PCR Amplification and Sequencing Analysis of Full-Length Turkey Coronavirus Spike Gene

Turkey coronaviral enteritis caused by turkey coronavirus (TCoV) continues to infect turkey flocks, resulting in significant economic loss. Determining and understanding genetic relationships among different TCoV isolates or strains is important for controlling the disease. Using two-step RT-PCR assays that amplify the full length of TCoV spike (S) gene, TCoV isolates can be sequenced, analyzed, and genotyped. Described in this chapter is the protocol on PCR amplification and sequencing analysis of full-length TCoV S gene. Such protocol is useful in molecular epidemiology for establishing an effective strategy to control the transmission of TCoV among turkey flocks. Copyright © Springer Science+Business Media New York 2016

Assessing the impact of the COVID-19 pandemic on tourism workers' health and well-being in Jordan

The current study assesses the impact of the coronavirus (COVID-19) pandemic on tourism workers' health and well-being in Jordan by investigating coronavirus threats, financial impacts, resources impacts, social isolation, depression and personal control experienced by tourism workers during the COVID-19 pandemic, and how the threats and impacts of the coronavirus pandemic affect workers' health and well-being. A quantitative survey method was applied using a questionnaire. Data were collected from a sample of 400 tourism workers in Jordan. The study's findings revealed that tourism workers are financially strained, socially isolated, psychologically depressed, experiencing a lack of personal control, and have limited resources due to their dread of the pandemic and its negative impacts. It was also found that a significant negative direct effect of perceived coronavirus threats and their impacts on workers' depression and personal control had a significant influence on workers' health and well-being. The current study proposed a model of the influence of coronavirus on tourism workers' health and well-being through their personal control and depression.

Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student's emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.

Jordanians' willingness to receive heterologous prime-boost COVID-19 vaccination and vaccine boosters

OBJECTIVE: Coronavirus-2019 (COVID-19) vaccination is the game-changing approach that tops all other strategies to contain the pandemic spread. A growing interest has been raised to heterologous prime-boost and booster COVID-19 vaccination to tackle vaccine shortage and to increase the vaccine's immunogenicity. This study aimed to evaluate the willingness and acceptance of Jordanians to receive heterologous prime-boost COVID-19 vaccination and vaccine boosters. MATERIALS AND METHODS: A web-based cross-sectional study was conducted using a validated online questionnaire. Adult Jordanian participants were recruited using several social media platforms. The questionnaire link was randomly posted by researchers on public groups in Jordan. Participant's demographics, medical history, knowledge of mixed and booster COVID-19 vaccination and their willingness to receive them were obtained and analyzed. RESULTS: Approximately 50.5% and 49.3% of the respondents stated former knowledge of the mixed and booster COVID-19 vaccination, respectively. Approximately 50% of respondents acknowledged that the side effects could preclude them from taking mixed and booster vaccines, and 45.3% responded that taking a third dose of the vaccine would increase the side effects. The respondents with previous history of COVID-19 and influenza vaccination were more likely to agree on mixed vaccines compared to those not vaccinated (29.5% vs. 6.5%, p <0.0001;38.0% vs. 24.5%, p=0.0078, respectively). Moreover, both previous history of COVID-19 and seasonal influenza vaccine was an encouraging response for acceptance of mixed and booster vaccines when compared to those who did not receive the vaccines (54.5% vs. 11.3%;p<0.0001, 69.0% vs. 45.5%;p <0.0001, respectively). CONCLUSIONS: The current study showed that nearly half of the respondents were familiar with COVID-19 mixed and booster vaccine programs;however, a high percentage still expressed reticence to receive the mixed vaccines. We consider these results to emphasize the importance and need of awareness campaigns that accentuate the safety profile of such updated vaccination programs.

The Role of Big Data and Machine Learning in COVID-19

The big rise in the existence of digital data contributed to creating many good chances, especially related to corporations, institutions and firms. Also, it gives the capability to scrimp data regarding its major or area, where the countries have benefited from the analysis of big data (BD) greatly in the face of epidemics and diseases, especially COVID-19 since BD is now available everywhere around us, from official reports and scientific studies related to virology and epidemiology. The general aim of this study is to clarify how the conjunction among both BD and machine learning (ML) created huge differences in data science and a big influence on the applications related to a lot of fields chiefly in COVID-19. The method which is used in this study 'relevance tree' by identifying papers related to ML and BD, especially in COVID-19. The results have been shown that the use of reinforcement learning in analyzing BD provides effective and tremendous results, although it faces many challenges and restrictions that have been explained in detail in this study. In addition, the results showed that most of the countries in the time of Corona turned into smart cities, totally dependent on smart applications based on the analysis of BD using ML, and one of the most important applications that were circulated around the world global positioning system. In addition to the results that have been found, data privacy is one of the most important challenges facing data analysis. Consequently, it recommended future researchers to focus on studying the challenges faced by ML in analyzing medical data in the COVID-19 era.

Sedating mechanically ventilated COVID-19 patients with volatile anesthetics: insights on the last-minute potential weapons

Coronavirus Disease 2019 (COVID-19) has spread globally with the number of cases exceeding seventy million. Although trials on potential treatments of COVID-19 Acute Respiratory Distress Syndrome (ARDS) are promising, the introduction of an effective therapeutic intervention seems elusive. In this review, we explored the potential therapeutic role of volatile anesthetics during mechanical ventilation in the late stages of the disease. COVID-19 is thought to hit the human body via five major mechanisms: direct viral damage, immune overactivation, capillary thrombosis, loss of alveolar capillary membrane integrity, and decreased tissue oxygenation. The overproduction of pro-inflammatory cytokines will eventually lead to the accumulation of inflammatory cells in the lungs, which will lead to ARDS requiring mechanical ventilation. Respiratory failure resulting from ARDS is thought to be the most common cause of death in COVID-19. The literature suggests that these effects could be directly countered by using volatile anesthetics for sedation. These agents possess multiple properties that affect viral replication, immunity, and coagulation. They also have proven benefits at the molecular, cellular, and tissue levels. Based on the comprehensive understanding of the literature, short-term sedation with volatile anesthetics may be beneficial in severe stages of COVID-19 ARDS and trials to study their effects should be encouraged.