Endothelial discoidin domain receptor 1 senses flow to modulate YAP activation.

Mechanotransduction in endothelial cells is critical to maintain vascular homeostasis and can contribute to disease development, yet the molecules responsible for sensing flow remain largely unknown. Here, we demonstrate that the discoidin domain receptor 1 (DDR1) tyrosine kinase is a direct mechanosensor and is essential for connecting the force imposed by shear to the endothelial responses. We identify the flow-induced activation of endothelial DDR1 to be atherogenic. Shear force likely causes conformational changes of DDR1 ectodomain by unfolding its DS-like domain to expose the buried cysteine-287, whose exposure facilitates force-induced receptor oligomerization and phase separation. Upon shearing, DDR1 forms liquid-like biomolecular condensates and co-condenses with YWHAE, leading to nuclear translocation of YAP. Our findings establish a previously uncharacterized role of DDR1 in directly sensing flow, propose a conceptual framework for understanding upstream regulation of the YAP signaling, and offer a mechanism by which endothelial activation of DDR1 promotes atherosclerosis.

TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation.

Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers' model in the field of computer vision, researchers have begun to gradually explore the application of Transformers in medical segmentation of images, especially in combination with convolutional neural networks with coding-decoding structure, which have achieved remarkable results in the field of medical segmentation. However, most studies have combined Transformers with CNNs at a single scale or processed only the highest-level semantic feature information, ignoring the rich location information in the lower-level semantic feature information. At the same time, for problems such as blurred structural boundaries and heterogeneous textures in images, most existing methods usually simply connect contour information to capture the boundaries of the target. However, these methods cannot capture the precise outline of the target and ignore the potential relationship between the boundary and the region. In this paper, we propose the TGDAUNet, which consists of a dual-branch backbone network of CNNs and Transformers and a parallel attention mechanism, to achieve accurate segmentation of lesions in medical images. Firstly, high-level semantic feature information of the CNN backbone branches is fused at multiple scales, and the high-level and low-level feature information complement each other's location and spatial information. We further use the polarised self-attentive (PSA) module to reduce the impact of redundant information caused by multiple scales, to better couple with the feature information extracted from the Transformers backbone branch, and to establish global contextual long-range dependencies at multiple scales. In addition, we have designed the Reverse Graph-reasoned Fusion (RGF) module and the Feature Aggregation (FA) module to jointly guide the global context. The FA module aggregates high-level semantic feature information to generate an original global predictive segmentation map. The RGF module captures non-significant features of the boundaries in the original or secondary global prediction segmentation graph through a reverse attention mechanism, establishing a graph reasoning module to explore the potential semantic relationships between boundaries and regions, further refining the target boundaries. Finally, to validate the effectiveness of our proposed method, we compare our proposed method with the current popular methods in the CVC-ClinicDB, Kvasir-SEG, ETIS, CVC-ColonDB, CVC-300,datasets as well as the skin cancer segmentation datasets ISIC-2016 and ISIC-2017. The large number of experimental results show that our method outperforms the currently popular methods. Source code is released at https://github.com/sd-spf/TGDAUNet.

Establishment and validation of a cuproptosis-related lncRNA signature that predicts prognosis and potential targeted therapy in hepatocellular carcinoma.

BACKGROUND: Cuproptosis is a recently identified form of programmed cell death and could be a new direction for tumour therapy, and it has important clinical implications. Long non-coding RNAs (lncRNAs) can intervene in diverse biological processes and have a decisive role in hepatocellular carcinoma (HCC). However, how cuproptosis-related lncRNAs (CRLs) participate in regulating HCC has yet to be recognised. This study aimed to establish and validate a prognostic signature of CRLs and to analyse their clinical value in HCC patients. METHODS: To analyse the function of CRLs in the prognosis of HCC, RNA sequencing data, mutation data, and clinically relevant data were collected from the Cancer Genome Atlas Database (TCGA). Then, TCGA cohort was randomly divided into training and test sets. The training set was utilized to define prognostic signature of CRLs using bioinformatics methods. Subsequently, we verified the accuracy of this prognostic signature in the test set. Finally, we performed immune-related analysis, the half-maximal inhibitory concentration (IC50) prediction, gene set enrichment analysis, and tumour mutational burden (TMB) analysis. RESULTS: We established a prognostic signature for the CRLs (SNHG4, AC026412.3, AL590705.3, and CDKN2A-DT). This signature-based risk group displayed an accurate predictive ability for the survival time of patients with HCC. We observed discrepancies in immune cells, immune function, the expression level of genes related to immune checkpoints, and TMB in high- and low-risk groups. CONCLUSION: This CRLs prognostic signature could predict clinical outcomes in patients with HCC as well as the efficacy of targeted and therapy immunotherapy.

A Segmented Redirection Mapping Method for Roadmaps of Large Constrained Virtual Environments.

Redirected walking (RDW) enables users to explore large virtual spaces by real walking in small real spaces. How to effectively reduce physical collisions and decrease user perceptions of redirection are important for most RDW methods. This article proposes a segmented redirection mapping method to calculate and map the roadmap of a large virtual space with inner obstacles to a mapped roadmap within a small real space. We adopt a Voronoi-based pruning method to extract the roadmap of the virtual space and design an RDW platform to interactively modify the virtual roadmap. We propose a roadmap mapping method based on divide-and-conquer and dynamic planning strategies to subdivide the virtual roadmap into several sub-virtual roads that are mapped individually. By recording connections of different sub-virtual roads, our method is applicable to virtual roadmaps with loop structures. During mapping, we apply the reset and redirection gains of the RDW technique as optimal aims and restrict conditions to obtain the mapped roadmap, which has small path curving and contains as few resets as possible. By real walking along the mapped roadmap, users perceive moving along the virtual roadmap to explore the entire virtual space. The experiment shows that our method works effectively for various virtual spaces with or without inner obstacles. Furthermore, our method is flexible in obtaining mapped roadmaps of different real spaces when the virtual space is fixed. Compared to prevalent RDW methods, our method can significantly reduce physical boundary collisions and maintain user experience of virtual roaming.

Redirected Walking for Exploring Immersive Virtual Spaces With HMD: A Comprehensive Review and Recent Advances.

Real walking techniques can provide the user with a more natural, highly immersive walking experience compared to the experience of other locomotion techniques. In contrast to the direct mapping between the virtual space and an equal-sized physical space that can be simply realized, the nonequivalent mapping that enables the user to explore a large virtual space by real walking within a confined physical space is complex. To address this issue, the redirected walking (RDW) technique is proposed by many works to adjust the user's virtual and physical movements based on some redirection manipulations. In this manner, subtle or overt motion deviations can be injected between the user's virtual and physical movements, allowing the user to undertake real walking in large virtual spaces by using different redirection controller methods. In this paper, we present a brief review to describe major concepts and methodologies in the field of redirected walking. First, we provide the fundamentals and basic criteria of RDW, and then we describe the redirection manipulations that can be applied to adjust the user's movements during virtual exploration. Furthermore, we clarify the redirection controller methods that properly adopt strategies for combining different redirection manipulations and present a classification of these methods by several categories. Finally, we summarize several experimental metrics to evaluate the performance of redirection controller methods and discuss current challenges and future work. Our study systematically classifies the relevant theories, concepts, and methods of RDW, and provides assistance to the newcomers in understanding and implementing the RDW technique.

DBMF: Dual Branch Multiscale Feature Fusion Network for polyp segmentation.

Accurate and reliable segmentation of colorectal polyps is important for the diagnosis and treatment of colorectal cancer. Most of the existing polyp segmentation methods innovatively combine CNN with Transformer. Due to the single combination approach, there are limitations in establishing connections between local feature information and utilizing global contextual information captured by Transformer. Still not a better solution to the problems in polyp segmentation. In this paper, we propose a Dual Branch Multiscale Feature Fusion Network for Polyp Segmentation, abbreviated as DBMF, for polyp segmentation to achieve accurate segmentation of polyps. DBMF uses CNN and Transformer in parallel to extract multi-scale local information and global contextual information respectively, with different regions and levels of information to make the network more accurate in identifying polyps and their surrounding tissues. Feature Super Decoder (FSD) fuses multi-level local features and global contextual information in dual branches to fully exploit the potential of combining CNN and Transformer to improve the network's ability to parse complex scenes and the detection rate of tiny polyps. The FSD generates an initial segmentation map to guide the second parallel decoder (SPD) to refine the segmentation boundary layer by layer. SPD consists of a multi-scale feature aggregation module (MFA) and parallel polarized self-attention (PSA) and reverse attention fusion modules (RAF). MFA aggregates multi-level local feature information extracted by CNN Brach to find consensus regions between multiple scales and improve the network's ability to identify polyp regions. PSA uses dual attention to enhance the fine-grained nature of segmented regions and reduce the redundancy introduced by MFA and interference information. RAF mines boundary cues and establishes relationships between regions and boundary cues. The three RAFs guide the network to explore lost targets and boundaries in a bottom-up manner. We used the CVC-ClinicDB, Kvasir, CVC-300, CVC-ColonDB, and ETIS datasets to conduct comparison experiments and ablation experiments between DBMF and mainstream polyp segmentation networks. The results showed that DBMF outperformed the current mainstream networks on five benchmark datasets.

Transjugular intrahepatic portosystemic shunt for the prevention of rebleeding in patients with cirrhosis and portal vein thrombosis: Systematic review and meta-analysis.

Background: Transjugular intrahepatic portosystemic shunt (TIPS) has been performed on patients with cirrhosis and portal vein thrombosis (PVT) to prevent rebleeding; however, the associated evidence is scarce. Hence, the study aimed to evaluate the feasibility and efficacy of TIPS in patients with cirrhosis and PVT and promote personalized treatment in such patients. Methods: Literature was systematically obtained from PubMed, EMBASE, Cochrane Library, and Web of Science. Data from the included studies were extracted, and meta-analyses by the random effects model were used to pool data across studies. Heterogeneity was assessed using Cochran's Q and I2 statistics. The source of heterogeneity was explored using subgroup analyses and meta-regressions. Results: A total of 11 studies comprising 703 patients with cirrhosis and portal vein thrombosis (PVT: complete, 32.2%; chronic, 90.2%; superior mesenteric vein or splenic vein involvement, 55.2%; cavernous transformation, 26.8%) were included. TIPS showed feasibility in 95% of the cases (95% confidence interval [CI]: 89%-99%) with heterogeneity (I2 = 84%, p < 0.01) due to cavernous transformation. The pooled rebleeding rate was 13% (95% CI: 7%-20%) with heterogeneity (I2 = 75%, p < 0.01) explained by chronic PVT and anticoagulation (AC) therapy. Hepatic encephalopathy occurred in 32% of patients. The survival rate, portal vein recanalization rate, and shunt patency rate were 80%, 82%, and 77%, respectively. Conclusion: TIPS is feasible and effectively prevents rebleeding in patients with cirrhosis and PVT, regardless of cavernous transformation of the portal vein. Due to a potentially high risk of rebleeding and no apparent benefits of AC, post-TIPS AC must be employed cautiously. Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=258765], identifier [CRD42021258765].

MFN2 Prevents Neointimal Hyperplasia in Vein Grafts via Destabilizing PFK1.

BACKGROUND: Mechanical forces play crucial roles in neointimal hyperplasia after vein grafting; yet, our understanding of their influences on vascular smooth muscle cell (VSMC) activation remains rudimentary. METHODS: A cuff mouse model was used to study vein graft hyperplasia. Fifteen percent to 1 Hz uniaxial cyclic stretch (arterial strain), 5% to 1 Hz uniaxial cyclic stretch or a static condition (venous strain) were applied to the cultured VSMCs. Metabolomics analysis, cell proliferation and migration assays, immunoblotting, co-immunoprecipitation, mutagenesis, pull-down and surface plasmon resonance assays were employed to elucidate the potential molecular mechanisms. RESULTS: RNA-sequencing in vein grafts and the controls identified changes in metabolic pathways and downregulation of mitochondrial protein MFN2 (mitofusin 2) in the vein grafts. Exposure of VSMCs to 15% stretch resulted in MFN2 downregulation, mitochondrial fragmentation, metabolic shift from mitochondrial oxidative phosphorylation to glycolysis, and cell proliferation and migration, as compared with that to a static condition or 5% stretch. Metabolomics analysis indicated an increased generation of fructose 1,6-bisphosphate, an intermediate in the glycolytic pathway converted by PFK1 (phosphofructokinase 1) from fructose-6-phosphate, in cells exposed to 15% stretch. Mechanistic study revealed that MFN2 physically interacts through its C-terminus with PFK1. MFN2 knockdown or exposure of cells to 15% stretch promoted stabilization of PFK1, likely through interfering the association between PFK1 and the E3 ubiquitin ligase TRIM21 (E3 ubiquitin ligase tripartite motif [TRIM]-containing protein 21), thus, decreasing the ubiquitin-protease-dependent PFK1 degradation. In addition, study of mechanotransduction utilizing pharmaceutical inhibition indicated that the MFN2 downregulation by 15% stretch was dependent on inactivation of the SP1 (specificity protein 1) and activation of the JNK (c-Jun N-terminal kinase) and ROCK (Rho-associated protein kinase). Adenovirus-mediated MFN2 overexpression or pharmaceutical inhibition of PFK1 suppressed the 15% stretch-induced VSMC proliferation and migration and alleviated neointimal hyperplasia in vein grafts. CONCLUSIONS: MFN2 is a mechanoresponsive protein that interacts with PFK1 to mediate PFK1 degradation and therefore suppresses glycolysis in VSMCs.

Low-Light Image Enhancement Network Based on Recursive Network.

In low-light environments, image acquisition devices do not obtain sufficient light sources, resulting in low brightness and contrast of images, which poses a great obstacle for other computer vision tasks to be performed. To enable other vision tasks to be performed smoothly, it is essential to enhance the research on low-light image enhancement algorithms. In this article, a multi-scale feature fusion image enhancement network based on recursive structure is proposed. The network uses a dual attention module-Convolutional Block Attention Module. It was abbreviated as CBAM, which includes two attention mechanisms: channel attention and spatial attention. To extract and fuse multi-scale features, we extend the U-Net model using the inception model to form the Multi-scale inception U-Net Module or MIU module for short. The learning of the whole network is divided into T recursive stages, and the input of each stage is the original low-light image and the inter-mediate estimation result of the output of the previous recursion. In the t-th recursion, CBAM is first used to extract channel feature information and spatial feature information to make the network focus more on the low-light region of the image. Next, the MIU module fuses features from three different scales to obtain inter-mediate enhanced image results. Finally, the inter-mediate enhanced image is stitched with the original input image and fed into the t + 1th recursive iteration. The inter-mediate enhancement result provides higher-order feature information, and the original input image provides lower-order feature information. The entire network outputs the enhanced image after several recursive cycles. We conduct experiments on several public datasets and analyze the experimental results subjectively and objectively. The experimental results show that although the structure of the network in this article is simple, the method in this article can recover the details and increase the brightness of the image better and reduce the image degradation compared with other methods.

Attention-Guided Multi-Scale Feature Fusion Network for Low-Light Image Enhancement.

Low-light image enhancement has been an important research branch in the field of computer vision. Low-light images are characterized by poor visibility, high noise and low contrast. To improve low-light images generated in low-light environments and night conditions, we propose an Attention-Guided Multi-scale feature fusion network (MSFFNet) for low-light image enhancement for enhancing the contrast and brightness of low-light images. First, to avoid the high cost computation arising from the stacking of multiple sub-networks, our network uses a single encoder and decoder for multi-scale input and output images. Multi-scale input images can make up for the lack of pixel information and loss of feature map information caused by a single input image. The multi-scale output image can effectively monitor the error loss in the image reconstruction process. Second, the Convolutional Block Attention Module (CBAM) is introduced in the encoder part to effectively suppress the noise and color difference generated during feature extraction and further guide the network to refine the color features. Feature calibration module (FCM) is introduced in the decoder section to enhance the mapping expression between channels. Attention fusion module (AFM) is also added to capture contextual information, which is more conducive to recovering image detail information. Last, the cascade fusion module (CFM) is introduced to effectively combine the feature map information under different perceptual fields. Sufficient qualitative and quantitative experiments have been conducted on a variety of publicly available datasets, and the proposed MSFFNet outperforms other low-light enhancement methods in terms of visual effects and metric scores.

USP49 mediates tumor progression and poor prognosis through a YAP1-dependent feedback loop in gastric cancer.

The importance of the Hippo-Yes-associated protein 1 (YAP1) pathway in gastric carcinogenesis and metastasis has attracted considerable research attention; however, the regulatory network of YAP1 in gastric cancer (GC) is not completely understood. In this study, ubiquitin-specific peptidase 49 (USP49) was identified as a novel deubiquitinase of YAP1, knockdown of USP49 inhibited the proliferation, metastasis, chemoresistance, and peritoneal metastasis of GC cells. Overexpression of USP49 showed opposing biological effects. Moreover, USP49 was transcriptionally activated by the YAP1/TEAD4 complex, which formed a positive feedback loop with YAP1 to promote the malignant progression of GC cells. Finally, we collected tissue samples and clinical follow-up information from 482 GC patients. The results showed that USP49 expression was high in GC cells and positively correlated with the expression of YAP1 and its target genes, connective tissue growth factor (CTGF) and cysteine-rich angiogenic inducer 61 (CYR61). Survival and Cox regression analysis showed that high USP49 expression was associated with poor prognosis and was an independent prognostic factor. Moreover, patients with high USP49 and YAP1 expression had extremely short overall survival. The findings of this study reveal that the aberrant activation of the USP49/YAP1 positive feedback loop plays a critical role in the malignant progression of GC, thus providing potential novel prognostic factors and therapeutic targets for GC.

Increased SNX20 and PD-L1 Levels Can Predict the Clinical Response to PD-1 Inhibitors in Lung Adenocarcinoma.

PURPOSE: Programmed death ligand 1 (PD-L1) is widely used for predicting immune checkpoint inhibitors but has a limited effect on predicting clinical response. The aim of this study was to examine the prognostic value and PD-1 inhibitor therapeutic efficiency of SNX20 in lung adenocarcinoma. METHODS: We evaluated the mRNA and protein expression levels of SNX20 and PD-L1 and confirmed their predictive role in clinical response to anti-PD-1 therapy in 56 patients with advanced, refractory lung adenocarcinoma treated with PD-1 inhibitors. The expression of SNX family in different cancer types and the relationship between SNX20 and immune cells were evaluated in TCGA. The protein expression levels of SNX20, PD-L1 in 56 lung adenocarcinoma tissues were evaluated by immunohistochemistry. RESULTS: SNX20 mRNA expression has the strongest relationship with CD8a of the sorting nexin (SNX) family in lung adenocarcinoma and is strongly correlated with immune infiltration levels in 30 cancer types, especially in lung adenocarcinoma. A positive correlation between SNX20 and PD-L1 was found based on immunohistochemical data (Pearson's r=0.3731 and p=0.0466). SNX20 and PD-L1 were also observed to have a significant positive correlation at the mRNA level. According to the receiver operating characteristic (ROC) curve, the best expression differentiation score of SNX20 and PD-L1 between responder versus non-responders in patients with lung adenocarcinoma using PD-1 inhibitors is 5. In univariate logistic regression analysis, both SNX20 (odds ratio [OR]=3.778, p=0.019) and PD-L1 (OR=5.727, p=0.004) expression levels are significant predictors of clinical response in the PD-1 inhibitor responder group, and SNX20 (OR=3.575, p=0.038) and PD-L1 (OR=5.484, p=0.007) are also predictors of the response to PD-1 inhibitors in the multivariate analysis. High SNX20/high PD-L1 expression group had longer overall survival than patients with high SNX20/low PD-L1 expression group or low SNX20/high PD-L1 expression group (p=0.013) and patients with low SNX20/low PD-L1 expression group (p=0.01). CONCLUSION: SNX20 expression can be a promising predictor for therapeutic decision-making and treatment response assessment regarding PD-1 inhibitors, and special attention is required for the subgroup of patients with lung adenocarcinoma whose tumors express both high SNX20 and PD-L1.

Brief review of image denoising techniques.

With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.