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Despite the controversy about the benefits of dietary supplements in treating or preventing COVID-19, their use has increased worldwide even with the introduction of relevant vaccines. Thus, this study aimed to investigate the perception of the Middle Eastern Arab public of dietary supplements as prophylactic or therapeutic agents against COVID-19, and their consumption during the second wave of the COVID-19 pandemic.
A validated, pilot tested online survey was distributed through social networking platforms in Lebanon, the Kingdom of Saudi Arabia, Palestine, Jordan, and the United Arab Emirates. Responses underwent various statistical analyses.
A total of 2,100 responses were included. Around 44% of participants reported changes in their dietary behavior during COVID-19, and 70% believed that healthy habits may prevent the infection. Moreover, 21% believed that dietary supplements surely protect against COVID-19 and 45% thought they aid in treating it. Users of supplements during the second wave of the pandemic counted for 47%, who declared they were influenced by the media, healthcare providers, or close contacts. The most used supplements included Vitamins C and D and zinc. Only 34% of participants read supplement leaflets. The use of supplements was significantly correlated with being female and exercising, as revealed by the odds ratio and logistic regression analysis.
In line with other areas of the world, the use of dietary supplements in the Middle East against COVID-19 is not evidence-based. Competent health authorities should play their role in spreading sound awareness among the public regarding this issue.
In line with other areas of the world, the use of dietary supplements in the Middle East against COVID-19 is not evidence-based. Competent health authorities should play their role in spreading sound awareness among the public regarding this issue.Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. Tyloxapol cell line The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.Today is a reality that the novel coronavirus SARS-Cov-2 has become a global pandemic. For this reason, the study of real microscopic images of this coronavirus is of great importance, as it allows us to carry out a more precise research on it. However, as we pointed out in a former paper as reported by Roberto Rodríguez (SARS-CoV-2 Enhancement and Segmentation of High-Resolution Microscopy Images. Part I", Sent to Signal, Image and Video Processing Video Processing, Springer, New York, 2020), many times these microscopic images present some blurring problems, which are always susceptible to be improved. The aim of this work is to carry out a theoretical analysis of the proposed algorithms to enhancement and segmentation of these microscopic images, which is important for the design and development of future algorithms before new epidemics.Coronavirus disease 2019 or COVID-19, starting from Wuhan, China, in December 2019, is a pandemic situation affecting millions worldwide and has exerted a huge burden on healthcare infrastructure. Therefore, there is an urgent need to understand the molecular mechanisms underlying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and design novel effective therapeutic strategies for combating this pandemic. In this regard, special attention has been paid to the exosomes. These nanoparticles are extracellular vesicles with critical function in the pathogenesis of several diseases including viral sepsis. Therefore, they may be involved in the pathogenesis of COVID-19 infection and also may be a way for transferring viral components and infecting other neighbor cells. Exosomes also can be considered as a therapeutic strategy for treating COVID-19 patients or used as a carrier for delivering effective therapeutic agents. Therefore, in this review, we discussed the biogenesis and contents of exosomes, their function in viral infection, and their potential as a therapeutic candidate in treating COVID-19.Polyethylene terephthalate is a common plastic in many products such as viscose rayon for clothing, and packaging material in the food and beverage industries. Polyethylene terephthalate has beneficial properties such as light weight, high tensile strength, transparency and gas barrier. Nonetheless, there is actually increasing concern about plastic pollution and toxicity. Here we review the properties, occurrence, toxicity, remediation and analysis of polyethylene terephthalate as macroplastic, mesoplastic, microplastic and nanoplastic. Polyethylene terephthalate occurs in groundwater, drinking water, soils and sediments. Plastic uptake by humans induces diseases such as reducing migration and proliferation of human mesenchymal stem cells of bone marrow and endothelial progenitor cells. Polyethylene terephthalate can be degraded by physical, chemical and biological methods.The present study examined latent class cluster group patterns based on measures of depression and anxiety symptom severity and alcohol consumption during the COVID-19 pandemic. Hypothesized correlates with latent class cluster groups including quarantining, self-isolation, suicidal ideations, sitting hours per day, and physical activity (vigorous intensity exercise in minutes per week) were examined. The delimited participant sample consisted of 606 university young adults 18 to 25 years of age (M = 21.24 ± 1.62). Latent cluster analysis (LCA) modeled patterns of depression and anxiety symptom severity and alcohol consumption during the COVID-19 pandemic. Between group analysis and multinomial logistic regression analysis were used to examine relationships between latent class clusters and correlates including quarantining, self-isolation, suicidal ideations, sitting hours per day, and physical activity (vigorous intensity exercise in minutes per week). LCA results showed that six latent cluster groups provid with cluster group membership. Findings from this study demonstrate associations between COVID-19 public health restrictions, suicidal ideations, and declines in mental health and increases in alcohol consumption among young adult university students.Identifying risk and protective factors for suicidal ideation during adolescence is essential for suicide prevention. One potential risk factor is body dissatisfaction which appears to peak during adolescence. The present study investigated the self-compassion buffering effects in the relationship between body dissatisfaction and suicidal ideation. A convenience sample comprising 580 adolescents (mean age 16.35 years; SD = .87; range 14-18 years) was recruited from public schools. The results indicated a strong positive association between body dissatisfaction and suicidal ideation (Cohen's f 2 = .25). The association was significantly moderated by the self-compassion (β =  - .16, SE = .04, p = .01, t  = 2.4.34, .95% CI [- .16, - .01]). Structural equation modeling analysis showed that the lack of self-kindness was associated with a moderate suicidal ideation level (Cohen's f 2 = .14). Also, higher levels of self-judgment predicted suicidal ideation with a moderate to large effect size (Cohen's f 2 = .28). The findings suggest that therapeutic programs designed to develop self-compassion should be implemented to reduce the risk of suicidal ideation among adolescents with body dissatisfaction. The findings empirically show that a higher degree of self-judgment is strongly associated with suicidal thoughts among adolescents, which must be systematically addressed in clinical studies on suicidal risk.
The online version contains supplementary material available at 10.1007/s11469-021-00727-4.
The online version contains supplementary material available at 10.1007/s11469-021-00727-4.The COVID-19 pandemic has impacted nurses transitioning to practice in a variety of ways over the past 2 years. Analysis from the Versant Database comparing new graduate nurses (NGNs) from 2018-2021 revealed a widened practice gap for NGNs in these specialty areas of practice critical care, perinatal, and emergency. Additionally, NGNs achieved 100% competency validation sooner in 2020-2021. The analysis also revealed greater diversity of NGNs who participated in a transition to practice program in 2020-2021. Based on these findings, this article proposes recommendations for nurse leaders to consider as NGNs transition into the workforce.