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.

COVID-19 Information Overload, Negative Emotions and Posttraumatic Stress Disorder: A Cross-Sectional Study.

Background: Under the context of the COVID-19 pandemic, a large amount of COVID-19-related information can cause an individual's perceived information overload, further halting the individual's psychological health. As a minor psychological discomfort could develop severe mental disorders such as posttraumatic stress disorder, it is necessary to understand the chain linkage of COVID-19 information overload turn to posttraumatic stress disorder to ensure timely intervention can be offered at each point of mental state transformation. Hence, we examined the negative outcomes of COVID-19 information overload and investigated the direct and indirect effects of COVID-19 on posttraumatic stress disorder. Methods: A convenient sample of Chinese adults (n = 1150) was investigated by an online survey from July 2020 to March 2021. The extent of COVID-19 information overload was measured by the information overload severity scale on the text of the COVID-19 pandemic. Psychological distress symptoms were measured using a 7-item anxiety scale (GAD-7), the 9-item Patient Health Questionnaire depression module (PHQ-9), and the psychometric properties of the PTSD Checklist (PCL-C). Structural equation modeling and bootstrap methods were utilized to analyze the relationships between variables. Results: COVID-19 information overload is positively related to an individual's anxiety, depression, and posttraumatic stress disorder. Furthermore, COVID-19 information overload can indirectly affect an individual's PTSD symptoms by increasing the feeling of depression. R2 values of anxiety, depression, and PTSD were 0.471, 0.324, and 0.795, respectively. Conclusion: COVID-19 information overload, anxiety, depression, and PTSD are negative psychological states, and each variable is closely linked with the others, suggesting the need for potential psychological interventions at specific times. Practical public training, such as crisis coping and information filtering, is essential. Regulation of technology companies is also essential.

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.

Effect of Repeated Home Quarantine on Anxiety, Depression, and PTSD Symptoms in a Chinese Population During the COVID-19 Pandemic: A Cross-sectional Study.

Background: Strict quarantines can prevent the spread of the COVID-19 pandemic, but also increase the risk of mental illness. This study examined whether the people who have experienced repeated home quarantine performance more negative emotions such as anxiety, depression, and post-traumatic stress disorder (PTSD) in a Chinese population. Methods: We collected data from 2,514 participants in Pi County, Chengdu City, and stratified them into two groups. Group 1 comprised 1,214 individuals who were quarantined only once in early 2020, while Group 2 comprised 1,300 individuals who were quarantined in early 2020 and again in late 2020. Both groups were from the same community. The GAD-7, PHQ-9, and PCL-C scales were used to assess symptoms of anxiety, depression, and PTSD between the two groups. Results: Analyses showed that total PHQ-9 scores were significantly higher in Group 2 than in Group 1 (p < 0.001) and the quarantine times and age are independent predictors of symptoms of depression (p < 0.001). The two groups did not differ significantly in total GAD-7 or PCL-C scores. Conclusion: Increasing quarantine times was associated with moderate to severe depression symptoms, but not with an increase in symptoms of anxiety or PTSD.

COVID-19 Information Overload, Negative Emotions and Posttraumatic Stress Disorder: A Cross-Sectional Study

Background Under the context of the COVID-19 pandemic, a large amount of COVID-19-related information can cause an individual's perceived information overload, further halting the individual's psychological health. As a minor psychological discomfort could develop severe mental disorders such as posttraumatic stress disorder, it is necessary to understand the chain linkage of COVID-19 information overload turn to posttraumatic stress disorder to ensure timely intervention can be offered at each point of mental state transformation. Hence, we examined the negative outcomes of COVID-19 information overload and investigated the direct and indirect effects of COVID-19 on posttraumatic stress disorder. Methods A convenient sample of Chinese adults (n = 1150) was investigated by an online survey from July 2020 to March 2021. The extent of COVID-19 information overload was measured by the information overload severity scale on the text of the COVID-19 pandemic. Psychological distress symptoms were measured using a 7-item anxiety scale (GAD-7), the 9-item Patient Health Questionnaire depression module (PHQ-9), and the psychometric properties of the PTSD Checklist (PCL-C). Structural equation modeling and bootstrap methods were utilized to analyze the relationships between variables. Results COVID-19 information overload is positively related to an individual's anxiety, depression, and posttraumatic stress disorder. Furthermore, COVID-19 information overload can indirectly affect an individual's PTSD symptoms by increasing the feeling of depression. R2 values of anxiety, depression, and PTSD were 0.471, 0.324, and 0.795, respectively. Conclusion COVID-19 information overload, anxiety, depression, and PTSD are negative psychological states, and each variable is closely linked with the others, suggesting the need for potential psychological interventions at specific times. Practical public training, such as crisis coping and information filtering, is essential. Regulation of technology companies is also essential.

Is amyloid fibrillation related to 3D domain swapping for the C-terminal domain of SARS-CoV main protease?

The C-terminal domain of SARS-CoV main protease (Mpro-C) can form 3D domain-swapped dimer by exchanging the α1-helices fully buried inside the protein hydrophobic core, under non-denaturing conditions. Here, we report that Mpro-C can also form amyloid fibrils under the 3D domain-swappable conditions in vitro, and the fibrils are not formed through runaway/propagated domain swapping. It is found that there are positive correlations between the rates of domain swapping dimerization and amyloid fibrillation at different temperatures, and for different mutants. However, some Mpro-C mutants incapable of 3D domain swapping can still form amyloid fibrils, indicating that 3D domain swapping is not essential for amyloid fibrillation. Furthermore, NMR H/D exchange data and molecular dynamics simulation results suggest that the protofibril core region tends to unpack at the early stage of 3D domain swapping, so that the amyloid fibrillation can proceed during the 3D domain swapping process. We propose that 3D domain swapping makes it possible for the unpacking of the amyloidogenic fragment of the protein and thus accelerates the amyloid fibrillation process kinetically, which explains the well-documented correlations between amyloid fibrillation and 3D domain swapping observed in many proteins.

Decision Intelligence for Nationwide Ventilator Allocation During the COVID-19 Pandemic.

Many states in the U.S. have faced shortages of medical resources because of the surge in the number of patients suffering from COVID-19. As many projections indicate, the situation will be far worse in coming months. The upcoming challenge is not only due to the exponential growth in cases but also because of inherent uncertainty and lags associated with disease progression. In this paper, we present a collection of models for decision intelligence which provide decision-support for ventilator allocation based on predictions from well-accepted oracles of disease progression. It is clear from our study that without coordination among states, there is a very high risk of ventilator shortages in certain states. However, such shortages can be reduced, provided neighboring states agree to share ventilators as suggested by our models. We show that despite the explosive growth in cases and associated uncertainty in ventilator demand, our simulation results hold the promise of reducing unmet demand, even in the face of significant uncertainty. This paper also provides the first evidence that coordination between neighboring states can lead to significant reduction in ventilator shortages across the U.S.