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1.
Epidemiologia ; 2(3):315-324, 2021.
Article in English | MDPI | ID: covidwho-1341667

ABSTRACT

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

2.
ArXiv; 2020.
Preprint | ArXiv | ID: ppcovidwho-476

ABSTRACT

As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that maand measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

3.
Preprint in English | medRxiv | ID: ppmedrxiv-20171371

ABSTRACT

The World Health Organization has declared SARS-CoV-2 virus outbreak a world-wide pandemic. Individuals infected by the virus exhibited different degrees of symptoms, the basis of which remains largely unclear. Currently, though convalescent individuals have been shown with both cellular and humoral immune responses, there is very limited understanding on the immune responses, especially adaptive immune responses, in patients with severe COVID-19. Here, we examined 10 blood samples from COVID-19 patients with acute respiratory distress syndrome (ARDS). The majority of them (70%) mounted SARS-CoV-2-specific humoral immunity with production of neutralizing antibodies. However, compared to healthy controls, the percentages and absolute numbers of both NK cells and CD8+ T cells were significantly reduced, accompanied with decreased IFN{gamma} expression in CD4+ T cells in peripheral blood from severe patients. Most notably, we failed in detecting SARS-CoV-2-specific IFN{gamma} production by peripheral blood lymphocytes from these patients. Our work thus indicates that COVID-19 patients with severe symptoms are associated with defective cellular immunity, which not only provides insights on understanding the pathogenesis of COVID-19, but also has implications in developing an effective vaccine to SARS-CoV-2.

4.
Preprint in English | medRxiv | ID: ppmedrxiv-20021584

ABSTRACT

BackgroundSevere ill patients with 2019 novel coronavirus (2019-nCoV) infection progressed rapidly to acute respiratory failure. We aimed to select the most useful prognostic factor for severe illness incidence. MethodsThe study prospectively included 61 patients with 2019-nCoV infection treated at Beijing Ditan Hospital from January 13, 2020 to January 31, 2020. Prognostic factor of severe illness was selected by the LASSO COX regression analyses, to predict the severe illness probability of 2019-CoV pneumonia. The predictive accuracy was evaluated by concordance index, calibration curve, decision curve and clinical impact curve. ResultsThe neutrophil-to-lymphocyte ratio (NLR) was identified as the independent risk factor for severe illness in patients with 2019-nCoV infection. The NLR had a c-index of 0.807 (95% confidence interval, 0.676-0.38), the calibration curves fitted well, and the decision curve and clinical impact curve showed that the NLR had superior standardized net benefit. In addition, the incidence of severe illness was 9.1% in age [≥] 50 and NLR < 3.13 patients, and half of patients with age [≥] 50 and NLR [≥] 3.13 would develop severe illness. Based on the risk stratification of NLR with age, the study developed a 2019-nCoV pneumonia management process. ConclusionsThe NLR was the early identification of risk factors for 2019-nCoV severe illness. Patients with age [≥] 50 and NLR [≥] 3.13 facilitated severe illness, and they should rapidly access to intensive care unit if necessary.

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