Summary
Periostin (PN), originally named as osteoblast‐specific factor‐2 (OSF‐2), has been involved in regulating adhesion and differentiation of osteoblasts. Recently many studies have shown that high‐level expression of PN is correlated significantly with tumour angiogenesis and prognosis in many kinds of human cancer. However, whether and how periostin expression influences prognosis in osteosarcoma remains unknown. This study aimed to examine the expression of PN in patients with osteosarcoma and explore the relationship of PN expression with clinicopathologic factors, tumour angiogenesis and prognosis. Immunohistochemistry was performed to determine the expression of PN in osteosarcoma and osteochondroma respectively. Vascular endothelial growth factor (VEGF) and CD34 were also examined in tissues from the osteosarcoma patients mentioned above. The results showed that PN expression was significantly (P < 0.05) higher in osteosarcoma (80.9%) than in osteochondroma (14.7%). Increased PN protein expression was associated with histological subtype (P = 0.000), Enneking stage (P = 0.027) and tumour size (P = 0.009). The result also showed that high expression of PN correlated with VEGF expression (r = 0.285; P = 0.019) and that tumours with PN‐positive expression significantly had higher microvessal density (44.6 ± 13.7 vs. 20.6 ± 6.5; P = 0.000) compared to those in normal bone tissues. Additionally, the expression of PN was found to be an independent prognostic factor in osteosarcoma patients. In conclusion, our findings suggest that PN may have an important role in tumour progression and may be used as a prognostic biomarker for patients with osteosarcoma.
With the widespread use of the Internet, network security issues have attracted more and more attention, and network intrusion detection has become one of the main security technologies. As for network intrusion detection, the original data source always has a high dimension and a large amount of data, which greatly influence the efficiency and the accuracy. Thus, both feature selection and the classifier then play a significant role in raising the performance of network intrusion detection. This paper takes the results of classification optimization of weighted K-nearest neighbor (KNN) with those of the feature selection algorithm into consideration, and proposes a combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN, in order to improve the performance of network intrusion detection. Experimental results show that the weighted KNN can increase the efficiency at the expense of a small amount of the accuracy. Thus, the proposed combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN can then improve both the efficiency and the accuracy of network intrusion detection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.