Pre-existing humoral immunity to low pathogenic human coronaviruses exhibits limited cross-reactive antibodies response against SARS-CoV-2 in children.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes asymptomatic or mild symptoms, even rare hospitalization in children. A major concern is whether the pre-existing antibodies induced by low pathogenic human coronaviruses (LPH-CoVs) in children can cross-react with SARS-CoV-2. To address this unresolved question, we analyzed the pre-existing spike (S)-specific immunoglobin (Ig) G antibodies against LPH-CoVs and the cross-reactive antibodies against SARS-CoV-2 in 658 serum samples collected from children prior to SARS-CoV-2 outbreak. We found that the seroprevalence of these four LPH-CoVs reached 75.84%, and about 24.64% of the seropositive samples had cross-reactive IgG antibodies against the nucleocapsid, S, and receptor binding domain antigens of SARS-CoV-2. Additionally, the re-infections with different LPH-CoVs occurred frequently in children and tended to increase the cross-reactive antibodies against SARS-CoV-2. From the forty-nine serum samples with cross-reactive anti-S IgG antibodies against SARS-CoV-2, we found that seven samples with a median age of 1.4 years old had detected neutralizing activity for the wild-type or mutant SARS-CoV-2 S pseudotypes. Interestingly, all of the seven samples contained anti-S IgG antibodies against HCoV-OC43. Together, these data suggest that children's pre-existing antibodies to LPH-CoVs have limited cross-reactive neutralizing antibodies against SRAS-CoV-2.

Collaborative Integration, Workplace Flexibility and Scholarly Productivity: Evidence from the COVID-19 Outbreak

In this paper, we exploit the natural experiment of the COVID-19 outbreak and investigate the role of collaborative integration and workplace flexibility in scholarly productivity. Using data on the quantity and quality of the journal and working paper submissions, we first identify a discontinuity pattern in the productivity of Chinese scholars around the Chinese New Year (CNY). Second, we find that COVID-19 has a negative impact on the productivity of Chinese scholars in terms of quantity and quality post-CNY. Furthermore, the short-term detrimental effect on scholarly productivity is induced mainly through the channel of collaborative integration and workplace flexibility due to mitigation policy shocks in terms of social distancing and working-from-home arrangements. The results suggest while advances in virtual communication technologies can facilitate productivity by lowering collaboration costs, virtual team communication cannot be a perfect substitute for face-to-face communication in collaborative integration. In addition, higher workplace flexibility might hinder productivity in sectors relying more on the skills of self-management and discipline.

Authenticity Mediates the Relationship between Risk Perception of COVID-19 and Subjective Well-Being: A Daily Diary Study

The present research investigated whether risk perception of COVID-19 relates to subjective well-being and the mediating role of authenticity in this association. We conducted a 12-day daily diary study with 133 undergraduates (Mage = 19.9 years, SD = 1.27 years;64 females). Participants self-reported risk perception of COVID-19, authenticity, and subjective well-being every day. Results revealed that (1) risk perception of COVID-19 was negatively related to subjective well-being at the interindividual level;(2) authenticity mediated the relationship between risk perception of COVID-19 and subjective well-being at the interindividual level but not at the intraindividual level. In general, findings suggested that risk perception of COVID-19 is negatively related to subjective well-being only at the interindividual level, and authenticity plays a mediating role in this relationship. The finding suggested that keeping authenticity is a good strategy for avoiding the disruption caused by COVID-19. Longitudinal studies on samples with a broader age range, larger sample size, and extended sociodemographic background, as well as experimental studies, should be conducted to explore the causal relationship among interested variables that the current research has not detected.

How does COVID-19 affect the implementation of CORSIA?

This paper investigates the impacts of COVID-19 on the implementation of Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). By using the Automatic Dependent Surveillance-Broadcast (ADS-B) aviation data, the forecast methods of Gompertz and Logistic curves and four COVID-19 scenarios, we find the following results. First, the international aviation activities of developing countries are on the track of rapid growth, while the trends of developed countries are relatively slow or even close to saturation. Second, our results provide retrospective support for the decision of the ICAO Council to revise the implementation baseline of CORSIA. The adjustment of the baseline has saved countries considerable purchasing offsetting costs, especially for China, the United States, the United Arab Emirates, and the United Kingdom. Third, although the adjustment of the baseline can lower the economic pressure of the global aviation industry, CORSIA will still bring considerable financial burden to international aviation enterprises.

Heterogeneous Actions, Beliefs, Constraints and Risk Tolerance During the COVID-19 Pandemic

During a pandemic, an individual's choices can determine outcomes not only for the individual but also for the entire community. Beliefs, constraints and preferences may shape behavior. This paper documents demographic differences in behaviors, beliefs, constraints and risk preferences across gender, income and political affiliation lines during the new coronavirus disease (COVID-19) pandemic. Our main analyses are based on data from an original nationally representative survey covering 5,500 adult respondents in the U.S. We find substantial gaps in behaviors and beliefs across gender, income and partisanship lines;in constraints across income levels;and in risk tolerance among men and women. Based on location data from a large sample of smartphones, we also document significant differences in mobility across demographics, which are consistent with our findings based on the survey data.

Using green finance to counteract the adverse effects of COVID-19 pandemic on renewable energy investment-The case of offshore wind power in China.

The outbreak of COVID-19 pandemic has increased the production costs of renewable energy facilities and undermines the profitability of renewable energy investment. Green finance polices, e.g. carbon pricing, tradable green certificate (TGC) and green credit, can provide low-cost finances and counteract the adverse effects of COVID-19 pandemic. In this work, the generation costs of offshore wind power before and after the COVID-19 pandemic in China are analyzed using the data of 97 offshore wind power projects implemented in the period of 2014-2020, and the effect of green finance policy on the generation cost and the project profitability are evaluated. The results show that the average levelized cost of electricity (LCOE) of offshore wind power decreased from 0.86 CNY/kWh in 2014 to 0.72 CNY/kWh in 2019, while it increased to 0.79 CNY/kWh in 2020, i.e. 10.85% increase relative to that in 2019. With the average carbon price of 50 CNY/t CO2, the average TGC price of 170 CNY and the green-credit policy being introduced, the average LCOE decreases to 0.76 CNY/kWh, 0.67 CNY/kWh and 0.74 CNY/kWh respectively. The green finance policy mix is still necessary to support the offshore wind power investment during the Covid-19 pandemic.

Risk Spillover Network Structure Learning for Correlated Financial Assets: A Directed Acyclic Graph Approach

Using cross-asset return data in global financial markets, we propose a novel empirical framework to identify the causal structure of the asset risk spillover network. The joint return distribution of the global financial system can be characterized using a directed acyclic graph approach. However, since assets tend to be highly correlated during market turbulence, when adopting a nodewise penalized regression approach for neighborhood estimation, parameter estimates will receive large standard errors, and edges cannot be reliably estimated. In this work, we propose a two-stage approach for directed acyclic graph skeleton estimation for highly correlated variables. In the first stage, a variable screening ensemble is incorporated into the sparse partial least squares regression method to both reduce the size of the active variables set and impose an adaptive penalization on the weight vectors. In the second stage, a modified PC algorithm based on Gram-Schmidt orthogonalization is applied to remove the false positive edges. Simulation studies are conducted to demonstrate the effectiveness of the proposed method. Finally, we apply our method to analyze the asset risk spillover channels for international financial assets during the COVID-19 pandemic.

The effects of narrative exposure therapy on COVID-19 patients with post-traumatic stress symptoms: A randomized controlled trial.

BACKGROUND: With the global attack of Coronavirus Disease 2019 (COVID-19), cases with Post-traumatic Stress Disorder (PTSD) have been increasing steadily, which seriously affects the quality of life of patients and as such, seeking effective treatments is an urgent matter. Narrative Exposure Therapy (NET) is a typical cognitive behavioral therapy targeting trauma-related psychological disorders and may be an effective intervention. METHODS: A total of 111 COVID-19 patients near the discharge stage with positive screening results for posttraumatic stress symptoms (PTSS) were randomly assigned (1:1) to either the study group or the control group. The study group received NET and personalized psychological intervention, while the control group only received personalized psychological intervention. PTSS, depression, anxiety and sleep quality were measured pre- and post-intervention to evaluate the effect of NET. This trial was registered with the International Standard Randomized Clinical Trial Registry (No. ChiCTR2000039369). RESULTS: NET participants showed a significantly greater PTSS reduction in comparison with the control group after the intervention. Improvement in sleep quality, anxiety and depression after the intervention were pronounced but not significantly different between the two treatment groups. LIMITATIONS: The assessors weren't blinded for the convenience of measurement and protection of participants' psychological security. CONCLUSIONS: NET likely had a positive impact on PTSS of COVID-19 patients. Clinical staff should consider applying NET to improve the psychological well-being of patients who have experienced an epidemic such as COVID-19.

Understanding the Anticontagion Process and Reopening of China during COVID-19 via Coevolution Network of Epidemic and Awareness

The novel coronavirus (COVID-19) pandemic is intensifying all over the world, but some countries, including China, have developed extensive and successful experience in controlling this pandemic. In this context, some questions arise naturally: What can countries caught up in the epidemic learn from China’s experience? In regions where the outbreak is under control, what would lead to a resurgence of the epidemic? To address these issues, we investigate China’s experience in anticontagion interventions and reopening process, focusing on the coevolution of epidemic and awareness during COVID-19 outbreak. Through an empirical analysis based on large-scale data and simulation based on a metapopulation and multilayer network model, we ascertain the impact of human movements and awareness diffusion on the epidemic, elucidate the inherent patterns and effective interventions of different epidemic prevention methods, and highlight the crunch time of each measure. The results are also employed to analyze COVID-19 evolution in other countries so as to find unified rules in complex situations around the world and provide advice on anticontagion and reopening policies. Our findings explain some key mechanisms of epidemic prevention and may help the epidemic analysis and decision-making in various countries suffering from COVID-19.

The Risk Factors for the Exacerbation of COVID-19 Disease: A Case-control Study.

AIMS AND OBJECTIVES: To investigate the factors associated with the exacerbations of COVID-19. BACKGROUND: At present, COVID-19 is prevalent in the world, seriously endangering the property and life safety of people around the world. Currently, there are many reports on the clinical features, complications and risk factors of death of COVID-19, but there are few reports on the factors associated with the exacerbation of COVID-19. DESIGN: Case-control Study. METHODS: Patients with COVID-19 were recruited from four designated hospitals for novel coronavirus pneumonia in Xiangyang City, Hubei Province from January to April 2020. The patients were divided into disease exacerbation group (n = 53) and disease stabilisation group (n = 265) according to the disease progression during hospitalisation. Univariate analysis and multivariate logistic regression were used to identify the factors associated with the exacerbation of COVID-19. The research was reported according to STROBE statement. RESULTS: Univariate analysis showed there were significant differences in gender, age, hypertension, heart disease, kidney disease, white blood cell count, percentage of neutrophil, percentage of lymphocyte, C-reactive protein, lactate dehydrogenase, total protein, albumin, creatinine, calcium ion, rate of erythrocyte sedimentation, cough, expectoration, chest tightness, gastrointestinal discomfort and dyspnoea between the two groups. The variables with p < 0.05 in the aforementioned difference analysis were included in binary logistic regression analysis, which showed that age, hypertension history, chest tightness, percentage of neutrophil, percentage of lymphocyte, lactate dehydrogenase and creatinine were independent factors associated with COVID-19 disease exacerbation. CONCLUSION: Clinicians may warn the exacerbation of COVID-19 facing above risk factors and associated characteristics, and adjust the diagnosis and treatment plan to delay the disease progression, reduce complications and mortality and improve the prognosis of patients. RELEVANCE TO CLINICAL PRACTICE: Patients with certain risk factors associated with COVID-19 diseases exacerbation should be observed and targeted by using effective early interventions.

Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.

To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.

Clinical Management of Gynecological Cancer Patients During COVID-19 Epidemic

During the epidemic of COVID-19, the routine clinical treatment for gynecological cancer patients has been disturbed due to the redistribution of medical resource. Due to the systemic immunosuppression caused by the malignancy and anticancer treatments, gynecological cancer patients are more susceptible to COVID-19. With the improvement of the epidemic, the treatment needs of gynecological cancer patients are extremely strong. During this special period, it should carefully identify fever and respiratory symptoms of gynecological cancer patients receiving chemotherapy, immunotherapy and operations. Therefore, it is quite necessary to carry out comprehensive clinical management. We introduce a clinical management of gynecological cancer patients in three aspects of outpatient, in-hospital and out-of-hospital management during this period, in order to maximize the treatment of tumors and effectively prevent COVID-19.

Liver injury in COVID-19: A minireview.

In December 2019, an outbreak of unexplained pneumonia was reported in Wuhan, China. The World Health Organization officially named this disease as novel coronavirus disease 2019 (COVID-19). Liver injury was observed in patients with COVID-19, and its severity varied depending on disease severity, geographical area, and patient age. Systemic inflammatory response, immune damage, ischemia-reperfusion injury, viral direct damage, drug induce, mechanical ventilation, and underlying diseases may contribute to liver injury. Although, in most cases, mild liver dysfunction is observed, which is usually temporary and does not require special treatment, the importance of monitoring liver injury should be emphasized for doctors. The risk of COVID-19 infection of liver transplantation recipients caused more and more concerns. In this article, we aimed to review the available literature on liver injury in COVID-19 to highlight the importance of monitoring and treating liver injury in COVID-19.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.

BACKGROUND: The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. OBJECTIVE: The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. METHODS: A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. RESULTS: One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097-0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916-0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. CONCLUSIONS: This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.

Differentiating Between 2019 Novel Coronavirus Pneumonia and Influenza Using a Nonspecific Laboratory Marker-Based Dynamic Nomogram.

BACKGROUND: There is currently a lack of nonspecific laboratory indicators as a quantitative standard to distinguish between the 2019 coronavirus disease (COVID-19) and an influenza A or B virus infection. Thus, the aim of this study was to establish a nomogram to detect COVID-19. METHODS: A nomogram was established using data collected from 457 patients (181 with COVID-19 and 276 with influenza A or B infection) in China. The nomogram used age, lymphocyte percentage, and monocyte count to differentiate COVID-19 from influenza. RESULTS: Our nomogram predicted probabilities of COVID-19 with an area under the receiver operating characteristic curve of 0.913 (95% confidence interval [CI], 0.883-0.937), greater than that of the lymphocyte:monocyte ratio (0.849; 95% CI, 0.812-0.880; P = .0007), lymphocyte percentage (0.808; 95% CI, 0.768-0.843; P < .0001), monocyte count (0.780; 95% CI, 0.739-0.817; P < .0001), or age (0.656; 95% CI, 0.610-0.699; P < .0001). The predicted probability conformed to the real observation outcomes of COVID-19, according to the calibration curves. CONCLUSIONS: We found that age, lymphocyte percentage, and monocyte count are risk factors for the early-stage prediction of patients infected with the 2019 novel coronavirus. As such, our research provides a useful test for doctors to differentiate COVID-19 from influenza.

Pattern of liver injury in adult patients with COVID-19: a retrospective analysis of 105 patients.

BACKGROUND: Recent studies reported that patients with coronavirus disease-2019 (COVID-19) might have liver injury. However, few data on the combined analysis and change patterns of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and total bilirubin (TBil) have been shown. METHODS: This is a single-center retrospective study. A total of 105 adult patients hospitalized for confirmed COVID-19 in Beijing Ditan Hospital between January 12, and March 17, 2020 were included, and divided into mild group (n = 79) and severe group(n = 26). We compared liver functional test results between the two groups. Category of ALT change during the disease course was also examined. RESULTS: 56.2% (59/105) of the patients had unnormal ALT, AST, or total TBil throughout the course of the disease, but in 91.4% (96/105) cases the level of ALT, AST or TBil ≤3 fold of the upper limit of normal reference range (ULN). The overall distribution of ALT, AST, and TBil were all significantly difference between mild and severe group (P <  0.05). The percentage of the patients with elevated both ALT and AST was 12.7% (10/79) in mild cases vs. 46.2% (12/26) in severe cases (P = 0.001). 34.6% (9/26) severe group patients started to have abnormal ALT after admission, and 73.3% (77/105) of all patients had normal ALT before discharge. CONCLUSIONS: Elevated liver function index is very common in patients with COVID-19 infection, and the level were less than 3 × ULN, but most are reversible. The abnormality of 2 or more indexes is low in the patients with COVID-19, but it is more likely to occur in the severe group.