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BackgroundPeriostin and the mammalian target of rapamycin (mTOR) are involved in several cancers. This study aimed to evaluate the expression level of periostin and mTOR in locally advanced esophageal squamous cell carcinoma (ESCC) and to analyze their correlations with prognostic value.Material/MethodsExpression levels of periostin and mTOR were examined by immunohistochemistry in locally advanced ESCC and corresponding adjacent normal tissue of 71 patients. The expression of periostin and mTOR were correlated with clinicopathologic characteristics by χ2 test or Kruskal-Wallis analysis. The prognostic factors of periostin and mTOR on overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan-Meier and Cox regression methods, respectively.ResultsThe high expression of periostin was significantly correlated to tumor stage (P=0.000), vascular invasion (P=0.027), differentiation (P=0.002), invasion depth (P=0.023), and lymph node metastasis (P=0.017). The high expression of mTOR was associated with tumor stage (P=0.001), lymphatic metastasis (P=0.014), and differentiation (P=0.036). Expression levels of periostin and mTOR was positively correlated (r=0.416, P=0.000). The OS and DFS in patients in the high-periostin group were significantly shorter than those in the low-periostin group, (both P<0.001). Similar results were found in mTOR analysis. Moreover, Cox regression analysis showed that the expressions of periostin and mTOR, along with tumor stage, were the independent factors affecting the survival time of ESCC patients.ConclusionsExpressions of periostin and mTOR are related to multiple clinicopathologic features. High expression of periostin and mTOR were independent risk factors of ESCC patients, which might offer a potential target strategy for ESCC treatment in the future.

A novel method based on support vector machine (SVM) is proposed for detecting computer virus. By utilizing SVM, the generalizing ability of virus detection system is still good even the sample dataset size is small. First, the research progress of computer virus detection is recalled and algorithm of SVM taxonomy is introduced. Then the model of a virus detection system based on SVM and virus detection engine are presented respectively. An experiment using system API function call trace is given to illustrate the performance of this model. Finally, comparison of detection ability between the above detection method and other is given. It is found that the detection system based on SVM needs less priori knowledge than other methods and can shorten the training time under the same detection performance condition.

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