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Neuronal regeneration related protein (NREP), also known as P311, has been reported to participate in multiple biological processes. The detection of tumor biomarker favored a non-invasive early entry for cancer diagnosis and disease monitoring to prevent its worsening symptoms. This study is intended to investigate the clinical roles of NREP in gastric cancer (GC) and its effect on gastric cancer cell proliferation and angiogenesis. Our results demonstrated that NREP was typically upregulated in GC tissues compared with normal control. The Kaplan–Meier analysis showed correlations between increased NREP level and poor survival, indicating the prognostic value of NREP in GC patients. The expression levels of NREP varied by races, clinical T stages, and histologic grades. NREP expression was associated with tumor-associated immune infiltration. The NREP expression was powerfully associated with clinical characteristics of GC patients, in particular, with T stage and histologic grade. Gene ontology and KEGG signaling analysis indicated that NREP-related genes were predominantly enriched in various pathways. Additionally, knockdown of NREP inhibited human gastric adenocarcinoma cell proliferation and angiogenesis. Collectively, our results suggested that NREP may be an excellent biomarker for the clinical diagnosis, prognosis, and therapy of GC. [ABSTRACT FROM AUTHOR]

At present, treatment options for osimertinib resistance are very limited. Dual inhibition of the vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) significantly improved the progression-free survival (PFS) of advanced EGFR-mutant non–small cell lung cancer (NSCLC). After EGFR-tyrosine kinase inhibitor (TKI) resistance, EGFR-TKI continuation combined with VEGF inhibitors still had clinical benefits. It is unclear whether the addition of bevacizumab after osimertinib progresses will prolong the duration of the osimertinib benefit. We screened 1289 patients with NSCLC and finally included 96 patients to evaluate osimertinib combined with bevacizumab (osi + bev) versus chemotherapy combined with bevacizumab (che + bev) for patients with acquired resistance to osimertinib. The overall response rate (ORR) for osi + bev and chem + bev was 15.8% (6 of 38) and 20.7% (12 of 58), respectively. The median PFS for osi + bev and che + bev was 7.0 and 4.9 months (HR 0.415 95%CI: 0.252–0.687 p = 0.001). The median OS for osi + bev and che + bev was 12.6 and 7.1 months (HR 0.430 95%CI: 0.266–0.696 p = 0.001). Multivariate analyses showed that no brain metastases and osi + bev treatment after osimertinib resistance correlated with longer PFS (p = 0.044, p = 0.001), while the median PFS of osimertinib less than 6 months (p = 0.021) had a detrimental effect on sequent treatment. Only osi + bev treatment was identified as an independent predictor of OS (p = 0.001). The most common adverse events (AEs) of grade ≥3 were hypertension (13.2%) and diarrhea (10.5%) in the osi + bevacizumab group. Neutropenia (24.1%) and thrombocytopenia (19%) were the most common grade ≥3 AEs in the che + bev group. The overall incidence of serious AEs (grade ≥3) was significantly higher in the chemotherapy plus bevacizumab group. Our study has shown the superiority of osi + bev compared to che + bev after the failure of osimertinib, making it a preferred option for patients with acquired resistance to osimertinib. [ABSTRACT FROM AUTHOR]

Android as an operating system is now increasingly being adopted in industrial information systems, especially with cyber–physical systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicious applications targeting the platform. The existence of such malware threats, therefore, call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and Internet of Things (IoT) devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android is challenging because of restrictions from the security and privacy model of the platform. In this article, the authors investigate how the latest advances in deep learning could address this security problem with better accuracy. Specifically, a deep learning engine is proposed that detects sensitive app behaviors by classifying patterns of system-wide statistics, such as available storage space and transmitted packet volume, using a customized deep neural network based on existing models called Encoder and ResNet. Meanwhile, to handle resource limitations on typical CPS and IoT devices, sparse learning is adopted to reduce the amount of valid parameters in the trained neural network. Evaluations show that the proposed model outperforms a well-established group of baselines on time series classification in identifying sensitive app behaviors with background noise and the targeted behaviors potentially overlapping. [ABSTRACT FROM AUTHOR]

Android has been the most popular smartphone system with multiple platform versions active in the market. To manage the application’s compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we conduct a systematic study of this modern software mechanism. Our objective is to measure the current practice of declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the (in)consistency between DSDK versions and their host apps’ API calls. To successfully analyze a modern dataset of 22,687 popular apps (with an average app size of 25MB), we design a scalable approach that operates on the Android bytecode level and employs a lightweight bytecode search for app analysis. This approach achieves a good performance suitable for online vetting in app markets, requiring only around 5 seconds to process an app on average. Besides shedding light on the characteristics of DSDK in the wild, our study quantitatively measures two side effects of inappropriate DSDK versions: (i) around 35% apps under-set the minimum DSDK versions and could incur runtime crashes, but fortunately, only 11.3% apps could crash on Android 6.0 and above; (ii) around 2% apps, due to under-claiming the targeted DSDK versions, are potentially exploitable by remote code execution, and half of them invoke the vulnerable API via embedded third-party libraries. These results indicate the importance and difficulty of declaring correct DSDK, and our work can help developers fulfill this goal. [ABSTRACT FROM AUTHOR]

Measuring and understanding the performance of mobile networks is becoming very important for end users and operators. Despite the availability of many measurement apps, their measurement accuracy has not received sufficient scrutiny. In this paper, we appraise the accuracy of smartphone-based network performance measurement using the Android platform and the network round-trip time (RTT) as the metric. We show that two of the most popular measurement apps—Ookla Speedtest and MobiPerf—have their RTT measurements inflated. We build three test apps for three common measurement methods and evaluate them in a testbed. We overcome the main challenge of obtaining a complete trace of packets and their timestamps using multiple sniffers and frame-based synchronization. Our multi-layer analysis reveals that the delay inflation can be introduced both in the user space and kernel space. The long path of subfunction invocations accounts for the majority of the delay overhead in the Android runtime (both Dalvik VM and ART), and the sleeping functions in the drivers are the major source of the delay overhead between the kernel and physical layer. We propose and implement a native measurement app to mitigate the delay overhead in the Android runtime, and the resulted delay inflation in the user space can be kept under 1.5 ms for almost all cases. [ABSTRACT FROM AUTHOR]