Mpox multi-antigen mRNA vaccine candidates by a simplified manufacturing strategy afford efficient protection against lethal orthopoxvirus challenge.

Current unprecedented mpox outbreaks in non-endemic regions represent a global public health concern. Although two live-attenuated vaccinia virus (VACV)-based vaccines have been urgently approved for people at high risk for mpox, a safer and more effective vaccine that can be available for the general public is desperately needed. By utilizing a simplified manufacturing strategy of mixing DNA plasmids before transcription, we developed two multi-antigen mRNA vaccine candidates, which encode four (M1, A29, B6, A35, termed as Rmix4) or six (M1, H3, A29, E8, B6, A35, termed as Rmix6) mpox virus antigens. We demonstrated that those mpox multi-antigen mRNA vaccine candidates elicited similar potent cross-neutralizing immune responses against VACV, and compared to Rmix4, Rmix6 elicited significantly stronger cellular immune responses. Moreover, immunization with both vaccine candidates protected mice from the lethal VACV challenge. Investigation of B-cell receptor (BCR) repertoire elicited by mpox individual antigen demonstrated that the M1 antigen efficiently induced neutralizing antibody responses, and all neutralizing antibodies among the top 20 frequent antibodies appeared to target the same conformational epitope as 7D11, revealing potential vulnerability to viral immune evasion. Our findings suggest that Rmix4 and Rmix6 from a simplified manufacturing process are promising candidates to combat mpox.

Molecular Insights into the Binding Behavior of Imidazolium Ionic Liquids to the Receptor Binding Domain of the SARS-CoV-2 Spike Protein.

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein is considered as a key target for the design and development of COVID-19 drugs and inhibitors. Due to their unique structure and properties, ionic liquids (ILs) have many special interactions with proteins, showing great potential in biomedicine. Nevertheless, few research studies have been carried out on ILs and the spike RBD protein. Here, we explore the interaction of ILs and the RBD protein through large-scale molecular dynamics simulations (4 µs in total). It was found that IL cations with long alkyl chain lengths (nchain) could spontaneously bind to the cavity region of the RBD protein. The longer the alkyl chain is, the stabler the cations bind to the protein. The binding free energy (ΔG) had the same trend, peaking at nchain = 12 with -101.19 kJ/mol. The cationic chain lengths and their fit to the pocket are decisive factors that influence the binding strength of cations and proteins. The cationic imidazole ring has a high contact frequency with phenylalanine and tryptophan, and the hydrophobic residues phenylalanine, valine, leucine, and isoleucine are the most interacting residues with side chains of cations. Meanwhile, through analysis of the interaction energy, the hydrophobic and π-π interactions are the main contributors to the high affinity between cations and the RBD protein. In addition, the long-chain ILs would also act on the protein through clustering. These studies not only provide insights into the molecular interaction between ILs and the RBD of SARS-CoV-2 but also contribute to the rational design of IL-based drugs, drug carriers, and selective inhibitors as a therapeutic for SARS-CoV-2.

Clustering method with axiomatization to support failure mode and effect analysis

Failure Mode and Effect Analysis (FMEA) is a highly structured risk-prevention management process that improves the reliability and safety of a system. This article investigates one of the most critical issues in FMEA practice: Clustering failure modes based on their risks. In the failure mode clustering problem, all identified failure modes need to be assigned to several predefined and risk-ordered categories to manage their risks. We model the clustering of failure modes through multi-expert multiple criteria decision making with an additive value function, and call it the additive N -clustering problem. We begin by proposing six axioms that describe an ideal clustering method in the additive N -clustering problem, and find that the EXogenous Clustering Method (EXCM), where category thresholds can be exogenously provided, is ideal (Exogenous Possibility Theorem), whereas any endogenous clustering method, where the clustering is determined endogenously in the given method, cannot satisfy all six axioms simultaneously (Endogenous Impossibility Theorem). In practice, endogenous clustering methods are important, due to the difficulty in providing accurate and reasonable category thresholds of the EXCM. Therefore, we propose the Consensus-based ENdogenous Clustering Method (CENCM) and discuss its axiomatic properties. We also apply the CENCM to the SARS-CoV-2 prevention case and justify the CENCM through axiomatic comparisons and a detailed simulation experiment. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Optimization and Improvement of qPCR Detection Sensitivity of SARS-CoV-2 in Saliva.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a major public health threat globally, especially during the beginning of the pandemic in 2020. Reverse transcription-quantitative PCR (RT-qPCR) is utilized for viral RNA detection as part of control measures to limit the spread of COVID-19. Collecting nasopharyngeal swabs for RT-qPCR is a routine diagnostic method for COVID-19 in clinical settings, but its large-scale implementation is hindered by a shortage of trained health professionals. Despite concerns over its sensitivity, saliva has been suggested as a practical alternative sampling approach to the nasopharyngeal swab for viral RNA detection. In this study, we spiked saliva from healthy donors with inactivated SARS-CoV-2 from an international standard to evaluate the effect of saliva on viral RNA detection. On average, the saliva increased the cycle threshold (CT) values of the SARS-CoV-2 RNA samples by 2.64 compared to the viral RNA in viral transport medium. Despite substantial variation among different donors in the effect of saliva on RNA quantification, the outcome of the RT-qPCR diagnosis was largely unaffected for viral RNA samples with CT values of <35 (1.55 log10 IU/mL). The saliva-treated viral RNA remained stable for up to 6 h at room temperature and 24 h at 4°C. Further supplementing protease and RNase inhibitors improved the detection of viral RNA in the saliva samples. Our data provide practical information on the storage conditions of saliva samples and suggest optimized sampling procedures for SARS-CoV-2 diagnosis. IMPORTANCE The primary method for detection of SARS-CoV-2 is using nasopharyngeal swabs, but a shortage of trained health professionals has hindered its large-scale implementation. Saliva-based nucleic acid detection is a widely adopted alternative, due to its convenience and minimally invasive nature, but the detection limit and direct impact of saliva on viral RNA remain poorly understood. To address this gap in knowledge, we used a WHO international standard to evaluate the effect of saliva on SARS-CoV-2 RNA detection. We describe the detection profile of saliva-treated SARS-CoV-2 samples under different storage temperatures and incubation periods. We also found that adding protease and RNase inhibitors could improve viral RNA detection in saliva. Our research provides practical recommendations for the optimal storage conditions and sampling procedures for saliva-based testing, which can improve the efficiency of COVID-19 testing and enhance public health responses to the pandemic.

The mediating role of job satisfaction between psychological capital and work engagement among Chinese nurses during COVID-19 outbreak: A comparative study between nurse specialists and general nurses.

Background: The outbreak of COVID-19 has become a global public health emergency, causing great psychological distress to nurses. It is unknown whether the pandemic will affect the work engagement of nurses, the relationship between psychological capital, job satisfaction, and work engagement among nurses, and whether there are differences between nurse specialists and general nurses during the pandemic. Objectives: The purpose of this study was to compare psychological capital, job satisfaction, and work engagement among nurse specialists and general nurses during the pandemic, as well as to test the role of job satisfaction as a mediator in the association between psychological capital and work engagement among nurses, and to examine whether the underlying mechanism of the relationship between psychological capital and job satisfaction differs between nurse specialists and general nurses. Materials and methods: A convenience sampling was used to assess a sample of 372 nurse specialists and 318 general nurses from nine provincial general hospitals in China to participate in the online survey. Data were collected using self-report questionnaires, including the following tools: self-designed socio-demographic questionnaire, psychological capital scale, job satisfaction scale, and work engagement scale. Results: Compared with general nurses, the nurse specialists had higher psychological capital, job satisfaction, and work engagement. Job satisfaction partially mediated the positive association between psychological capital and work engagement and the indirect effect was stronger in nurse specialists in comparison to general nurses during the COVID-19 pandemic. Conclusion: The findings provide important practical implications for future intervention programs aimed at enhancing nurses' work engagement, which may be realized through strengthening psychological capital and job satisfaction during the pandemic. Moreover, considering the cost-effectiveness of limited health care spending, nursing managers should pay more attention to the continuing professional development of young general nurses.

Taiwan and the WHO: negotiating the deconstruction of racialized discourse during the COVID-19 pandemic

Racialized and politicized discourses on individuals and countries have accompanied the emergence and spread of COVID-19. Adopting critical discourse analysis (CDA) and investigating the WHO's news releases, press conferences and the Taiwanese government's formal responses, this article examines three discursive events involving the deconstruction of racialized discourses during the pandemic by the WHO, the WHO's Director-General and Taiwan. Specifically, I focus on the following: the WHO's calls on the international community to cease using the term 'China virus';Dr Ghebreyesus' calls on Taiwan to cease leveling racist insults against the Black community;and the Taiwanese government's refutation of racist allegations by deconstructing racialized meanings and highlighting its marginal status in the global health system due to international politics. The findings demonstrate the different ways each subject framed moral and immoral practice, highlighted or downplayed racialized discourse, and applied moral vs immoral distinction to manipulate and reinforce the audience's thoughts. This article contributes to the field of international relations and its connection with racism by showing how racial injustices 'travel' between places and are negotiated and re-politicized in the global health sphere. [ FROM AUTHOR]

The mediating role of job satisfaction between psychological capital and work engagement among Chinese nurses during COVID-19 outbreak: A comparative study between nurse specialists and general nurses

Background The outbreak of COVID-19 has become a global public health emergency, causing great psychological distress to nurses. It is unknown whether the pandemic will affect the work engagement of nurses, the relationship between psychological capital, job satisfaction, and work engagement among nurses, and whether there are differences between nurse specialists and general nurses during the pandemic. Objectives The purpose of this study was to compare psychological capital, job satisfaction, and work engagement among nurse specialists and general nurses during the pandemic, as well as to test the role of job satisfaction as a mediator in the association between psychological capital and work engagement among nurses, and to examine whether the underlying mechanism of the relationship between psychological capital and job satisfaction differs between nurse specialists and general nurses. Materials and methods A convenience sampling was used to assess a sample of 372 nurse specialists and 318 general nurses from nine provincial general hospitals in China to participate in the online survey. Data were collected using self-report questionnaires, including the following tools: self-designed socio-demographic questionnaire, psychological capital scale, job satisfaction scale, and work engagement scale. Results Compared with general nurses, the nurse specialists had higher psychological capital, job satisfaction, and work engagement. Job satisfaction partially mediated the positive association between psychological capital and work engagement and the indirect effect was stronger in nurse specialists in comparison to general nurses during the COVID-19 pandemic. Conclusion The findings provide important practical implications for future intervention programs aimed at enhancing nurses' work engagement, which may be realized through strengthening psychological capital and job satisfaction during the pandemic. Moreover, considering the cost-effectiveness of limited health care spending, nursing managers should pay more attention to the continuing professional development of young general nurses.

Does transformational leadership and psychological empowerment improve nurses' innovative behaviour during COVID-19 outbreak? A cross-sectional study.

AIMS: To investigate the relationships between transformational leadership, psychological empowerment and innovative behaviour among nurses in China. BACKGROUND: The innovative behaviour of nurses is important to adapt to the changing medical environment. However, there is currently a limited understanding of the relationship between nurses' innovative behaviour and transformational leadership and psychological empowerment during the pandemic. METHODS: Convenience sampling was used to conduct an investigation involving 1317 nurses from 10 hospitals in China from January 2022 to April 2022. Data analysis was performed using correlation analysis, univariate analysis and multiple regression analysis. The STROBE checklist was followed when writing this manuscript. RESULTS: High transformational leadership and high psychological empowerment were associated with high innovative behaviour. The results of the multiple linear regression analysis showed that physical condition, whether or not you have attended academic conferences or whether or not you have participated in fund research projects, transformational leadership and psychological empowerment were the main factors on nurses' innovative behaviour, together explaining 64.5% of the total variance. CONCLUSION: Promotion of transformational leadership and psychological empowerment is vital for nurses to promote innovation, thereby meeting the urgent demand for innovative nurses and the rapid development of nursing disciplines. IMPLICATIONS FOR NURSING MANAGEMENT: This study highlights the importance of transformational leadership in developing nurses' innovative behaviours. Understanding the role of psychological empowerment can help nurse managers formulate relevant intervention strategies and cultivate nurses' innovative behaviour.

Machine learning based regional epidemic transmission risks precaution in digital society.

The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.

Visual Framing: The Use of COVID-19 in the Mobilization of Hong Kong Protest

This study focuses on the Hong Kong Lennon Walls and the communications posted there. We assert that the physical placement of COVID-19 related images on the Lennon Walls of Hong Kong and the replication of symbols and iconography from the Umbrella Movement and the Anti-ELAB Movement situated COVID-19 discourse not only physically within but also symbolically within the contentious politics of Hong Kong. We conclude that the messages and images posted on Lennon Walls between January and April 2020 have used COVID-19 to extend public expression of sentiment on the debates around the Hong Kong government and to further mobilize a sense of Hong Kong identity against China. The findings contribute to the understandings of how the cultural politics surrounding the pandemic became a collective action frame in the mobilization of a localized Hong Kong political identity against the Hong Kong and Chinese governments.

The effect of innovation on tourists’ revisit intention toward tourism destinations

Purpose Drawing on Schumpeter's theory of innovation and stereotype content model, this study aims to arrive at an integrated model that relates destination innovation type, destination innovativeness and revisit intention to uncover more about the drivers and outcomes of destination innovativeness from a consumer-centric perspective. Design/methodology/approach Three studies, including content analysis of news media, an onsite survey and an online survey in Chinese special featured towns, were conducted. Findings This study develops a consumer-centric destination innovation measure. The results reveal that input innovation and product innovation positively influence revisit intention through the serial mediation of destination innovativeness and perceived competence. Research limitations/implications As the data was collected from tourists in China, any generalization of the results to other regions should be made with caution;accordingly, replication is needed to test the proposed model in different cultural contexts. Second, during the onsite data collection period, special featured town destinations were still recovering from the COVID-19 pandemic, which may have affected the perceptions of tourists. Third, the second round of data was collected using an online survey, which may have introduced bias due to a potential lack of representativeness. Fourth, some potential missing variables could also influence the links among innovation, destination innovativeness and revisit intention. Originality/value This study presents the first empirical test of the impact of innovation type and innovativeness on tourists' response to tourism destinations. The results of this study could guide destinations to deliver more effective consumer-centric innovations to generate competitiveness.

A Seven-Layer Convolutional Neural Network for Chest CT-Based COVID-19 Diagnosis Using Stochastic Pooling.

(Aim) COVID-19 pandemic causes numerous death tolls till now. Chest CT is an effective imaging sensor system to make accurate diagnosis. (Method) This article proposed a novel seven layer convolutional neural network based smart diagnosis model for COVID-19 diagnosis (7L-CNN-CD). We proposed a 14-way data augmentation to enhance the training set, and introduced stochastic pooling to replace traditional pooling methods. (Results) The 10 runs of 10-fold cross validation experiment show that our 7L-CNN-CD approach achieves a sensitivity of 94.44±0.73, a specificity of 93.63±1.60, and an accuracy of 94.03±0.80. (Conclusion) Our proposed 7L-CNN-CD is effective in diagnosing COVID-19 in chest CT images. It gives better performance than several state-of-the-art algorithms. The data augmentation and stochastic pooling methods are proven to be effective.

Lineage BA.2 dominated the Omicron SARS-CoV-2 epidemic wave in the Philippines.

The Omicron severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant led to a dramatic global epidemic wave following detection in South Africa in November 2021. The BA.1 Omicron lineage was dominant and responsible for most SARS-CoV-2 outbreaks in countries around the world during December 2021-January 2022, while other Omicron lineages, including BA.2, accounted for the minority of global isolates. Here, we describe the Omicron wave in the Philippines by analysing genomic data. Our results identify the presence of both BA.1 and BA.2 lineages in the Philippines in December 2021, before cases surged in January 2022. We infer that only the BA.2 lineage underwent sustained transmission in the country, with an estimated emergence around 18 November 2021 (95 per cent highest posterior density: 6-28 November), while despite multiple introductions, BA.1 transmission remained limited. These results suggest that the Philippines was one of the earliest areas affected by BA.2 and reiterate the importance of whole genome sequencing for monitoring outbreaks.

Evaluating risk of SARS-CoV-2 infection of the elderly in the public bus under personalized air supply.

In developing countries, public transportation is the first choice for the elderly because of its convenience and cheapness. The high density population of public transportation increases the risk of passengers contracting infectious diseases, so it is extremely critical to determine healthy transportation systems to safeguard the health of passengers. The propagation characteristics of droplets in the ZK-type public bus were studied by computational fluid simulation employing the Realizable k-ε turbulence model and discrete phase model. The modified Wells-Riley model was used to quantitatively assess the infection risk of SARS-CoV-2 spread by droplets on the elderly. The risk assessment shows that when the personalized air supply angle is 30°, the number of infected passengers is the least, reaching 14, which shows that the infection risk of passengers can be reduced through the design of personalized air supply angle. Regardless of the angle of the personalized air supply, the rear seats are in a low-risk area. Therefore, it's recommended that elderly passengers choose the rear seats of the public bus during the epidemic to prevent being infected. This study can provide a reference for healthy transportation systems to construct a healthy environment inside the public bus.

Structural insights into ORF10 recognition by ZYG11B.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a major threat to human health. As a unique putative protein of SARS-CoV-2, the N-terminus of ORF10 can be recognized by ZYG11B, a substrate receptor of the Cullin 2-RING E3 ubiquitin ligase (CRL2). Here we elucidated recognition mechanism of ORF10 N-terminus by ZYG11B through presenting the crystal structure of ZYG11B bound to ORF10 N-terminal peptide. Our work expands the current understanding of ORF10 interaction with ZYG11B, and may also inspire the development of novel therapies for COVID-19.

Bus Predictive-Control Method considering the Impact of Traffic Lights

Amid the COVID-19 pandemic, many travelers have switched from public transit to other modes. How to maintain the stability and service quality of the bus system under regular pandemic prevention and control, so as to maintain the attractiveness of the bus, is an important research direction. Predicting operation states and adopting appropriate control measures for running buses are effective means of improving the bus system's schedule reliability and service quality. Focusing on the impacts of intersection traffic lights on the link's travel time durations, we establish a probabilistic prediction model for bus headways, classifying the bus headways into three states: bunching, stable, and big gap states. Based on the prediction of bus headways, the most suitable control strategy is selected by the proposed method from the plan set, such as holding control, speed-adjusting control, and stop-skipping control to minimize the bus headway deviation. Simulation experiments were employed to verify the effectiveness of the proposed method. Compared with the no-control situation, the expected headway variation, average passenger waiting time, and bus bunching frequency for 100 simulations by the proposed method are reduced by 77.73%, 41.66%, and 87.11%, respectively. Compared with some control methods without prediction, the proposed method is more robust, maintains good control performance, and reduces bus bunching despite significant variations in environmental parameters. In addition, the model still performs well when considering the execution errors of bus drivers. [ FROM AUTHOR] Copyright of Journal of Advanced Transportation is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Clustering method with axiomatization to support failure mode and effect analysis

Failure mode and effect analysis (FMEA) is a highly structured risk-prevention management process that improves the reliability and safety of a system. This paper investigates one of the most critical issues in FMEA practice: Clustering failure modes based on their risks. In the failure mode clustering problem, all identified failure modes need to be assigned to several predefined and risk-ordered categories to manage their risks. We model the failure mode clustering through multi-expert multiple criteria decision making with an additive value function and call it the additive N -clustering problem. We begin by proposing six axioms that describe an ideal clustering method in the additive N -clustering problem, and find that the exogenous clustering method (EXCM), where category thresholds can be exogenously provided, is ideal (Exogenous Possibility Theorem), while any endogenous clustering method, where the clustering is determined endogenously in the given method, cannot satisfy all six axioms simultaneously (Endogenous Impossibility Theorem). In practice, endogenous clustering methods are important because of the difficulty in providing accurate and reasonable category thresholds of the EXCM. Therefore, we propose the consensus-based endogenous clustering method (CENCM) and discuss its axiomatic properties. We also apply the CENCM to the SARS-CoV-2 prevention case and justify the CENCM through axiomatic comparisons and a detailed simulation experiment. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Impact of Online Learning on Physical and Mental Health in University Students during the COVID-19 Pandemic.

Higher education organizations have been influenced by the COVID-19 pandemic. During school closures, online teaching and learning has become a new routine that may lead to changes in lifestyles and adversely affect university students' health. Therefore, this study was to understand the potential impact of online learning on physical and mental health by investigating the differences in physical activity, psychological distress, and student life stress in Taiwanese university students between the in-class learning and online learning periods. A total of 181 students were recruited from a local university. All participants were requested to complete an online survey and self-report physical activity, psychological distress, and life stress in the in-class learning and online learning periods, respectively. The results indicated a significant reduction in physical activity of various intensities (p < 0.05). Specifically, male university students showed a greater decrease in vigorous physical activity compared to their female peers. Yet, there was no significant increase in psychological distress and life stress from the in-class learning period to the online learning period. In summary, physical activity drastically reduces during the online learning period in Taiwanese university students. Notably, male students may be at greater risk of insufficient participation in vigorous physical activity.

Does Proton Pump Inhibitor Use Lead to a Higher Risk of Coronavirus Disease 2019 Infection and Progression to Severe Disease? a Meta-analysis.

The findings of previous research on the association between proton pump inhibitor (PPI) use and the treatment and prevention of coronavirus disease 2019 (COVID-19) are inconsistent. Therefore, this meta-analysis was conducted to clarify the outcomes of patients taking PPIs. This analysis included 14 articles with more than 268,683 subjects. PPI use was not associated with increased or decreased risk of COVID-19 infection (odds ratio [OR] 1.64, 95% confidence interval [CI] = 0.54-5.00, P = 0.39) or mortality (OR = 1.91, 95% CI = 0.86-4.24, P = 0.11). However, PPI use increased the risks of severe disease (OR 1.67, 95% CI = 1.37-2.02, P < 0.00001) and secondary infection (OR 4.62, 95% CI = 2.55-8.39, P < 0.00001). In summary, PPI use was not associated with an increased risk of infection and mortality in COVID-19 but appeared to be associated with an increased risk of progression to severe disease and secondary infection. However, more original studies are urgently needed to further clarify the relationship between PPI use and COVID-19.