Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China.

The human social and behavioral activities play significant roles in the spread of COVID-19. Social-distancing centered non-pharmaceutical interventions (NPIs) are the best strategies to curb the spread of COVID-19 prior to an effective pharmaceutical or vaccine solution. This study investigates various social-distancing measures' impact on the spread of COVID-19 using advanced global and novel local geospatial techniques. Social distancing measures are acquired through website analysis, document text analysis, and other big data extraction strategies. A spatial panel regression model and a newly proposed geographically weighted panel regression model are applied to investigate the global and local relationships between the spread of COVID-19 and the various social distancing measures. Results from the combined global and local analyses confirm the effectiveness of NPI strategies to curb the spread of COVID-19. While global level strategies allow a nation to implement social distancing measures immediately at the beginning to minimize the impact of the disease, local level strategies fine tune such measures based on different times and places to provide targeted implementation to balance conflicting demands during the pandemic. The local level analysis further suggests that implementing different NPI strategies in different locations might allow us to battle unknown global pandemic more efficiently.

Redox-Mediated Artificial Non-Enzymatic Antioxidant MXene Nanoplatforms for Acute Kidney Injury Alleviation.

Acute kidney injury (AKI), as a common oxidative stress-related renal disease, causes high mortality in clinics annually, and many other clinical diseases, including the pandemic COVID-19, have a high potential to cause AKI, yet only rehydration, renal dialysis, and other supportive therapies are available for AKI in the clinics. Nanotechnology-mediated antioxidant therapy represents a promising therapeutic strategy for AKI treatment. However, current enzyme-mimicking nanoantioxidants show poor biocompatibility and biodegradability, as well as non-specific ROS level regulation, further potentially causing deleterious adverse effects. Herein, the authors report a novel non-enzymatic antioxidant strategy based on ultrathin Ti3 C2 -PVP nanosheets (TPNS) with excellent biocompatibility and great chemical reactivity toward multiple ROS for AKI treatment. These TPNS nanosheets exhibit enzyme/ROS-triggered biodegradability and broad-spectrum ROS scavenging ability through the readily occurring redox reaction between Ti3 C2 and various ROS, as verified by theoretical calculations. Furthermore, both in vivo and in vitro experiments demonstrate that TPNS can serve as efficient antioxidant platforms to scavenge the overexpressed ROS and subsequently suppress oxidative stress-induced inflammatory response through inhibition of NF-κB signal pathway for AKI treatment. This study highlights a new type of therapeutic agent, that is, the redox-mediated non-enzymatic antioxidant MXene nanoplatforms in treatment of AKI and other ROS-associated diseases.

The Impact of Risk Cognitive and Characteristics of Mobile Phone Using on the Sleep Quality of College Students in China during COVID-19 (preprint)

Objectives: This study aimed to assess the influence of risk cognitive and characteristics of mobile phones using on sleep quality during the COVID-19 epidemic.Methods: We used the Pittsburgh Sleep Quality Index (PSQI), mobile phone use characteristics and a mobile phone use risk cognitive questionnaire, which was answered by 1207 college students. The data were statistically analyzed with SPSS 21.0 software.Results: There were significant differences in the general and poor sleep quality groups (p=0.013 and 0.037, respectively) between before and during the COVID-19 period. In the PSQI scores there were significant differences of the participants between before and during COVID-19 period with respect to dimensions other than sleep quality. Generalized linear regression analysis showed that the “pros and cons” (p=0.007) of mobile phone use for the items “How often do you take a break during use time?” (p=0.003), “Will subjectively increase the distance between the screen and the eyes?” (p=0.003), “Daily accumulated use time (hours)” (p=0.003) and “use time before bed with the lights off (hours)” (p<0.001) were significantly correlated with sleep quality.Conclusions: Risk cognitive and characteristics of mobile phone using influence sleep quality during the COVID-19 epidemic.

Learning from Large-Scale Wearable Device Data for Predicting the Epidemic Trend of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has triggered a new response involving public health surveillance. The popularity of personal wearable devices creates a new opportunity for tracking and precaution of spread of such infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. In addition to a physiological anomaly detection algorithm defined based on data from wearable devices, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. Four models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The anonymised sensor data from approximately 1.3 million wearable device users are used for model verification. Our experiment's results indicate that the prediction models can be utilized to alert to an outbreak of COVID-19 in advance, which suggests there is potential for a health surveillance system utilising wearable device data.