Linsiegel5578

Viral infections pose significant health challenges globally by affecting millions of people worldwide and consequently resulting in a negative impact on both socioeconomic development and health. Corona virus disease 2019 (COVID-19) is a clear example of how a virus can have a global impact in the society and has demonstrated the limitations of detection and diagnostic capabilities globally. Another virus which has posed serious threats to world health is the human immunodeficiency virus (HIV) which is a lentivirus of the retroviridae family responsible for causing acquired immunodeficiency syndrome (AIDS). Even though there has been a significant progress in the HIV biosensing over the past years, there is still a great need for the development of point of care (POC) biosensors that are affordable, robust, portable, easy to use and sensitive enough to provide accurate results to enable clinical decision making. The aim of this study was to present a proof of concept for detecting HIV-1 pseudoviruses by usinferentiate between substrates with and without the HIV pseudoviruses. Raman spectroscopy confirmed the presence of biomolecules related to HIV and therefore this system has potential in HIV biosensing applications.The novel coronavirus outbreak has reported to be rapidly spreading across the countries and becomes a foremost community health alarm. At present, no vaccine or specific drug is on hand for the treatment of this infectious disease. This review investigates the drugs, which are being evaluated and found to be effective against nCOVID-19 infection. A thorough literature search was performedon the recently published research papers in between January 2020 to May 2020, through various databases like "Science Direct", "Google Scholar", "PubMed","Medline", "Web of Science", and "World Health Organization (WHO)". We reviewed and documented the information related with the current and future aspects for the management and cure of COVID-19. As of 21st July 2020 a total of 14,562,550 confirmed cases of coronavirus and 607,781 deaths have been reported world-wide. The main clinical feature of COVID-19 ranges from asymptomatic disease to mild lower respiratory tract illness to severe pneumonia, acute lung injury, acute respiratory distress syndrome (ARDS), multiple organ dysfunction, and death. The drugs at present used in COVID-19 patients and ongoing clinical trials focusing on drug repurposing of various therapeutic classes of drug e.g. antiviral, anti-inflammatory and/or immunomodulatory drugs along with adjuvant/supportive care. Many drugs on clinical trials shows effective results on preliminary scale and now used currently in patients. Adjuvant/supportive care therapy are used in patients to get the best results in order to minimize the short and long-term complications. However, further studies and clinical trials are needed on large scale of population to reach any firm conclusion in terms of its efficacy and safety.The purpose of this research is to test the ex-post cost structure effects in horizontal mergers and acquisitions (M&A). Our proposed methodology quantifies cost structure effects empirically to inform competition policy around M&As in the airline industry. The results show that horizontal M&As involving unprofitable firms significantly reduce variable costs and increase fixed costs ex-post. M&As involving only profitable firms show no significant impact on the cost structure. We offer support that the ex-post cost structure effects of airline M&As depend on the incentives to improve efficiency, reflected in the ex-ante performance of the merging firms. We further argue that market behavior may not just depend on market structure but cost structures too, all of which should be accounted for in antitrust decision making and regulation around airline M&As.Focusing on deterministic AIDS model proposed by Hyman (2000) and the detailed data from the World Health Organization (WHO), there are three stages of AIDS process which are described as Acute infection period, Asymptomatic phase and AIDS stage. Our paper is therefore concerned with a stochastic staged progression AIDS model with staged treatment. In view of the complexity of random disturbances, we reasonably take second-order perturbation into consideration for realistic sense. By means of our creative transformation technique and stochastic Lyapunov method, a critical value R 0 H > 1 is firstly obtained for the existence and uniqueness of ergodic stationary distribution to the stochastic system. Not only does it respectively reveal the corresponding dynamical effects of the linear and second-order perturbations to the model, but the unified form of second-order and linear fluctuations is derived. Next, some sufficient conditions about extinction of stochastic system are established in view of the basic reproduction number R 0 . Finally, some examples and numerical simulations are introduced to illustrate our analytical results. In addition, some advantages of our new method and theory are highlighted by comparison with other existing results at the end of this paper.One of the key indicators used in tracking the evolution of an infectious disease is the reproduction number. This quantity is usually computed using the reported number of cases, but ignoring that many more individuals may be infected (e.g. asymptomatic carriers). We develop a Bayesian procedure to quantify the impact of undetected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmental model. see more It is applied to the COVID-19 outbreak in eight different countries and all Italian regions, showing that the effect of undetected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten.Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus's infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.In the Groninger Veenkoloniën, a former peat region in the northeast of the Netherlands, persistent poverty is more prevalent compared to other rural regions in the country. Grounded in participant observations and supplemented by in-depth interviews capturing the social life history of 21 participants, this paper paints a detailed picture of the social networks and class practices of those experiencing persisting poverty in the examined town and surrounding region. In addition, we explore the relations between the rural context and lived experiences of class and poverty. Our findings highlight the complex experience as well as spatial embeddedness of persisting poverty. We find that, although the specific circumstances to which the participants are exposed vary greatly, the repercussions in terms of the characteristics of their social networks and practices are very similar. In general, the social networks of participants are fragmented and small, tightly knit, and characterized by clear power imbalances. The most formative experiences that result in the isolation of networks of poor are found to occur in the home and family situation during childhood years. We argue that poverty and the region's history are intricately interwoven resulting in a socio-spatial stigma which in turn contributes to the persistent and intergenerational character of poverty in the rural context of our study. Due to the long history of stigmatization, dismantling the socio-spatial stigma attached to the Groninger Veenkoloniën will presumably take multiple generations.As widely known, the basic reproduction number plays a key role in weighing birth/infection and death/recovery processes in several models of population dynamics. In this general setting, its characterization as the spectral radius of next generation operators is rather elegant, but simultaneously poses serious obstacles to its practical determination. In this work we address the problem numerically by reducing the relevant operators to matrices through a pseudospectral collocation, eventually computing the sought quantity by solving finite-dimensional eigenvalue problems. The approach is illustrated for two classes of models, respectively from ecology and epidemiology. Several numerical tests demonstrate experimentally important features of the method, like fast convergence and influence of the smoothness of the models' coefficients. Examples of robust analysis of instances of specific models are also presented to show potentialities and ease of application.