Aims: Apoptosis plays a critical role in cardiomyocyte loss during ischaemic heart injury. A detailed understanding of the mechanism involved has a substantial impact on the optimization and development of treatment strategies. Here, we report that the expression of SIRT4, a mitochondrial sirtuin, is markedly down-regulated in hypoxia-induced apoptosis of H9c2 cardiomyoblast cells. Methods and Results: SIRT4 interference significantly alters H9c2 cell viability, apoptotic cell number and caspase-3/7 activity. Furthermore, SIRT4 expression can affect the ratio of pro-caspase 9/caspase 9 or pro-caspase 3/caspase 3, an affect Bax translocation, which in turn alters the development of H9c2 cell apoptosis. Conclusion: These results suggest that SIRT4 is a key player in hypoxia-induced cardiomyocyte apoptosis, and that strategies based on its enhancement might be of benefit in the treatment of ischaemic heart disease.

Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has become a hotspot in the research field of brain computer interface (BCI). More recently, deep learning has emerged as a promising technique to automatically extract features of raw MI EEG signals and then classify them. However, deep learning-based methods still face two challenging problems in practical MI EEG signal classification applications: (1) Generally, training a deep learning model successfully needs a large amount of labeled data. However, most of the EEG signal data is unlabeled and it is quite difficult or even impossible for human experts to label all the signal samples manually. (2) It is extremely time-consuming and computationally expensive to train a deep learning model from scratch. To cope with these two challenges, a deep transfer convolutional neural network (CNN) framework based on VGG-16 is proposed for EEG signal classification. The proposed framework consists of a VGG-16 CNN model pre-trained on the ImageNet and a target CNN model which shares the same structure with VGG-16 except for the softmax output layer. The parameters of the pre-trained VGG-16 CNN model are directly transferred to the target CNN model used for MI EEG signal classification. Then, front-layers parameters in the target model are frozen, while later-layers parameters are fine-tuned by the target MI dataset. The target dataset is composed of timefrequency spectrum images of EEG signals. The performance of the proposed framework is verified on the public benchmark dataset 2b from the BCI competition IV. The experimental results show that the proposed framework improves the accuracy and efficiency performance of EEG signal classification compared with traditional methods, including support vector machine (SVM), artificial neural network (ANN), and standard CNN. INDEX TERMS Motor imagery (MI), electroencephalogram (EEG), signal classification, short time Fourier transform (STFT), VGG-16, transfer learning.

Background: SIRT5 is located in the mitochondria, and plays a crucial role in the regulation of metabolic process and cellular apoptosis. Cardiomyocytes are abundant in mitochondria. However, the role of SIRT5 in oxidative stress-induced apoptosis is still unknown in cardiomyocytes. Methods and Results: Western blots analysis revealed that SIRT5 is significantly down-regulated in cardiomyocytes upon oxidative stress. MTT assay, DAPI staining, and caspase 3/7 activity assay were used to estimate apoptosis development. The result suggested that compared with the wild-type group, SIRT5 knockdown results in a marked reduction in cell viability, and a significant increase in the number of apoptotic cells and the caspase 3/7 activity. Protein immunoprecipitation revealed a direct interaction between Bcl-Xl and SIRT5. Apoptosis assay and western blot anaylsis suggested that SIRT5 levels could affect the levels of Bcl-Xl expression, but have no effect on the apoptosis development in Bcl-Xl knockdown cells. Conclusion: This study reveals a novel role of SIRT5 in the regulation of oxidative stress-induced apoptosis in cardiomyocytes. Pharmacological interventions on SIRT5 expression may be useful in the treatment of oxidative stress-related cardiac injury.

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Atrial fibrillation (AF) is the most common irregular heart rhythm which influence approximately 1–2% of the general population. As a potential factor for ischemic stroke, AF could also cause heart failure. The mechanisms behind AF pathogenesis is complex and remains elusive. As a new category of non-coding RNAs (ncRNAs), circular RNAs (circRNAs) have been known as the key of developmental processes, regulation of cell function, pathogenesis of heart diseases and pathological responses which could provide novel sight into the pathogenesis of AF. circRNAs function as modulators of microRNAs in cardiac disease. To investigate the regulatory mechanism of circRNA in AF, especially the complex interactions among circRNA, microRNA and mRNA, we collected the heart tissues from three AF patients and three healthy controls and profiled their circRNA expressions with circRNA Microarray. The differentially expressed circRNAs were identified and the biological functions of their interaction microRNAs and mRNAs were analyzed. Our results provided novel insights of the circRNA roles in AF and proposed highly possible interaction mechanisms among circRNAs, microRNAs, and mRNAs.

Myocardial infarction with nonobstructive coronary arteries (MINOCA) remains a puzzling clinical entity that is characterized by clinical evidence of myocardial infarction (MI) with normal or near-normal coronary arteries on angiography (stenosis < 50%). Major advances in understanding this condition have been made in recent years. The precise pathogenesis is poorly understood and is being studied and examined further. Guidelines indicate that MINOCA is a group of heterogeneous diseases with different mechanisms of pathology. Since there are multiple possible pathological mechanisms, it is not certain that the classical secondary prevention and treatment strategy for MI with obstructive coronary artery disease (MI-CAD) is optimal for MINOCA patients. The prognosis and predictors for MINOCA patients remain unclear. Although the prognosis is slightly better for MINO-CA patients than for MI-CAD patients, MINOCA isn't always benign. The aim of this paper was to review the literature and evaluate MINOCA epidemiology, clinical features, etiology, diagnosis, treatment, and prognosis.

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

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