Reliability of plasma HIV viral load testing beyond 24 hours: Insights gained from a study in a routine diagnostic laboratory

Background Viral load testing is key to monitoring response to anti-retroviral therapy (ART). However, in lower and middle income countries with large epidemics, pre-analytical challenges threaten the quality of testing. It is unknown how much delayed processing and adverse storage affects the validity of results. The aim of this study was to determine the impact of delayed testing and warmer storage conditions on HIV RNA stability in diagnostic samples. Methods 1194 samples, collected in EDTA or plasma preparation (PPT) tubes, were studied. Immediately after initial testing, primary tubes were stored for 72, 96 or 168 hours at 4°C, 20°C or 30°C. The viral load was then repeated and the 2 results were compared. Results Viral loads were very stable, with <0.5 log copies/ml median difference noted between paired tests for all storage times and temperatures. The viral load in samples stored for up to a week reliably differentiated between ART-suppressed and failing patients in 98.83% of instances. However, re-centrifugation immediately prior to repeat testing was essential to avoid falsely elevated readings, probably due to contamination of plasma with cell-associated viral nucleic acids. Approximately 20% of samples with initially undetectable viral loads were weakly positive (<100 copies/mL) on repeat. This was not exacerbated by duration or temperature of storage. Conclusion Viral RNA in diagnostic samples is stable well beyond currently recommended limits. However, when testing stored primary samples, contamination of plasma with cellular material easily occurs. Low viral loads (<100copies/mL) in samples stored in this way should be interpreted with caution.

Correction: Transcriptome profiling of mouse brain and lung under Dip2a regulation using RNA-sequencing

Mixed Micellar Cloud Point Extraction and Spectrophotometric Determination of Meldola′s Blue Dye in Tap and Sea Water Samples

Mixed Micellar Cloud Point Extraction and Spectrophotometric Determination of Iron(II) Using 1,10-Phenanthroline: Effect of Sodium Nitroprusside on Extraction Efficiency

700 Mild Endoscopic Activity Is Associated With an Increased Risk of Relapse in Patients With Ulcerative Colitis

760 Disease Location Is Associated With Non-Alcoholic Fatty Liver Disease in Patients With Crohn's Disease

Sustentabilidade, gerenciamento de resultados e decisões de investimento: um estudo no mercado de capitais brasileiro

The literature points out that companies with better practices of corporate sustainability outperform their counterparts in several characteristics, such as better performance, lower risk, and greater transparency in the disclosure of information (Ameer & Othman, 2012; Eccles et al., 2014; Kantabutra, 2011; Khan, Serafeim, & Yoon, 2015; Lameira, Ness, Quelhas, & Pereira, 2013; Weber, 2017). The disclosure of sustainable practices by companies around the world has been associated with a greater level of transparency, especially in environmental and social aspects (Siew, 2015) and a way to measure this transparency is through the earnings management (Martinez, 2008). Earnings management, according to Schipper (1989), is a purposeful intervention in the process of presenting the financial data of firms with the intention of obtaining some private or personal gain. According to the literature, sustainable companies tend to do less earnings management (Kim, Park, & Wier, 2012). Considering these antecedents, the objectives of the paper are (a) to study the relationship between corporate sustainability and earnings management and (b) investigate the possible effects that the sustainability and transparency could have in the decision making of the investors when they allocate their capital in the stock market. To achieve the objective (a) there were developed tests and regressions in Brazilian open capital companies from the B3 for the period from 2010 to 2017. Additionally, to achieve objective (b) an experiment was developed throughout an anonymous opinion survey applied to university students with knowledge and/or experience in finances and/or investments. The result of the investigation related to objective (a) wasn ́t any significant relationship between earnings management and corporate earnings. However, a significant relationship between earnings management size and ROA was found. The result of the investigation related to objective (b) indicate that sustainability and transparency do not affect the decision making of the investors. As the main limitations of the investigation stand out the disproportionality in the number of sustainable companies in the Brazilian stock market and the difficulty of treating respondents in a homogeneous way regarding to sociodemographic factors. For further investigations, it is suggested to relate other variables related to corporate sustainability as social responsibility or environmental impact to the analyses the issues previously exposed.

Comparative Analysis of Virtualization Methods in Big Data Processing

Cloud computing systems have become widely used for Big Data processing, providing access to a wide variety of computing resources and a greater distribution between multi-clouds. This trend has been strengthened by the rapid development of the Internet of Things (IoT) concept. Virtualization via virtual machines and containers is a traditional way of organization of cloud computing infrastructure. Containerization technology provides a lightweight virtual runtime environment. In addition to the advantages of traditional virtual machines in terms of size and flexibility, containers are particularly important for integration tasks for PaaS solutions, such as application packaging and service orchestration. In this paper, we overview the current state-of-the-art of virtualization and containerization approaches and technologies in the context of Big Data tasks solution. We present the results of studies which compare the efficiency of containerization and virtualization technologies to solve Big Data problems. We also analyze containerized and virtualized services collaboration solutions to support automation of the deployment and execution of Big Data applications in the cloud infrastructure.

Techniques, Tools and Applications of Graph Analytic

Graphs have acute significance because of poly-tropic nature and have wide spread real world big data appli-cations, e.g., search engines, social media, knowledge discovery, network systems, etc. Major challenge is to develop efficient systems to store, process and analyze large graphs generated by these applications. Graph analytic is important research area in big data graphs dealing with efficient extraction of useful knowledge and interesting patterns from rapidly growing big data streams. Tremendously huge and complex data of graph applications requires specially designed graph databases having special data structures and effective features for data modeling and querying. The manipulation of large size of data requires effective scalable and distributed computational techniques for efficient graph partitioning and communication. Researchers have proposed different analytical techniques, storage structures, and processing models. This study provides insight of different graph analytical techniques and compares existing graph storage and computational technologies. This work also assesses the perfor-mance, strengths and limitations of various graph databases and processing models.

Bethune Round Table 2019 Conference on Global SurgeryCauses of delay in the management of surgical conditions at Tamale Teaching Hospital in GhanaBloodless obstetric surgery in University of Calabar Teaching Hospital (UCTH)Innovative strategies to improve the quality of surgical care services in Abim Hospital in UgandaInnovative mobile solutions for ambulances in low-resource settingsCost-effectiveness analysis of cesarean delivery rates and accessCapacity and averted surgical burden in the …