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Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of “few data, poor information”, this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.

Despite the promise of immune checkpoint blockade (ICB) therapy against cancer, challenges associated with low objective response rates and severe systemic side effects still remain and limit its clinical applications. Here, we described a cold atmospheric plasma (CAP)-mediated ICB therapy integrated with microneedles (MN) for the transdermal delivery of ICB. We found that a hollow-structured MN (hMN) patch facilitates the transportation of CAP through the skin, causing tumor cell death. The release of tumor-associated antigens then promotes the maturation of dendritic cells in the tumor-draining lymph nodes, subsequently initiating T cell-mediated immune response. Anti-programmed death-ligand 1 antibody (aPDL1), an immune checkpoint inhibitor, released from the MN patch further augments the antitumor immunity. Our findings indicate that the proposed transdermal combined CAP and ICB therapy can inhibit the tumor growth of both primary tumors and distant tumors, prolonging the survival of tumor-bearing mice.

Semantic segmentation of large-scale indoor 3D point cloud scenes is crucial for scene understanding but faces challenges in effectively modeling long-range dependencies and multi-scale features. In this paper, we present RegionPVT, a novel Regional-to-Local Point-Voxel Transformer that synergistically integrates voxel-based regional self-attention and window-based point-voxel self-attention for concurrent coarse-grained and fine-grained feature learning. The voxel-based regional branch focuses on capturing regional context and facilitating inter-window communication. The window-based point-voxel branch concentrates on local feature learning while integrating voxel-level information within each window. This unique design enables the model to jointly extract local details and regional structures efficiently and provides an effective and efficient solution for multi-scale feature fusion and a comprehensive understanding of 3D point clouds. Extensive experiments on S3DIS and ScanNet v2 datasets demonstrate that our RegionPVT achieves competitive or superior performance compared with state-of-the-art approaches, attaining mIoUs of 71.0% and 73.9% respectively, with significantly lower memory footprint.

The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. For the highly similar morphology of different WBC subtypes, it is too confused to classify the WBC effectively and accurately for visual observation of blood cell smears. This paper proposes a Deep Convolutional Neural Network (DCNN) with feature fusion strategies, named WBC-AMNet, for automatically classifying WBC subtypes based on focalized attention mechanism. To obtain more localized attention of CNN, the fusion features of the first and the last convolutional layer are extracted by focalized attention mechanism combining Squeeze-and-Excitation (SE) and Gather-Excite (GE) modules. The new method performs successfully in classifying monocytes, neutrophils, lymphocytes, and eosinophils on the complex background with an overall accuracy of 95.66%, better than that of general CNNs. The multi-classification accuracy of WBC-AMNet with the background segmentation is over 98% in all cases. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize the attention heatmaps of different feature maps.

With the popularity of video surveillance technology, people are paying more and more attention to how to detect abnormal states or events in videos in time. Therefore, real-time, automatic and accurate detection of abnormal events has become the main goal of video-based surveillance systems. To achieve this goal, many researchers have conducted in-depth research on online video anomaly detection. This paper presents the background of the research in this field and briefly explains the research methods of offline video anomaly detection. Then, we sort out and classify the research methods of online video anomaly detection and expound on the basic ideas and characteristics of each method. In addition, we summarize the datasets commonly used in online video anomaly detection and compare and analyze the performance of the current mainstream algorithms according to the evaluation criteria of each dataset. Finally, we summarize the future trends in the field of online video anomaly detection.

Inflammatory bowel disease (IBD) is a recurring chronic inflammatory disease. Current treatment strategies are aimed at alleviating clinical symptoms and are associated with gastrointestinal or systemic adverse effects. New delivery strategies are needed for the treatment of IBD. Bacteria are promising biocarriers, which can produce drugs in situ and sense the gut in real time. Herein, we focus on recent studies of engineered bacteria used for IBD treatment and introduce the application of engineered bacteria in the diagnosis. On this basis, the current dilemmas and future developments of bacterial delivery systems are discussed. Recent studies on bacteria‐based platforms for the treatment of inflammatory bowel disease (IBD) through immunomodulation, antioxidative stress, barrier restoration, and microbial regulation are comprehensively reviewed. Current dilemmas and future development prospects of integrated IBD‐targeting bacterial platforms were also discussed.

Background Circular RNAs (circRNAs), a subclass of non-coding RNAs, play essential roles in tumorigenesis and aggressiveness. Our previous study has identified that circAGO2 drives gastric cancer progression through activating human antigen R (HuR), a protein stabilizing AU-rich element-containing mRNAs. However, the functions and underlying mechanisms of circRNAs derived from HuR in gastric cancer progression remain elusive. Methods CircRNAs derived from HuR were detected by real-time quantitative RT-PCR and validated by Sanger sequencing. Biotin-labeled RNA pull-down, mass spectrometry, RNA immunoprecipitation, RNA electrophoretic mobility shift, and in vitro binding assays were applied to identify proteins interacting with circRNA. Gene expression regulation was observed by chromatin immunoprecipitation, dual-luciferase assay, real-time quantitative RT-PCR, and western blot assays. Gain- and loss-of-function studies were performed to observe the impacts of circRNA and its protein partner on the growth, invasion, and metastasis of gastric cancer cells in vitro and in vivo . Results Circ-HuR ( hsa_circ_0049027 ) was predominantly detected in the nucleus, and was down-regulated in gastric cancer tissues and cell lines. Ectopic expression of circ-HuR suppressed the growth, invasion, and metastasis of gastric cancer cells in vitro and in vivo. Mechanistically, circ-HuR interacted with CCHC-type zinc finger nucleic acid binding protein (CNBP), and subsequently restrained its binding to HuR promoter, resulting in down-regulation of HuR and repression of tumor progression. Conclusions Circ-HuR serves as a tumor suppressor to inhibit CNBP-facilitated HuR expression and gastric cancer progression, indicating a potential therapeutic target for gastric cancer.

Background Coronavirus disease 2019 (COVID-19) induces myocardial injury, either direct myocarditis or indirect injury due to systemic inflammatory response. Myocardial involvement has been proved to be one of the primary manifestations of COVID-19 infection, according to laboratory test, autopsy, and cardiovascular magnetic resonance (CMR). However, the middle-term outcome of cardiac involvement after the patients were discharged from the hospital is yet unknown. The present study aimed to evaluate mid-term cardiac sequelae in recovered COVID-19 patients by CMR Methods A total of 47 recovered COVID-19 patients were prospectively recruited and underwent CMR examination. The CMR protocol consisted of black blood fat-suppressed T2 weighted imaging, T2 star mapping, left ventricle (LV) cine imaging, pre- and post-contrast T1 mapping, and late gadolinium enhancement (LGE). LGE were assessed in mixed both recovered COVID-19 patients and healthy controls. The LV and right ventricle (RV) function and LV mass were assessed and compared with healthy controls. Results A total of 44 recovered COVID-19 patients and 31 healthy controls were studied. LGE was found in 13 (30%) of COVID-19 patients. All LGE lesions were located in the mid myocardium and/or sub-epicardium with a scattered distribution. Further analysis showed that LGE-positive patients had significantly decreased LV peak global circumferential strain (GCS), RV peak GCS, RV peak global longitudinal strain (GLS) as compared to non-LGE patients ( p  < 0.05), while no difference was found between the non-LGE patients and healthy controls. Conclusion Myocardium injury existed in 30% of COVID-19 patients. These patients have depressed LV GCS and peak RV strains at the 3-month follow-up. CMR can monitor the COVID-19-induced myocarditis progression, and CMR strain analysis is a sensitive tool to evaluate the recovery of LV and RV dysfunction.

Approximately half of HIV patients continue to experience HIV-associated neurocognitive disorders (HAND). Our study aims to evaluate changes in the dynamic activity patterns of functional brain communities in the early stages of HIV infection by comparing time-varying multilayer network metrics. A total of 165 persons living with HIV but without neurocognitive disorders (PWND), 173 individuals with asymptomatic neurocognitive impairment (ANI), and 100 matched healthy controls (HC) were enrolled. A time-varying multilayer network model was constructed, and global modularity (Q value) and nodal flexibility were calculated using different parameter settings (γ = [0.9, 1, 1.1], ω = [0.5, 0.75, 1]). Brain functional alterations in the PWND and ANI groups were evaluated from both global and nodal perspectives. Associations between network measures, clinical variables, and cognitive performance were also explored. Using the full connectivity matrix, no significant differences in global modularity (Q value) were found among the three groups. However, when thresholding the matrix to retain the top 10% of strongest connections, the ANI group showed significantly lower modularity than the HC group across all γ and ω combinations ( p  < 0.05). At γ = 0.9 and ω = 0.5, reduced nodal flexibility was observed in visual network regions in the PWND group, while the ANI group showed reduced flexibility in regions belonging to the default mode network (DMN), sensorimotor network (SMN), and limbic network (LIM). At γ = 0.9 and ω = 1, the ANI group exhibited increased flexibility in DMN regions compared to HC. Additionally, thresholding the top 10% connections revealed increased flexibility in the right lingual gyrus (visual network) in ANI compared to HC (FDR corrected, p  < 0.05). Nodal flexibility was positively correlated with neurocognitive performance in the PWND group, whereas a significant negative correlation was observed in the ANI group. Regardless of cognitive impairment, HIV patients exhibit abnormalities in dynamic community structures. These findings provide new insights and perspectives for the early detection of brain damage, advancing our current understanding of time-varying multilayer networks in HIV patients.

Additive manufacturing as a major component of the “fourth Industrial Revolution” is getting more and more attention. Friction additive manufacturing technology (FAM) is a subdivision of additive manufacturing technology. Because of its solid-state characteristics, deposition by FAM shows better mechanical performance than other technologies such as powder bed fusion technologies. This paper presents a state-of-the-art survey on the development of FAM in three categories: (i) Friction stir additive manufacturing; (ii) Friction surfacing additive manufacturing; (iii) Metal powder assisted additive manufacturing. The underlying principles, process parameters, microstructure, mechanical properties, and existing problems are described and discussed in detail.