Construction of Cell–Extracellular Matrix Microenvironments by Conjugating ECM Proteins on Supported Lipid Bilayers

The cell membrane is an organized and fluid structure that modulates cellular activities in response to specific extracellular signals, and maintains the critical communication, integration, and homeostasis between the cytosol and the extracellular matrix (ECM). In recent years, tissue engineering and cell biology research has been rapidly progressed by a remarkable understanding of cell and ECM interfaces. In this review, the design of new biomimetic platforms based on the conjugation of ECM proteins on solid supported lipid bilayers (SLBs) will be summarized. The platforms provide a better system to evaluate cellular responses to specific recognition events, gradient, mechanical property, nanostructures, and inter- and intra-molecular interactions of ECM proteins on a non-fouling and fluid membrane. Moreover, the findings from the molecular interactions and cellular activities will be highlighted to look into the cell-materials mechanisms

Three-Phase Detection and Classification for Android Malware Based on Common Behaviors

Android is one of the most popular operating systems used in mobile devices. Its popularity also renders it a common target for attackers. We propose an efficient and accurate three-phase behavior-based approach for detecting and classifying malicious Android applications. In the proposedapproach, the first two phases detect a malicious application and the final phase classifies the detected malware. The first phase quickly filters out benign applications based on requested permissions and the remaining samples are passed to the slower second phase, which detects malicious applications based on system call sequences. The final phase classifies malware into known or unknown types based on behavioral or permission similarities. Our contributions are three-fold: First, we propose a self-contained approach for Android malware identification and classification. Second, we show that permission requests from an Application are beneficial to benign application filtering. Third, we show that system call sequences generated from an application running inside a virtual machine can be used for malware detection. The experiment results indicate that the multi-phase approach is more accurate than the single-phase approach. The proposed approach registered true positive and false positive rates of 97% and 3%, respectively. In addition, more than 98% of the samples were correctly classified into known or unknown types of malware based on permission similarities.We believe that our findings shed some lights on future development of malware detection and classification

Three-Phase Detection and Classification for Android Malware Based on Common Behaviors

Android is one of the most popular operating systems used in mobile devices. Its popularity also renders it a common target for attackers. We propose an efficient and accurate three-phase behavior-based approach for detecting and classifying malicious Android applications. In the proposed approach, the first two phases detect a malicious application and the final phase classifies the detected malware. The first phase quickly filters out benign applications based on requested permissions and the remaining samples are passed to the slower second phase, which detects malicious applications based on system call sequences. The final phase classifies malware into known or unknown types based on behavioral or permission similarities. Our contributions are three-fold: First, we propose a self-contained approach for Android malware identification and classification. Second, we show that permission requests from an Application are beneficial to benign application filtering. Third, we show that system call sequences generated from an application running inside a virtual machine can be used for malware detection. The experiment results indicate that the multi-phase approach is more accurate than the single-phase approach. The proposed approach registered true positive and false positive rates of 97% and 3%, respectively. In addition, more than 98% of the samples were correctly classified into known or unknown types of malware based on permission similarities.We believe that our findings shed some lights on future development of malware detection and classification

Targeting delivery of paclitaxel into tumor cells via somatostatin receptor endocytosis

AbstractBackground: The binding of somatostatin (SST) to endogenous G-protein-coupled receptors (SST receptors or SSTRs) is followed by internalization of SST, and, several reports have shown that a high density of SSTRs is present on most hormone-secreting tissue tumors. Facile synthesis of the long-acting SST analog, octreotide, has previously been described. Octreotide might be of practical value in developing tumor tracers and in serving as a carrier of cytotoxic antitumor drugs.Results: Fluorescein-labeled octreotide was internalized into the cytosol of human breast MCF-7 carcinoma cells via binding to SSTRs. Octreotide-conjugated paclitaxel (taxol) was created by coupling taxol–succinate to the amino-terminal end of octreotide. This conjugate retains the biological activity of taxol in inducing formation of tubulin bundles, eventually causing apoptosis of MCF-7 cells. Cytotoxicity of octreotide-conjugated taxol is mainly mediated by SSTR, as shown by the observation that octreotide pretreatment can rescue the induced cell death. In comparison with free taxol, this conjugate shows much less toxicity in Chinese hamster ovary cells.Conclusions: Octreotide-conjugated taxol exerts the same antitumor effect of free taxol on stabilizing microtubule formation and inducing cell death. This conjugate triggers tumor cell apoptosis mediated by SSTRs and is exclusively toxic to SSTR-expressing cells. Octreotide-conjugated taxol is less toxic to low-SSTR-expressing cells compared with free taxol. Our results strongly indicated that octreotide-conjugated taxol demonstrates cell selectivity and may be used as a targeting agent for cancer therapy