How are we going to treat Chinese people during the pandemic? Media cultivation of intergroup threat and blame.

This study integrates cultivation and intergroup threat theories to examine media cultivation effects during the COVID-19 pandemic. We argue that U.S. media have consistently portrayed China as a threat and target of blame. The cultivation of media has thus resulted in perceived threat of and blame on Chinese people for the COVID-19 pandemic. Results of a cross-sectional survey in two samples (MTurk: N = 375; college: N = 566) showed that the amount of media consumption predicted stronger perceptions that Chinese people were a health threat, and also predicted blame on Chinese people for the COVID-19 outbreak. Threat perception and blame were further associated with support of media content that derogated China, stronger intentions to attack, and weaker intentions to help Chinese people. The findings have profound implications for intergroup threat and cultivation research, and practical importance for intergroup relations, especially when the global community finds itself in a public crisis.

Internet-of-Things-Enabled Data Fusion Method for Sleep Healthcare Applications.

The Internet of Medical Things (IoMT) aims to exploit the Internet-of-Things (IoT) techniques to provide better medical treatment scheme for patients with smart, automatic, timely, and emotion-aware clinical services. One of the IoMT instances is applying IoT techniques to sleep-aware smartphones or wearable devices' applications to provide better sleep healthcare services. As we all know, sleep is vital to our daily health. What is more, studies have shown a strong relationship between sleep difficulties and various diseases such as COVID-19. Therefore, leveraging IoT techniques to develop a longer lifetime sleep healthcare IoMT system, with a tradeoff between data transferring/processing speed and battery energy efficiency, to provide longer time services for bad sleep condition persons, especially the COVID-19 patients or survivors, is a meaningful research topic. In this study, we propose an IoT-enabled sleep data fusion networks (SDFN) module with a star topology Bluetooth network to fuse data of sleep-aware applications. A machine learning model is built to detect sleep events through an audio signal. We design two data reprocessing mechanisms running on our IoT devices to alleviate the data jam problem and save the IoT devices' battery energy. The experiments manifest that the presented module and mechanisms can save the energy of the system and alleviate the data jam problem of the device.

Predicting Intentions to Vaccinate against COVID-19 and Seasonal Flu: The Role of Consideration of Future and Immediate Consequences.

Understanding the social-psychological determinants of the public's perceptions and intentions related to vaccination is key to promoting vaccination. The current study examines how individual differences in consideration of future and immediate consequences (CFC-F and CFC-I) impact risk perceptions of, and intentions to vaccinate against, COVID-19 and seasonal flu. A survey of 395 adults on Amazon Mechanical Turk during April and May of 2020 showed that CFC-F predicted vaccination intentions, whereas CFC-I did not. Moreover, CFC-F and CFC-I positively predicted affective risk perceptions, perceived susceptibility, and perceived severity of both COVID-19 and seasonal flu. Last, both CFC constructs had a positive indirect effect on vaccination intentions of COVID-19 and seasonal flu through increasing perceived severity of the corresponding disease. This study makes theoretical contributions to the CFC literature and offers valuable insights for the design of effective vaccine promotion messages.

Prognostic value of bedside lung ultrasound score in patients with COVID-19.

BACKGROUND: Bedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19. METHOD: The LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score based on B-lines, lung consolidation and pleural line abnormalities was evaluated. RESULTS: The median time from admission to LUS examinations was 7 days (interquartile range [IQR] 3-10). Patients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10-20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI] 1.02 ~ 1.08; P < 0.001; Akaike information criterion [AIC] = 272; C-index = 0.903) or as a categorical variable (HR 10.76, 95% CI 2.75 ~ 42.05; P = 0.001; AIC = 272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model (AIC = 286; C-index = 0.866). An LUS score cut-off > 12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively. CONCLUSIONS: The LUS score devised by our group performs well at predicting adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.