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Acute myocardial infarction (AMI) is a fatal cardiovascular disease with a high mortality rate. The discovery of effective biomarkers is crucial for the diagnosis and treatment of AMI. In the present study, miRNA sequencing and reverse transcription-quantitative polymerase chain reaction techniques revealed that the expression of exosome derived miR-152-5p was significantly downregulated in patients with AMI compared with healthy controls. A series of functional validation experiments were then performed using H9c2 cardiomyocytes. Following transfection of the cardiomyocytes using an miR-152-5p inhibitor, immunofluorescence staining of a-smooth muscle actin revealed a marked increase in fibrosis. Western blotting revealed that the expression levels of the apoptotic protein Bax, TNF-α and collagen-associated proteins were significantly increased, whereas those of the apoptosis-inhibiting factor Bcl-2 and vascular endothelial growth factor A were significantly decreased. Furthermore, the binding of Rho GTPase-activating protein 6 (ARHGAP6) to miR-152-5p was predicted using an online database and verified using a dual-luciferase reporter gene assay. The transfection of cardiomyocytes with miR-152-5p mimics was found to inhibit the activation of ARHGAP6 and Rho-associated coiled-coil containing kinase 2 (ROCK2). These results suggest that miR-152-5p targets ARHGAP6 through the ROCK signaling pathway to inhibit AMI, which implies that miR-152-5p may be a diagnostic indicator and potential target for treatment of myocardial infarction.

Nowadays cardiovascular diseases ( CVD ) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention of CVD . Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgment and several electrocardiographic (ECG) or vectorcardiographic ( VCG ) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. This vector is decomposed into a series of proper rotation components ( PRCs ) by using the intrinsic time-scale decomposition ( ITD ) method. Second, four levels discrete wavelet transform ( DWT ) is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies ( db3 ) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3 , in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional ( 3D ) phase space reconstruction ( PSR ) together with Euclidean distance (ED) has been utilized to derive features. Fourth, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.20 % . The result verifies the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

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