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  • Hindawi Limited  (2)
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  • Hindawi Limited  (2)
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  • 1
    Online Resource
    Online Resource
    FAIM Enhances the Efficacy of Mesenchymal Stem Cell Transplantation by Inhibiting JNK-Induced c-FLIP Ubiquitination and Degradation
    Hindawi Limited ; 2022
    In:  Stem Cells International Vol. 2022 ( 2022-9-30), p. 1-18
    Details
    In: Stem Cells International, Hindawi Limited, Vol. 2022 ( 2022-9-30), p. 1-18
    Abstract: Background. The poor survival rates of transplanted mesenchymal stem cells (MSCs) in harsh microenvironments impair the efficacy of MSCs transplantation in myocardial infarction (MI). Extrinsic apoptosis pathways play an important role in the apoptosis of transplanted MSCs, and Fas apoptosis inhibitory molecule (FAIM) is involved in regulation of the extrinsic apoptosis pathway. Thus, we aimed to explore whether FAIM augmentation protects MSCs against stress-induced apoptosis and thereby improves the therapeutic efficacy of MSCs. Methods. We ligated the left anterior descending coronary artery (LAD) in the mouse heart to generate an MI model and then injected FAIM-overexpressing MSCs (MSCsFAIM) into the peri-infarction area in vivo. Moreover, FAIM-overexpressing MSCs were challenged with oxygen, serum, and glucose deprivation (OGD) in vitro, which mimicked the harsh microenvironment that occurs in cardiac infarction. Results. FAIM was markedly downregulated under OGD conditions, and FAIM overexpression protected MSCs against OGD-induced apoptosis. MSCsFAIM transplantation improved cell retention, strengthened angiogenesis, and ameliorated heart function. The antiapoptotic effect of FAIM was mediated by cellular-FLICE inhibitory protein (c-FLIP), and FAIM augmentation improved the protein expression of c-FLIP by reducing ubiquitin–proteasome-dependent c-FLIP degradation. Furthermore, FAIM inhibited the activation of JNK, and treatment with the JNK inhibitor SP600125 abrogated the reduction in c-FLIP protein expression caused by FAIM silencing. Conclusions. Overall, these results indicated that FAIM curbed the JNK-mediated, ubiquitination–proteasome-dependent degradation of c-FLIP, thereby improving the survival of transplanted MSCs and enhancing their efficacy in MI. This study may provide a novel approach to strengthen the therapeutic effect of MSC-based therapy.
    Type of Medium: Online Resource
    ISSN: 1687-9678 , 1687-966X
    URL: Article
    DOI: 10.1155/2022/3705637
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2573856-2
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  • 2
    Online Resource
    Online Resource
    Research on Multi-Precision Fabric Modeling Method Based on Machine Learning
    Hindawi Limited ; 2022
    In:  Scientific Programming Vol. 2022 ( 2022-4-11), p. 1-13
    Details
    In: Scientific Programming, Hindawi Limited, Vol. 2022 ( 2022-4-11), p. 1-13
    Abstract: In order to solve the shortcomings of complex calculation and time consumption of fabric physical simulation methods, many single-precision fabric simulation technologies based on machine learning methods have emerged and the amount of calculation is increased for regions with small changes. Aiming at this problem, this paper proposes two multi-precision fabric modeling method based on machine learning. Firstly, the fabric mesh is simulated by the physical method, the initial position of the vertex is calculated, and the deformation of each region of the fabric is measured by Rayleigh quotient curvature, and the multi-precision fabric mesh is constructed. Secondly, the multi-precision fabric graph structure and geometry image are extracted from the multiprecision fabric mesh. Finally, the subgraph convolutional neural network and super-resolution network are trained to model the multi-precision fabric, and we compared the two different multi-precision fabric machine learning modeling methods. Through the experimental verification, in the garment modeling, the garment modeled by the subgraph convolutional neural network is no longer only dependent on the change of human joints, resulting in a more realistic effect. At the same time, the efficiency of the subgraph convolutional neural network is 25.3% higher than that of the single-precision garment modeling based on the machine learning method. In the cloth simulation, speed of the super-resolution network is nearly 16 times faster than that of the physical simulation, which supplements the imperfection of insufficient flexibility of the subgraph convolutional neural network modeling.
    Type of Medium: Online Resource
    ISSN: 1875-919X , 1058-9244
    URL: Article
    DOI: 10.1155/2022/4339095
    RVK:
    SA 7860
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2070004-0
    Location Call Number Limitation Availability
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