Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low-or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI).Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression.

COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded more than 210 countries around the world and as of February 18 th , 2021, just after a year has passed, a total of 110,533,973 confirmed cases of COVID-19 were reported and its death toll reached about 2,443,091. COVID-19 is a new member of the family of corona viruses, its nature, behaviour, transmission, spread, prevention, and treatment are to be investigated. Generally, a huge amount of data is accumulating regarding the COVID-19 pandemic, which makes hot research topics for machine learning researchers. However, the panicked world’s population is asking when the COVID-19 will be over? This study considered machine learning approaches to predict the spread of the COVID-19 in many countries. The experimental results of the proposed model showed that the overall R2 is 0.99 from the perspective of confirmed cases. A machine learning model has been developed to predict the estimation of the spread of the COVID-19 infection in many countries and the expected period after which the virus can be stopped. Globally, our results forecasted that the COVID-19 infections will greatly decline during the first week of September 2021 when it will be going to an end shortly afterward.

SummaryWe performed a series of ELISAs to evaluate the diagnostic significance of two Schistosoma mansoni proteins, Sm31 (cysteine proteinase, cathepsin B) and Sm32 (asparaginyl endopeptidase). Our study populations were chosen from two villages in an endemic area close to Alexandria. Using fusion proteins MS2-Sm31 and MS2-Sm32 as antigens, 70% and 78.9%, respectively, of patient sera from 134 parasitologically confirmed cases reacted positively. The percentage of seropositivity increased to 84.5% when parasite-derived proteins Sm31 and Sm32 were used. The serum levels of antibodies to these two proteins in recombinant or native forms do not correlate with intensity of infection and hence are detected even when egg counts are low, which makes proteins Sm31 and Sm32 useful antigens in the identification of S. mansoni infected cases, particularly in endemic areas in Egypt.keywords Schistosomiasis mansoni, diagnosis, ELISA correspondence Prof.

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COVID-19 illness has been recognized as an International Public Health Emergency (PHEIC). According to the World Health Organization (WHO), more than 200 countries worldwide have been impacted by the COVID-19. More than 4.7 M people in the globe have lost their lives due to the coronavirus pandemic. Governments and hospitals have taken several actions and measures to prevent the further spread of infectious diseases, protect all people, and minimize illness and death rates. Artificial Intelligence (AI) and the Internet of Things (IoT) have great potential to create prevailing tools for battling COVID-19. Several technologies such as deep learning, machine learning, natural language processing, and the Internet of Things have been used to properly address healthcare concerns, including diagnosis, drug and vaccine development, vaccine distribution, sentiment analysis, and fake news identification regarding COVID-19 reviews. The primary goal of this chapter is to focus on the potential of Artificial Intelligence (AI) and the Internet of Things (IoT). This chapter aims to provide a comprehensive overview of different approaches of AI and IoT related to combating COVID-19. In addition, the chapter discusses several actions and measurements that governments have used to prevent the spread of COVID-19. This chapter covers all methodologies applied to forecast the acceptance and the demand of the COVID-19 vaccine.

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Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research topics for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; 1) when this COVID-19 pandemic will be over? and 2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic?. This research developed a predictive model that can estimate the expected period of time that the virus can possibly stopped and the risk of a second rebound of COVID-19 pandemic. Therefore, this study considered SARIMA model to predict the spread of the virus on several selected countries and is used for pandemic life cycle and end date predictions. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The advantages of this study are that it helps the governments in making decisions and planning now for the future, reduces anxiety and prepares the mentality of people for the next phases of the pandemic. The most striking finding to emerge from this experimental and simulation study is that the proposed algorithm show that the expected COVID-19 infections for the top countries of highest number of confirmed case will slowdown in October, 2020. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time, if the current taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, what are ultimately required are the flexibility, robustness and resilience to cope up the unexpected future events and scenarios.