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Cheng Luo,Kunlun Zhang,Wenlong Zhang,Yongzhi Jing 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.4
In this paper, a 3D analytical model of permanent magnet (PM) and electromagnetic hybrid Halbach array electrodynamic suspension (EDS) system for a conductive plate is presented. When the hybrid Halbach array moves to cut the conductive plate, the 3D eddy current forces are derived using the second-order vector potential (SOVP) based on y-component of the source magnetic fux density, which is derived using the Biot–Savart law and surface current method. The accuracy of the derived equations is verifed by a 3D fnite-element analysis (FEA) model. Furthermore, the efects of the main characteristic parameters on the levitation force are analysed.
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Yang Liu,Wenlong Luo 한국분자세포생물학회 2012 Molecules and cells Vol.33 No.5
Betulinic acid (BetA) is an effective and potential anti-cancer chemical derived from plants. BetA can kill a broad range of tumor cell lines, but has no effect on untransformed cells. The chemical also kills melanoma, leukemia, lung, colon, breast, prostate and ovarian cancer cells via induction of apoptosis, which depends on caspase activation. However, no reports are yet available about the effects of BetA on nasopharyngeal carcinoma (NPC), a widely spread malignancy in the world, especially in East Asia. In this study, we first showed that BetA can effectively kill CNE2 cells, a cell line derived from NPC. BetA-induced CNE2 apoptosis was characterized by typical apoptosis hallmarks: caspase activation, DNA fragmentation, and cytochrome c release. Overexpression of Bcl-2 and Bcl-xL could partially prevent apoptosis caused by BetA. Moreover, Bax was not activated during the induction of apoptosis. Bax/Bak knockdown and wild-type CNE2 cells showed the same kinetics of cytochrome c release. We then showed that BetA may impair mitochondrial permeability transition pores (mPTPs), which may partially contribute to cytochrome c release. These observations suggest that BetA may serve as a potent and effective anti-cancer agent in NPC treatment. Further exploration of the mechanism of action of BetA could yield novel break-throughs in anti-cancer drug discovery.
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Song Zuhua,Guo Dajing,Tang Zhuoyue,Liu Huan,Li Xin,Luo Sha,Yao Xueying,Song Wenlong,Song Junjie,Zhou Zhiming 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.3
Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initialNCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.
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