Profiling lipid mediators in serum from children with H1N1 influenza.

Influenza A virus subtype H1N1 can cause severe acute respiratory distress syndrome and death in young children and elderly individuals. H1N1 initiates inflammatory responses that aim to contain and eliminate microbial invaders. Various lipid mediators (LMs) are biosynthesized and play a critical role in fighting viruses during inflammation; thus, by profiling the LMs in patients, researchers can obtain mechanistic insights into diseases, such as the pathways disrupted. To date, the relationship between molecular alterations in LMs and the pathogenesis of H1N1 influenza in children is poorly understood. Here, we employed a targeted liquid chromatography coupled with tandem mass spectrometry (LC‒MS/MS) to profile LMs in serum from children with H1N1 influenza (H1N1 children) and recovered children. We found that 22 LM species were altered in H1N1 children with mild symptoms. Analysis of the LM profiles of recovered children revealed a decrease in the levels of thromboxane B2 (TxB2) and thromboxane B3 (TxB3) and an increase in the levels of other 8 altered LM species associated with H1N1 influenza, including cytochrome P450 (CYP) enzyme-derived dihydroxyeicosatrienoic acids (DiHETrEs) and hydroxyeicosatetraenoic acids (HETEs) from arachidonic acid (AA), and epoxyoctadecamonoenoic acids (EpOMEs) from linoleic acid (LA). Taken together, the results of this study revealed that serum LMs change dynamically in H1N1 children with mild symptoms. The dramatically altered LMs in H1N1 children could serve as a basis for potential therapeutics or adjuvants against H1N1 influenza.

A Multi-Task Group Bi-LSTM Networks Application on Electrocardiogram Classification.

BACKGROUND: Cardiovascular diseases (CVD) are the leading cause of death globally. Electrocardiogram (ECG) analysis can provide thoroughly assessment for different CVDs efficiently. We propose a multi-task group bidirectional long short-term memory (MTGBi-LSTM) framework to intelligent recognize multiple CVDs based on multi-lead ECG signals. METHODS: This model employs a Group Bi-LSTM (GBi-LSTM) and Residual Group Convolutional Neural Network (Res-GCNN) to learn the dual feature representation of ECG space and time series. GBi-LSTM is divided into Global Bi-LSTM and Intra-Group Bi-LSTM, which can learn the features of each ECG lead and the relationship between leads. Then, through attention mechanism, the different lead information of ECG is integrated to make the model to possess the powerful feature discriminability. Through multi-task learning, the model can fully mine the association information between diseases and obtain more accurate diagnostic results. In addition, we propose a dynamic weighted loss function to better quantify the loss to overcome the imbalance between classes. RESULTS: Based on more than 170,000 clinical 12-lead ECG analysis, the MTGBi-LSTM method achieved accuracy, precision, recall and F1 of 88.86%, 90.67%, 94.19% and 92.39%, respectively. The experimental results show that the proposed MTGBi-LSTM method can reliably realize ECG analysis and provide an effective tool for computer-aided diagnosis of CVD.

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases.

Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN) is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart) and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.