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BackgroundThe COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people’s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences. ObjectiveThis study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese. MethodsWe used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19–related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures. ResultsWe identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak’s seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior. ConclusionsBetween the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

BackgroundNeonatal resuscitation involves a complex sequence of actions to establish an infant’s cardiorespiratory function at birth. Many of these responses, which identify the best action sequence in each situation, are taught as part of the recurrent Neonatal Resuscitation Program training, but they have a low incidence in practice, which leaves health care providers (HCPs) less prepared to respond appropriately and efficiently when they do occur. Computer-based simulators are increasingly used to complement traditional training in medical education, especially in the COVID-19 pandemic era of mass transition to digital education. However, it is not known how learners’ attitudes toward computer-based learning and assessment environments influence their performance. ObjectiveThis study explores the relation between HCPs’ attitudes toward a computer-based simulator and their performance in the computer-based simulator, RETAIN (REsuscitation TrAINing), to uncover the predictors of performance in computer-based simulation environments for neonatal resuscitation. MethodsParticipants were 50 neonatal HCPs (45 females, 4 males, 1 not reported; 16 respiratory therapists, 33 registered nurses and nurse practitioners, and 1 physician) affiliated with a large university hospital. Participants completed a demographic presurvey before playing the game and an attitudinal postsurvey after completing the RETAIN game. Participants’ survey responses were collected to measure attitudes toward the computer-based simulator, among other factors. Knowledge on neonatal resuscitation was assessed in each round of the game through increasingly difficult neonatal resuscitation scenarios. This study investigated the moderating role of mindset on the association between the perceived benefits of understanding the terminology used in the computer-based simulator, RETAIN, and their performance on the neonatal resuscitation tasks covered by RETAIN. ResultsThe results revealed that mindset moderated the relation between participants’ perceived terminology used in RETAIN and their actual performance in the game (F3,44=4.56, R2=0.24, adjusted R2=0.19; P=.007; estimate=–1.19, SE=0.38, t44=–3.12, 95% CI –1.96 to –0.42; P=.003). Specifically, participants who perceived the terminology useful also performed better but only when endorsing more of a growth mindset; they also performed worse when endorsing more of a fixed mindset. Most participants reported that they enjoyed playing the game. The more the HCPs agreed that the terminology in the tutorial and in the game was accessible, the better they performed in the game, but only when they reported endorsing a growth mindset exceeding the average mindset of all the participants (F3,44=6.31, R2=0.30, adjusted R2=0.25; P=.001; estimate=–1.21, SE=0.38, t44=−3.16, 95% CI –1.99 to –0.44; P=.003). ConclusionsMindset moderates the strength of the relationship between HCPs’ perception of the role that the terminology employed in a game simulator has on their performance and their actual performance in a computer-based simulator designed for neonatal resuscitation training. Implications of this research include the design and development of interactive learning environments that can support HCPs in performing better on neonatal resuscitation tasks.

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

Simarteriviruses (order Nidovirales, family Arteriviridae, subfamily Simarterivirinae) are genetically diverse viruses that naturally infect cercopithecoid monkeys throughout sub-Saharan Africa. Many divergent simarteriviruses can infect macaques, although severity of disease ranges [...]