Severe acute respiratory syndrome coronavirus 2 virus-like particles induce dendritic cell maturation and modulate T cell immunity.

Dendritic cells (DCs) are professional antigen-presenting cells that play an important role in both innate and acquired immune responses against pathogens. However, the role of DCs in coronavirus disease 2019 (COVID-19) is unclear. Virus-like particles (VLPs) that structurally mimic the original virus are one of the candidates COVID-19 vaccines. In the present study, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) VLPs were used as an alternative to live virus to evaluate the interaction of the virus with DCs. The results revealed that SARS-CoV-2 VLPs induced DC maturation by augmenting cell surface molecule expression (CD80, CD86, and major histocompatibility complex class II (MHC-II)) and inflammatory cytokine production (tumor necrosis factor-α, interleukin (IL)-1ß, IL-6, and IL-12p70) in DCs via the mitogen-activated protein kinase and nuclear factor-κB signaling pathways. In addition, mature DCs induced by SARS-CoV-2 VLPs promoted T cell proliferation, which was dependent on VLPs concentration. Our results suggest that SARS-CoV-2 VLPs regulate the immune response by interacting with DCs. These findings will improve the understanding of SARS-CoV-2 pathogenesis and SARS-CoV-2 vaccine development.

The Influence of Personality and Demographic Characteristics on Aggressive Driving Behaviors in Eastern Chinese Drivers.

PURPOSE: Road safety research is important due to the large number of road traffic fatalities globally. This study investigated the influences of age, driving experience and other covariates on aggressive driving behavior. METHODS: A cross-sectional survey was conducted in Yixing City, Wuxi City, Jiangsu Province, China. Regression analysis was applied to explore the influences of age and driving experience and their interactions with other covariates on aggressive driving behavior. Two analyses methodologies were used to assess the simple effect of the interactions. Firstly, the Jamovi automatic analysis classification program was used to calculate the simple slope test. Second, the SPSS macro program was also used to calculate the simple slope test also. RESULTS: A total of 570 drivers (247 males, 282 females) participated in the survey. A negative correlation was found between age and aggressive driving behaviors, and a positive correlation was found between neuroticism and aggressive driving behaviors in the multiple regression analysis. Significant associations were also found between age, driving experience, and depression, as well as age, driving experience, and neuroticism. Simple slope tests showed that depressive symptoms could increase aggressive behaviors in the elderly and experienced drivers. When experiencing neuroticism, individuals with higher driving experience were more aggressive in driving than shorter experienced drivers. CONCLUSION: Age and neuroticism influenced aggressive driving behaviors. Veteran drivers could be aggressive drivers when experiencing depressive symptoms or neuroticism. Mobile intervention could be sent to the potentially risky drivers, which would be safe and broadly feasible to prevent aggressive driving behavior in the background of COVID-19.

Association between perceived stress and depression among medical students during the outbreak of COVID-19: The mediating role of insomnia.

BACKGROUND: The purpose of this study was to explore the association between perceived stress and depression among medical students and the mediating role of insomnia in this relationship during the COVID-19 pandemic in China. METHODS: A cross-sectional survey was conducted from March to April 2020 in medical university. Levels of perceived stress, insomnia and depression were measured using Perceived Stress Scale (PSS), Insomnia Severity Index (ISI) and Patient Health Questionnaire 9 (PHQ-9). The descriptive analyses of the demographic characteristics and correlation analyses of the three variables were calculated. The significance of the mediation effect was obtained using a bootstrap approach with SPSS PROCESS macro. RESULTS: The mean age of medical students was 21.46 years (SD=2.50). Of these medical students, 10,185 (34.3%) were male and 19,478 (65.7%) were female. Perceived stress was significantly associated with depression (ß=0.513, P < 0.001). Insomnia mediated the association between perceived stress and depression (ß=0.513, P < 0.001). The results of the non-parametric bootstrapping method confirmed the significance of the indirect effect of perceived stress through insomnia (95% bootstrap CI =0.137, 0.149). The indirect effect of insomnia accounted for 44.13% of the total variance in depression. CONCLUSIONS: These findings contribute to a better understanding of the interactive mechanisms underlying perceived stress and depression, and elucidating the mediating effects of insomnia on the association. This research provides a useful theoretical and methodological approach for prevention of depression in medical students. Findings from this study indicated that it may be effective to reduce depression among medical students by improving sleep quality and easing perceived stress.

Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic.

OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on depression and anxiety and explore the influencing factors during the COVID-19 epidemic in China. METHODS: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Depression and anxiety symptoms were assessed using Patient Health Questionnaire 9 (PHQ9) and Generalized Anxiety Disorder 7 (GAD7) respectively. Latent class analysis was performed based on depression and anxiety symptoms in medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS: In this study, three distinct subgroups were identified, namely, the poor mental health group, the mild mental health group and the low symptoms group. The number of medical students in each class is 4325, 9321 and 16,017 respectively. The multinomial logistic regression results showed that compared with the low symptoms group, the factors influencing depression and anxiety in the poor mental health group and mild mental health group were sex, educational level, drinking, individual psychiatric disorders, family psychiatric disorders, knowledge of COVID-19, fear of being infected, and participate in mental health education on COVID-19. CONCLUSIONS: Our findings suggested that latent class analysis can be used to categorize different medical students according to their depression and anxiety symptoms during the outbreak of COVID-19. The main factors influencing the poor mental health group and the mild mental health group are basic demographic characteristics, disease history, COVID-19 related factors and behavioural lifestyle. School administrative departments can carry out targeted psychological counseling according to different subgroups to promote the physical and mental health of medical students.