Curcumin exhibits therapeutic effect against spinal cord injury via inhibition of neuronal inflammation and apoptosis

Purpose: To investigate the effect of curcumin on spinal cord injury (SCI) in a rat model. Methods: SCI was induced in the rats using mid thoracic spinal cord compression, after which curcumin was injected intraperitoneally. Western blotting was used for assay of expressions of apoptotic proteins, viz, IL-1β, NF-κB p65, TLR4, TNF-α, LC3, Bax and Bcl-2. Malondialdehyde (MDA) and myeloperoxidase were measured using standard methods. Neuronal loss in spinal cord tissues was determined with TUNEL staining and NeuN labelling. Results: Curcumin treatment significantly (p < 0.05) suppressed SCI-mediated upregulation of myeloperoxidase activity and increase in MDA level in rat spinal cord. The reduction of glutathione (GSH) and superoxide dismutase (SOD) activities in the spinal cord of SCI rats were suppressed by curcumin treatment. Curcumin treatment also led to a significant (p < 0.02) increase in the proportion of NeuN positive cells and marked reduction in TUNEL positive cells, but it decreased caspase-3 in the spinal cord tissues of SCI rats. Moreover, curcumin reversed the effect of SCI on protein expressions of Bax and Bcl 2 in a dose-based manner. There was marked curcumin-induced decline in CD11b and GFAP levels in the spinal cord tissues of the SCI rats. Conclusion: These results demonstrate that curcumin protects rats against SCI via inhibition of oxidative stress-mediated neuronal apoptosis. Therefore, curcumin may be useful for the development of an effective treatment for spinal cord injury

An Approach Based on the Improved SVM Algorithm for Identifying Malware in Network Traffic

Due to the growth and popularity of the internet, cyber security remains, and will continue, to be an important issue. There are many network traffic classification methods or malware identification approaches that have been proposed to solve this problem. However, the existing methods are not well suited to help security experts effectively solve this challenge due to their low accuracy and high false positive rate. To this end, we employ a machine learning-based classification approach to identify malware. The approach extracts features from network traffic and reduces the dimensionality of the features, which can effectively improve the accuracy of identification. Furthermore, we propose an improved SVM algorithm for classifying the network traffic dubbed Optimized Facile Support Vector Machine (OFSVM). The OFSVM algorithm solves the problem that the original SVM algorithm is not satisfactory for classification from two aspects, i.e., parameter optimization and kernel function selection. Therefore, in this paper, we present an approach for identifying malware in network traffic, called Network Traffic Malware Identification (NTMI). To evaluate the effectiveness of the NTMI approach proposed in this paper, we collect four real network traffic datasets and use a publicly available dataset CAIDA for our experiments. Evaluation results suggest that the NTMI approach can lead to higher accuracy while achieving a lower false positive rate compared with other identification methods. On average, the NTMI approach achieves an accuracy of 92.5% and a false positive rate of 5.527%