NLRC4-mediated pyroptosis was involved in coagulation disorders of acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a potentially lethal acute disease highly involved in coagulation disorders. Pyroptosis has been reported to exacerbate coagulation disorders, yet this implication has not been illustrated completely in AP. METHODS: RNA sequencing data of peripheral blood of AP patients were downloaded from the Gene Expression Omnibus database. Gene set variation analysis and single sample gene set enrichment analysis were used to calculate the enrichment score of coagulation-related signatures and pyroptosis. Spearman and Pearson correlation analysis was used for correlation analysis. Peripheral blood samples and related clinical parameters were collected from patients with AP and healthy individuals. A severe AP (SAP) model of mice was established using caerulein and lipopolysaccharide. Enzyme-linked immunosorbent assay, chemiluminescence immunoassay and immunohistochemical analysis were employed to detect the level of coagulation indicators and pyroptosis markers in serum and pancreas tissues. Additionally, we evaluated the effect of pyroptosis inhibition and NLRC4 silence on the function of human umbilical vein endothelial cells (HUVECs). RESULTS: Coagulation disorders were significantly positively correlated to the severity of AP, and they could be a predictor for AP severity. Further analyses indicated that six genes-DOCK9, GATA3, FCER1G, NLRC4, C1QB and C1QC-may be involved in coagulation disorders of AP. Among them, NLRC4 was positively related to pyroptosis that had a positive association with most coagulation-related signatures. Data from patients showed that NLRC4 and other pyroptosis markers, including IL-1ß, IL-18, caspase1 and GSDMD, were significant correlation to AP severity. In addition, NLRC4 was positively associated with coagulation indicators in AP patients. Data from mice showed that NLRC4 was increased in the pancreas tissues of SAP mice. Treatment with a pyroptosis inhibitor effectively alleviated SAP and coagulation disorders in mice. Finally, inhibiting pyroptosis or silencing NLRC4 could relieve endothelial dysfunction in HUVECs. CONCLUSIONS: NLRC4-mediated pyroptosis damages the function of endothelial cells and thereby exacerbates coagulation disorders of AP. Inhibiting pyroptosis could improve coagulation function and alleviate AP.

Long-term effects of the COVID-19 pandemic on five mental and psychological disorders: in terms of the number of disease visits, drug consumption, and scale scores.

BACKGROUND: COVID-19 caused mild to severe infections in humans. The long-term epidemic environment harms people's mental health. To explore the impact of the epidemic on people's mental and psychological conditions, we surveyed in Wenzhou. METHODS: We collected the data of people who visited the First Affiliated Hospital of Wenzhou Medical University for five types of mental and psychological diseases from January 2018 to December 2021. Then, taking December 2019 as the cut-off point, the 48-month data were divided into the pre-epidemic group and the dur-epidemic group. Based on the above data, statistical analysis was done. RESULTS: From 2018 to 2021, the number of initial diagnoses, the number of disease visits, and drug consumption for these five types of mental and psychological diseases were all on the rise. Compared with the number of disease visits for all disorders in both psychiatry and neurology departments, it was found that the growth rate of these five diseases was higher than the growth rate of all disorders. We found that the number of disease visits, drug consumption, and scale scores after the COVID-19 outbreak were significantly different from those before the outbreak (P < 0.05). And the number of disease visits positively correlated with drug consumption (P < 0.0001, r = 0.9503), which verified the stability of the data. CONCLUSION: The epidemic environment has had a long-term and negative impact on people's mental and psychological conditions. Therefore, whether or not the epidemic is receding, we still need to be concerned about the impact of COVID-19 on mental and psychological health.

Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation.

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

Video texture synthesis with multi-frame LBP-TOP and diffeomorphic growth model.

Video texture synthesis is the process of providing a continuous and infinitely varying stream of frames, which plays an important role in computer vision and graphics. However, it still remains a challenging problem to generate high-quality synthesis results. Considering the two key factors that affect the synthesis performance, frame representation and blending artifacts, we improve the synthesis performance from two aspects: 1) Effective frame representation is designed to capture both the image appearance information in spatial domain and the longitudinal information in temporal domain. 2) Artifacts that degrade the synthesis quality are significantly suppressed on the basis of a diffeomorphic growth model. The proposed video texture synthesis approach has two major stages: video stitching stage and transition smoothing stage. In the first stage, a video texture synthesis model is proposed to generate an infinite video flow. To find similar frames for stitching video clips, we present a new spatial-temporal descriptor to provide an effective representation for different types of dynamic textures. In the second stage, a smoothing method is proposed to improve synthesis quality, especially in the aspect of temporal continuity. It aims to establish a diffeomorphic growth model to emulate local dynamics around stitched frames. The proposed approach is thoroughly tested on public databases and videos from the Internet, and is evaluated in both qualitative and quantitative ways.