The novel coronavirus pneumonia, namely COVID-19, has become a global Journal Pre-proof J o u r n a l P r e -p r o o f public health problem. Previous studies have found that air pollution is a risk factor for respiratory infection by carrying microorganisms and affecting body's immunity. This study aimed to explore the relationship between ambient air pollutants and the infection caused by the novel coronavirus. Daily confirmed cases, air pollution concentration and meteorological variables in 120 cities were obtained from January 23, 2020 to February 29, 2020 in China. We applied a generalized additive model to investigate the associations of six air pollutants (PM 2.5 , PM 10 , SO 2 , CO, NO 2 and O 3 ) with COVID-19 confirmed cases. We observed significantly positive associations of PM 2.5 , PM 10 , NO 2 and O 3 in the last two weeks with newly COVID-19 confirmed cases. A 10-μg/m 3 increase (lag0-14) in PM 2.5 , PM 10 , NO 2 , and O 3 was associated with a 2.24% (95% CI:1.02 to 3.46), 1.76% (95% CI: 0.89 to 2.63), 6.94% (95% CI: 2.38 to 11.51), and 4.76% (95% CI: 1.99 to 7.52) increase in the daily counts of confirmed cases, respectively. However, a 10-μg/m 3 increase (lag0-14) in SO 2 was associated with a 7.79% decrease (95% CI: -14.57 to -1.01) in COVID-19 confirmed cases. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease.

Background: Previous studies have found that human mobility restrictions could not only prevent the spread of COVID-19, but also improve the air quality because of the reduction of industrial production, transportation and traffic. It is noteworthy that air quality is also closely related to the risk of COVID-19 infection. Therefore, we aimed to assess the mediating role of air quality on the association between human mobility and the infection caused by this novel coronavirus. Methods: We collected daily confirmed cases, human mobility data, air quality data and meteorological variables in 120 cities from China between January 23, 2020 and February 29, 2020. We applied the generalized additive model to examine the association of human mobility index with COVID-19 confirmed cases, and to assess the mediating effects of air quality index and each pollutant. Results: We observed a significant positive relationship between human mobility index and the daily counts of COVID-19 confirmed cases. A unit increase in human mobility index (lag0-14) was associated with a 6.45% increase in daily COVID-19 confirmed cases, and air quality index significantly mediated 19.47% of this association. We also observed a positive relationship between human mobility index and air quality index. In the pollutant level analyses, we found significant mediating effects of PM 2.5 , PM 10 , and NO 2 . Conclusions: Our study suggests that limiting human movements could reduce COVID-19 cases by improving air quality besides decreasing social contact.

Background The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) not only caused an adverse impact on physical health, but also brought about mental health problems among the public. Methods To assess the causal impact of COVID-19 on psychological changes in China, we constructed a city-level panel data set based on the expressed sentiment in the contents of 13 million geotagged tweets on Sina Weibo, the Chinese largest microblog platform. Results Applying a difference-in-differences approach, we found a significant deterioration in mental health status after the occurrence of COVID-19. We also observed that this psychological effect faded out over time during our study period and was more pronounced among women, teenagers and older adults. The mental health impact was more likely to be observed in cities with low levels of initial mental health status, economic development, medical resources and social security. Conclusions Our findings may assist in the understanding of mental health impact of COVID-19 and yield useful insights into how to make effective psychological interventions in this kind of sudden public health event.

Plaque rupture or erosion represents a major cause of stroke and the acute coronary syndrome. Information on plaque morphology and composition is essential to identify patients at increased risk for acute cardio- and cerebrovascular events, and may play a key role in individualizing therapeutic strategies and monitoring the effect of interventions. The rapidly emerging field of MR and targeted molecular MRI allows to qualitatively and quantitatively characterize plaque features. We aimed to review the literature regarding the current most promising advanced techniques to image vulnerable atherosclerotic plaques.

SARS-CoV-2 and its mutant strains continue to rapidly spread with high infection and fatality. Large-scale SARS-CoV-2 vaccination provides an important guarantee for effective resistance to existing or mutated SARS-CoV-2 virus infection. However, whether the host metabolite levels respond to SARS-CoV-2 vaccine-influenced host immunity remains unclear. To help delineate the serum metabolome profile of SARS-CoV-2 vaccinated volunteers and determine that the metabolites tightly respond to host immune antibodies and cytokines, in this study, a total of 59 sera samples were collected from 30 individuals before SARS-CoV-2 vaccination and from 29 COVID-19 vaccines 2 weeks after the two-dose vaccination. Next, untargeted metabolomics was performed and a distinct metabolic composition was revealed between the pre-vaccination (VB) group and two-dose vaccination (SV) group by partial least squares-discriminant and principal component analyses. Based on the criteria: FDR < 0.05, absolute log2 fold change greater than 0.25, and VIP >1, we found that L-glutamic acid, gamma-aminobutyric acid (GABA), succinic acid, and taurine showed increasing trends from SV to VB. Furthermore, SV-associated metabolites were mainly annotated to butanoate metabolism and glutamate metabolism pathways. Moreover, two metabolite biomarkers classified SV from VB individuals with an area under the curve (AUC) of 0.96. Correlation analysis identified a positive association between four metabolites enriched in glutamate metabolism and serum antibodies in relation to IgG, IgM, and IgA. These results suggest that the contents of gamma-aminobutyric acid and indole in serum could be applied as biomarkers in distinguishing vaccinated volunteers from the unvaccinated. What’s more, metabolites such as GABA and taurine may serve as a metabolic target for adjuvant vaccines to boost the ability of the individuals to improve immunity.

Cataract is the first cause of blindness and the major cause of visual impairment worldwide. Under conditions of global warming, researchers have begun to give attention to the influence of increasing temperature on cataract patients. Our paper aimed to investigate the association between extreme heat and hospital admissions for cataract in Hefei, China. Based on data from the New Rural Cooperative Medical System and National Meteorological Information Center, we used a generalized additive model and a distributed lag nonlinear model to examine the relationship between extreme heat and hospitalizations for cataract, with consideration of cumulative and lagged effects. When current mean temperature was above 28 °C, each 1 °C rise was associated with a 4% decrease in the number of cataract admissions (RR = 0.96, 95% CI = 0.94–0.98). The cumulative relative risk over 11 days of lag was the lowest, which indicated that every 1 °C increase in mean temperature above 28 °C was associated with a 19% decrease in the number of hospital admissions for cataract (RR = 0.81, 95% CI = 0.75–0.88). In subgroup analyses, the negative association between extreme heat and hospital admissions for cataract was stronger among patients who were not admitted to provincial-level hospitals. In conclusion, this paper found that extreme heat was negatively associated with cataract hospitalizations in Hefei, providing useful information for hospitals and policymakers.

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Background. The outbreak and rapid spread of COVID-19 not only caused an adverse impact on physical health but also brought about mental health problems among the public.Methods. To assess the causal impact of COVID-19 on psychological changes in China, we constructed a city-level panel data set based on the expressed sentiment in the contents of 13 million geotagged tweets on Sina Weibo, the Chinese largest microblog platform.Results. Applying a difference-in-differences approach, we found a significant deterioration in mental health status after the occurrence of COVID-19. We also observed that this psychological effect faded out over time during our study period and was more pronounced among women, teenagers and older adults. The mental health impact was more likely to be observed in cities with low levels of initial mental health status, economic development, medical resources, and social security.Conclusions. Our findings may assist the understanding of COVID-19’s mental health impact and yield useful insights on how to make effective psychological interventions in this kind of sudden public health event.