We used mice to test our hypothesis that in response to viral invasion, stem cells may migrate into the heart and attenuate the effect of viral myocarditis. Male BALB/c mice were divided into three groups: mouse embryonic stem (ES) cell control, encephalomyocarditis virus (EMCV), and EMCV + ES cells. After administration of ES cells via tail vein, mice were immediately inoculated with EMCV. Mice were sacrificed at different days after EMCV inoculation. Mortality was recorded. Inflammatory cell infiltration and necrosis (major pathological changes of viral myocarditis) were evaluated by hematoxylin-eosin staining. ES cell migration and differentiation were identified by immunofluorescence. The survival rate in the EMCV + ES cell group (80%) was significantly increased (p < 0.05) over the EMCV-alone group (64%). Also, the incidence of inflammatory cell infiltration and myocardial lesions was lower in the EMCV + ES cell mice. Furthermore, the result of green fluorescent protein (GFP) and α-actinin analysis indicated that ES cells migrated into the heart and differentiated into myocytes after virus inoculation. In conclusion, ES cells significantly increased the survival of viral myocarditis mice and also decreased the necrosis and infiltration of inflammatory cells. These results demonstrated the ability of stem cells to mitigate the effects of viral infection on the heart and illustrated their potential therapeutic application to other mammalian species, including humans. 753 754 WANG ET AL.
Density functional theory (DFT) based computational methods have shown great significance in developing high-performance electro-catalysts. In this perspective, we briefly summarized the state-of the-art research progress of electro-catalysts for nitrogen...
Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some “dark genes” that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.
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