Emerging evidence suggests that atherosclerosis, one of the leading phenotypes of cardiovascular diseases, is a chronic inflammatory disease. During the atherosclerotic process, immune cells play critical roles in vascular inflammation and plaque formation. Meanwhile, gastrointestinal disorder is considered a risk factor in mediating the atherosclerotic process. The present study aimed to utilize sivelestat, a selective inhibitor of neutrophil elastase, to investigate its pharmacological benefits on atherosclerosis and disclose the gastrointestinal–vascular interaction. The activation of intestinal neutrophil was increased during atherosclerotic development in Western diet-fed ApoE-/- mice. Administration of sivelestat attenuated atherosclerotic phenotypes, including decreasing toxic lipid accumulation, vascular monocyte infiltration, and inflammatory cytokines. Sivelestat decreased intestinal permeability and endotoxemia in atherosclerotic mice. Mechanistically, sivelestat upregulated the expression of zonula occludens-1 in the atherosclerotic mice and recombinant neutrophil elastase protein-treated intestinal epithelial cells. Meanwhile, treatment of sivelestat suppressed the intestinal expression of inflammatory cytokines and NF-κB activity. In contrast, administration of lipopolysaccharides abolished the anti-atherosclerotic benefits of sivelestat in the Western diet-fed ApoE-/- mice. Further clinical correlation study showed that the circulating endotoxin level and intestinal neutrophil elastase activity were positively correlated with carotid intima-medial thickness in recruited subjects. In conclusion, sivelestat had pharmacological applications in protection against atherosclerosis, and intestinal homeostasis played one of the critical roles in atherosclerotic development.

BACKGROUND:There is a big difference in the expression of miRNAs of plasma exosomes of patients with HBV infection. This study aims to analyze four molecular markers of peripheral blood plasma exosomes to evaluate their potential diagnostic values in HBV infection.METHODS:A total of 55 cases of patients with chronic hepatitis B were in Experimental Group 1; 49 cases of hepatitis B carriers were in Experimental Group 2, and 46 cases were in the healthy control group. The total RNA of the plasma exosome was used to analyze the specificity and sensitivity and draw ROC curves.RESULTS:There was a significant difference in the expression of miRNA-1246, miRNA-150-5p, miRNA-5787, and miRNA-8069 down-regulated by plasma exosomes in Experimental Group 1 and Group 2 and Control Group, with a p value of less than 0.05.CONCLUSIONS:The molecular markers down-regulated were miRNA-1246, miRNA-150-5p, miRNA-5787, and miRNA-8069. The four miRNAs were initially identified as new markers of miRNAs of peripheral blood plasma exosomes after HBV infection. It is better to use multiple markers for combined diagnosis.

ObjectiveTo investigate the effect of hepatitis B virus (HBV) infection on the activation of hepatic stellate cells (HSCs) and its mechanism of action. MethodsA total of 30 plasma samples of chronic hepatitis B patients, 42 plasma samples of hepatitis B cirrhosis patients, 30 plasma samples of hepatocellular carcinoma patients, and 18 plasma samples of the individuals undergoing physical examination were collected from November 2020 to January 2021, and ELISA was used to measure the content of hepatitis B X protein (HBx), transforming growth factor-β1 (TGFβ1), dopamine beta-hydroxylase (DBH), and hydroxyproline (HYP) in plasma and conditioned medium. LO2 cells were used to establish a cell line with stable overexpression of HBx (LO2-HBx) and negative control cells (LO2-con), and a conditioned medium was prepared for LO2-HBx, LO2-Con, and LO2 cells (Mock), respectively; human HSC cell line LX-2 was incubated and divided into LX-2/LO2-HBx, LX-2/LO2-con, and LX-2/Mock groups, and CCK-8 assay was used to measure the change in cell proliferation. LX-2 cells were stimulated by rhTGFβ1, and the cells in the LX-2/LO2-HBx group were treated with a TGFβ1 receptor inhibitor. Quantitative real-time PCR and Western blot were used to measure the expression of HBx in LO2 cells and the expression of alpha-smooth muscle actin (α-SMA), collagen type I alpha 1 (Col1A1), DBH, and TGFβ1 in the above LX-2 cells. An analysis of variance was used for comparison between multiple groups, and the Bonferroni method was used for further comparison; the t-test was used for comparison between two groups; the Pearson method was used for correlation analysis. ResultsLO2-HBx stably expressed HBx protein and showed an increase in the content of TGFβ1 in supernatant (F=324.701, P＜0.01). The co-cultured LX-2/LO2-HBx group had a significant change in cell morphology, with the presence of cell shrinkage, extended cytoplasmic process, and reduced lipid droplets, and compared with the LX-2/LO2-con group, the LX-2/LO2-HBx group had significant increases in proliferative activity (P＜005) and the mRNA and protein expression levels of α-SMA and Col1A1 (mRNA: F=144.712 and 76.680, both P＜001; protein: F=234.142 and 528.708, both P＜0.001). The LX-2/LO2-HBx group had significant increases in the content of TGFβ1 (F=29.382, P＜001) and DBH (F=42.662, P＜0.01). With the increase in the stimulating concentration of rhTGFβ1, there were significant increases in the expression of α-SMA (F=1 794.031, P＜0.01), Col1A1 (F=91.340, P＜0.01), and DBH (F=2 501.011, P＜0.01), which reached the peak values at the rhTGFβ1 concentration of 10 ng/ml, and after a TGFβ1 receptor inhibitor was added to the conditioned medium, the LO2-HBx group had significant reductions in the expression of DBH and Col1A1 compared with the control group (t=3.603 and 5798, both P＜0.05). Compared with the healthy control group, the chronic hepatitis B, liver cirrhosis, and hepatocellular carcinoma groups had significant increases in the plasma levels of TGFβ1 (F=51.188, P＜0.001), HBx (F=39.227, P＜0.001), DBH (F=34431, P＜0.001), and HYP (F=16.211, P＜0.001), and a positive correlation was observed between plasma HBx and TGFβ1, between TGFβ1 and DBH, and between HYP and DBH (r=0.931, 0.863, and 0.765, all P＜0.001). ConclusionHBx protein can promote the secretion of TGFβ1 in LO2 cells, induce the proliferation and activation of LX-2 cells, promote the development of liver fibrosis, and upregulate the expression of TGFβ1 and DBH in LX-2 cells, and rhTGFβ1 stimulation can induce the activation of LX-2 cells and the upregulation of DBH expression.

目的:探究肝纤维化标志物和HBV-DNA联合检测在慢性HBV感染后疾病进程中的应用价值.方法:回顾性分析2017年1月-2019年12月诊断为HBV携带者58例(对照组)、慢性乙型肝炎患者107例(慢性乙型肝炎组)、乙肝肝硬化患者34例(肝硬化组)的病历资料,检测并观察三组的肝纤维化标志物HA、PCⅢ、Ⅳ-C、LN和HBV-DNA载量,分析各指标之间及各指标与疾病进程的相关性,并绘制受试者工作特征(ROC)曲线.结果:肝纤维化四项指标HA、PCⅢ、Ⅳ-C、LN在对照组、慢性乙型肝炎组、肝硬化组中的含量依次增加,HBV-DNA载量在慢性乙型肝炎组中最高,其次是对照组;各指标在各组中的检测结果比较,差异均有统计学意义(P<0.05).血清肝纤维化四项指标HA、PCⅢ、Ⅳ-C、LN之间均呈正相关关系(P<0.01),其中PCⅢ与Ⅳ-C之间为高度相关(rs=0.897,P<0.01).血清肝纤维化四项指标HA、PCⅢ、Ⅳ-C、LN与HBV感染后疾病发展呈正相关(P<0.05),其中HA与HBV感染后疾病进程显著相关(rs=0.548,P<0.01).HA、PCⅢ、Ⅳ-C、LN、HBV-DNA单一检测对应AUC分别为0.925、0.929、0.916、0.899、0.630,联合检测对应AUC为0.959.结论:HBV在慢性乙型肝炎患者中呈现较高水平的复制,在肝硬化阶段复制水平降低.肝纤维化四项指标在HBV感染后随着疾病发展含量升高,其含量与疾病发展呈正相关关系,HA、PCⅢ、Ⅳ-C、LN四项指标之间也具有正相关关系,其中PCⅢ与Ⅳ-C含量高度相关,HA含量与疾病进程明显相关.联合检测肝纤维化四项血清学标志物与HBV-DNA载量可提高肝纤维化的诊断效能.

Web 2.0时代,消费者在在线购物、学习和娱乐时越来越多地依赖在线评论信息,而虚假的评论会误导消费者的决策,影响商家的真实信用,因此有效识别虚假评论具有重要意义.文中首先对虚假评论的范围进行了界定,并从虚假评论识别、形成动机、对消费者的影响以及治理策略4个方面归纳了虚假评论的研究内容,给出了虚假评论研究框架和一般识别方法的工作流程.然后从评论文本内容和评论者及其群组行为两个角度,对近十年来国内外的相关研究成果进行了综述,介绍了虚假评论效果评估的相关数据集和评价指标,统计分析了在公开数据集上实现的虚假评论有效识别方法,并从特征选取、模型方法、训练数据集、评价指标值等方面进行了对比分析.最后对虚假评论识别领域的有标注语料规模限制等未来研究方向进行了探讨.

The degradation of soil fertility in mining areas poses great risks to agricultural production and the ecological environment, and has increasingly become a worldwide concern. In this study, soil assessments were conducted to evaluate the spatial and temporal variations of soil fertility indicators and characteristics of soil fertility degradation under different types of land use and mining disturbances in a coalfield on the Loess Plateau of China, where soil fertility degradation caused by mining activities has become a serious environmental issue. Soil samples (depth: 0–20 cm) were collected twice from the same 50 sampling points in 2017 and 2019. The sampling points covered three land use types (cropland, shrubland, and grassland) and three years of mining disturbance (2011, 2013, and 2016). Soil organic matter (SOM), total nitrogen (TN), soil-available phosphorus, soil-available potassium, and the fine soil particles in topsoils were measured for each sample. The spatial distributions of the properties and degradation of soil fertility were analysed using kriging interpolation, and the degree to which fertility degraded was analysed via density-based spatial clustering of applications with noise (DBSCAN) and validated using SoftMax and random forest algorithms. The study revealed that the intensity of the degradation of soil fertility could be classified into three clusters (i.e. severely degraded, moderately degraded, and slightly degraded), as indicated by the DBSCAN results, and based on the variation in soil fertility indicators. Validation using random forest and SoftMax suggested that the accuracy of clustering was over 95%. Land use types and coal mining years significantly affected the degree of degradation, and total nitrogen and soil organic matter had the most noticeable impacts on the classification of soil fertility degradation.

Soil total nitrogen (TN) is one of the most important nutrients for plant and crop growth. It is essential to estimate the spatial distribution of TN to evaluate soil productivity, land reclamation efficiency, and agricultural management. This study was performed to investigate the spatial distribution of TN and its influencing factors in a coalfield in the Loess Plateau, China. A total of 143 topsoil samples were collected from five land use types and different slope positions. Classical statistical and geostatistical methods were used to quantify the spatial variation in TN. Redundancy analysis (RDA) was used to detect correlations between environmental factors and TN, whereas the random forest (RF) algorithm and LightGBM were applied to simulate the correlations. The results showed that the degree of spatial dependence ranged from 31.7%-45.2%, indicating moderate spatial variability in TN. Land use type significantly affected TN. The highest TN contents were found in the conservation tillage lands, followed by conventional tillage land, shrub land, grassland, and coal mining areas. The spatial distribution of the TN was also influenced by the slope position. The sites located downslope had higher TN than those located on the upper and middle slopes. RDA indicated that available potassium (AK) and soil organic matter (SOM) significantly affected the spatial distribution of TN. It was also demonstrated by LightGBM and RF that the TN content could be simulated by AK, SOM, normalised vegetation index, elevation, and available phosphorus. These results are helpful in understanding the effects of anthropogenic and natural factors on soil TN in coalfield areas.

Underground coal mining can result in land deformation (e.g., land subsidence and ground fissures), and may consequently change the soil nutrients. Soil organic matter (SOM), total nitrogen (TN), and available phosphorus (AP) are critical indicators of soil fertility and eco-restoration in mining areas. In this study, soil samples (depth: 0–20 cm) were collected twice from 20 sampling points in pre-mining and post-mining in the No.12 panel of Caojiatan coalfield, in the Loess Plateau of China. SOM, TN, and AP in soil samples were measured, and the nutrient loss was evaluated. Ten environmental factors affecting soil nutrient loss were identified from a 5-m resolution digital elevation map (DEM). The paired t-test was utilized to evaluate the differences between SOM, TN, and AP in pre-mining and post-mining soil. The mechanisms of the effects of environmental factors on soil nutrient loss were revealed based on multiple linear regression, redundancy analysis (RDA), and the random forest algorithm (RF). Ordinary kriging and RF were utilized to predict and optimize the spatial distribution of the soil nutrient loss. The results showed that significant differences existed between the SOM, TN, and AP in the pre-mining and post-mining soil. The model established by RF provided a higher accuracy in terms of fitting the correlation between soil nutrient loss and environmental factors compared to the model established by multiple linear regression, and the feature importance obtained by RF showed that profile curvature, distance to working panel margin, and surface roughness were the most significant factors affecting the loss of SOM, TN, and AP, respectively. This study provides a theoretical reference for eco-restoration, as well as soil and water conservation, in subsided lands in coalfields.

The nonlinear and heterogeneous responses of nutrients to eutrophication control measures are a major challenge for in situ treatment engineering design, especially for large water bodies. Tackling the problem calls for a full understanding of potential water quality responses to various treatment schemes, which cannot be fulfilled by empirical-based methods or small-scale tests. This paper presents a methodology for Phoslock application based on the idea of object-oriented intelligent engineering design (OOID), which includes numerical simulation to explore the features of responses to numerous assumed schemes. A large plateau lake in Southwestern China was employed as a case study to illustrate the characteristics of the water quality response and demonstrate the applicability of this new approach. It was shown by the simulation and scenario analysis that the water quality response to Phoslock application always reflected nonlinearity and spatiotemporal heterogeneity, and always varied with objects, boundary conditions, and engineering design parameters. It was also found that some design parameters, like release position, had a significant impact on efficiency. Thus, a remarkable improvement could be obtained by cost-effective analysis based on scenarios using combinations of design parameters.

Precise delineation of river networks is important for accurate hydrological and flood modelling. Whilst remote sensing (RS) has showed great potential in monitoring hydrological changes over space and time, the existing RS-based methods extract river networks based on local morphologies and seldom take into account the overall hydrological connectivity of the rivers. The existing methods also commonly neglect the effect of seasonal variation of water surfaces and the existence of temporary water bodies, which deteriorate the precision of positioning river networks. To address these challenges, a new two-stage method is developed to Extract spatiotemporal variation of water surfaces based on Multi-temporal remote sensing Imagery and Delineate connected river networks with improved accuracy (EMID method for short) using a path tracking technique. The EMID method delineates connected river networks using (a) multi-temporal imagery and a Random Forest model to synoptically map the location and extent of water surfaces under different hydrological conditions, and (b) an optimization algorithm to find the best river paths based on water-occurrence frequency. Four drainage basins with various river morphologies are considered to validate EMID. Comparing with alternative methods, the EMID method consistently produces river network results with improved accuracy in terms of stream location, river coverage and network connectivity.

BACKGROUND The treatment options of systemic lupus erythematosus (SLE) patients in active and inactive phases are very different clinically, and the prognosis of patients with active SLE is much worse than inactive patients. However, the present indicators for diagnosis of SLE in activity are limited and inefficient. METHODS Three hundred thirty patients with SLE were included. All patients are classified as SLEDAI (systemic lupus erythematosus disease activity index) > 4 as active and SLEDAI ≤ 4 as inactive. The linear correlation between variables was assessed by Pearson's correlation analysis. The difference between parameters in active and inactive patients was evaluated by the Mann-Whitney U test. The evaluation capacity of erythrocyte sedimenta-tion/red blood cell (ERR) and red blood cell/albumin ratio (RAR) on SLE activity was determined by bivariate regression analysis. Sensitivity and specificity are assessed by receiver operating characteristic curve (ROC). RESULTS Compared with the inactive SLE, ESR (52.97 ± 35.66 vs. 32.38 ± 29.16 p < 0.001), ERR (15.40 ± 12.41 vs. 8.19 ± 8.10 p < 0.001) and RAR (0.13 ± 0.10 vs. 0.11 ± 0.20 p = 0.038) are all elevated in active SLE (52.97 ± 35.66 vs. 32.38 ±2 9.16 p < 0.001). ERR shows better correlation than RAR with ESR (p < 0.001 vs. p = 0.911). Patients with active SLE exhibited higher SLEDAI than those with inactive SLE (8.67 ± 2.67 vs. 3.27 ± 1.36, p < 0.001). According to ROC analysis, when ESR levels > 58.5 and ERR levels > 13.18, the sensitivity is 37.6% and 45.2%, the specificity is 83.0% and 83.2%. CONCLUSIONS ESR and ERR are potential indicators for diagnosis of active and inactive SLE.

目的 检测并分析门诊和体检中心就诊的女性人群中人乳头瘤病毒(HPV)感染情况并进行危险因素分析.方法 回顾性分析2016年5月至2018年8月接受HPV感染检测的9409例体检女性和门诊患者的临床资料,包括:患者姓名、年龄、月经史、初次性生活年龄、细胞学检查情况,其中有6201例调查对象同时进行宫颈液基细胞学(TCT)检测.按照患者的年龄阶段分为五组,另根据TCT细胞病理结果分为五组,观察年龄和细胞病变程度与HPV感染的关系.并根据临床资料进行危险因素分析.结果 HPV检测结果阳性2740例,阳性率为29.12％,高危型HPV检出2187例,低危型HPV检出553例;在高危型HPV感染中,以HPV-52、HPV-16、HPV-58为主,感染率最高的是HPV-52;在低危型HPV感染中,以HPV-81、HPV-42、HPV-43为主,感染率最高的是HPV-81.在各年龄组患者中,31～40岁组阳性率最高(39.23％),HPV阳性率差异有统计学意义(P<0.001).未见上皮内病变或恶性病变(NILM)组、宫颈炎症(cervitis)组、非典型鳞状上皮细胞病变(ASC)组、低级别鳞状上皮内病变(LSIL)组、高级别鳞状上皮内病变(HSIL)组的HPV阳性例数分别为889例(16.08％)、36例(27.91％)、261例(63.66％)、67例(88.16％)和56例(94.92％),HPV阳性率差异有统计学意义(P<0.001).危险因素分析提示初次性生活年龄与HPV感染有关,25岁以前有性生活者感染概率是25岁以前无性生活者的3.444倍.结论 深圳地区女性人群感染HPV以高危型为主,31～40岁人群HPV的感染率最高,其次是绝经后的中老年女性.且HPV阳性率与TCT细胞病理学病变程度有线性关系.性生活年龄<25岁是HPV感染的独立危险因素,针对重点人群开展HPV感染筛查,对于本地宫颈癌的防控具有重要的意义.

The utilization of complicated water quality models is the primary approach used to forecast water quality. These models, however, are not easy to employ because of constraints, such as data limitation, extensive computations, and future boundary conditions. Long short-term memory (LSTM) can overcome such constraints; however, its applications in water quality forecast have rarely been explored. In this study, the ability of LSTM to simulate the forecast capacity of a complicated water quality model (i.e., environmental fluid dynamics code, EFDC) is investigated. First, the EFDC is run to produce a long-term (12 years) time series of six water quality variables. These variables are intrinsically associated with equations embedded in the EFDC that represent the dynamics of the simulated system. The LSTM is developed to forecast the concentration of Chlorophyll a 1-31 d ahead of time using six water quality variables. The generated data are thereafter employed to train a number of LSTMs with different model structures (combinations of input variables, numbers of hidden layers, and lag times). The LSTM performances are evaluated by the Nash-Sutcliffe efficiency coefficient, and random forest is applied to identify the key drivers of LSTM performance. The results show that many LSTMs could achieve an acceptable performance level. Chlorophyll a, water temperature, and total phosphorus are identified as the key drivers of LSTM performances, which are consistent with limnological theories. The number of hidden layers and lag time practically have no impact on the LSTM performance. It is thereby confirmed that an LSTM with a simple structure could simulate the forecast capacity of EFDC. The results also reveal that the mechanism-guided LSTM in our study may capture certain mechanism features. The LSTM is thus expected to be a promising approach for water quality forecast.

Reliable decision⁃making based on complex three⁃dimensional hydrodynamic and water quality modeling becomes essential under the circumstances of increasingly demand for water management requirement. However, due to the complicated modeling structure, enormous parameters and governing equations, it is extremely difficult if not impossible to obtain reasonable parameters to represent the underlying mechanisms in lake systems, which is the prerequisite of robust decision⁃making support. It is hence critical to explore highly effective parameter estimation techniques for complex water quality models. Traditional automatic parameter estimation techniques are usually computationally intensive, while Bayesian optimization algorithm has been shown to be able to tackle optimization problems for computational expensive models in a timely manner. In this study, we proposed a Bayesian optimization⁃based parameter estimation strategy, which includes 1 critical parameters identification; 2 critical parameters sensitivity analysis, sorting and filtering; 3parameter estimation using Bayesian optimization; and 4 method applicability evolution. We have successfully applied this strategy in parameter estimation for a three⁃dimensional hydrodynamic and water quality model of Lake Yilong in Southwestern China. The lg(NSE) for models using parameters identified by this strategy was all above 0.65, indicating a satisfactory representation of the lake system. Our results show that lg(NSE) could reach 0.766 after only 141 iterations when using EI as the acquisition function for the Bayesian optimization algorithm, indicating that the method proposed in this study has the potential to be applied in real world water quality modeling practices.

The self-organizing feature map (SOFM) and random forest (RF) method were integrated to recognize water quality patterns of nine water quality indicators for 63 lakes in China for 11 years (5110 data). The SOFM was built firstly to cluster lakes to identify the pollution conditions. Then, the RF was used to explore the good-of-fitness of water quality variables on the clustering result and to determine the important water quality indicators. The result of SOFM shows that the lakes can be clustered into three types. And the result of RF shows that permanganate index and chlorophyll a can determine the pollution condition when the classification accuracy is 80%. The integrated method can identify the water quality indicators reflecting the pollution conditions from complex data. In practice, the method can be used to determine the pollution conditions and direct the monitoring indicators.

Nutrient criteria is the foundation of lake eutrophication control and management. To assess the applicability of ecoregional nutrient criteria, a relationship⁃based clustering approach (RCA) is proposed in this study. The RCA includes two major steps, including 1the hierarchical clustering based on the linear mixed⁃effect model and the mean absolute percentage error and 2the model identification based on the Akaike information criterion (AIC). Considering the availability of long⁃term data, the Yungui Plateau Ecoregion and the Eastern Plain Ecoregion were selected as the study areas. The RCA was then employed to explore the Chlorophyll a⁃nutrient ( total nitrogen and total phosphorus) relationship. The results show that the ecoregional models have the highest AIC values among all the four groups of relationships with Akaike weights of less than 0.001. The ecoregional relationship may result in the ecological fallacy. We thereby concluded that the ecoregional nutrient criteria is not applicable to all the lakes within the ecoregion. It is believed that the modeling results and the RCA approach will enhance the understanding of proper spatial scale and values of nutrient criteria in China.