The present study was designed to identify potential diagnostic markers for acute myocardial infarction (AMI) and determine the significance of immune cell infiltration in this pathology. Methods: Two publicly available gene expression profiles (GSE66360 and GSE48060 datasets) from human AMI and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 80 AMI and 71 control samples. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic curve (AUC) value was obtained and used to evaluate discriminatory ability. The expression level and diagnostic value of the biomarkers in AMI were further validated in the GSE60993 dataset (17 AMI patients and 7 controls). The compositional patterns of the 22 types of immune cell fraction in AMI were estimated based on the merged cohorts using CIBERSORT. Results: A total of 27 genes were identified. The identified DEGs were mainly involved in carbohydrate binding, Kawasaki disease, atherosclerosis, and arteriosclerotic cardiovascular disease. Gene sets related to atherosclerosis signaling, primary immunodeficiency, IL-17, and TNF signaling pathways were differentially activated in AMI compared with the control. IL1R2, IRAK3, and THBD were identified as diagnostic markers of AMI (AUC = 0.877) and validated in the GSE60993 dataset (AUC = 0.941). Immune cell infiltration analysis revealed that IL1R2, IRAK3, and THBD were correlated with M2 macrophages, neutrophils, monocytes, CD4 + resting memory T cells, activated natural killer (NK) cells, and gamma delta T cells. Conclusion: IL1R2, IRAK3, and THBD can be used as diagnostic markers of AMI, and can provide new insights for future studies on the occurrence and the molecular mechanisms of AMI.
Prenatal diagnosis of fetal congenital heart disease (CHD) has been shown to have a significant effect on prenatal and postnatal management and outcomes. However, the factors influencing the diagnostic accuracy and which pregnant trimester is the most adaptive for fetal heart disease remain uncertain despite of extensive researches. The aim of the present study was to evaluate the accuracy of echocardiography for detecting CHD and potential influence factors.We searched Chinese Biomedical Database (CBM), Medline, ISI Web of Knowledge, the Cochrane Library, and China National Knowledge Infrastructure (CNKI) to identify relevant studies from January 1, 1990 to August 13, 2015.Overall, the pooled sensitivity, specificity, diagnostic odds ratio, positive likelihood ratio, and negative likelihood ratio were 68.5% (95% confidence interval [CI], 66.8%–70.2%), 99.8% (95% CI, 99.7%–99.8%), 3026.9 (95% CI, 1417.9–6461.8), 659.41 (95% CI, 346.38–1255.3), and 0.246 (95% CI, 0.187–0.324) respectively (AUC = 0.9924). The pooled sensitivity of basic cardiac echocardiographic examination (BCEE), extended cardiac echocardiographic examination (ECEE), BCEE plus outflow tract view (BCEE + OTV), BCEE + OTV + 3VTV (BCEE plus outflow tract view plus three vessel and trachea view) for the prenatal diagnosis of CHD were 49.0%, 75.5%, 66.1%, and 83.7% respectively. The pooled sensitivity of the prenatal echocardiographic diagnosis of CHD during the first trimester, second trimester, the second to third trimester were 60.3%, 60.9%, and 77.4%, respectively. The pooled sensitivity of BCEE and ECEE for the prenatal diagnosis of CHD during the second to third trimester was significantly higher than that during the second trimester. The pooled sensitivity of the prenatal echocardiographic diagnosis of CHD for pregnancies with low risk, high risk, low and high risk, and unselected risk were 45.4%, 85.1%, 89.1%, and 66.2%, respectively. The sensitivity analysis was robust and risk level was significant source of heterogeneity. Deek test indicated no potential significant publication bias.Prenatal ultrasound is a powerful tool for the diagnosis of CHD; however, echocardiography has individual sensitivity for different gestation period, different levels of risk, and different echo-views.
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