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With the emerging demand for high-definition videos in recent years, Multi-access Edge Computing (MEC) has become a promising solution to leverage Quality of Experience (QoE) of users in the 5G mobile network, which provides computing and cache resource at network edges to serve end users with less latency. Also, since mobile users tend to be influenced by the trends in social media, the performance of video caching will become more effective if we can extract the hidden information from interaction among them. In this paper, we propose a novel Collaborative Social-aware QoE-driven video Caching and Adaption (CSQCA) framework. Specifically, we first design a 2-tier MEC collaborative video caching architecture, which partially caches popular videos among multiple edge servers. Second, we propose a social-aware proactive cache strategy, which embeds interactions of users and video dissemination process in social networks into the caching mechanism. Third, a QoE-driven video adaptation algorithm is presented to dynamically transcode the cached videos into appropriate resolution on edge server for each request. Finally, we conduct our simulation based on real-world datasets. The simulation results show that the proposed CSQCA framework outperforms traditional cache algorithms, in terms of the average hit ratio and QoE.

Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.

Perforated gastric cancer (PGC) is a rare condition of gastric cancer (GC). In this study, we sought to assess the outcome of PGC from the aspects of both acute care surgery and surgical oncology at a single institute, Chang Gung Memorial Hospital (CGMH).From 1997 to 2013, 6864 patients were diagnosed with GC and 2738 were diagnosed with gastroduodenal perforation at CGMH. In total, 29 patients with PGC were identified. Immediate surgical and long-term oncologic outcomes were evaluated after an appropriate matching process was performed.The immediate surgical outcome of PGC, i.e., the hospital mortality rate within 30 d after surgery, did not significantly differ from that of non-cancer related gastroduodenal perforation. The long-term oncologic outcome, with matching by age, gender, year of surgery and AJCC 7th stage grouping, also did not significantly differ from that of GC without perforation.Aggressive surgical treatment, including an initial emergency procedure for containing peritonitis and radical surgery for GC, may benefit PGC patients in terms of both the immediate and oncologic outcomes.

Background and Objectives: The standard treatment for carcinomas of the ampulla of Vater (CAV) is surgical resection. Nevertheless, recurrence is observed in half of the patients within five years after cancer surgery. In this study, we aimed to identify predictive factors and develop a nomogram capable of predicting the risk of recurrence in post-resection CAV patients. Methods: Our study cohort was composed of two hundred and twelve patients who had undergone radical surgery for CAV between 2000 and 2012. Clinicopathological variables predictive of recurrence were identified using univariate analysis. A Cox proportional hazards model was used to select the predictive factors used for the nomogram. Results: The 5-year overall survival, cancer-specific survival and disease-free survival rates were 44.3%, 51.1%, and 42.8%, respectively. The five most predictive variables for tumor recurrence were chosen to develop the nomogram. The nomogram had a highly predictive performance, with a bootstrap-corrected concordance index of 0.72 (95% confidence interval, 0.67-0.79). Furthermore, the performance of the nomogram was found to be better than the American Joint Committee on Cancer (AJCC) staging system due to better homogeneity, higher ability of discrimination and higher risk stratification of the model. Conclusions: We developed a predictive nomogram for estimating tumor relapse probability in CAV patients after radical surgery. This nomogram might be more informative than the AJCC staging system and may allow for better estimation of the probability of relapse after surgical resections.

The continuous advancement in molecular biology and information technology aided the development of a rich molecular simulation repertoire that can be applied in system biology, proteomics, molecular biology, bioinformatics, and materials science. We attempt to introduce the latest developments in drug design based on computational techniques, including protein structure modeling, docking, binding site prediction, quantitative structure–activity relationship (QSAR), and molecular dynamics simulation. Furthermore, a brief discussion on current docking issues, including accuracy of protein structure and protein–ligand interaction, is also included. Weight equation and rules and a new concept on flexibility are also described here as possible solution for these issues.

Human epidermal growth factor receptor 2, HER2, is a commonly over-expressed tyrosine kinase receptor found in many types of carcinoma. Despite that there are several HER2 inhibitors, namely Iressa, Tarceva and Tykerb, currently in clinical trials, all can cause several side effects. In this study, both structure-based and ligand-based drug design were employed to design novel HER2 inhibitors from traditional Chinese medicine (TCM). The HER2 structure model was built in homology modeling based on known receptors of the same family. Docking and de novo evolution experiments were performed to identify candidates and to build derivatives. A training set of 32 compounds with inhibitory activities to HER2 was used to formulate the pharmacophore hypotheses that were subsequently used to examine candidates obtained from the docking study. Hydrogen bond interactions, salt-bridge formations and pi-stacking were observed between the ligands and Phe731, Lys753, Asp863 and Asp808 of HER2 protein. Combining results from both docking and pharmacophore mapping analysis, CLC015-5, CLC604-11 and CLC604-18 were well accepted and consistent in both approaches and were considered as the most potential HER2 inhibitors.

Patients with carcinoma of the ampulla of Vater (CAV) have better outcomes among periampullary malignancies. However, little is known about the metastatic lymph node ratio (LNR) as a prognostic factor for resectable CAV. We retrospectively reviewed our CAV patients undergoing curative surgery and analyzed their prognostic factors. A total of 212 CAV patients who received radical surgery at Chang Gung Memorial Hospital, Linkou, between 2000 and 2010 were admitted in this study. The lymph node ratio was defined as the number of metastatic lymph nodes (LNs) divided by the total number of LNs removed. The patients’ demographic data, comorbidities, operation type, and tumor features were analyzed retrospectively for survival prediction of patients. The median age of the patients was 62 years, and 57% of the patients were men. The surgical procedure was standard pancreaticoduodenectomy and pylorus-preserving pancreaticoduodenectomy in 53% and 47% of the patients, respectively. The median follow-up duration was 32.6 months, and 50% of the patients had died by the end of the study. The median overall survival time (OS) and disease-free survival time (DFS) were 65.8 and 33.7 months, respectively. In multivariate analysis, patients with a metastatic LNR >0.056 had a significantly poor prognosis in both OS and DFS. A metastatic LNR >0.056 predicted a poor DFS and OS in CAV patients after radical surgery. Greater awareness on the impact of metastatic LNR may help clinicians provide appropriate adjuvant treatment for high-risk CAV patients.

Background Infective endocarditis (IE) in intravenous drug users has been increasing in incidence. The major pathogen used to be methicillin-susceptible Staphylococcus aureus , but resistant isolates have also been increasing. This study aimed to investigate the clinical characteristics of IE in intravenous drug users and to evaluate the molecular patterns of methicillin-resistant S. aureus (MRSA) that cause IE in these drug users. Methods A total of 37 episodes of IE in intravenous drug users hospitalized from 1980 to 2006 at a 1,250-bed teaching hospital in Southern Taiwan were evaluated retrospectively. The genetic relatedness of S. aureus strains was assessed using pulsed-field gel electrophoresis. Polymerase chain reaction was used to detect Panton-Valentine leukocidin ( PVL ) and staphylococcal γ-hemolysin ( Hlg ), and to determine the staphylococcal chromosomal cassette carrying the mec A methicillin-resistant gene (SCC mec ) type. Results The patients had a mean ± standard deviation age of 31.5 ± 9.25 years, with a male predominance of 76%. Hepatitis C was present in all patients. Methicillin-susceptible S. aureus accounted for 76% of infections, and the most common clinical symptoms were fever (97%) and embolic phenomenon (68%). There were 4 MRSA isolates, 3 of which were SCC mec type III. PVL and Hlg genes were found in 2 and 3 MRSA isolates, respectively. Eighty percent similarity was found among the MRSA isolates by pulsed-field gel electrophoresis. Conclusion Our results suggest that coinfection with hepatitis C was common in intravenous drug users with IE, and that molecular patterns of MRSA isolates had high similarity. SCC mec type III, which is usually hospital-acquired, could have caused the community-associated MRSA endocarditis in our patients.