[Detection method of early heart valve diseases based on heart sound features].

Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.

HBO regulates the Warburg effect of hypoxic HCC cells through miR-103a-3p/TRIM35.

BACKGROUND: There are a lot of studies on the treatment of tumors with hyperbaric oxygen, while most of them are in breast cancer, prostate cancer and so on. However, there are still few studies on hyperbaric oxygen in treating hepatocellular carcinoma (HCC). According to the current data, hyperbaric oxygen is an effective means to intervene in tumors. The Warburg effect is a unique marker of glucose metabolism in tumors related to hypoxia, making it possible for hyperbaric oxygen to interfere with the tumor through the Warburg effect. METHOD: We used the hypoxia/hyperbaric oxygen(HBO)-exposed HCC cells for in vitro studies. Glucose uptake, lactic acid, and adenosine triphosphate (ATP) assessed the Warburg effect. The expression of miR-103a-3p in HCC was detected by using qRT-PCR. The effect of miR-103a-3p/TRIM35 expression level on the cells was measured using the CCK8 method and flow cytometry. The molecular biological mechanism of miR-103a-3p in HCC was examined using the luciferase reporter, MS2-RIP assays. RESULT: HBO inhibited the Warburg effect in hypoxic HCC cells. HBO suppressed the expression of miR-103a-3p in hypoxic HCC cells, and miR-103a-3p inhibited the expression of TRIM35 in hypoxic HCC cells. With HBO exposure, miR-103a-3p/TRIM35 regulated the Warburg effect of hypoxic HCC cells. CONCLUSION: These findings reveal that HBO regulates the Warburg effect of hypoxic HCC cells through miR-103a-3p/TRIM35 and inhibits tumor growth.

Knockdown of ETV4 promotes autophagy-dependent apoptosis in GBM cells by reducing the transcriptional activation of EMP1.

ETS variant transcription factor 4 (ETV4) is a common cancer-promoting transcription factor and its expression has been found to be significantly upregulated in glioblastoma multiforme (GBM), as determined via analysis of the Gene Expression Profiling Interactive Analysis (GEPIA) database. In addition, our previous study demonstrated that ETV4 expression was highly positively correlated with epithelial membrane protein 1 (EMP1). The present study aimed to determine whether ETV4 could influence the activation of the PI3K/AKT/mTOR signaling pathway to affect the autophagy and apoptosis of GBM cells by regulating the transcriptional activity of EMP1. In addition to the analysis of the GEPIA database, the expression levels of ETV4 were also investigated in several different GBM cell lines. After interfering with the expression of ETV4, western blotting was used to detect the expression levels of autophagy- and apoptosis-related proteins, and a TUNEL assay was used to detect the levels of cell apoptosis. Dual luciferase reporter and chromatin immunoprecipitation assays were used to verify the potential binding site of ETV4 on EMP1. Western blotting was also used to analyze the expression levels of PI3K/AKT/mTOR signaling pathway-related proteins. The results of the current study revealed that the expression levels of ETV4 were significantly upregulated in GBM cell lines compared with those in normal glial cells. In the GBM cell line, LN-229, ETV4 was discovered to bind to the EMP1 promoter and positively regulate the expression of EMP1. The knockdown of ETV4 expression inhibited the PI3K/AKT/mTOR signaling pathway activity to promote autophagy and apoptosis, and this effect could be partially reversed by overexpressing EMP1. In conclusion, these findings indicated that the knockdown of ETV4 in GBM cells may reduce the transcriptional activation of EMP1 and thereby inhibit PI3K/AKT/mTOR signaling pathway activity to promote autophagy and apoptosis. This provides a novel insight into potential strategies for the treatment of GBM via the induction of autophagy-dependent apoptosis.

ECG_SegNet: An ECG delineation model based on the encoder-decoder structure.

With the increasing usage of wearable electrocardiogram (ECG) monitoring devices, it is necessary to develop models and algorithms that can analyze the large amounts of ECG data obtained in real-time. Accurate ECG delineation is key to assisting cardiologists in diagnosing cardiac diseases. The main objective of this study is to design a delineation model based on the encoder-decoder structure to detect different heartbeat waveforms, including P-waves, QRS complexes, T-waves, and No waves (NW), as well as the onset and offset of these waveforms. First, the introduction of a standard dilated convolution module (SDCM) into the encoder path enabled the model to extract more useful ECG signal-informative features. Subsequently, bidirectional long short-term memory (BiLSTM) was added to the encoding structure to obtain numerous temporal features. Moreover, the feature sets of the ECG signals at each level in the encoder path were connected to the decoder part for multi-scale decoding to mitigate the information loss caused by the pooling operation in the encoding process. Finally, the proposed model was trained and tested on both QT and LU databases, and it achieved accurate results compared to other state-of-the-art methods. Regarding the QT database, the average accuracy of ECG waveform classification was 96.90%, and an average classification accuracy of 95.40% was obtained on the LU database. In addition, average F1 values of 99.58% and 97.05% were achieved in the ECG delineation task of the QT and LU databases, respectively. The results show that the proposed ECG_SegNet model has good flexibility and reliability when applied to ECG delineation, and it is a reliable method for analyzing ECG signals in real-time.

Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a G-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level G-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.

[Celastrol in the inhibition of neovascularization].

OBJECTIVE: To study the inhibition effect of celastrol on neovascularization. METHODS: The effect of celastrol on the in vitro proliferation of endothelial cell of vessel (ECV) was examined by MTT assay. The effect of celastrol on endothelial cell migration, tube formation on Matrigel and Chick chorioallantoic membrane angiogenesis was also examined. Matrigel plug assay was used to evaluate the effect of celastrol on angiogenesis in vivo. RESULTS: The proliferation of ECV was inhibited significantly by celastrol with IC(50) being 1.33 microg/ml. Celastrol inhibited endothelial cell migration and tube formation in a dose-dependent manner. Celastrol also inhibited angiogenesis both in Matrigel plug of mouse model and in chick chorioallantoic membranes. CONCLUSION: Celastrol, which can inhibit angiogenesis, could be developed as an antiangiogenic drug.

[Several monomes from Tripterygium wilfordii inhibit proliferation of glioma cells in vitro].

BACKGROUND AND OBJECTIVE: Researches indicated that Tripterygium wilfordii possess antitumor activity. The current study was designed to investigate inhibitive effect of several monomes of Tripterygium wilfordii on the proliferation of glioma cells. METHODS: The effect of three monomes from Tripterygium wilfordii on the proliferation of glioma cell lines SHG44, C6, and U251 in vitro was examined by using MTT assay. Immunohistochemistry was used to examine the expression of Bax, Bcl-2 after treatment of triptolide and celastrol. RESULT: The proliferation of glioma cells was remarkably inhibited by triptolide and celastrol. They both increased expression of Bax and decreased expression of Bcl-2 in the SHG44 cells. CONCLUSION: Triptolide and celastrol inhibit the proliferation of glioma cells in vitro, which was associated with promoting the expression of Bax and inhibiting the expression of Bcl-2 and accelerating cell apotosis.