Gold-based paper for antigen detection of monkeypox virus.

In 2022, the outbreak of the monkeypox virus occurred in many non-endemic countries, and the World Health Organization (WHO) assessed that this outbreak was "atypical". The establishment of a rapid and effective assay that can be used for the early diagnosis of monkeypox virus infection is crucial for outbreak prevention and control. In this study, the monkeypox virus A29 protein and the homologous vaccinia virus A27 protein and cowpox virus 162 protein were expressed in Escherichia coli BL21 for screening. We synthesized the monkeypox virus A2917-49 peptide as the immunogen and obtained 25 monoclonal antibodies (mAbs) against the A29 protein using mouse hybridoma techniques. Then an immunochromatographic test strip method for detecting A29 was established. The strips utilizing mAb-7C5 and 5D8 showed the best sensitivity and lowest limit of detection: 50 pg mL-1 for purified A29 and specificity tests showed that the strips did not cross-react with other orthopox viruses (vaccinia virus or cowpox virus) as well as common respiratory pathogens (SARS-CoV-2, influenza A and influenza B). Therefore, this method can be used for early and rapid diagnosis of monkeypox virus infection by antigen detection.

The Incidence of Myocarditis Following an Influenza Vaccination: A Population-Based Observational Study.

BACKGROUND AND OBJECTIVE: Recently, studies have pointed to a link between coronavirus disease 2019 vaccinations and myocarditis. Myocarditis following an influenza vaccine has been sporadically reported. However, it is not known whether this adverse event occurs among elderly individuals who have received influenza vaccines. We used a population-based database and a self-controlled case-series design to estimate the incidence of myocarditis following an influenza vaccination. METHODS: Data were extracted from Taiwan's National Health Insurance Research Database. The study population consisted of elderly people aged ≥ 65 years who had de novo myocarditis, which required hospitalization, within 6 months after receiving an influenza vaccination between 2003 and 2017. The first 1-7, 1-14, and 1-42 days after vaccination were defined as risk intervals, and the other periods were defined as control intervals. Poisson regression was used to calculate the incidence rate ratio for myocarditis between the risk and control periods. RESULTS: Within 180 days following a vaccination, 191 people were hospitalized for myocarditis among 19,678,904 people. In comparison with control intervals, the incidence rate ratios of an admission for myocarditis for days 1-7, 1-14, and 1-42 were 0.80 (95% confidence interval 0.36-1.81), 0.72 (95% confidence interval 0.39-1.32), and 0.73 (95% confidence interval 0.50-1.05), respectively. Subgroup analyses by sex, age, Charlson Comorbidity Index scores, and comorbidities did not yield significant differences in the incidence rate ratio. CONCLUSIONS: Regardless of the post-vaccination time and underlying baseline characteristics, the incidence risk of myocarditis is not significantly increased in the elderly following an influenza vaccination.

Website analytics for government user behavior during COVID-19 pandemic

Purpose>The present study aims to clarify the following two research objectives: (1) the user behavior of government websites during the coronavirus disease (COVID-19) period and (2) how the government improved government's website design during the COVID-19 period.Design/methodology/approach>The authors used website analytics to examine usage patterns and behaviors of the government website via personal computer (PC) and mobile devices during the COVID-19 pandemic. In-depth interviews were conducted to understand the user experience of government website users and to gather users' opinions about how government websites should be redesigned.Findings>With the rising of the COIVID-19 pandemic, most studies expect that the use of government websites through a mobile device will grow astonishingly. The authors uncovered that the COVID-19 pandemic did not increase the use of government websites. Instead, severe declines in website usage were observed for all device users with the declines being more pronounced in mobile device users than in PC users. This is an admonitory caveat that reveals public health and pandemic prevention information announced on government websites cannot be effectively transmitted to the general public through official online platforms.Originality/value>The study highlights the gap in information behavior and usage patterns between PC and mobile device users when visiting government websites. Although mobile devices brought many new visitors, mobile devices are ineffective in retaining visitors and continuous long-term use. The results of localize experience is helpful in the improvement of government website evaluation worldwide.

A Fusion Decision-Making Architecture for COVID-19 Crisis Analysis and Management

The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is not appropriate or sufficient. Thus, there is an urgent requirement to reexamine and revise an enterprise's inherent crisis management architecture so as to help it recover sooner after having encountered extremely negative economic effects. To fulfill this need, the present paper introduces a fusion architecture that integrates artificial intelligence and multiple criteria decision making to exploit essential risk factors and identify the intertwined relations between dimensions/criteria for managers to prioritize improvement plans and deploy resources to key areas without any waste. The result indicated the accurate improvement priorities, which ran in the order of financial sustainability (A), customer and stakeholders (B), enablers' learning and growth (D), and internal business process (C) based on the measurement of the impact. The method herein will help to effectively and efficiently support crisis management for an organization confronting COVID-19. Among all the criteria, maintaining fixed reserves was the most successful factor regarding crisis management.

Rapid colloidal gold immunochromatographic assay for the detection of SARS-CoV-2 total antibodies after vaccination.

An epidemic caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spread globally in just a few months. To prevent the further spread of the virus, millions of people around the world have been vaccinated for COVID-19. Although the plaque reduction neutralization test (PRNT) has become the gold standard method for determining neutralizing antibodies, this method has many limitations; therefore, there remains an urgent need for a quick and accurate technique to evaluate the immune efficacy of COVID-19 vaccines. Here, after the recombinant expression of the SARS-CoV-2 spike protein receptor binding domain (S-RBD), we established a colloidal gold immunochromatographic assay (GICA) based on the principle of a double antigen sandwich for the detection of total antibodies in sera. Under the developed conditions, the GICA was capable of the rapid detection of SARS-CoV-2 total antibodies within 15 min. In addition, the anti-S-RBD antibodies measured by the GICA had a good correlation with the results measured by ELISA, indicating that the GICA may be used as a rapid tool for the detection of neutralizing antibodies derived from SARS-CoV-2 infection. Clinical detection was performed using serum samples obtained from 40 subjects who had received their two doses of the COVID-19 vaccine and 20 unvaccinated serum samples. We found that our method had high sensitivity and specificity; therefore, our convenient and rapid GICA method could preliminarily evaluate the protection rate and effectiveness of vaccines by monitoring total antibody levels.

Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices

Under the Basel II and Basel III agreements, the probability of default (PD) is a key parameter used in calculating expected credit loss (ECL), which is typically defined as: PD × Loss Given Default × Exposure at Default. In practice or in regulatory requirements, gross domestic product (GDP) has been adopted in the PD estimation model. Due to the problem of excessive fluctuation and highly volatile ECL estimation, models that produce satisfactory PD and thus ECL estimations in the context of existing risk management techniques are lacking. In this study, we explore the usage of the credit default swap index (CDX), a market’s expectation of future PD, as a predictor of the default rate (DR). By comparing the goodness-of-fit of logistic regression, several conclusions are drawn. Firstly, in general, GDP has considerable explanatory power for the default rate which is consistent with current models in practice. Secondly, although both GDP and CDX fit the DR well for rating B class, CDX has a significantly better fit of DR for ratings [A, Baa, Ba]. Thirdly, compared with low-rated companies, the relationship between the DR and GDP is relatively weak for rating A. This phenomenon implies that, in addition to using macroeconomic variables and firm-specific explanatory variables in the PD estimation model, high-rated companies exhibit a greater need to use market supplemental information, such as CDX, to capture the changes in the DR.

Identification of the Critical Factors for Global Supply Chain Management under the COVID-19 Outbreak via a Fusion Intelligent Decision Support System

Under the ravages of COVID-19, global supply chains have encountered unprecedented disruptions. Past experiences cannot fully explain the situations nor provide any suitable responses to these fatal shocks on supply chain management (SCM), especially in todays’ highly intertwined/globalized business environment. This research thus revisits and rechecks the crucial components for global SCM during such special periods, and the basic essence of such management covers numerous perspectives that can be categorized into a multiple criteria decision making (MCDM) approach. To handle this complex issue appropriately, one can introduce a fusion intelligent system that involves data envelopment analysis (DEA), rough set theory (RST), and MCDM to understand the reality of the analyzed problem in a faster and better manner. Based on the empirical results, we rank the priorities in order as cash management and information (D), raw material supply (B), global management strategy (C), and productivity and logistics (A) for improvement in SCM. This finding is confirmed by companies now undergoing a downsizing strategy in order to survive in this harsh business environment.