HLA-B*13:01 was associated with the development of the dapsone hypersensitivity syndrome among patients with leprosy. (Funded by the National Natural Science Foundation of China and others.).

The current pandemic of the coronavirus disease (COVID-19) has highlighted the importance of rapid control of the transmission of infectious diseases. This is particularly important for COVID-19, where many individuals are asymptomatic or have only mild symptoms but can still spread the disease. Current systems for controlling transmission rely on patients to report their symptoms to medical professionals and be able to recall and trace all their contacts from the previous few days. This is unrealistic in the modern world. However, existing smartphone-based GPS and social media technology may provide a suitable alternative. We, therefore, developed a mini-program within the app WeChat. This analyzes data from all users and traces close contacts of all patients. This permits early tracing and quarantine of potential sources of infection. Data from the mini-program can also be merged with other data to predict epidemic trends, calculate individual and population risks, and provide recommendations for individual and population protection action. It may also improve our understanding of how the disease spreads. However, there are a number of unresolved questions about the use of smartphone data for health surveillance, including how to protect individual privacy and provide safeguards against data breaches.

Particulate matter (PM) combined with meteorological factors cause the haze, which brings inconvenience to people's daily life and deeply endanger people's health. Accumulating literature, to date, reported that PM are closely related to cardiopulmonary disease. Outpatient visits and admissions as a result of asthma and heart attacks gradually increase with an elevated concentration of PM. Owing to its special physicochemical property, the brain could be a potential target beyond the cardiopulmonary system. Possible routes of PM to the brain via a direct route or stimulation of pro-inflammatory cytokines have been reported in several documents concerning toxicity of engineered nanoparticles in rodents. Recent studies have demonstrated that PM have implications in oxidative stress, inflammation, dysfunction of cellular organelles, as well as the disturbance of protein homeostasis, promoting neuron loss and exaggerating the burden of central nervous system (CNS). Moreover, the smallest particles (nano-sized particles), which were involved in inflammation, reactive oxygen species (ROS), microglial activation and neuron loss, may accelerate the process of the neurodevelopmental disorder and neurodegenerative disease. Potential or other undiscovered mechanisms are not mutually exclusive but complementary aspects of each other. Epidemiology studies have shown that exposure to PM could bring about neurotoxicity and play a significant role in the etiology of CNS disease, which has been gradually corroborated by in vivo and in vitro studies. This review highlights research advances on the health effects of PM with an emphasis on neurotoxicity. With the hope of enhancing awareness in the public and calling for prevention and protective measures, it is a critical topic that requires proceeding exploration. Copyright © 2017 John Wiley & Sons, Ltd.

Indoor physical environments appear to influence learning efficiency nowadays. For improvement in learning efficiency, environmental scenarios need to be designed when occupants engage in different learning tasks. However, how learning efficiency is affected by indoor physical environment based on task types are still not well understood. The present study aims to explore the impacts of three physical environmental factors (i.e., temperature, noise, and illuminance) on learning efficiency according to different types of tasks, including perception, memory, problem-solving, and attention-oriented tasks. A 3 × 4 × 3 full factorial design experiment was employed in a university classroom with 10 subjects recruited. Environmental scenarios were generated based on different levels of temperature (17 °C, 22 °C, and 27 °C), noise (40 dB(A), 50 dB(A), 60 dB(A), and 70 dB(A)) and illuminance (60 lx, 300 lx, and 2200 lx). Accuracy rate (AC), reaction time (RT), and the final performance indicator (PI) were used to quantify learning efficiency. The results showed ambient temperature, noise, and illuminance exerted significant main effect on learning efficiency based on four task types. Significant concurrent effects of the three factors on final learning efficiency was found in all tasks except problem-solving-oriented task. The optimal environmental scenarios for top learning efficiency were further identified under different environmental interactions. The highest learning efficiency came in thermoneutral, relatively quiet, and bright conditions in perception-oriented task. Subjects performed best under warm, relatively quiet, and moderately light exposure when recalling images in the memory-oriented task. Learning efficiency peaked to maxima in thermoneutral, fairly quiet, and moderately light environment in problem-solving process while in cool, fairly quiet and bright environment with regard to attention-oriented task. The study provides guidance for building users to conduct effective environmental intervention with simultaneous controls of ambient temperature, noise, and illuminance. It contributes to creating the most suitable indoor physical environment for improving occupants learning efficiency according to different task types. The findings could further supplement the present indoor environment-related standards or norms with providing empirical reference on environmental interactions.