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The ongoing COVID-19 pandemic has produced substantial impacts on our society. Wastewater surveillance has increasingly been introduced to support the monitoring, and thus mitigation, of COVID-19 outbreaks and transmission. Monitoring of buildings and sub-sewershed areas via a wastewater surveillance approach has been a cost-effective strategy for mass testing of residents in congregate living situations such as universities. A series of spatial and spatiotemporal data are involved with wastewater surveillance, and these data must be interpreted and integrated with other information to better serve as guidance on response to a positive wastewater signal. The management and analysis of these data poses a significant challenge, in particular, for the need of supporting timely decision making. In this study, we present a web-based spatial decision support system framework to address this challenge. Our study area is the main campus of the University of North Carolina at Charlotte. We develop a spatiotemporal data model that facilitates the management of space-time data related to wastewater surveillance. We use spatiotemporal analysis and modeling to discover spatio-temporal patterns of COVID-19 virus abundance at wastewater collection sites that may not be readily apparent in wastewater data as they are routinely collected. Web-based GIS dashboards are implemented to support the automatic update and sharing of wastewater testing results. Our web-based SDSS framework enables the efficient and automated management, analytics, and sharing of spatiotemporal data of wastewater testing results for our study area. This framework provides substantial support for informing critical decisions or guidelines for the prevention of COVID-19 outbreak and the mitigation of virus transmission on campus.
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Objective To investigate the effect of atractylone on the viability and apoptosis of hepatoma HepG2 cells and its mechanism of action. Methods Hepatoma HepG2 cells were selected and divided into low-, middle-, and high-dose atractylone groups (5, 10, and 20 μmol/L), and the cells in the control group were added with an equal volume of DMSO. MTT colorimetry was used to measure the viability of HepG2 cells after treatment with different concentrations of atractylone; flow cytometry was used to measure the apoptosis rate and mitochondrial membrane potential of HepG2 cells; the DCFH-DA fluorescent probe labeling method was used to measure the level of reactive oxygen species (ROS) in HepG2 cells; Transwell assay was used to evaluate the effect of atractylone on the migration ability of HepG2 cells; Western blot was used to measure the protein expression levels of Bcl-2, Bax, and cleaved caspase-3. A one-way analysis of variance was used for comparison of continuous data between multiple groups, and the least significant difference t -test was used for comparison between two groups. Results After 24 and 48 hours of treatment with atractylone, compared with the control group, the low-, middle-, and high-dose atractylone groups had a tendency of reduction in cell viability (all P < 0.05), with a half inhibitory concentration of 26.19 μmol/L in atractylone treatment of HepG2 cells for 72 hours. The low-, middle-, and high-dose atractylone groups had a significantly higher apoptosis rate than the control group (14.34%/29.32%/50.12% vs 0.32%, all P < 0.05). Compared with the control group, the low-, middle-, and high-dose atractylone groups had a significant increase in the fluorescence intensity of ROS in HepG2 cells (all P < 0.05). After 48 hours of treatment with atractylone, compared with the control group, the low-, middle-, and high-dose atractylone groups had a significant reduction in the number of migrated cells (132.67±18.36/57.00±9.26/31.00±2.45 vs 258.11±38.54, P < 0.05). Compared with the control group, the low-, middle-, and high-dose atractylone groups had a significant reduction in the expression of the anti-apoptotic factor Bcl-2 and significant increases in the expression of the apoptotic factors Bax and cleaved caspase-3 (all P < 0.05). Conclusion Atractylone can induce the apoptosis and inhibit the migration of HepG2 cells, which provides an experimental basis for further development and utilization of atractylone.
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Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features. One sentence summaryWe trained and validated late fusion deep learning-machine learning models to predict non-severe COVID-19, severe COVID-19, non-COVID viral infection, and healthy classes from clinical, lab testing, and CT scan features extracted from convolutional neural network and achieved predictive accuracy of > 96% to differentiate all four classes at once based on a large dataset of 689 participants.
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Objective To investigate the effect of miRNA-384 (miR-384)expression on hepatic steatosis in mice with nonalcoholic fatty liver disease (NAFLD)induced by high-fat diet (HFD). Methods A total of 30 male C57BL/6J mice were fed for 7 days to adapt to the environment and then randomly divided into 2 groups,with 15 mice in each group. The mice in the control group were given normal diet, and those in the model group were given HFD for 8 weeks and then the liver tissue was harvested. HE and Nile red staining were used to ob-serve the pathological changes of the liver. Microarray sequencing was performed to determine the whole-genome miRNA expression profile of liver tissue,and PCR was used to measure the relative expression of miR-384. The t-test was used for the comparison of continuous da-ta between groups. Results In the control group,the liver was red with sharp edges,the lobular structure was clear,and there was no he-patic steatosis;in the model group,the liver was yellow with blunt edges,and the hepatocytes were swollen with a large number of fat vacu-oles in the cytoplasm and nuclear deviation caused by the fusion of lipid droplets. Compared with the normal mice,the NAFLD mice had 12 upregulated miRNAs and 18 downregulated miRNAs in liver tissue. Some of the differentially expressed miRNAs between the control group and the model group were screened to obtain the same cluster diagram. Among the 8 miRNAs with significant changes,miR-384 showed a significant fold change. Conclusion The upregulation of miR -384 is closely associated with hepatic steatosis,but its mechanism still needs further study.
