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.

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.