BackgroundMetabolic and energy disorders are considered central to the etiology of diabetic cardiomyopathy (DCM). Sodium-glucose cotransporter-2 inhibitors (SGLT2i) can effectively reduce the risk of cardiovascular death and heart failure in patients with DCM. However, the underlying mechanism has not been elucidated.MethodsWe established a DCM rat model followed by treatment with empagliflozin (EMPA) for 12 weeks. Echocardiography, blood tests, histopathology, and transmission electron microscopy (TEM) were used to evaluate the phenotypic characteristics of the rats. The proteomics and metabolomics of the myocardium in the rat model were performed to identify the potential targets and signaling pathways associated with the cardiovascular benefit of SGLT2i.ResultsThe diabetic rat showed pronounced DCM characterized by mitochondrial pleomorphic, impaired lipid metabolism, myocardial fibrosis, and associated diastolic and systolic functional impairments in the heart. To some extent, these changes were ameliorated after treatment with EMPA. A total of 43 proteins and 34 metabolites were identified as targets in the myocardium of diabetic rats treated with EMPA. The KEGG analysis showed that arachidonic acid is associated with the maximum number of related pathways and may be a potential target of EMPA treatment. Fatty acid (FA) metabolism was enhanced in diabetic hearts, and the perturbation of biosynthesis of unsaturated FAs and arachidonic acid metabolism was a potential enabler for the cardiovascular benefit of EMPA.ConclusionSGLT2i ameliorated lipid accumulation and mitochondrial damage in the myocardium of diabetic rats. The metabolomic and proteomic data revealed the potential targets and signaling pathways associated with the cardiovascular benefit of SGLT2i, which provides a valuable resource for the mechanism of SGLT2i.

Background: Diabetic cardiomyopathy (DCM) is one of the major causes of heart failure in diabetic patients; however, its pathogenesis remains unclear. Long non-coding RNAs (lncRNAs) are involved in the development of various cardiovascular diseases, but little is known in DCM.Objective: The present study was conducted to investigate the altered expression signature of lncRNAs and mRNAs by RNA-sequencing and uncovers the potential targets of DCM.Methods: A DCM rat model was established, and the genome-wide expression profile of cardiac lncRNAs and mRNAs was investigated in the rat model with and without DCM by RNA-sequencing. Bioinformatics analysis included the co-expression, competitive endogenous RNA (ceRNA) network, and functional enrichment analysis of deregulated lncRNAs and mRNAs.Results: A total of 355 lncRNA transcripts and 828 mRNA transcripts were aberrantly expressed. The ceRNA network showed that lncRNA XR_351927.3, ENSRNOT00000089581, XR_597359.2, XR_591602.2, and XR_001842089.1 are associated with the greatest number of differentially expressed mRNAs and AURKB, MELK, and CDK1 may be the potential regulatory targets of these lncRNAs. Functional analysis showed that these five lncRNAs are closely associated with fibration, cell proliferation, and energy metabolism of cardiac myocytes, indicating that these core lncRNAs have high significance in DCM.Conclusions: The present study profiled the DCM-specific lncRNAs and mRNAs, constructed the lncRNA-related ceRNA regulatory network, and identified the potential prognostic biomarkers, which provided new insights into the pathogenesis of DCM.

In this study, we considered preoperative prediction of microvascular invasion (MVI) status with deep learning (DL) models for patients with early-stage hepatocellular carcinoma (HCC) (tumor size ≤ 5 cm). Two types of DL models based only on venous phase (VP) of contrast-enhanced computed tomography (CECT) were constructed and validated. From our hospital (First Affiliated Hospital of Zhejiang University, Zhejiang, P.R. China), 559 patients, who had histopathological confirmed MVI status, participated in this study. All preoperative CECT were collected, and the patients were randomly divided into training and validation cohorts at a ratio of 4:1. We proposed a novel transformer-based end-to-end DL model, named MVI-TR, which is a supervised learning method. MVI-TR can capture features automatically from radiomics and perform MVI preoperative assessments. In addition, a popular self-supervised learning method, the contrastive learning model, and the widely used residual networks (ResNets family) were constructed for fair comparisons. With an accuracy of 99.1%, a precision of 99.3%, an area under the curve (AUC) of 0.98, a recalling rate of 98.8%, and an F1-score of 99.1% in the training cohort, MVI-TR achieved superior outcomes. Additionally, the validation cohort’s MVI status prediction had the best accuracy (97.2%), precision (97.3%), AUC (0.935), recalling rate (93.1%), and F1-score (95.2%). MVI-TR outperformed other models for predicting MVI status, and showed great preoperative predictive value for early-stage HCC patients.

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Background. The effect of intensive glucose-lowering treatment on the risk of cardiovascular events in type 2 diabetes remains uncertain, especially the effect on the occurrence of myocardial infarction in patients with type 2 diabetes is still unclear. The purpose of this study was to conduct a systematic review and meta-analysis of relevant RCTs. Methods. We performed a systematic review of randomized clinical trials (RCTS) and observational studies relevant to this study question. We searched the PubMed and Cochrane databases until June 2022. Results. We included data on 14 RCTs and 144,334 patients, all of whom had type 2 diabetes. When all studies were considered, intensive glucose-lowering treatment significantly reduced the incidence of MI compared with conventional therapy and the total OR value is 0.90 (CI 0.84, 0.97; P = 0.004 ) when considering all the studies. When the target value of intensive glucose-lowering treatment was considered as HbA1c decrease of more than 0.5%, there was no significant protective effect on MI, the total OR value is 0.88 (CI 0.81, 0.96; P = 0.003 ). When considering all available RCTS, the intensive glucose-lowering treatment group had a protective effect for MACE compared to the conventional treatment group, and the total OR value is 0.92 (CI 0.88, 0.96; P < 0.00001 ). In the available RCTs, for the patients with a history of prior CAD, the total OR value is 0.94 (CI 0.89, 0.99; P = 0.002 ). And there was no difference in the incidence of hypoglycemic events between the intensive and conservative treatment groups. Conclusion. Our data support the positive protective effect of glucose-lowering therapy on MI in patients with T2DM, but there is no significant effect of intensive glucose-lowering. In addition, we found no greater protective effect of enhanced glucose control in the HbA1c reduction of more than 0.5%, and no difference in the incidence of adverse events compared with the HbA1c reduction of less than 0.5%.