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        A methylprednisolone-loaded and core-shell nanofiber-covered stent-graft to prevent inflammation and reduce degradation in aortic dissection

        Junjun Liu,Hongqiao Zhu,Yifei Pei,Heng Zhang,Jian Zhou,Zaiping Jing 한국생체재료학회 2022 생체재료학회지 Vol.26 No.2

        Background: Stent-graft-induced inflammation is an independent risk factor for adverse aortic remodeling in aortic dissection. In this context, we asked that whether a methylprednisolone-loaded stent-graft could reduce inflammation and degradation. Methods: First, a coaxial electrospinning technique was used to create a core-shell film with methylprednisolone encapsulated in the inner of poly (L-lactide-co-caprolactone) nanofibers for controllable drug release. Second, an in vitro study was conducted to evaluate the biocompatibility of the nanofiber meshes. Third, the porcine aortic dissection model was developed to investigate the therapeutic effects of the ethylprednisolone-loaded stent-graft. Results: The results demonstrated that the nanofiber-coated film with a methylprednisolone-poly-caprolactone core layer and a poly (L-lactide-co-caprolactone) shell layer could effectively sustain drug release in vitro. In vivo study showed that the methylprednisolone-loaded stent-graft could reduce degradtion of aortic dissection by regulating inflammation. Conclusions: Overall, the controllable drug release by coaxial nanofiber is a promising approach to alleviate aortic inflammation and promote aortic remodeling after stent-graft implantation.

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        Investigation of Different Conduction States on the Performance of NMOS-Based Power Clamp ESD Device

        Wei Weipeng,Wang Yang,Chen Xijun,Zheng Yifei,Li Jieyu,Cao Pei,Cao Wenmiao 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.3

        This article investigates the eff ects of diff erent gate coupling voltage and gate voltage duration on electro-static discharge (ESD) performance of several NMOS-based power rail protection devices. Through simulation and transmission line pulse (TLP) test, it is found that there are two modes in the conduction process of the main clamping NMOS: channel conduction state and parasitic NPN conduction state. Diff erent gate voltage and duration bring the two conduction states diff erent proportions in the whole working process, which give the device very diff erent robustness. The results show that under the condition of small gate voltage and long duration and the condition of large gate voltage and short duration, the device can achieve optimal performance because the trigger voltage can be reduced, and the parasitic NPN can be turned on in time to release most of the current

      • Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI

        Wang, Yan,Shen, Dinggang,Ma, Guangkai,An, Le,Shi, Feng,Zhang, Pei,Lalush, David S.,Wu, Xi,Pu, Yifei,Zhou, Jiliu IEEE 2017 IEEE Transactions on Biomedical Engineering Vol.64 No.3

        <P>Objective: To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). Methods: It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semisupervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. Results: Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. Conclusion: This paper proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. Significance: The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients.</P>

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