The association between team resilience and team performance in nurses during COVID-19 pandemic: a network analysis.

BACKGROUND: Compared to other healthcare workers, nurses are more vulnerable to the potentially devastating effects of pandemic-related stressors. Studies have not yet investigated the deeper characteristics of the relationship between team resilience and team performance among nurses during the COVID-19 pandemic. This study aimed to elucidate the characteristics of team resilience and performance networks among nurses during the pandemic. METHODS: A cross-sectional study involving 118 nursing teams comprising 1627 practice nurses from four tertiary-A and secondary-A hospitals in Shandong Province, China, was conducted. Analyzing and Developing Adaptability and Performance in Teams to Enhance Resilience Scale and the Team Effectiveness Scale were used to measure team resilience and performance, respectively. The estimation of the network model and calculation of related metrics, network stability and accuracy, and network comparison tests were performed using R 4.0.2. RESULTS: Node monitoring had the highest centralities in the team resilience and performance network model, followed by node anticipation, cooperation satisfaction, and cooperation with other departments. Moreover, node cooperation satisfaction and learning had the highest levels of bridge centrality in the entire network. CONCLUSION: Monitoring, anticipation, cooperation satisfaction, cooperation with other departments, and learning constituted core variables maintaining the team resilience-performance network structure of nurses during the pandemic. Clinical interventions targeting core variables may be effective in maintaining or promoting both team resilience and performance in this population.

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning

Vaccination offers health, economic, and social benefits. However, three major issues—vaccine quality, demand forecasting, and trust among stakeholders—persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.

Inducing supplier backup via manufacturer information sharing under supply disruption risk.

What comes along with the repeating and wide-range COVID-19 outbreak is the increasingly latent supply disruption risk encountered by global supply chains. Among many instruments to enhance supply chain resilience, backup production may be an appropriate choice, whereas how to induce the supplier backup becomes an obstacle. In this study, we investigate a supply chain in the context of the crisis-like new normal with supply disruption risk, wherein a manufacturer uses private demand information as a strategic lever, according to which a supplier decides whether to adopt backup production. Our findings reveal that the supplier's equilibrium decision on the adoption of backup production exhibits a cutoff structure when the manufacturer shares demand information. Moreover, we uncover the effect of information sharing on backup decision. In specific, information sharing impedes the adoption of backup production under low demand potential while promoting it under high demand potential. Interestingly, the manufacturer may have the incentive to share the demand information with the upstream supplier if the demand variability is low and the backup cost is moderate, and such information sharing stimulates the supplier to adopt the backup production. Counterintuitively, the manufacturer and the whole supply chain may display nonmonotonic relations to the backup cost as a result.

Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning.

Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.

Antibodies Can Last for More Than 1 Year After SARS-CoV-2 Infection: A Follow-Up Study From Survivors of COVID-19.

Background: COVID-19 is a global pandemic. The prevention of SARS-CoV-2 infection and the rehabilitation of survivors are currently the most urgent tasks. However, after patients with COVID-19 are discharged from the hospital, how long the antibodies persist, whether the lung lesions can be completely absorbed, and whether cardiopulmonary abnormalities exist remain unclear. Methods: A total of 56 COVID-19 survivors were followed up for 12 months, with examinations including serum virus-specific antibodies, chest CT, and cardiopulmonary exercise testing. Results: The IgG titer of the COVID-19 survivors decreased gradually, especially in the first 6 months after discharge. At 6 and 12 months after discharge, the IgG titer decreased by 68.9 and 86.0%, respectively. The IgG titer in patients with severe disease was higher than that in patients with non-severe disease at each time point, but the difference did not reach statistical significance. Among the patients, 11.8% were IgG negative up to 12 months after discharge. Chest CT scans showed that at 3 and 10 months after discharge, the lung opacity had decreased by 91.9 and 95.5%, respectively, as compared with that at admission. 10 months after discharge, 12.5% of the patients had an opacity percentage >1%, and 18.8% of patients had pulmonary fibrosis (38.5% in the severe group and 5.3% in the non-severe group, P < 0.001). Cardiopulmonary exercise testing showed that 22.9% of patients had FEV1/FVC%Pred <92%, 17.1% of patients had FEV1%Pred <80%, 20.0% of patients had a VO2 AT <14 mlO2/kg/min, and 22.9% of patients had a VE/VCO2 slope >30%. Conclusions: IgG antibodies in most patients with COVID-19 can last for at least 12 months after discharge. The IgG titers decreased significantly in the first 6 months and remained stable in the following 6 months. The lung lesions of most patients with COVID-19 can be absorbed without sequelae, and a few patients in severe condition are more likely to develop pulmonary fibrosis. Approximately one-fifth of the patients had cardiopulmonary dysfunction 6 months after discharge.

A prospective cohort study on radiological and physiological outcomes of recovered COVID-19 patients 6 months after discharge.

BACKGROUND: This study investigated patients' long-term radiological and physiological outcomes with coronavirus disease 2019 (COVID-19). METHODS: A total of 52 patients (26 men and 26 women, 32 with moderate COVID-19 and 20 with severe COVID-19, with a median age of 50.5 years) who had COVID-19 participated in this study. Follow-up thin-section chest computed tomography (CT) scans were performed at 1, 3, and 6 months after discharge. Cardiopulmonary exercise testing was performed on 37 patients 6 months after discharge. The clinical data and the chest CT findings were recorded and analyzed. RESULTS: The predominant chest CT patterns of abnormalities observed at 6 months after discharge were parenchymal band, interlobular septal thickening, and traction bronchiectasis. The cumulative percentage of the complete radiological resolution was 17%, 42%, 67%, and 75% at discharge and 1, 3, and 6 months after discharge, respectively. A subgroup analysis revealed that 88% of patients with moderate type and 55% of patients with severe type COVID-19 achieved complete radiological resolution at 6 months after discharge, and the difference between the 2 groups was significant (P<0.001). The following risk factors were found to be associated with an incomplete radiological resolution at 6 months after discharge: an age >50 years old, the severe type of COVID-19, a hospital stay >18 days, mechanical ventilation, steroid therapy, immunoglobin therapy, an opacity score at discharge >4, and a volume of opacity at discharge >235 mL. CONCLUSIONS: Chest CT lesions were absorbed without any sequelae in most patients with COVID-19; however, fibrotic-like changes and cardiopulmonary insufficiency were still present in a considerable proportion of COVID-19 survivors at 6 months after discharge, especially in patients with severe type COVID-19.

Intestinal Microbiota-A Promising Target for Antiviral Therapy?

The intestinal microbiota is thought to be an important biological barrier against enteric pathogens. Its depletion, however, also has curative effects against some viral infections, suggesting that different components of the intestinal microbiota can play both promoting and inhibitory roles depending on the type of viral infection. The two primary mechanisms by which the microbiota facilitates or inhibits viral invasion involve participation in the innate and adaptive immune responses and direct or indirect interaction with the virus, during which the abundance and composition of the intestinal microbiota might be changed by the virus. Oral administration of probiotics, faecal microbiota transplantation (FMT), and antibiotics are major therapeutic strategies for regulating intestinal microbiota balance. However, these three methods have shown limited curative effects in clinical trials. Therefore, the intestinal microbiota might represent a new and promising supplementary antiviral therapeutic target, and more efficient and safer methods for regulating the microbiota require deeper investigation. This review summarizes the latest research on the relationship among the intestinal microbiota, anti-viral immunity and viruses and the most commonly used methods for regulating the intestinal microbiota with the goal of providing new insight into the antiviral effects of the gut microbiota.

Impact of Cardiovascular Diseases on COVID-19: A Systematic Review.

In December 2019, pneumonia of unknown cause broke out, and currently more than 150 countries around the world have been affected. Globally, as of 5: 46 pm CET, 6 November 2020, the World Health Organization (WHO) had reported 48 534 508 confirmed cases of COVID-19, including 1 231 017 deaths. The novel coronavirus disease (COVID-19) outbreak, caused by the SARS-CoV-2 virus, is the most important medical challenge in decades. Previous research mainly focused on the exploration of lung changes. However, with development of the disease and deepening research, more and more patients showed cardiovascular diseases, even in those without respiratory symptoms, and some researchers have found that underlying cardiovascular diseases increase the risk of infection. Although the related mechanism is not thoroughly studied, based on existing research, we speculate that the interaction between the virus and its receptor, inflammatory factors, various forms of the stress response, hypoxic environment, and drug administration could all induce the development of cardiac adverse events. Interventions to control these pathogenic factors may effectively reduce the occurrence of cardiovascular complications. This review summarizes the latest research on the relationship between COVID-19 and its associated cardiovascular complications, and we also explore possible mechanisms and treatments.

Follow-Up Study of the Chest CT Characteristics of COVID-19 Survivors Seven Months After Recovery.

Background: It has remained a concern whether any long-term pulmonary sequelae exist for COVID-19 survivors. Methods: Forty-one patients (22 men and 19 women, 50 ± 14 years) confirmed with COVID-19 performed follow-up chest CT and cardiopulmonary exercise testing at 7 months after discharge. Patients were divided into fibrosis group and non-fibrosis group according to the evidence of fibrosis on follow-up CT. The clinical data and the CT findings were recorded and analyzed. Results: The predominant CT patterns of abnormalities observed at 7 months after discharge were parenchymal band (41%), interlobular septal thickening (32%), and traction bronchiectasis (29%). Sixty-one percent of the patients achieved complete radiological resolution, and 29% of patients developed pulmonary fibrosis. Compared with the patients in the non-fibrosis group, the patients in the fibrosis group were older, with a longer hospital stay, a higher rate of steroid and mechanical ventilation therapy, lower levels of lymphocyte and T cell count, higher levels of D-dimer and lactic dehydrogenase, and higher quantitative CT parameters (opacity score, volume of opacity, and percentage of opacity) at discharge. Besides, oxygen consumption and metabolic equations were decreased and ventilatory equivalent for carbon dioxide was increased in patients in the fibrosis group. Logistic regression analyses revealed that age, steroid therapy, presence of traction bronchiectasis on chest CT at discharge, and opacity score at discharge, were independent risk factors for developing pulmonary fibrosis at 7 months after discharge. Receiver operating characteristic analysis revealed that the combined clinical-radiological model was better than the clinical-only model in the prediction of pulmonary fibrosis. Conclusions: The chest CT lesions could be absorbed without any sequelae for most patients with COVID-19, whereas older patients with severe conditions are more prone to develop fibrosis, which may further lead to cardiopulmonary insufficiency. The combined clinical-radiological model may predict the formation of pulmonary fibrosis early.

Potential effects of SARS-CoV-2 on the gastrointestinal tract and liver.

COVID-19 is a pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early reported symptoms include fever, cough, and respiratory symptoms. There were few reports of digestive symptoms. However, with COVID-19 spreading worldwide, symptoms such as vomiting, diarrhoea, and abdominal pain have gained increasing attention. Research has found that angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 receptor, is strongly expressed in the gastrointestinal tract and liver. Whether theoretically or clinically, many studies have suggested a close connection between COVID-19 and the digestive system. In this review, we summarize the digestive symptoms reported in existing research, discuss the impact of SARS-CoV-2 on the gastrointestinal tract and liver, and determine the possible mechanisms and aetiology, such as cytokine storm. In-depth exploration of the relationship between COVID-19 and the digestive system is urgently needed.

High-Resolution Computed Tomography Manifestations of 5 Pediatric Patients With 2019 Novel Coronavirus.

We present clinical and chest computed tomography (CT) features of 5 cases of pediatric patients with 2019 novel coronavirus. Two patients had fever and dry cough, whereas the rest of 3 patients were asymptomatic. Three patients had unilateral ground glass opacities with or without consolidation in the subpleural region on high-resolution chest CT, 1 patient had bilateral ground glass opacities, and 1 patient was negative for CT. We note that up to 66.7% asymptomatic patients had pulmonary lesions, so the asymptomatic children with Wuhan contact are recommended to do a 2019 novel coronavirus real-time fluorescence polymerase chain reaction screening. Unlike adult patients, only a small amount of patients had multilobes affected, so we speculate that the pediatric patients generally have milder CT findings than adults.

COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia.

OBJECTIVES: To investigate the clinical and chest CT characteristics of COVID-19 pneumonia and explore the radiological differences between COVID-19 and influenza. MATERIALS AND METHODS: A total of 122 patients (61 men and 61 women, 48 ± 15 years) confirmed with COVID-19 and 48 patients (23 men and 25 women, 47 ± 19 years) confirmed with influenza were enrolled in the study. Thin-section CT was performed. The clinical data and the chest CT findings were recorded. RESULTS: The most common symptoms of COVID-19 were fever (74%) and cough (63%), and 102 patients (83%) had Wuhan contact. Pneumonia in 50 patients with COVID-19 (45%) distributed in the peripheral regions of the lung, while it showed mixed distribution in 26 patients (74%) with influenza (p = 0.022). The most common CT features of the COVID-19 group were pure ground-glass opacities (GGO, 36%), GGO with consolidation (51%), rounded opacities (35%), linear opacities (64%), bronchiolar wall thickening (49%), and interlobular septal thickening (66%). Compared with the influenza group, the COVID-19 group was more likely to have rounded opacities (35% vs. 17%, p = 0.048) and interlobular septal thickening (66% vs. 43%, p = 0.014), but less likely to have nodules (28% vs. 71%, p < 0.001), tree-in-bud sign (9% vs. 40%, p < 0.001), and pleural effusion (6% vs. 31%, p < 0.001). CONCLUSIONS: There are significant differences in the CT manifestations of patients with COVID-19 and influenza. Presence of rounded opacities and interlobular septal thickening, with the absence of nodules and tree-in-bud sign, and with the typical peripheral distribution, may help us differentiate COVID-19 from influenza. KEY POINTS: • Typical CT features of COVID-19 include pure ground-glass opacities (GGO), GGO with consolidation, rounded opacities, bronchiolar wall thickening, interlobular septal thickening, and a peripheral distribution. • Presence of rounded opacities and interlobular septal thickening, with the absence of nodules and tree-in-bud sign, and with the typical peripheral distribution, may help us differentiate COVID-19 from influenza.