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Regional Landslide Susceptibility Assessment Based on Combined Model with Certainty Factor Method
( Peihua Xu ),( Guang Yang ),( Chen Cao ),( Wen Zhang ) 대한지질공학회 2019 대한지질공학회 학술발표회논문집 Vol.2019 No.2
This paper takes the case of Diexi Township to Shidaguan Township, Maoxian County, Sichuan Province as the research area. According to the field data and the basic conditions of the study area, 10 impact factors such as the slope, slope direction, section curvature, elevation, undulation, stratum, distance from the river, distance from the fault, land type and vegetation coverage are selected. Using GIS technology as the operating platform, three methods, i.e., deterministic coefficient + analytic hierarchy process (CF-AHP), deterministic coefficient + logistic regression method (CF-LR) and deterministic coefficient + multi-layer perceptron method of neural network (CF-MLP), were used to evaluate the sensitivity of landslide hazard in the study area. The landslide hazard in this area was divided into four categories: extremely low, low, medium and high sensitive areas, and the effect of the model was tested by receiver operating characteristic curve (ROC). The under-line area (AUC) of the ROC curves of the CF-AHP, CF-LR and CF-MLP combined models were 0.850, 0.884 and 0.867, respectively. The CF-LR combination model worked best. In the CF-LR combination model, the areas of high sensitive area, medium sensitive area, low sensitive area and extremely low sensitive area accounted for 11.3%, 25.1%, 22.5% and 41.1% of the total area, respectively. The results show that the high sensitive area is mainly concentrated around the main water system and the concentrated area of the fault. The results are close to the actual situation and play an important role in the geological disaster risk assessment.
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The Combination of Gefitinib and Acetaminophen Exacerbates Hepatotoxicity via ROS-Mediated Apoptosis
Xu Jiangxin,Huang Xiangliang,Zhou Yourong,Xu Zhifei,Cai Xinjun,Yang Bo,He Qiaojun,Luo Peihua,Yan Hao,Jin Jie 한국응용약물학회 2024 Biomolecules & Therapeutics(구 응용약물학회지) Vol.32 No.5
Gefitinib is the well-tolerated first-line treatment of non-small cell lung cancer. As it needs analgesics during oncology treatment, particularly in the context of the coronavirus disease, where patients are more susceptible to contract high fever and sore throat. This has increased the likelihood of taking both gefitinib and antipyretic analgesic acetaminophen (APAP). Given that gefitinib and APAP overdose can predispose patients to liver injury or even acute liver failure, there is a risk of severe hepatotoxicity when these two drugs are used concomitantly. However, little is known regarding their safety at therapeutic doses. This study simulated the administration of gefitinib and APAP at clinically relevant doses in an animal model and confirmed that gefitinib in combination with APAP exhibited additional hepatotoxicity. We found that gefitinib plus APAP significantly exacerbated cell death, whereas each drug by itself had little or minor effect on hepatocyte survival. Mechanistically, combination of gefitinib and APAP induces hepatocyte death via the apoptotic pathway obviously. Reactive oxygen species (ROS) generation and DNA damage accumulation are involved in hepatocyte apoptosis. Gefitinib plus APAP also promotes the expression of Kelch-like ECH-associated protein 1 (Keap1) and downregulated the antioxidant factor, Nuclear factor erythroid 2-related factor 2 (Nrf2), by inhibiting p62 expression. Taken together, this study revealed the potential ROS-mediated apoptosis-dependent hepatotoxicity effect of the combination of gefitinib and APAP, in which the p62/Keap1/Nrf2 signaling pathway participates and plays an important regulatory role.
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Flash Flood Hazard Susceptibility Mapping Using Two Statistical Methods in Coalmine Subsidence Areas
( Chen Cao ),( Peihua Xu ),( Jianping Chen ),( Qing Wang ) 대한지질공학회 2019 대한지질공학회 학술발표회논문집 Vol.2019 No.2
This study focused on producing flash flood hazard susceptibility maps (FFHSM) using frequency ratio (FR) and statistical index (SI) models in the Xiqu Gully (XQG) of Beijing, China. First, a total of 85 flash flood hazard locations (n= 85) were surveyed in the field and plotted using geographic information system (GIS) software. Based on the flash flood hazard locations, a flood hazard inventory map was built. Seventy percent of the flooding hazard locations were randomly selected for building the models. The remaining 30% of the flooded hazard locations were used for validation. XQG used to be a coal mining area, coalmine caves and subsidence caused by coal mining exist in this catchment, as well as many ground fissures. Thus, this study took the subsidence risk level into consideration for FFHSM. The ten conditioning parameters were elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI), topographic wetness index (TWI), and short-term heavy rain. This study also tested different classification schemes for the values for each conditional parameter and checked their impacts on the results. The accuracy of the FFHSM was validated using area under the curve (AUC) analysis. Classification accuracies were 86.61%, 83.35%, and 78.52% using frequency ratio (FR)-natural breaks, statistical index (SI)-natural breaks and FR-manual classification schemes, respectively. Associated prediction accuracies were 83.69%, 81.22%, and 74.23%, respectively. It was found that FR modeling using a natural breaks classification method was more appropriate for generating FFHSM for the Xiqu Gully.
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