Expression of GSK-3β in renal allograft tissue and its significance in pathogenesis of chronic allograft dysfunction

<p>Abstract</p> <p>Objective</p> <p>To explore the expression of Glycogen synthase kinase 3 beta (GSK-3β) in renal allograft tissue and its significance in the pathogenesis of chronic allograft dysfunction.</p> <p>Methods</p> <p>Renal allograft biopsy was performed in all of the renal allograft recipients with proteinuria or increased serum creatinine level who came into our hospital from January 2007 to December 2009. Among them 28 cases was diagnosed as chronic allograft dysfunction based on pahtological observation, including 21 males with a mean age of 45 ± 10 years old and 7 females with a mean age of 42 ± 9 years old. The time from kidney transplantation to biopsy were 1-9 (3.5) years. Their serum creatinine level were 206 ± 122 umol/L. Immunohistochemical assay and computer-assisted genuine color image analysis system (imagepro-plus 6.0) were used to detect the expression of GSK-3β in the renal allografts of 28 cases of recipients with chronic allograft dysfunction. Mean area and mean integrated optical density of GSK-3β expression were calculated. The relationship between expression level of GSK-3β and either the grade of inflammatory cell infiltration or interstitial fibrosis/tubular atrophy in renal allograft was analyzed. Five specimens of healthy renal tissue were used as controls.</p> <p>Results</p> <p>The expression level of the GSK-3β was significantly increased in the renal allograft tissue of recipients with chronic allograft dysfunction, compared to normal renal tissues, and GSK-3β expression became stronger along with the increasing of the grade of either inflammatory cell infiltration or interstitial fibrosis/tubular atrophy in renal allograft tissue.</p> <p>Conclusion</p> <p>There might be a positive correlation between either inflammatory cell infiltration or interstitial fibrosis/tubular atrophy and high GSK-3β expression in renal allograft tissue.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here:</p> <p><url>http://www.diagnosticpathology.diagnomx.eu/vs/9924478946162998</url>.</p

False Data Injection Attack Based on Hyperplane Migration of Support Vector Machine in Transmission Network of the Smart Grid

The smart grid is a key piece of infrastructure and its security has attracted widespread attention. The false data injection (FDI) attack is one of the important research issues in the field of smart grid security. Because this kind of attack has a great impact on the safe and stable operation of the smart grid, many effective detection methods have been proposed, such as an FDI detector based on the support vector machine (SVM). In this paper, we first analyze the problem existing in the detector based on SVM. Then, we propose a new attack method to reduce the detection effect of the FDI detector based on SVM and give a proof. The core of the method is that the FDI detector based on SVM cannot detect the attack vectors which are specially constructed and can replace the attack vectors into the training set when it is updated. Therefore, the training set is changed and then the next training result will be affected. With the increase of the number of the attack vectors which are injected into the positive space, the hyperplane moves to the side of the negative space, and the detection effect of the FDI detector based on SVM is reduced. Finally, we analyze the impact of different data injection modes for training results. Simulation experiments show that this attack method can impact the effectiveness of the FDI detector based on SVM

False Sequential Command Attack of Large-Scale Cyber-Physical Systems

Previous studies have demonstrated that false commands can cause severe damage to large-scale cyber-physical systems (CPSs). We focus on a kind of threat called false sequential command attack, with which attackers can generate false sequential commands, resulting in the illegal control of the physical process. We present a feasible attack model. Attackers delay the disaggregation of former commands by manipulating maliciously sub-controllers. Simultaneously, bad feedback data is injected to defeat the controller to issue latter commands. Thus, false command sequence is executed and the disruption of physical process can be obtained. It is also difficult for the detector to identify such attacks as injecting bad data. We also discuss other possible attack paths and analyze the corresponding disadvantages. Compared with other paths, the proposed model is more feasible and has more difficulties to be detected. A case study is given to validate the feasibility and effectiveness of proposed false sequential command attack model. Finally, we discuss the possible countermeasure