Internet Rumors During the COVID-19 Pandemic: Dynamics of Topics and Public Psychologies.

The capturing of social opinions, especially rumors, is a crucial issue in digital public health. With the outbreak of the COVID-19 pandemic, the discussions of related topics have increased exponentially in social media, with a large number of rumors on the Internet, which highly impede the harmony and sustainable development of society. As human health has never suffered a threat of this magnitude since the Internet era, past studies have lacked in-depth analysis of rumors regarding such a globally sweeping pandemic. This text-based analysis explores the dynamic features of Internet rumors during the COVID-19 pandemic considering the progress of the pandemic as time-series. Specifically, a Latent Dirichlet Allocation (LDA) model is used to extract rumor topics that spread widely during the pandemic, and the extracted six rumor topics, i.e., "Human Immunity," "Technology R&D," "Virus Protection," "People's Livelihood," "Virus Spreading," and "Psychosomatic Health" are found to show a certain degree of concentrated distribution at different stages of the pandemic. Linguistic Inquiry and Word Count (LIWC) is used to statistically test the psychosocial dynamics reflected in the rumor texts, and the results show differences in psychosocial characteristics of rumors at different stages of the pandemic progression. There are also differences in the indicators of psychosocial characteristics between truth and disinformation. Our results reveal which topics of rumors and which psychosocial characteristics are more likely to spread at each stage of progress of the pandemic. The findings contribute to a comprehensive understanding of the changing public opinions and psychological dynamics during the pandemic, and also provide reference for public opinion responses to major public health emergencies that may arise in the future.

Features of α-HBDH in COVID-19 patients: A cohort study.

BACKGROUND: Coronavirus disease-2019 (COVID-19) has spread all over the world and brought extremely huge losses. At present, there is a lack of study to systematically analyze the features of hydroxybutyrate dehydrogenase (α-HBDH) in COVID-19 patients. METHODS: Electronic medical records including demographics, clinical manifestation, α-HBDH results and outcomes of all included patients were extracted. RESULTS: α-HBDH in COVID-19 group was higher than that in excluded group (p < 0.001), and there was no significant difference in α-HBDH before and after the exclusion of 5 patients with comorbidity in heart or kidney (p = 0.671). In COVID-19 group, the α-HBDH value in ≥61 years old group, severe group, and critical group, death group all increased at first and then decreased, while no obvious changes were observed in other groups. And there were significant differences of the α-HBDH value among different age groups (p < 0.001), clinical type groups (p < 0.001), and outcome groups (p < 0.001). The optimal scale regression model showed that α-HBDH value (p < 0.001) and age (p < 0.001) were related to clinical type. CONCLUSIONS: α-HBDH was increased in COVID-19 patients, obviously in ≥61 years old, death and critical group, indicating that patients in these three groups suffer from more serious heart and kidney and other tissues and organs damage, higher α-HBDH value, and risk of death. The difference between death and survival group in early stage might provide a approach to judge the prognosis. The accuracy of the model to distinguish severe/critical type and other types was 85.84%, suggesting that α-HBDH could judge the clinical type accurately.