Association of Night Shift Work With Chronic Spontaneous Urticaria and Effect Modification by Circadian Dysfunction Among Workers.

Purpose: Night shift work is common in the current working environment and is a risk factor for many diseases. The study aimed to explore the relationship between night shift work with chronic spontaneous urticaria (CSU), and the modification effect of circadian dysfunction on it. Methods: A cross-sectional survey was conducted among Chinese workers. Exposure was measured by night work history and duration. Circadian dysfunction was characterized by excessive daytime sleepiness (EDS). The diagnosis of CSU was made by dermatologists who were investigating on the spot. The effect size was expressed as odds ratios (ORs). Results: A total of 8,057 participants were recruited, and 7,411 (92%) with complete information were included in the final analyses. The prevalence rates of CSU for workers without night shift and those with night shift history were 0.73 and 1.28%, respectively. Compared with workers who never worked night shifts, the risk of CSU increased with the length of night shift work: OR = 1.55 (95% confidence interval [CI]: 0.78-3.06) for duration <5 years and OR = 1.91 (95% CI: 1.12-3.26) for duration ≥5 years. EDS s EDS has been shown to modify this combination. Among workers without EDS, there was no association between night shift and CSU (OR = 0.94; 95% CI: 0.49-1.79). Whereas, in participants with EDS, the correlation was significant (OR = 3.58; 95% CI: 1.14-11.20). However, the effect modification by sleep disturbance was not observed. Conclusions: Night shift work is a risk factor for CSU, and there is a dose-response relationship between night shift work hours and the risk of CSU. This connection may be modified by circadian dysfunction.

Anti-SARS-CoV-2 IgG responses are powerful predicting signatures for the outcome of COVID-19 patients.

Introduction: The COVID-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for early predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies evolved with disease progression and severity in COIVD-19 patients. Objectives: We assumed that antibodies may serve as biomarkers for predicting the clinical outcome of hospitalized COVID-19 patients on admission. Methods: By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgG responses against 20 proteins of SARS-CoV-2 in 1034 hospitalized COVID-19 patients on admission and followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality. Results: Nonsurvivors (n = 955) induced higher levels of IgG responses against most of non-structural proteins than survivors (n = 79) on admission. In particular, the magnitude of IgG antibodies against 8 non-structural proteins (NSP1, NSP4, NSP7, NSP8, NSP9, NSP10, RdRp, and NSP14) and 2 accessory proteins (ORF3b and ORF9b) possessed significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory biomarkers for disease severity (all with p trend < 0.05). Additionally, IgG responses to all of these 10 non-structural/accessory proteins were also associated with the severity of disease, and differential kinetics and serum positive rate of these IgG responses were confirmed in COVID-19 patients of varying severities within 20 days after symptoms onset. The area under curves (AUCs) for these IgG responses, determined by computational cross-validations, were between 0.62 and 0.71. Conclusions: Our findings might have important implications for improving clinical management of COVID-19 patients.

Road traffic and air pollution: Evidence from a nationwide traffic control during coronavirus disease 2019 outbreak.

Existing estimations of air pollution from automobile sources are based on either experiments or small-scale governmental interventions. China's nationwide traffic control during the coronavirus disease 2019 outbreak provided us a unique opportunity to assess the direct dose-effect relationship between vehicle density and air pollution. We found that, during the coronavirus disease 2019 outbreak, the nationwide reduced air pollution (except for O3) could be largely explained by traffic control measures. During the traffic control period, every doubling of vehicle density was associated with a decrease of 4.2 (2.0, 6.4) µg/m3 in PM2.5, 5.5 (2.9, 8.1) µg/m3 in PM10, 1.5 (0.9, 2.0) µg/m3 in NO2, and 0.04 (0.02, 0.07) mg/m3 in CO comparing cities with different vehicle densities. Similarly, for every 10% increase in the truck proportion, PM2.5 decreased by 12.3 (4.1, 20.6) µg/m3, PM10 decreased by 14.3 (4.6, 23.9) µg/m3, and CO decreased by 0.14 (0.05, 0.23) mg/m3. Moreover, the associations between vehicle density and reduction in PM2.5, PM10, and CO during the traffic control period were stronger and showed near-complete linearity in cities with low green coverage rate (All P < 0.05 for interaction). According to our estimation, PM2.5 emissions from every doubling of vehicle density can lead to over 8000 excess deaths per year, 66% of which were caused by cardiopulmonary diseases. This natural experiment study is the first to observe the dose-effect relationship between on-road traffic and traffic-generated air pollution, as well as the mitigating effect of urban greening. Findings provide key evidence to the assessment and control of traffic-generated air pollution and its public health impact.

Clinical Characteristics and Prognosis of 244 Cardiovascular Patients Suffering From Coronavirus Disease in Wuhan, China.

Background The coronavirus disease 2019 (COVID-19) has developed into a global outbreak. Patients with cardiovascular disease (CVD) with COVID-19 have different clinical characteristics and prognostic outcomes. This study aimed to summarize the clinical characteristics and laboratory indicators of patients with COVID-19 with CVD, especially the critically ill patients. Methods and Results This study included 244 patients diagnosed with COVID-19 and CVD (hypertension, coronary heart disease, or heart failure). The patients were categorized into critical (n=36) and noncritical (n=208) groups according to the interim guidance of China's National Health Commission. Clinical, laboratory, and outcome data were collected from the patients' medical records and compared between the 2 groups. The average body mass index of patients was significantly higher in the critical group than in the noncritical group. Neutrophil/lymphocyte ratio, and C-reactive protein, procalcitonin, and fibrinogen, and d-dimer levels at admission were significantly increased in the critical group. The all-cause mortality rate among cases of COVID-19 combined with CVD was 19.26%; the proportion of coronary heart disease and heart failure was significantly higher in deceased patients than in recovered patients. High body mass index, previous history of coronary heart disease, lactic acid accumulation, and a decrease in the partial pressure of oxygen were associated with death. Conclusions All-cause mortality in patients with COVID-19 with CVD in hospitals is high. The high neutrophil/lymphocyte ratio may be a predictor of critical patients. Overweight/obesity combined with coronary heart disease, severe hypoxia, and lactic acid accumulation resulting from respiratory failure are related to poor outcomes. Registration URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2000029865.