Articles: coronavirus.
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Pan-immunoglobulin assays can simultaneously detect IgG, IgM and IgA directed against the receptor binding domain (RBD) of the S1 subunit of the spike protein (S) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 S1-RBD Ig). In this work, we aim to evaluate a quantitative SARS-CoV-2 S1-RBD Ig electrochemiluminescence immunoassay (ECLIA) regarding analytical, diagnostic, operational and clinical characteristics. Our work takes the form of a population-based study in the principality of Liechtenstein, including 125 cases with clinically well-described and laboratory confirmed SARS-CoV-2 infection and 1159 individuals without evidence of coronavirus disease 2019 (COVID-19). ⋯ A substantial proportion of individuals without evidence of past SARS-CoV-2 infection displayed non-S1-RBD antibody reactivities (248/1159, i.e., 21.4%, 95% CI, 19.1-23.4). In conclusion, a quantitative SARS-CoV-2 S1-RBD Ig assay offers favorable and sustained assay characteristics allowing the determination of quantitative associations between clinical characteristics (e.g., disease severity, smoking or fever) and antibody levels. The assay could also help to identify individuals with antibodies of non-S1-RBD specificity with potential clinical cross-reactivity to SARS-CoV-2.
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BMC pulmonary medicine · Dec 2020
COVID-19 related concerns of people with long-term respiratory conditions: a qualitative study.
The COVID-19 pandemic is having profound psychological impacts on populations globally, with increasing levels of stress, anxiety, and depression being reported, especially in people with pre-existing medical conditions who appear to be particularly vulnerable. There are limited data on the specific concerns people have about COVID-19 and what these are based on. ⋯ The COVID-19 pandemic is having profound psychological impacts. The concerns we identified largely reflect contextual factors, as well as their subjective experience of the current situation. Hence, key approaches to reducing these concerns require changes to the reality of their situation, and are likely to include (1) helping people optimise their health, limit risk of infection, and access necessities; (2) minimising the negative experience of disease where possible, (3) providing up-to-date, accurate and consistent information, (4) improving the government and healthcare response.
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A lower respiratory tract infection caused by novel coronavirus termed as Corona Virus Disease (COVID-19) was first identified in China and subsequently took the form of pandemic. Studies on disease outbreak in the past and recent COVID-19 outbreak have demonstrated increased psychological distress and adverse impacts on mental health and psychological wellbeing of people. However, the impact of COVID-19 on psychological wellbeing of people in Nepal hasn't been studied adequately. So, this paper aims to report the findings from a social media survey on psychological impacts of COVID-19 in Nepal. ⋯ The study has shown high prevalence of psychological distress amongst the Nepalese respondents following COVID-19 outbreak. Appropriate mental health and psychosocial support response needs to be instituted to adequately respond to psychological impacts of the epidemic.
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Int J Environ Res Public Health · Dec 2020
Alcohol Consumption during the COVID-19 Pandemic: A Cross-Sectional Survey of US Adults.
Emerging but limited evidence suggests that alcohol consumption has increased during the COVID-19 pandemic. This study assessed: (1) whether drinking behaviors changed during the pandemic; and, (2) how those changes were impacted by COVID-19-related stress. We conducted a cross-sectional online survey with a convenience sample of U. ⋯ Additionally, 60% reported increased drinking but 13% reported decreased drinking, compared to pre-COVID-19. Reasons for increased drinking included increased stress (45.7%), increased alcohol availability (34.4%), and boredom (30.1%). Participants who reported being stressed by the pandemic consumed more drinks over a greater number of days, which raises concerns from both an individual and public health perspective.
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J. Med. Internet Res. · Dec 2020
Detection of Hate Speech in COVID-19-Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach.
The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. ⋯ The COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19-related tweets in the Arab region.