Objective To report the progression of breviscapine’s protective effect to hepatic ischemia-reperfusion injury. Methods Pertinent literatures and journal articles published in recent years were reviewed, and the progression of breviscapine protecting hepatic ischemia-reperfusion injury in the experimental and clinical research were analyzed and summarized. Results The role of breviscapine is considerable extensive. It can protect hepatic ischemia-reperfusion injury by anti-oxyradical and anti-lipid peroxidation, inhibiting mitochondrial damage, intracellular calcium overload, intra-thromboxane and apoptosis, improving microcirculation, and so on. Conclusion Breviscapine plays a protective role in hepatic ischemia-reperfusion injury, and it will be of great value to application and research.
Objective To investigate the protective effects and the mechanism of recombinant human growth hormone on the intestinal barrier function. Methods The literatures of recent years were reviewed and summarized． Results The recombinant human growth hormone not only prevent mucosal cells and immunological cells from apoptosis, but also antagonize the damage of NO, cytokines, as well as endotoxin on intestinal barrier. What’s more, it increases the intestinal uptake and utilization of glutamine. All of the above could maintain the integrity and functions of the intestinal barrier. Conclusion The recombinant human growth hormone protects the intestinal barrier function through different ways.
Recently, deep neural networks (DNNs) have been widely used in the field of electrocardiogram (ECG) signal classification, but the previous models have limited ability to extract features from raw ECG data. In this paper, a deep residual network model based on pyramidal convolutional layers (PC-DRN) was proposed to implement ECG signal classification. The pyramidal convolutional (PC) layer could simultaneously extract multi-scale features from the original ECG data. And then, a deep residual network was designed to train the classification model for arrhythmia detection. The public dataset provided by the physionet computing in cardiology challenge 2017(CinC2017) was used to validate the classification experiment of 4 types of ECG data. In this paper, the harmonic mean F1 of classification accuracy and recall was selected as the evaluation indexes. The experimental results showed that the average sequence level F1 (SeqF1) of PC-DRN was improved from 0.857 to 0.920, and the average set level F1 (SetF1) was improved from 0.876 to 0.925. Therefore, the PC-DRN model proposed in this paper provided a promising way for the feature extraction and classification of ECG signals, and provided an effective tool for arrhythmia classification.