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Coronary restenosis is an important cause of poor long-term prognosis in patients with coronary heart disease. Here, we show that lysine methyltransferase SMYD2 expression in the nucleus is significantly elevated in serum- and PDGF-BB-induced vascular smooth muscle cells (VSMCs), and in tissues of carotid artery injury-induced neointimal hyperplasia. Smyd2 overexpression in VSMCs (Smyd2-vTg) facilitates, but treatment with its specific inhibitor LLY-507 or SMYD2 knockdown significantly inhibits VSMC phenotypic switching and carotid artery injury-induced neointima formation in mice. Transcriptome sequencing revealed that SMYD2 knockdown represses the expression of serum response factor (SRF) target genes and that SRF overexpression largely reverses the inhibitory effect of SMYD2 knockdown on VSMC proliferation. HDAC3 directly interacts with and deacetylates SRF, which enhances SRF transcriptional activity in VSMCs. Moreover, SMYD2 promotes HDAC3 expression via tri-methylation of H3K36 at its promoter. RGFP966, a specific inhibitor of HDAC3, not only counteracts the pro-proliferation effect of SMYD2 overexpression on VSMCs, but also inhibits carotid artery injury-induced neointima formation in mice. HDAC3 partially abolishes the inhibitory effect of SMYD2 knockdown on VSMC proliferation in a deacetylase activity-dependent manner. Our results reveal that the SMYD2-HDAC3-SRF axis constitutes a novel and critical epigenetic mechanism that regulates VSMC phenotypic switching and neointimal hyperplasia.
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Background/Aims@#The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. @*Methods@#We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. @*Results@#A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers.The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. @*Conclusions@#We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.
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Objective@#To clarify the role of cytochrome P450 in nonalcoholic fatty liver disease (NAFLD) by RNA-Seq and bioinformatics analysis.@*Methods@#A total of 20 male C57BL/6 mice were used. Ten mice were fed with high-fat diet (D12492, 60% kcal fat) for 16 weeks to establish a mouse model of NAFLD, and the other 10 mice were fed with low-fat diet (D12450B, 10% kcal fat) as control group. At the end of the experiment, the body weight, liver weight, and hepatic triglyceride (TG) content were measured. Meanwhile, HE staining and RNA-Seq analysis were performed for the liver tissues. The differentially expressed genes were screened out and subjected to bioinformatics analysis, including KEGG and GO BP enrichment analyses and interaction network analysis. Comparison of means between the two groups was made using t-test.@*Results@#Compared with the control group, the mice in the model group were obviously obese, with significantly increased body weight (41.41 ± 6.01 g vs 28.78 ± 1.79 g, t = 6.04, P < 0.01) and liver weight (1.38 ± 0.30 g vs 1.08 ± 0.10 g, t = 2.89, P < 0.01). The mice in the model group showed obvious steatosis, accompanied by a small amount of inflammatory cell infiltration, but with no obvious fibrosis, according to the results of HE staining. In addition, the hepatic TG content in the model group was significantly increased compared with that in the control group (0.64 ± 0.01 mg/mg vs 0.29 ± 0.06 mg/mg, t = 10.11, P = 0.04). Compared with the control group, a total of 367 differentially expressed genes, including 211 down-regulated and 156 up-regulated ones, were identified in the model group according to the RNA-seq results. Meanwhile, 19 CYP450 subtypes, accounting for 5% of the differentially expressed genes, were identified, and CYP2E1, CYP2C70, CYP3A11, CYP3A25, CYP2D26, CYP4A10, CYP17A1, CYP2B10, and CYP2C38 were involved in oxidative stress, steroid hormone metabolism, fatty acid metabolism, arachidonic acid metabolism, and the PPAR signaling pathway. An interaction network was constructed with 30 nodes, and CYP2E1 and CYP2C70 were identified as key nodes. RT-PCR validation results showed that the expression changes of CYP450 subtypes and lipid metabolism-related genes were consistent with the findings of sequencing.@*Conclusion@#The CYP450 family plays a vital role in the pathogenesis of fatty liver by regulating lipid metabolism-related pathways, including oxidative stress, arachidonic acid metabolism, steroid hormone metabolism , and fatty acid metabolism.
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Objective To explore risk factors of unintentional injury among left-behind children in a rural poverty county and to provide evidence for injury intervention.Methods 2 909 students,including 1 630 left-behind children,were recruited from a poor counties of five schools by Multi-stage stratified cluster random sampling method,and were surveyed applying designed questionnaire,interview method and observation method to collect the data of the injury accidents and related factors in the last one year.Results The incidence of unintentional injury among the left-behind children in rural area was 34.0％,higher than the rate of 27.7％ among non-left-behind children and the difference was statistically significant (c2 =13.541,P＜0.05);in the top five injury type were falling injury (14.0％),burus (5.6％) and sharp instrument injury (3.6％),bite (3.0％),parts (1.6％) among left-behind children.Low age,often miss my mother,fighting in school,outdoor activities,road and traffic state were risk factors for injury of left-behind children.Corcltsion The unintentional injury of left-behind children has become a social problem that can not be ignored,and targeted intervention should be carried out.