Kikuchi-Fujimoto disease evolves into lupus encephalopathy characterized by venous sinus thrombosis: a case report

Kikuchi-Fujimoto disease (KFD) is a benign, self-limiting illness that can progress to systemic lupus erythematosus (SLE) in approximately 30% of cases. Neurological injuries can occur in both diseases, albeit with distinct presentations. Venous sinus thrombosis is a serious cerebrovascular complication in patients with neuropsychiatric SLE but is rarely observed in patients with KFD. The involvement of various antibodies, particularly antiphospholipid antibodies, can cause vascular endothelial cell injury, resulting in focal cerebral ischemia and intracranial vascular embolism in SLE. However, there are cases in which thrombotic pathology occurs without antiphospholipid antibody positivity, attributed to vascular lesions. In this report, we present a case of KFD and lupus encephalopathy featuring cerebral venous sinus thrombosis, despite the patient being negative for antiphospholipid antibody. We also conducted a comparative analysis of C3 and C4 levels in cerebrospinal fluid (CSF) and peripheral blood, along with the protein ratio in CSF and serum, to elucidate the pathological changes and characteristics of lupus encephalopathy

Application of different watershed units to debris flow susceptibility mapping: A case study of Northeast China

The main purpose of this study was to compare two types of watershed units divided by the hydrological analysis method (HWUs) and mean curvature method (CWUs) for debris flow susceptibility mapping (DFSM) in Northeast China. Firstly, a debris flow inventory map consisting of 129 debris flows and 129 non-debris flows was randomly divided into a ratio of 70% and 30% for training and testing. Secondly, 13 influencing factors were selected and the correlations between these factors and the debris flows were determined by frequency ration analysis. Then, two types of watershed units (HWUs and CWUs) were divided and logistic regression (LR), multilayer perceptron (MLP), classification and regression tree (CART) and Bayesian network (BN) were selected as the evaluation models. Finally, the predictive capabilities of the models were verified using the predictive accuracy (ACC), the Kappa coefficient and the area under the receiver operating characteristic curve (AUC). The mean AUC, ACC and Kappa of four models (LR, MLP, CART and BN) in the training stage were 0.977, 0.931, and 0.861, respectively, for the HWUs, while 0.961, 0.905, and 0.810, respectively, for the CWUs; in the testing stage, were 0.904, 0.818, and 0.635, respectively, for the HWUs, while 0.883, 0.800, and 0.601, respectively, for the CWUs, which showed that HWU model has a higher debris flow prediction performance compared with the CWU model. The CWU-based model can reflect the spatial distribution probability of debris flows in the study area overall and can be used as an alternative model

Epimorphin Regulates Bile Duct Formation via Effects on Mitosis Orientation in Rat Liver Epithelial Stem-Like Cells

Understanding how hepatic precursor cells can generate differentiated bile ducts is crucial for studies on epithelial morphogenesis and for development of cell therapies for hepatobiliary diseases. Epimorphin (EPM) is a key morphogen for duct morphogenesis in various epithelial organs. The role of EPM in bile duct formation (DF) from hepatic precursor cells, however, is not known. To address this issue, we used WB-F344 rat epithelial stem-like cells as model for bile duct formation. A micropattern and a uniaxial static stretch device was used to investigate the effects of EPM and stress fiber bundles on the mitosis orientation (MO) of WB cells. Immunohistochemistry of liver tissue sections demonstrated high EPM expression around bile ducts in vivo. In vitro, recombinant EPM selectively induced DF through upregulation of CK19 expression and suppression of HNF3α and HNF6, with no effects on other hepatocytic genes investigated. Our data provide evidence that EPM guides MO of WB-F344 cells via effects on stress fiber bundles and focal adhesion assembly, as supported by blockade EPM, β1 integrin, and F-actin assembly. These blockers can also inhibit EPM-induced DF. These results demonstrate a new biophysical action of EPM in bile duct formation, during which determination of MO plays a crucial role

Interrelation between the lipid accumulation product index and diabetic kidney disease in patients with type 2 diabetes mellitus

ObjectiveThe purpose of this study was to determine the relation between the lipid accumulation product index (LAPI) and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).MethodsHerein, 931 patients were enrolled and their data were collected. Then the interrelation between LAPI and DKD was assessed using multivariate logistic regression analyses (LRAs) and by a restricted cubic spline (RCS).ResultsIn total, 931 participants (352 females and 579 males) aged 55 years on average were included in the study. After adjusting for several confounders, the odds ratio for DKD was increased evidently in the third LAPI tertile compared with that in the first LAPI tertile. In addition, the RCS revealed a positive interrelation between LAPI and DKD. In the subgroup analyses, age, sex, hyperlipidemia, hypertension, and HbA1c did not significantly interact with LAPI.ConclusionsLAPI was higher in the DKD group than in the no-DKD group, and LAPI is positively linked with DKD, which may have potential value to diagnose DKD in clinical practice

Proteomics and network pharmacology of Ganshu Nuodan capsules in the prevention of alcoholic liver disease

IntroductionGanshu Nuodan is a liver-protecting dietary supplement composed of Ganoderma lucidum (G. lucidum) spore powder, Pueraria montana (Lour.) Merr. (P. montana), Salvia miltiorrhiza Bunge (S. miltiorrhiza) and Astragalus membranaceus (Fisch.) Bunge. (A. membranaceus). However, its pharmacodynamic material basis and mechanism of action remain unknown.MethodsA mouse model of acute alcohol liver disease (ALD) induced by intragastric administration of 50% alcohol was used to evaluate the hepatoprotective effect of Ganshu Nuodan. The chemical constituents of Ganshu Nuodan were comprehensively identified by UPLC-QTOF/MS, and then its pharmacodynamic material basis and potential mechanism of action were explored by proteomics and network pharmacology.ResultsGanshu Nuodan could ameliorate acute ALD, which is mainly manifested in the significant reduction of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum and malondialdehyde (MDA) content in liver and the remarkably increase of glutathione (GSH) content and superoxide dismutase (SOD) activity in liver. Totally 76 chemical constituents were identified from Ganshu Nuodan by UPLC-QTOF/MS, including 21 quinones, 18 flavonoids, 11 organic acids, 7 terpenoids, 5 ketones, 4 sterols, 3 coumarins and 7 others. Three key signaling pathways were identified via proteomics studies, namely Arachidonic acid metabolism, Retinol metabolism, and HIF-1 signaling pathway respectively. Combined with network pharmacology and molecular docking, six key targets were subsequently obtained, including Ephx2, Lta4h, Map2k1, Stat3, Mtor and Dgat1. Finally, these six key targets and their related components were verified by molecular docking, which could explain the material basis of the hepatoprotective effect of Ganshu Nuodan.ConclusionGanshu Nuodan can protect acute alcohol-induced liver injury in mice by inhibiting oxidative stress, lipid accumulation and apoptosis. Our study provides a scientific basis for the hepatoprotective effect of Ganshu Nuodan in acute ALD mice and supports its traditional application

A study on the treatment effects of Crataegus pinnatifida polysaccharide on non-alcoholic fatty liver in mice by modulating gut microbiota

The objective of this study was to investigate the protective effect of Crataegus pinnatifida polysaccharide (CPP) on non-alcoholic fatty liver disease (NAFLD) induced by a high-fat diet (HFD) in mice. The findings demonstrated that CPP improved free fatty acid (FFA)-induced lipid accumulation in HepG2 cells and effectively reduced liver steatosis and epididymal fat weight in NAFLD mice, as well as decreased serum levels of TG, TC, AST, ALT, and LDL-C. Furthermore, CPP exhibited inhibitory effects on the expression of fatty acid synthesis genes FASN and ACC while activating the expression of fatty acid oxidation genes CPT1A and PPARα. Additionally, CPP reversed disturbances in intestinal microbiota composition caused by HFD consumption. CPP decreased the firmicutes/Bacteroidetes ratio, increased Akkermansia abundance, and elevated levels of total short-chain fatty acid (SCFA) content specifically butyric acid and acetic acid. Our results concluded that CPP may intervene in the development of NAFLD by regulating of intes-tinal microbiota imbalance and SCFAs production. Our study highlights that CPP has a potential to modulate lipid-related pathways via alterations to gut microbiome composition thereby ex-erting inhibitory effects on obesity and NAFLD development

The Application of a Three-Dimensional Deterministic Model in the Study of Debris Flow Prediction Based on the Rainfall-Unstable Soil Coupling Mechanism

As debris flow is one of the most destructive natural disasters in many parts of the world, the assessment and management of future debris flows with proper forecasting methods are crucial for the safety of life and property. So increasing attention has been paid to the forecasting methods on debris flows. A debris flow forecasting method based on the rainfall-unstable soil coupling mechanism (R-USCM) is presented in the current study. This method is based on the debris flow formation mechanism. The density of sediment is introduced as an evaluation index to determine the susceptibility of debris flow occurrence. The forecasting method includes two phases: (1) rainfall and soil coupling and (2) runoff and unstable soil coupling. Scoops3D, a three-dimensional (3D) model for analyzing slope stability, was introduced into the debris flow forecasting method. In order to test the forecasting accuracy of this method, Jiaohe County was selected as a research area, and the serious debris flow disasters attributed to strong rainfall on 20 July 2017 were taken as the research case. By comparing the forecasting results with the debris flow distribution map for Jiaohe County, the method based on the R-USCM is feasible for forecasting debris flows at the regional scale. The application of the Scoops3D model can more reasonably analyze the slope stability than the traditional two dimensional (2D) method and improve the forecasting ability of debris flows

Modeling of the Magnetic Turbulence Level and Source Function of Particle Injection from Multiple SEP Events

Solar energetic particles (SEPs) are produced by solar eruptions and are harmful to spacecraft and astronauts. The four source function parameters of particle injection for SEP events and the magnetic turbulence level can be collectively referred to as key parameters. We reproduce the electron intensity-time profiles with simulations for five SEP events observed by multispacecraft such as ACE, STEREO-A, and STEREO-B, so we can obtain the five fitted key parameters for each of the events. We analyze the relationship among the five fitted key parameters, and also the relationship between these parameters and the observed event features. Thus, the model of key parameters are established. Next, we simulate another 12 SEP events with the key parameters model. Though the predicted electron intensity-time profiles do not fit the observed ones well, the peak flux and event-integrated fluence can be predicted accurately. Therefore, the model can be used to estimate the radiation hazards

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification

Abstract Clarification of sugarcane juice is an important operation in the production process of sugar industry. The gravity purity and the color value of juice are the two most important evaluation indexes in the cane sugar production using the sulphitation clarification method. However, in the actual operation, the measurement of these two indexes is usually obtained by offline experimental titration, which makes it impossible to timely adjust the system indicators. A data‐driven modeling based on kernel extreme learning machine is proposed to predict the gravity purity of juice and the color value of clear juice. The model parameters are optimized by particle swarm optimization. Experiments are conducted to verify the effectiveness and superiority of the modeling method. Compared with BP neural network, radial basis neural network, and support vector machine, the model has a good performance, which proves the reliability of the model