scheme. The experimental results showed that the user needs evaluation results were positively correlated with the "excellent" grade in the FAHP method. This indicated that the evaluation system realized a symmetry, reliability, and effectiveness between the user needs evaluation and FAHP. Therefore, it can be concluded that this evaluation system based on AHP and FAHP proposed in this study has reliability and validity, and it can be used for design evaluation to judge the popularity of products, enhance the competitiveness of products, and reduce product design costs.

The investigation of the low-cost deoxygenation of guaiacol (GUA, a model bio-oil compound) is of importance for upgrading bio-oil. At present, common sulfide catalysts for GUA deoxygenation reactions cause contamination of the liquid product, and noble metal catalysts are economically disadvantageous. In this study, four reduced Fe-based oxides with different Ce doping ratios were prepared and their effects on the in-situ deoxygenation performance of GUA in aqueous/methanol hydrogen donor solvents were explored. The results based on the deoxygenation degree, conversion degree, and higher heating value (HHV) of the products showed that the oxide catalyst with a Fe/Ce molar ratio of 2:1 in the methanol solvent performed very well. After selecting an excellent catalyst and a better hydrogen donor solvent, four factors (reaction temperature, reaction time, volume ratio of GUA dosage and methanol dosage, and the ratio of catalyst dosage at the bottom of the reactor to that at the top) in the deoxygenation degree of GUA were investigated using an orthogonal experimental method to further explore the performance of the catalyst. The results showed that the reaction temperature and time greatly influenced GUA deoxygenation. Under optimal experimental conditions, the deoxygenation degree and conversion degree of GUA could reach 34.36% and 92.56%, respectively, based on the relative peak area of gas chromatography–mass spectrometry, and the HHV of the liquid product was 32.27 MJ/kg. Although Fe/Ce catalysts mainly promote demethoxylation, demethylation, and methylation, the stability and quality of the liquid products were improved compared with GUA owing to the reduction in phenolic hydroxyl and ether content. The reduced catalyst in the process of GUA in-situ deoxygenation reactions in methanol maintained a steady performance, as revealed by X-ray diffraction and X-ray fluorescence.

BackgroundBrain metastases (BMs) are the most common central nervous system (CNS) malignant tumors, with rapid disease progression and extremely poor prognosis. The heterogeneity between primary lung cancers and BMs leads to the divergent efficacy of the adjuvant therapy response to primary tumors and BMs. However, the extent of heterogeneity between primary lung cancers and BMs, and the evolutionary process remains little known.MethodsTo deeply insight into the extent of inter-tumor heterogeneity at a single-patient level and the process of these evolutions, we retrospectively analyzed a total of 26 tumor samples from 10 patients with matched primary lung cancers and BMs. One patient underwent four times brain metastatic lesion surgery with diverse locations and one operation for the primary lesion. The genomic and immune heterogeneity between primary lung cancers and BMs were evaluated by utilizing whole-exome sequencing (WESeq) and immunohistochemical analysis.ResultsIn addition to inheriting genomic phenotype and molecular phenotype from the primary lung cancers, massive unique genomic phenotype and molecular phenotype were also observed in BMs, which revealed unimaginable complexity of tumor evolution and extensive heterogeneity among lesions at a single-patient level. By analysis of a multi-metastases case (Case 3) of cancer cells’ subclonal composition, we found similar multiple subclonal clusters in the four spatial and temporal isolated brain metastatic focus, with the characteristics of polyclonal dissemination. Our study also verified that the expression level of immune checkpoints-related molecule Programmed Death-Ligand 1 (PD-L1) (P = 0.0002) and the density of tumor-infiltrating lymphocytes (TILs) (P = 0.0248) in BMs were significantly lower than that in paired primary lung cancers. Additionally, tumor microvascular density (MVD) also differed between primary tumors and paired BMs, indicating that temporal and spatial diversity profoundly contributes to the evolution of BMs heterogeneity.ConclusionOur study revealed the significance of temporal and spatial factors to the evolution of tumor heterogeneity by multi-dimensional analysis of matched primary lung cancers and BMs, which also provided novel insight for formulating individualized treatment strategies for BMs.

AimsDiabetes mellitus (DM), one of the most common chronic diseases in China, is a risk factor for SARS-COV-2 infection and poor prognosis of COVID-19. The COVID-19 vaccine is one of the key measures to control the pandemic. However, the actual coverage of COVID-19 vaccination and associated factors remain unclear among DM patients in China. We conducted this study to investigate the COVID-19 vaccine coverage, safety, and perceptions among patients with DM in China.MethodsA cross-sectional study of a sample of 2200 DM patients from 180 tertiary hospitals in China was performed using a questionnaire developed through the Wen Juan Xing survey platform to collect information regarding their coverage, safety, and perceptions of COVID-19 vaccination. A multinomial logistic regression analysis model was performed to determine any independent relationships with COVID-19 vaccination behavior among DM patients.ResultsIn total, 1929 (87.7%) DM patients have received at least one dose COVID-19 vaccine, and 271 (12.3%) DM patients were unvaccinated. In addition, 65.2% (n = 1434) were booster vaccinated against COVID-19, while 16.2% (n = 357) were only fully vaccinated and 6.3% (n = 138) were only partially vaccinated. The prevalence of adverse effects after the first dose of vaccine, the second dose of vaccine, and the third dose of vaccine were 6.0%, 6.0%, and 4.3% respectively. Multinomial logistic regression analysis showed that DM patients complicated with immune and inflammatory diseases (partially vaccinated: OR = 0.12; fully vaccinated: OR = 0.11; booster vaccinated: OR = 0.28), diabetic nephropathy (partially vaccinated: OR = 0.23; fully vaccinated: OR = 0.50; booster vaccinated: OR = 0.30), and perceptions on the safety of COVID-19 vaccine (partially vaccinated: OR = 0.44; fully vaccinated: OR = 0.48; booster vaccinated: OR = 0.45) were all associated with the three of vaccination status.ConclusionThis study showed that higher proportion of COVID-19 vaccine coverage among patients with DM in China. The concern about the safety of the COVID-19 vaccine affected the vaccine behavior in patients with DM. The COVID-19 vaccine was relatively safe for DM patients due to all side effects were self-limiting.

Bio-oil is a renewable energy and a promising alternative to oil, but its disadvantages such as high oxygen content, low higher heating value (HHV), and instability are needed upgrading. For ketones account for a relatively large proportion of bio-oil, acetone is chosen as a single model compound for hydrodeoxygenation (HDO) in this work. The acetone HDO investigation was carried out in a high pressure reactor, using the reduced Fe-based oxygen carrier1 (OC1) and Ce-doping reduced Fe/Al oxygen carrier2 (OC2). The OCs’ evolution was analyzed by XRD, and the liquid phase products were tested by GC–MS. The results showed the OCs played the dual role of carrying oxygen and catalysis and the HDO capability of OC2 on acetone was better than that of OC1. In addition, two molecular structure models of OC1* (reduced iron-based oxygen carrier) and OC2* (Ce-modified Fe/Al reduced oxygen carrier) were established, and the acetone adsorption on OCs* and the H2O dissociation process were simulated with density functional theory (DFT). The analysis showed that the oxygen vacancies generated by Ce-doping and the Al2O3 carrier changed the optimal adsorption sites of acetone and H2O, then promoted the acetone adsorption and H2O dissociation reaction, improved the OCs performance, verifying that OC2 had better HDO performance on acetone. This work provides guidance for the improvement of crude bio-oil upgrading.

Cerebral cavernous malformations (CCMs) are common vascular anomaly diseases in the central nervous system associated with seizures, cerebral microbleeds, or asymptomatic mostly. CCMs can be classified as sporadic or familial, with familial cerebral cavernous malformations (fCCMs) being the autosomal dominant manner with incomplete penetrance. Germline mutations of KRIT1, CCM2, and PDCD10 are associated with the pathogenesis of fCCMs. Till now, little is known about the fCCMs mutation spectrum in the Han Chinese population. In this study, we enrolled a large, aggregated family, 11/26 of the family members were diagnosed with CCMs by pathological or neuroradiological examination, with a high percentage (5/9) of focal spinal cord involvement. Genomic DNA sequencing verified a novel duplication mutation (c.1119dupT, p.L374Sfs*9) in exon 9 of the Krev interaction trapped 1 (KRIT1) gene. The mutation causes a frameshift and is predicted to generate a truncated KRIT1/CCM1 protein of 381 amino acids. All our findings confirm that c.1119dupT mutation of KRIT1 is associated with fCCMs, which enriched the CCM genes’ mutational spectrum in the Chinese population and will be beneficial for deep insight into the pathogenesis of Chinese fCCMs. Additionally, with a retrospective study, we analyzed the molecular genetic features of Chinese fCCMs, most of the Chinese fCCMs variants are in the KRIT1 gene, and all these variants result in the functional deletion or insufficiency of the C-terminal FERM domain of the KRIT1 protein.

Abstract Nuclear transcription factor Mesenchyme Homeobox 2 (MEOX2) is a homeobox gene that is originally discovered to suppress the growth of vascular smooth muscle and endothelial cells. However, whether or not it is connected to cancer is yet unknown. Here, we report that MEOX2 functions as a tumor-initiating element in glioma. Bioinformatic analyses of public databases and investigation of MEOX2 expression in patients with glioma demonstrated that MEOX2 was abundant at both mRNA and protein levels in glioma. MEOX2 expression was shown to be inversely linked with the prognosis of glioma patients. MEOX2 inhibition changed the morphology of glioma cells, inhibited cell proliferation and motility, whereas had no effect on cell apoptosis. Besides, silencing MEOX2 also hampered the epithelial-mesenchymal transition (EMT), focal adhesion formation, and F-actin assembly. Overexpression of MEOX2 exhibited opposite effects. Importantly, RNA-sequencing, ChIP-qPCR assay, and luciferase reporter assay revealed Cathepsin S (CTSS) as a novel transcriptional target of MEOX2 in glioma cells. Consistently, MEOX2 causes glioma tumor development in mice and greatly lowers the survival period of tumor-bearing mice. Our findings indicate that MEOX2 promotes tumorigenesis and progression of glioma partially through the regulation of CTSS. Targeting MEOX2-CTSS axis might be a promising alternative for the treatment of glioma.

Capacitor-current-feedback active damping has been widely adopted in digitally controlled LCL-type grid-connected inverters (GCIs). However, their digital control delay causes the system stability to deteriorate within a negative damping range. Particularly, in a weak grid, such GCIs are prone to be unstable against the grid impedance variation. To address the above problem, a delay compensation strategy is proposed for an LCL-type GCI, in which a second-order low-pass filter (LPF) is inserted into the capacitor-current-feedback path. This increases the virtual-positive-resistance-region from one-sixth of the sampling frequency to almost within the Nyquist frequency. Therefore, the robustness of the grid-connected inverter can be significantly enhanced against the grid impedance variation. Furthermore, to ensure the system stability and improve the injecting current tracking performance, a proportional complex integral (PCI) strategy is adopted. More specifically, a robustness analysis is conducted under discrete domain. Finally, a 2-kW prototype is constructed, and the experimental results confirm the effectiveness of the theoretical expectations.

Bio-based PA56 is as matrix resin, maleic anhydride (MAH), and glycidyl methacrylate (GMA) dual monomer grafted POE (POE-g-MAH/GMA) polyphenyl ether(PPO) are simultaneous as toughening agents. The PA56/POE-g-MAH-GMA/PPO ternary alloys containing 10 wt% POE-g-MAH-GMA with different PPO content prepared by the melt blending method through twin screw extruder. The structure and property of PA56/POE-g-MAH-GMA/PPO ternary alloys were studied. The results showed that the microscopic morphology of each alloy showed an obvious “sea-island” structure. PA56 is a continuous sea phase, and the island phase assumes the “core–shell structure,” the etched POE-g-MAH-GMA particles are as the nucleus, and PPO are as the shell. When the PPO mass fraction is 30%, the relative crystallinity of the alloy is 11.56%, which is 1.68 times lower compared to the pure PA56, the alloy notch impact strength is 42.1 kJ/m2, and reaches 11.7 times of pure PA56; the water absorption rate of 1.22%, which is 3.22 times lower than pure PA56.

Abstract Background Spatial allocation of medical resources is closely related to people’s health. Thus, it is important to evaluate the abundance of medical resources regionally and explore the spatial heterogeneity of medical resource allocation. Methods Using medical geographic big data, this study analyzed 369 Chinese cities and constructed a medical resource evaluation model based on the grading of medical institutions using the Delphi method. It evaluated China’s medical resources at three levels (economic sectors, economic zones, and provinces) and discussed their spatial clustering patterns. Geographically weighted regression was used to explore the correlations between the evaluation results and population and gross domestic product (GDP). Results The spatial heterogeneity of medical resource allocation in China was significant, and the following general regularities were observed: 1) The abundance and balance of medical resources were typically better in the east than in the west, and in coastal areas compared to inland ones. 2) The average primacy ratio of medical resources in Chinese cities by province was 2.30. The spatial distribution of medical resources in the provinces was unbalanced, showing high concentrations in the primate cities. 3) The allocation of medical resources at the provincial level in China was summarized as following a single-growth pole pattern supplemented by bipolar circular allocation and balanced allocation patterns. The agglomeration patterns of medical resources in typical cities were categorized into single-center and balanced development patterns. GDP was highly correlated to the medical evaluation results, while demographic factors showed, low correlations. Large cities and their surrounding areas exhibited obvious response characteristics. Conclusions These findings provide policy-relevant guidance for improving the spatial imbalance of medical resources, strengthening regional public health systems, and promoting government coordination efforts for medical resource allocation at different levels to improve the overall functioning of the medical and health service system and bolster its balanced and synergistic development.

Neurotrophic tyrosine receptor kinase (NTRK) rearrangements are oncogenic drivers of various types of adult and pediatric tumors, including gliomas. However, NTRK rearrangements are extremely rare in glioneuronal tumors. Here, we report a novel NTRK2 rearrangement in a 24-year-old female with dysembryoplastic neuroepithelial tumor (DNT), a circumscribed WHO grade I benign tumor associated with epilepsy. By utilizing targeted RNA next-generation sequencing (NGS), fluorescence in situ hybridization (FISH), reverse transcriptase PCR (RT-PCR), and Sanger sequencing, we verified an in-frame fusion between NTRK2 and the lipoma HMGIC fusion partner-like 3 (LHFPL3). This oncogenic gene rearrangement involves 5’ LHFPL3 and 3’ NTRK2, retaining the entire tyrosine kinase domain of NTRK2 genes. Moreover, the targeted DNA NGS analysis revealed an IDH1 (p.R132H) mutation, a surprising finding in this type of tumor. The pathogenic mechanism of the LHFPL3::NTRK2 in this case likely involves aberrant dimerization and constitutive activation of RTK signaling pathways.

Abstract Due to the extremely intrinsic heterogeneity among glioma patients, the outcomes of these patients are tremendously different. Therefore, the exploitation of novel biomarker classification of glioma is vitally important for deep insight into the essence and predicting the prognosis of glioma. We aim to analyze the correlation between TP73 mRNA expression, DNA methylated alteration and the prognosis of WHO grade II/III glioma, utilizing bioinformatics to evaluate its significance as a risk‐factor in predicting the prognosis of these glioma patients. The analysis found that TP73 expression was positively correlated with the grade of glioma, and showed a strong correlation with glioma molecular classification, which revealed significantly higher TP73 expression in IDH‐wildtype than in IDH‐mutant subtype of WHO grade II/III glioma. Cox regression analysis indicated that high expression of TP73 shared an independent high‐risk factor impacting the prognosis of WHO grade II/III glioma. We discovered 8 DNA promoter methylation sites with prognostic significance, which were negatively associated with TP73 expression, and positively associated with beneficial overall survival (OS) and progression‐free survival (PFS). Integrating with four independent glioma datasets, subsequent Meta‐analysis verified that low expression of TP73 was closely related to favorable OS, especially in IDH‐mutant subtype. Moreover, we found that 1p/19qCodel/TP73low subgroup shared the most favorable OS, 1p/19qNon−codel/TP73high subgroup suffered the worst OS. Meanwhile, the enrichment analysis of TP73‐related differential mRNAs demonstrated that TP73 aberration in WHO grade II/III glioma might be closely related to cell cycle and P53 signaling pathways. Finally, TP73 expression of 53 glioma specimens was measured by qRT‐PCR, which was consistent with the previous analytical result, and TP73 high‐expression subgroup suffered worse PFS than TP73 low‐expression subgroup. In summary, our funding supports that TP73 gene can perform as a reliable biomarker to evaluate the survival outcome of patients diagnosed with WHO grade II/III glioma.

BackgroundAfter the universal two-child policy has been fully implemented, challenges regarding pregnancy complications seemed to be more severe in China. This study aimed to evaluate the prevalence of gestational diabetes mellitus (GDM) and the main risk factors for GDM before and after the implementation of the universal two-child policy in China.MethodsA retrospective study was performed with 128,270 pregnant women who delivered at Ningbo Women & Children’s Hospital from January 2010 to December 2020. Univariate and multivariate logistic regression analysis was applied to estimate the risk factors associated with GDM prevalence. Segmented regression analyses of interrupted time series (ITS) were conducted to assess the effect of the universal two-child policy on the trends of GDM.ResultsThe prevalence of GDM increased remarkably from 4% in 2010 to 21% in 2020. ITS analysis presented that the prevalence of GDM increased by 0.190% (β1) per month from 2010 to 2016 (P30 years), multigravidity, multiparity, multiple gestation and gestational hypertension were significantly associated with GDM. Advanced age remained an independent risk factor for GDM even after cross stratification with gravidity and parity. The proportion of women with advanced maternal age (>30 years) increased by 0.161% per month before the implementation of the universal two-child policy and increased by 5.25% during the policy took effect month, and gradually increased by 0.124% (β1+β3) per month after then.ConclusionsThe prevalence of GDM has sharply increased in the past decade. The growth rate of GDM slowed down after the implementation of the universal two-child policy in China, but the rate would maintain at a high plateau. The rise in the proportion of older pregnant women could increase the GDM rate. We recommend having children at a relatively optimal reproductive age when encouraging childbearing.

BackgroundCurrent electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability of automatic feature extraction of ECG. However, DL was rarely applied in LVH diagnosis. Our study aimed to construct a DL model for rapid and effective detection of LVH using 12-lead ECG.MethodsWe built a DL model based on convolutional neural network-long short-term memory (CNN-LSTM) to detect LVH using 12-lead ECG. The echocardiogram and ECG of 1,863 patients obtained within 1 week after hospital admission were analyzed. Patients were evenly allocated into 3 sets at 3:1:1 ratio: the training set (n = 1,120), the validation set (n = 371) and the test set 1 (n = 372). In addition, we recruited 453 hospitalized patients into the internal test set 2. Different DL model of each subgroup was developed according to gender and relative wall thickness (RWT).ResultsThe LVH was predicted by the CNN-LSTM model with an area under the curve (AUC) of 0.62 (sensitivity 68%, specificity 57%) in the test set 1, which outperformed Cornell voltage criteria (AUC: 0.57, sensitivity 48%, specificity 72%) and Sokolow-Lyon voltage (AUC: 0.51, sensitivity 14%, specificity 96%). In the internal test set 2, the CNN-LSTM model had a stable performance in predicting LVH with an AUC of 0.59 (sensitivity 65%, specificity 57%). In the subgroup analysis, the CNN-LSTM model predicted LVH by 12-lead ECG with an AUC of 0.66 (sensitivity 72%, specificity 60%) for male patients, which performed better than that for female patients (AUC: 0.59, sensitivity 50%, specificity 71%).ConclusionOur study established a CNN-LSTM model to diagnose LVH by 12-lead ECG with higher sensitivity than current ECG diagnostic criteria. This CNN-LSTM model may be a simple and effective screening tool of LVH.

Endoplasmic reticulum (ER) is the principal organelle for protein synthesis, such as hepatokines and transmembrane proteins, and is critical for maintaining physiological function. Dysfunction of ER is associated with metabolic disorders. However, the role of ER homeostasis as well as hepatokines in the progression of non-alcoholic fatty liver disease (NAFLD) remains to be elucidated. Here we comprehensively analyzed the RNA-seq profiles of liver biopsies from 206 NAFLD patients and 10 controls from dataset GSE135251. The co-expression modules were constructed based on weighted gene co-expression network analysis and six co-expression modules were identified, of which brown module stood out to be significantly associated with fibrosis stage and NAFLD activity score (NAS). Subsequently, cytoscape with cytoHubba plugin was applied to identify hub genes in the brown module. GO and KEGG enrichment analysis of the top 20 hub genes were performed and showed the involvement of extracellular matrix formation, collagen synthesis and decomposition, etc. Further, the expression of the top 20 hub genes were found to be a consistent increasing trend as the fibrosis stages and NAS increased, which have been validated both in HFD fed and HFHC fed mice. Among these genes, THY1, PTGDS, TMPRSS3, SPON1, COL1A2, RHBDF1, COL3A1, COL5A1, COL1A1 and IGFBP7 performed well in distinguishing fibrosis stage, while COL1A2, COL3A1, THY1, RHBDF1 and COL1A2 exhibited good capacity to discriminate NAS. Besides, RHBDF1, COL3A1, QSOX1, STING1, COL5A1, IGFBP7, COL4A2, COL1A1, FKBP10 and COL1A2 also showed a strong power in the diagnosis of NAFLD. In addition, COL1A1, COL1A2, COL3A1, COL8A2, IGFBP7, PGF, PTGDS, SPON1, THY1 and TIMP1 were identified as secretome genes from the top 20 hub genes. Of them, circulated THY1 and collagen III level were validated to be significantly elevated in the MCD diet-induced mice. Thus, we provided a systemic view on understanding the pathological roles and mechanisms of ER as well as secretome in NAFLD progression. THY1, COL1A1, COL1A2, COL3A1 and RHBDF1 could be served as candidate biomarkers to evaluate the progression of NAFLD.

BackgroundAdolescence is a critical period for bone development, and peak bone mass may be reached in late adolescence. Boosting bone accumulation at this time can help preserve adult bone health and avoid osteoporosis later in life. Body mass index (BMI) has been found to have a favorable impact on bone mineral density (BMD) in previous research. However, excessive obesity is harmful to health and may lead to various systemic diseases. Therefore, finding an appropriate BMI to maintain a balance between obesity and BMD is critical for adolescents.MethodsThe datasets from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 were used in a cross-sectional investigation. Multivariate linear regression models were used to examine the linear connection between BMI and BMD. Fitted smoothing curves and threshold effect analysis were used to describe the nonlinear relationship. Subgroup analyses were then conducted based on gender and age.ResultsThis population-based study included a total of 6,143 adolescents aged 8–19 years. In a multivariate linear regression analysis, a good association between BMI and total BMD was shown [0.014 (0.013, 0.014)]. This positive association was maintained in all subgroup analyses grouped by sex and age. Furthermore, the association between BMI and BMD was nonlinear with a saturation point present, as evidenced by smoothed curve fitting. According to the threshold effect study, with an age group of two years, adolescents of different ages had different BMI saturation values with respect to BMD.ConclusionsOur study showed a significant positive and saturated association between BMI and BMD in adolescents aged 8–19 years. Maintaining BMI at saturation values may reduce other adverse effects while achieving optimal BMD.

The inhibition of alpha-ketoglutarate (α-KG)-dependent dioxygenases is thought to contribute to isocitrate dehydrogenase (IDH) mutation-derived malignancy. Herein, we aim to thoroughly investigate the expression pattern and prognostic significance of genes encoding α-KG-dependent enzymes for lower-grade glioma (LGG) patients. In this retrospective study, a total of 775 LGG patients were enrolled. The generalized linear model, least absolute shrinkage and selection operator Cox regression, and nomogram were applied to identify the enzyme-based signature. With the use of gene set enrichment analysis and Gene Ontology, the probable molecular abnormalities underlying high-risk patients were investigated. By comprehensively analyzing mRNA data, we observed that 41 genes were differentially expressed between IDHMUT and IDHWT LGG patients. A risk signature comprising 10 genes, which could divide samples into high- and low-risk groups of distinct prognoses, was developed and independently validated. This enzyme-based signature was indicative of a more malignant phenotype. The nomogram model incorporating the risk signature, molecular biomarkers, and clinicopathological parameters proved the incremental utility of the α-KG-dependent signature by achieving a more accurate prediction impact. Our study demonstrates that the α-KG-dependent enzyme-encoding genes were differentially expressed in relation to the IDH phenotype and may serve as a promising indicator for clinical outcomes of LGG patients.

This paper proposes an improved common mode (CM) electromagnetic interference (EMI) suppression method in switching power supplies. The lossless snubber circuit can reduce du/dt and EMI in the high-frequency band. Nevertheless, it has a weak EMI suppression effect on the low-frequency band. A method combining the chaotic spread spectrum and the lossless snubber (CSS–LS) is proposed to improve the EMI suppression effect of the lossless snubber. It is an effective means to suppress CM EMI further. The paper used a Boost PFC converter as the object of analysis to study the CM EMI suppression effect of CSS–LS. Firstly, a CM EMI-equivalent model of the lossless snubber PFC converter was established. Then, the power spectral density function under chaotic spread-spectrum modulation was derived. The simulation analysis was performed. Finally, an experimental prototype was built, and relevant EMI tests were carried out. The experimental results show that CSS–LS can reduce CM EMI by 4~20 dBµV with little impact on converter stability. Fewer extra costs are needed for this optimization method, which is suitable for high-power-density power electronic devices.

The fuel cell grid-tied power generation system usually includes a dc-dc converter and a dc-ac inverter. In a single-phase system, inherent low-order current pulsations are introduced into the system, which can have harmful effects on the fuel cell stack. For example, reducing the output voltage and output efficiency, a reduction in service life, and even accelerates the degradation rate of the membrane electrode of a proton exchange membrane fuel cell (PEMFC). In addition, dc/ac coupling power can cause distortion in the dc input current and ac grid current. To eliminate the input ripple and ensure high ac power quality on the grid side, this paper proposes a novel power decoupling control for single-phase grid-tied PEMFC systems, which uses an improved model predictive control (MPC) algorithm. With the help of the virtual vector methods, which are realized by a two-stage optimization method, excellent tracking effect and robustness can be ensured. Simulations and experimental results show that the proposed algorithm can not only completely eliminate the input current ripple and reduce the total harmonic distortion (THD) of ac current on the grid side, but also improve the transient performance of the system.

The stability of grid-connected inverters is very sensitive to varying grid impedance, especially in the weak grid condition. In this paper, based on the Lyapunov method and with the micro-grid impedance identification, an active damping adaptive control strategy is proposed to guarantee local asymptotic stability for an LCL inverter system. First, using the backstepping design and taking the state variable errors as new variables, an appropriate Lyapunov function is constructed, so that the asymptotic stability control law can be obtained. Then, with this asymptotic stability controller, taking the varying impedance into account, an adaptive controller is developed using the Lyapunov direct method. Hence, the controller presented here can assure asymptotic stability, the system performance can also be improved instantly according to the adaptive control, and the expected stability and performance can also be achieved. Finally, the feasibility of the proposed control strategy was verified via a simulation and an experiment.

Abstract Background Glycemic variability (GV) confers a risk of cardiovascular events. In this study, we aimed to investigate whether long-term GV has an impact on coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM). Methods A total of 396 patients with T2DM who had coronary computed tomography angiography and laboratory data available at baseline and for follow-up evaluations [median 2.3 (1.8–3.1) years] were included. Fasting plasma glucose (FPG) was measured every 1–3 months, and HbA1c was measured quarterly. The coefficient of variation (CV) of HbA1c and FPG were calculated as measures of GV. Quantitative assessment of coronary plaques was performed by measuring the annual change and progression rate of total plaque volume (TPV). Significant progression was defined as annual TPV progression ≥ 15%. Multivariable regression analyses were used to assess the effects of GV on atherosclerosis progression. Results In the 396 patients, the annual change in TPV was 12.35 ± 14.23 mm3, and annual progression rate was 13.36 ± 12.69%. There were 143 (36.11%) patients with significant progression, and they had a significantly higher CV-HbA1c (P

Synchronous reference frame phase-locked loop (SRF-PLL) is widely used for grid voltage synchronization in single-phase grid-connected power converters. However, in the actual situation, dc offset component may be introduced in the input of the PLL, due to the transient fault of the power grid, sampling and measurement error, or A/D signal processing. A simple yet effective approach with additional all-pass filter based dc rejecter is presented for SRF-PLL, in this paper. Thereby, correct estimation and undesirable periodic ripple free can be achieved in SRF-PLL, when the input signal contains dc offset. The second order generalized integrator based PLL (SOGI-PLL) is first introduced, followed by the analysis on influence of the input dc offsets in SRF-PLL. The structure of enhanced-SOGI (ESOGI) with its analysis of dc offset rejection effects and performance have been then formulated in detail. Finally, experimental results are presented to demonstrate the effectiveness of the proposed ESOGI based PLL.

Early diagnosis of acute ST-segment elevation myocardial infarction (STEMI) and early determination of the culprit vessel are associated with a better clinical outcome. We developed three deep learning (DL) models for detecting STEMIs and culprit vessels based on 12-lead electrocardiography (ECG) and compared them with conclusions of experienced doctors, including cardiologists, emergency physicians, and internists. After screening the coronary angiography (CAG) results, 883 cases (506 control and 377 STEMI) from internal and external datasets were enrolled for testing DL models. Convolutional neural network-long short-term memory (CNN-LSTM) (AUC: 0.99) performed better than CNN, LSTM, and doctors in detecting STEMI. Deep learning models (AUC: 0.96) performed similarly to experienced cardiologists and emergency physicians in discriminating the left anterior descending (LAD) artery. Regarding distinguishing RCA from LCX, DL models were comparable to doctors (AUC: 0.81). In summary, we developed ECG-based DL diagnosis systems to detect STEMI and predict culprit vessel occlusion, thus enhancing the accuracy and effectiveness of STEMI diagnosis.

The weak interfacial adhesion has significantly affected the durability, long-term reliability, and performance of glass fiber–reinforced epoxy composites. The coating of graphene and carbon nanotubes on the glass fiber can have a positive effect on the strength, toughness, and thermal insulation performance of glass fiber-reinforced composites. However, the strengthening mechanism of carbon nanomaterial coating on the interfacial adhesion between glass fiber and epoxy has not been fully explored. In this work, the effect of graphene and single-walled carbon nanotubes (SWCNTs) on the interfacial properties of the glass fiber–reinforced epoxy has been investigated at atomistic scale. The graphene and SWCNTs are sandwiched between epoxy and silica to study the debonding behavior of the sandwiched structures. It is found that the interfacial energy is significantly improved with the incorporation of graphene and SWCNTs between epoxy and silica, causing an obvious improvement in adhesion stress for graphene coating and in debonding displacement for SWCNT coating. Compared with the epoxy/silica without coatings where the silica and epoxy detach from the contact surface, the sandwiched structures display different failure modes. The sandwiched structure with graphene coating fails at the epoxy matrix close to the interface, exhibiting a cohesive failure mode because of the relatively stronger interfacial interactions. The structures with SWCNTs fail at the interface between silica and SWCNTs, representing an adhesive failure mode due to the interlocking between SWCNTs and polymer chains. This work provides a theoretical guideline to optimize the interface adhesion of coated glass fiber–reinforced epoxy via structure design and surface modification of coating materials.

Objective: Diabetes mellitus is a chronic progressive inflammatory metabolic disease with pancreatic β-cells dysfunction. The present study aimed to investigate whether miR-17-5p plays a protective effect on pancreatic β-cells function in diabetes mellitus (DM) mice and dissect the underlying mechanism.Methods: C57BL/6J mice were randomly divided into control, DM, DM + Lentivirus negative control (LV-NC), and DM + Lenti-OE™ miR-17-5p (LV-miR-17-5) groups. DM was established by feeding a high-fat diet and intraperitoneal injection with streptozotocin (STZ) in mice. Blood glucose and glucose tolerance in circulation were measured. Meanwhile, the activation of nod-like receptor protein 3 (NLRP3) inflammasome, pancreas pyroptosis, and the expression of miR-17-5p and thioredoxin-interacting protein (TXNIP) were detected in the pancreas of DM mice. Pancreatic β-cell line INS-1 subjected to different concentrations of glucose was used in in vitro experiments.Results: Compared with control mice, glucose tolerance deficit, elevated blood glucose level, and decreased pancreatic islet size, were presented in DM mice, which was associated with a downregulation of miR-17-5p. Importantly, exogenous miR-17-5p alleviated pancreas injury, and consequently improved glucose tolerance and decreased blood glucose in DM mice. In vitro experiments showed that high glucose decreased miR-17-5p expression and impaired insulin secretion in INS-1 cells. Mechanistically, miR-17-5p inhibited the expression of TXNIP and NLRP3 inflammasome activation, and thus decreased pancreatic β-cell pyroptosis.Conclusion: Our results demonstrated that miR-17-5p improves glucose tolerance, and pancreatic β-cell function and inhibits TXNIP/NLRP3 inflammasome pathway-related pyroptosis in DM mice.

Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI.Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis.Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

Background: Chronic inflammation in type 2 diabetes mellitus (T2DM) is an essential contributor to the development of diabetic retinopathy (DR). The monocyte–to–high-density lipoprotein cholesterol (HDL-C) ratio (MHR) is a novel and simple measure related to inflammatory and oxidative stress status. However, little is known regarding the role of the MHR in evaluating the development of DR.Methods: A total of 771 patients with T2DM and 607 healthy controls were enrolled in this cross-sectional study. MHR determination and eye examination were performed. The association of MHR with the prevalence of DR in T2DM patients was analyzed.Results: The MHR in patients with DR was significantly higher than that in both non-DR diabetic patients (P < 0.05) and healthy controls (P < 0.01). No significance was observed in the MHR of different DR severity grades. Moreover, the MHR was similar between patients with non-macular oedema and those with macular oedema. Logistic regression analysis demonstrated that MHR was independently associated with the prevalence of DR in diabetic patients [odds ratio (OR) = 1.438, 95% confidence interval (CI): 1.249–1.655, P < 0.01]. After additional stratification by HbA1c level and diabetic duration, the MHR was still independently associated with the prevalence of DR.Conclusions: Our study suggests that the MHR can be used as a marker to indicate the prevalence of DR in patients with T2DM.

Purpose: Diabetic retinopathy (DR) is one of the most common diabetic microvascular complications. However, the pathogenesis of DR has not yet been fully elucidated. This study aimed to discover novel and key molecules involved in the pathogenesis of DR, which could potentially be targets for therapeutic DR intervention.Methods: To identify potential genes involved in the pathogenesis of DR, we analyzed the public database of neovascular membranes (NVMs) from patients with proliferative diabetic retinopathy (PDR) and healthy controls (HCs) (GSE102485, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102485). Further, we compared these findings by performing RNA-sequencing analysis of peripheral blood mononuclear cells (PBMC) from patients with DR, control patients with non-complicated diabetes mellitus (DMC), and HCs. To determine the critical role of candidate genes in DR, knockdown or knockout was performed in human retinal vascular endothelial cells (HRVECs). The oxidative stress pathway, as well as tight junction integrity, was analyzed.Results: Transcriptional profiles showed distinct patterns between the NVMs of patients with DR and those of the HCs. Those genes enriched in either extracellular matrix (ECM)-receptor interaction or focal adhesion pathways were considerably upregulated. Both pathways were important for maintaining the integrity of retinal vascular structure and function. Importantly, the gene encoding the matricellular protein CCN1, a key gene in cell physiology, was differentially expressed in both pathways. Knockdown of CCN1 by small interfering RNA (siRNA) or knockout of CCN1 by the CRISPR-Cas9 technique in HRVECs significantly increased the levels of VE-cadherin, reduced the level of NADPH oxidase 4 (NOX4), and inhibited the generation of reactive oxygen species (ROS).Conclusion: The present study identifies CCN1 as an important regulator in the pathogenesis of DR. Increased expression of CCN1 stimulates oxidative stress and disrupts tight junction integrity in endothelial cells by inducing NOX4. Thus, targeting the CCN1/NOX4 axis provides a therapeutic strategy for treating DR by alleviating endothelial cell injury.

Abstract Background To investigate the effect of visit-to-visit fasting plasma glucose (FPG) variability on the left cardiac structure and function in patients with type 2 diabetes mellitus (T2DM). Methods In this prospective cohort study, 455 T2DM patients were included and follow-up for a median of 4.7 years. FPG measured on every hospital visit was collected. FPG variability was calculated by its coefficient of variation (CV-FPG). Left cardiac structure and function were assessed using echocardiography at baseline and after follow-up. Multivariable linear regression analyses were used to estimate the effect of FPG variability on the annualized changes in left cardiac structure and function. Subgroup analysis stratified by mean HbA1c levels (