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( Zhenghao Wang ),( Chunying Liu ),( Hongshan Yu ),( Bo Wu ),( Baoyu Huai ),( Ziyu Zhuang ),( Changkai Sun ),( Longquan Xu ),( Fengxie Jin ) 한국미생물생명공학회 2022 Journal of microbiology and biotechnology Vol.32 No.4
In this study, to obtain icaritin with high pharmacological activities from icariin, which has a content ratio of over 58% in the total flavonoids of Epimedium herb, a special Epimedium flavonoid-glycosidase was produced, purified and characterized from Aspergillus sp.y848 strain. The optimal enzyme production was gained in a medium containing 5% (w/v) wheat bran extract and 0.7% (w/v) Epimedium leaf powder as the enzyme inducer, and strain culture at 30℃ for 6-7 days. The molecular weight of the enzyme was approximately 73.2 kDa; the optimal pH and temperature were 5.0 and 40°C. The enzyme Km and Vmax values for icariin were 15.63 mM and 55.56 mM/h. Moreover, the enzyme hydrolyzed the 7-O-glucosides of icariin into icariside II, and finally hydrolyzed 3-Orhamnoside of icariside II into icaritin. The enzyme also hydrolyzed 7-O-glucosides of epimedin B to sagittatoside B, and then further hydrolyzed terminal 3-O-xyloside of sagittatoside B to icarisiede II, before finally hydrolyzing 3-O-rhamnoside of icarisiede II into icaritin. The enzyme only hydrolyzed 7-O-glucoside of epimedin A or epimedin C into sagittatoside A or sagittatoside C. It is possible to prepare icaritin from the high-content icariin in Epimedium herb using this enzyme. When 2.5% icariin was reacted at 40℃ for 18-20 h by the low-cost crude enzyme, 5.04 g icaritin with 98% purity was obtained from 10 g icariin. Also, the icaritin molar yield was 92.5%. Our results showed icaritin was successfully produced via cost-effective and relatively simple methods from icariin by crude enzyme. Our results should be very useful for the development of medicines from Epimedium herb.
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Xinyu Cao,Yin Fang,Chunguang Yang,Zhenghao Liu,Guoping Xu,Yan Jiang,Peiyan Wu,Wenbo Song,Hanshuo Xing,Xinglong Wu 대한배뇨장애요실금학회 2024 International Neurourology Journal Vol.28 No.1
Purpose: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Methods: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Results: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. Conclusions: This study aimed to improve clinicians’ ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.
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Changhao Wang,Yu Luo,Jinru Luo,Xiao Song,Wei Zhang,Pinghuai Wang,Zhenghao Liu,Maoyin Wang,Xinghui He,Linjiang Chai 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.4
Titanium alloys are one of the most important structural materials for the aerospace applications, which bring active studyon microstructural and mechanical optimization of the alloys. In the present study, commercial TA2 titanium sheets withdifferent initial textures were rolled to a strain of 4% at liquid nitrogen temperature. Microstructures of the rolled sheets werecharacterized using a scanning electron microscope equipped with an EBSD detector and their mechanical properties weretested through quasi-static uniaxial tension tests at room temperature. The influence of the initial texture on the microstructureand mechanical performance of the cryorolled sheet were discussed. Twinning is found to occur in the TA2 sheets after rollingeven at a small strain. Initial texture is an important factor affecting both numbers and types of the twins. {11–22} contractiontwinning is always the dominating type of twins for all three sheets having different initial textures. A lot of {11–22}and {11–24} twins can be found in the sheets with the initial texture of C-axes//ND, while the sheet with an initial texture ofC-axes away from the ND contains few twins, with a high density of dislocations developed instead. Along with contractiontwinning, many {10–12} and {11–21} twins are detected in the sheet with the initial texture of C-axes⊥ND. The cryorolledsheet having the densest twins shows the best mechanical properties, indicating a beneficial effect of a small pre-strain.
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