Forecasting the Impact of the COVID-19 Outbreak on China's Cotton Exports by Modified Discrete Grey Model with Limited Data

The sudden outbreak of COVID-19 has created dramatic challenges for public health and textile export trade worldwide. Such abrupt changes are difficult to predict due to the inherently high complexity and nonlinearity, especially with limited data. This article proposes a novel modified discrete grey model with weakening buffer operators, called BODGM (1,1), for forecasting the impact of pandemic-induced uncertainty on the volatility of cotton exports in China under limited samples. First, the Mann–Kendall test examines how pandemic-induced uncertainty affects cotton exports, based on China's monthly cotton export data from June 2014 to August 2022. Second, buffer operators are employed to weaken the nonlinear trends and correct the tentative predictions of the discrete grey model. Then, the BODGM (1,1) model was validated by comparison with four alternative models. The results indicate that the BODGM (1,1) model was particularly promising for identifying mutational fluctuations in cotton exports and outperformed the GM (1,1), DGM (1,1), ARIMA and linear regression models in fitting and prediction accuracy under volatility and limited data. The BODGM (1,1) model forecast results for China showed that cotton export volume was expected to show signs of recovery over the next 12 months. The findings of this study may provide a basis for formulating trade policies to mitigate the impact of the COVID-19 outbreak on export resources and build their resilience to future pandemics.

Stress load of Chinese nurses in Fangcang Shelter Hospitals during the COVID-19 pandemic: A latent profile analysis

The Omicron wave of the COVID-19 pandemic significantly affected Shanghai, China, from March to June 2022. Numbers of Fangcang Shelter Hospitals (FSHs) were conversed from stadiums and exhibition centers to tackle the pandemic. This study aimed to identify the stress load profiles of nurses working in FSHs and explore the characteristics and factors influencing stress load profiles. Totally, 609 out of 700 FSH nurses (with an effective response rate of 87%) participated in an online survey investigating their socio-demographic information, work-related stressors, and stress load. Results of the latent profile analysis identified four classes of stress load, which were labeled as the low (Class 1), mild (Class 2), moderate (Class 3), and high (Class 4) stress load class. Maternity status and self-perceived health condition were significantly different between the four stress load classes by comparisons using the Chi-square test and the Kruskal–Wallis test. The contributors to the stress load profiles were determined by the multinomial logistic regression analysis, including age, education, maternity status, self-perceived health condition, working time in FSHs, and the four dimensions of work-related stressors. Participants who were less healthy (OR = 0.045, 95% CI:0.012,0.171), worked longer time in FSHs (OR = 40.483, 95% CI: 12.103,135.410), faced with more workload (OR = 3.664, 95% CI: 1.047,12.815), and worse working environment (OR = 12.274, 95% CI: 3.029,49.729) were more likely to be classified to the high stress load class. The task arrangement and working environment for FSH nurses should be optimized, and psychological training should be conducted routinely.

Evaluating the impact of coronavirus disease on burnout among healthcare workers using maslach burnout inventory tool: A systematic review

Background Burnout has been prevalent among healthcare workers (HCWs). However, the effect of the Coronavirus Disease (COVID-19) pandemic on this phenomenon in HCWs is unclear. Objective This systematic review aims to evaluate the impact of COVID-19 on burnout of HCWs using Maslach Burnout Inventory (MBI). Methods A systematic search was performed on PubMed database for articles published between 1 December 2019 and 30 June 2021. Search strategy combined terms for HCWs, COVID-19, burnout, and MBI. The main outcome of interest was burnout, including both mean prevalence and MBI scores for high emotional exhaustion (EE), high depersonalisation (DP) and low personal accomplishment (PA). Results Four cohort studies, 90 cross-sectional studies and one randomised-controlled trial were included for review. Only one cohort study compared burnout data among HCWs before and during COVID-19. It reported a statistically significant increase in mean EE and PA scores from 21.9 to 24.8 (p = .001), and 42.7 to 48.7 (p = .001), respectively. The remaining studies only evaluated burnout data during COVID-19 but were missing burnout data prior to the pandemic for comparison. Across these studies, the overall mean prevalence of burnout among HCWs was 39.95%, with mean MBI EE scores of 22.07, DP scores of 7.83, and PA scores of 32.53. Burnout outcomes were generally comparable across specific healthcare professions such as doctors and nurses. Conclusion Whilst quality research elucidating the effect of pandemic on burnout is lacking, current burnout prevalence among HCWs during COVID-19 is notable.

Explaining customer satisfaction via hotel reviews: A comparison between pre- and post-COVID-19 reviews

This study aims to examine whether and how COVID-19 has changed the effects of consumer evaluations of hotel attributes on customer satisfaction. We extracted positive and negative evaluations of hotel attributes from online reviews both pre- and post-COVID-19 and examined their effects on customer satisfaction. Using a sample of 1,947,391 reviews of 35,022 Chinese hotels collected from ctrip.com, we conducted a fine-grained sentiment analysis based on sentiment triples to identify important positive and negative evaluations of hotel attributes. Subsequently, we applied regression analyses to examine how these evaluations of hotel attributes influenced customer satisfaction. The results revealed that positive and negative evaluations of hotel attributes had differentiated effects on customer satisfaction. We classified these attributes into basic, excitement, and performance attributes, from which management implications can be derived.

Prevalence of psychological symptoms and associated risk factors among nurses in 30 provinces during the COVID-19 pandemic in China

Summary Background With the outbreak of the coronavirus disease 2019 (COVID-19), nurses have won well-deserved recognition for their indispensable roles in providing humane and professional healthcare for patients. However, by the nature of their role working at the forefront of patient care, nurses are prone to experiencing mental health consequences. Therefore, we pay attention to measuring the magnitude of psychological symptoms and identifying associated factors among nurses in China. Methods We launched a nationwide, cross-sectional survey of nurses who worked in secondary or tertiary hospitals and public or private hospitals from 30 provinces in China. The prevalence and severity of symptoms of burnout, depression, and anxiety were investigated, respectively. Multivariable logistic regression analyses were performed to identify factors associated with each psychological symptom. Findings A total of 138 279 respondents who worked in 243 hospitals completed this survey. A substantial proportion of nurses reported symptoms of burnout (34%), depression (55·5%), and anxiety (41·8%). In line with the disproportionality of economic development, we noted that the middle or western region was an independent risk factor for depression and anxiety. Compared with those working in the secondary hospital, nurses who worked in tertiary hospitals were associated with a higher likelihood of burnout and depression. Interpretation Nurses are experiencing emotional, physical, and mental exhaustion during the COVID-19 epidemic. Governments and health policymakers need to draw attention to reinforcing prevention and ameliorating countermeasures to safeguard nurses' health. Funding The strategic consulting project of the Chinese Academy of Engineering [2021-32-5]. Advanced Institute of Infomation Technology, Peking University, Zhejiang Province [2020-Z-17]

Public Health System and Socio-Economic Development Coupling Based on Systematic Theory: Evidence from China

This paper focus on the quantitative measurement of public health systems and its mismatch with socio-economic development. Based on systematic theory, we divide the public health system into four sub-systems: resource inputs, planning in decision-making, operations, and service outputs. We also provide a method to analyse the ability to match between the public health system and social-economic development by using the grey correlation and coupling method. Then we introduce data from China as a case of empirical research. The main findings are as follows: (1) China’s public health system has progressed from 2012 to 2019, and the development of China’s public health system is typically “input-driven”. Second, the level of public health management in China lacks sustainability. (2) The main reason for this problem is the mismatch between the central and local governments in China in terms of public health management authority. (3) Third, the coupling between China’s public health system and socio-economics development has shown a decreasing trend, which indicates an increasingly significant mismatch problem between public health and economic growth, urbanization, and population aging. Our study will enrich the understanding of the relationship between the public health system and socio-economics development.

Intellectual Capital and Financial Performance of Chinese Manufacturing SMEs: An Analysis from the Perspective of Different Industry Types

In the knowledge era, intellectual capital (IC) has been recognized as the determinant of firm performance. The main goal of the current study is to analyze the relationship between IC and its elements and financial performance of Chinese manufacturing small and medium-sized enterprises (SMEs). We also examine whether industry type has an impact on this relationship. This study uses the data of 588 Chinese listed SMEs in the manufacturing industry between 2015 and 2020 and employs the modified value-added intellectual coefficient (MVAIC) model to assess IC. The results show that IC improves SMEs’ financial performance, and physical and human capitals are the main contributor. In addition, the impact of IC and its elements on the financial performance of Chinese manufacturing SMEs is different in different types of industries. Specifically, capital-intensive SMEs have a greater impact of IC on financial performance than labor- and technology-intensive SMEs;labor-intensive SMEs have a higher efficiency of physical capital, while technology-intensive SMEs have higher human capital efficiency. The findings could help SMEs’ managers improve corporate performance by the effective utilization of their IC.

Adaptation-proof SARS-CoV-2 vaccine design (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface spike glycoprotein - a major antibody target - is critical for virus entry via engagement of human angiotensin-converting enzyme 2 (ACE2) receptor. Despite successes with existing vaccines and therapies that primarily target the receptor binding domain (RBD) of the spike protein, the susceptibility of RBD to mutations provides escape routes for the SARS-CoV-2 from neutralizing antibodies. On the other hand, structural conservation in the spike protein can be targeted to reduce escape mutations and achieve broad protection. Here, we designed candidate stable immunogens that mimic surface features of selected conserved regions of spike protein through 'epitope grafting', in which we present the target epitope topology on diverse heterologous scaffolds that can structurally accommodate the spike epitopes. Structural characterization of the epitope-scaffolds showed stark agreement with our computational models and target epitopes. The sera from mice immunized with engineered designs display epitope-scaffolds and spike binding activity. We also demonstrated the utility of the designed epitope-scaffolds in diagnostic applications. Taken all together, our study provides important methodology for targeting the conserved, non-RBD structural motifs of spike protein for SARS-CoV-2 epitope vaccine design and demonstrates the potential utility of 'epitope grafting' in rational vaccine design.

Spatiotemporal landscape of SARS-CoV-2 pulmonary infection reveals Slamf9+Spp1+ macrophages promoting viral clearance and inflammation resolution (preprint)

While SARS-CoV-2 pathogenesis has been intensively investigated, the host mechanisms of viral clearance and inflammation resolution are still elusive because of the ethical limitation of human studies based on COVID-19 convalescents. Here we infected Syrian hamsters by authentic SARS-CoV-2 and built an ideal model to simulate the natural recovery process of SARS-CoV-2 infection from severe pneumonia. We developed and applied a spatial transcriptomic sequencing technique with subcellular resolution and tissue-scale extensibility, i.e., Stereo-seq, together with single-cell RNA sequencing (scRNA-seq), to the entire lung lobes of 45 hamsters and obtained an elaborate map of the pulmonary spatiotemporal changes from acute infection, severe pneumonia to the late viral clearance and inflammation resolution. While SARS-CoV-2 infection caused massive damages to the hamster lungs, including naive T cell infection and deaths related to lymphopenia, we identified a group of monocyte-derived proliferating Slamf9+Spp1+ macrophages, which were SARS-CoV-2 infection-inducible and cell death-resistant, recruiting neutrophils to clear viruses together. After viral clearance, the Slamf9+Spp1+ macrophages differentiated into Trem2+ and Fbp1+ macrophages, both responsible for inflammation resolution and replenishment of alveolar macrophages. The existence of this specific macrophage subpopulation and its descendants were validated by RNAscope in hamsters, immunofluorescence in hACE2 mice, and public human autopsy scRNA-seq data of COVID-19 patients. The spatiotemporal landscape of SARS-CoV-2 infection in hamster lungs and the identification of Slamf9+Spp1+ macrophages that is pivotal to viral clearance and inflammation resolution are important to better understand the critical molecular and cellular players of COVID-19 host defense and also develop potential interventions of COVID-19 immunopathology.

Perceived stress, competitive state anxiety, depression and sleep quality in Chinese athletes During the COVID-19 Pandemic (preprint)

Background: The 2019 coronavirus disease (COVID-19) has spread worldwide, and its associated stressors have resulted in decreased sleep quality among athletes. This study aimed to test the correlation between perceived stress and sleep quality and to figure out whether competitive state anxiety and depression mediate this association.Methods: The Perceived Stress Scale, Self-rating Depression Scale, CSA Inventory-2, and Pittsburgh SQ Index were administered in 590 Chinese athletes (275 females, age = 19.93+2.90 years). Mediation analyses were used to investigate whether competitive state anxiety and depression mediate the correlation between perceived stress and sleep quality in athletes.Results: The results indicated that perceived stress was positively related to poor sleep quality (r = 0.232, p < 0.001). Moreover, competitive state anxiety (ab = 0.0347, Boot SE =0.0179, Boot 95% CI = [0.0252, 0.1091]) and depression (ab = 0.0235, Boot SE = 0.0120, Boot 95% CI = [0.0032, 0.0502]) mediated the correlation between perceived stress and sleep quality in athletes.Conclusions: These findings contributed to a better understanding of how perceived stress related to sleep quality in athletes during the COVID-19 pandemic and indicated that perceived stress, competitive state anxiety, and depression are risk factors in the development of athletes’ poor sleep quality.

Cross-species tropism and antigenic landscapes of circulating SARS-CoV-2 variants

Zhang et al. show in vitro cross-species infectivity and neutralization-escape characteristics of 153 SARS-CoV-2 RBD mutants and 11 globally circulating VOC/VOI variants. They reveal an association between enhanced cross-species infection potential and the current cumulative prevalence of mutations, which can inform surveillance and forecasting of SARS-CoV-2 spike mutations.

15-Month Health Outcomes of Hospitalized COVID-19 Patients from Onset: A Cohort Study (preprint)

Background: The long-term impact of COVID-19 on patient health has been a recent focus. This study aims to determine the persistent symptoms and psychological conditions of patients hospitalized with COVID-19 15 months after onset. The potential risk factors were also explored.Methods: A cohort of COVID-19 patients discharged from February 20, 2020 to March 31, 2020 was recruited. Follow-ups were conducted using validated questionnaires and psychological screening scales at 15 months after onset to evaluate the patients’ health status. The risk factors for long-term health impacts and their associations with disease severity was analyzed.Findings: 534 COVID-19 patients were enrolled. The median age of the patients was 62.0 years old (IQR 52.0-70.0) and 295 were female (55.2%). The median time from onset to follow-up was 460.0 (451.0-467.0) days. Sleep disturbance (18.5%, 99/534) and fatigue (17.2%, 92/534) were the most common persistent symptoms. 6.4% (34/534) of the patients had depression, 9.2% (49/534) were anxious, 13.0% (70/534) had insomnia and 4.7% (25/534) suffered from posttraumatic stress disorder (PTSD). Multivariate adjusted logistic regression analysis showed that glucocorticoid use during hospitalization (OR 3.58, 95% CI 1.12-11.44) was significantly associated with an increased risk of fatigue. The OR values for anxiety and sleep disorders were 2.36 (95% CI 1.07-5.20) and 2.16 (95% CI 1.13-4.14) in females compared with males. The OR value of PTSD was 25.6 (95% CI 3.3-198.4) in patients with persistent symptoms to those without persistent symptoms. No significant associations were observed between fatigue syndrome or adverse mental outcomes and disease severity.Interpretation: 15-month follow-up in this study aroused the need of extended rehabilitation intervention for complete recovery in COVID-19 patients. Funding: None to declare. Declaration of Interest: All the authors declare no competing interests.Ethical Approval: The Research Ethics Committee of Shanghai Changzheng Hospital approved this study (2020SL007).

Risk-adapted treatment strategy for COVID-19 patients. (Coronavirus (COVID-19) collection.)

Background: There are no clear expert consensus or guidelines on how to treat 2019 coronavirus disease (COVID-19). The objective of this study is to investigate the short-term effect of risk-adapted treatment strategy on patients with COVID-19.

CT-based Radiomic Nomogram for Predicting the Severity of Patients With COVID-19 (preprint)

Background: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. We aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19. To develop and validate a radiomics nomogram for identifying severe patients with COVID-19. Methods: : There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness. Results: : The radiomics signature consisting of 4 selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts ( P <0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts, showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19. Conclusion: We present an easy-to-use radiomics nomogram to identify the severe patients with COVID-19 for better guiding a prompt management and treatment.

Plasma cell-free DNA promise disease monitoring and tissue injury assessment of COVID-19 (preprint)

COVID-19 is a huge threat to global health. Due to the lack of definitive etiological therapeutics currently, effective disease monitoring is of high clinical value for better healthcare and management of the large number of COVID-19 patients. In this study, we recruited 37 COVID-19 patients, collected 176 blood samples upon diagnosis and during treatment, and analyzed cell-free DNA (cfDNA) in these samples. We report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA characteristics reflect patient-specific physiological conditions during treatment. Further analysis on tissue origin tracing of cfDNA reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, we demonstrate the translational merit of cfDNA as valuable analyte for effective disease monitoring, as well as tissue injury assessment in COVID-19 patients.

Clinical characteristics and outcome analysis of 8 cases with COVID-19

Objective: To investigate the clinical characteristics and outcome of patients with COVID-19 in Shenyang. Method: Data including clinical characteristics, outcome and laboratory index were obtained and analyzed in eight patients who were diagnosed with COVID-19 and admitted to the First hospital of China Medical University between January 21, 2020 to February 8, 2020.

A Chinese host genetic study discovered type I interferons and causality of cholesterol levels and WBC counts on COVID-19 severity (preprint)

As of early May 2021, the ongoing pandemic COVID-19 has caused over 160 million of infections and over 3 million deaths worldwide. Many risk factors, such as age, gender, and comorbidities, have been studied to explain the variable symptoms of infected patients. However, these effects may not fully account for the diversity in disease severity. Here, we present a comprehensive analysis of a broad range of patients laboratory and clinical assessments to investigate the genetic contributions to COVID-19 severity. By performing GWAS analysis, we discovered several concrete associations for laboratory features. Based on these findings, we performed Mendelian randomization (MR) analysis to investigate the causality of laboratory traits on disease severity. From the MR study, we identified two causal traits, cholesterol levels and WBC counts. The functional gene related to cholesterol levels is ApoE and people with particular ApoE genotype are more likely to have higher cholesterol levels, facilitating the process that SARS-CoV-2 binds on its receptor ACE2 and aggravating COVID-19 disease. The functional gene related to WBC counts is MHC system that plays a central role in the immune system. The host immune response to the SARS-CoV-2 infection greatly affects the patients severity status and clinical outcome. Additionally, our gene-based and GSEA analysis revealed interferon pathways, including type I interferon receptor binding, regulation of IFNA signaling, and SARS coronavirus and innate immunity. We hope that our work will make a contribution in studying the genetic mechanisms of disease illness and serve as useful reference for the clinical diagnosis and treatment of COVID-19.