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Objective: To investigate the wearing of masks and the knowledge of masks among high-risk positions for overseas import and pollution transmission. Methods: From May 14 to 17, 2022, a convenient sampling method was used to conduct an online survey among 963 workers in high-risk positions for overseas import and pollution transmission in Beijing. The behaviors of individual use and wearing masks, the distribution and supervision of the unit, the knowledge of personal mask protection and the subjective feelings of wearing masks were analyzed. The χ(2) test and logistic regression model were used to analyze the influencing factors of the correct selection of masks. Results: The majority of the workers in high-risk positions for overseas import and pollution transmission were male (86.0%, 828/963), age concentration in 18-44 years old (68.2%, 657/963), and the majority of them had college or bachelor degrees (49.4%, 476/963). 79.4%(765/963) of the workers chose the right type of masks, female, 45-59 years old and high school education or above were the risk factors for correct selection of masks (P <0.05). Workers had good behaviors such as wearing/removing masks, but only 10.5% (101/963) could correctly rank the protective effect of different masks. 98.4% (948/963) of the workers believed that their work units had provided masks to their employees, and 99.1% (954/963) and 98.2%(946/963) of them had organized training and supervision on the use of masks, respectively. 47.4%(456/963) of the workers were uncomfortable while wearing masks. Conclusion: The overall selection and use of masks among occupational groups in high-risk positions for overseas import and pollution transmission in China need to be further standardized. It is necessary to strengthen supervision and inspection on the use of masks among occupational groups, and take improvement measures to improve the comfort of wearing masks.
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Masks , Humans , Male , Female , Adolescent , Young Adult , Adult , Middle Aged , Cross-Sectional Studies , China , Surveys and Questionnaires , BeijingABSTRACT
Aim To analyze the differences in clinical characteristics and outcomes of coronavirus disease 2019 (COVID-19) critically ill patients with or without vascular calcification. Methods COVID-19 critically ill patients admitted to the intensive care unit of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology in February 2020 were analyzed retrospectively. According to the chest CT findings, the patients were divided into vascular calcification group and non-vascular calcification group. The vascular calcification group was further divided into aortic calcification group, coronary calcification group and simultaneous calcification group (both aorta and coronary artery calcification). The clinical characteristics and outcomes of patients were compared in different groups. Results Compared with the non-vascular calcification group, the patients in the vascular calcification group were older and had a higher proportion of hypertension and coronary heart disease, which showed higher levels of leukocyte count, neutro-phil count, C-reactive protein, globulin, lactate dehydrogenase, international normalized ratio, D-dimer, creatinine, crea-tine kinase-MB, high-sensitivity cardiac troponin, myohemoglobin and N-terminal pro-B-type natriuretic peptide, lower levels of lymphocyte count, platelet count, albumin, estimated glomerular filtration rate, and higher risk of death. Compared with aortic calcification group, the outcomes of coronary calcification group and simultaneous calcification group were worse. Conclusion Vascular calcification, especially coronary artery calcification, may be a risk factor for poor prognosis in COVID-19 critically ill patients. © 2022, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.
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Background During the containment of COVID-19，the traditional face-to-face interventions conducted at the rehabilitation center were plagued by many limitations，while internet-based interventions can overcome the limitations of geographic location，working hours and transportation，with less medical costs. Objective To examine the effects of internet-based interventions on knowledge，attitude/belief and practice（KAP） toward rehabilitation exercises，physical activity（PA）level，and exercise compliance inpatients after PCI. Methods The subjects were 76 patients who received their first PCI in Department of Cardiology，Tangshan Gongren Hospital from November 2021 to June 2022. They were randomly and equally divided into two groups to receive either internet-based intervention with routine nursing（experimental group） or routine nursing （control group）. Before and three months after the intervention，the Rehabilitation Exercise Knowledge-Belief-Practice Scale for Patients with Coronary Heart Disease（REKBPCHD），the International Physical Activity Questionnaire-Short Form （IPAQ-SF），and Patients' Exercise Log were used to assess the KAP level，PA level，and exercise adherence，respectively. The impact of network intervention on exercise adherence in patients after PCI by univariate Logistic regression analysis. Results After 3 months of intervention，a significant increase was found in the average total score of REKBPCHD，and the average scores of the knowledge dimension，attitude dimension and practice dimension of the scale in the experimental group（P<0.05），and the increase was more notable than that in the control group（P<0.05）. Moreover，both post-intervention low PA level and total PA level in the experimental group were higher than those in the control group（P<0.05）. Both post-intervention low PA level and total PA level were higher than the baseline levels in the experimental group（P<0.05）. The post-intervention exercise compliance of experimental group were higher than that in the control group（P=0.003）. Univariate Logistic regression analysis showed that the risk of non-adherence to exercise in the experimental group was relatively lower than that in the control group 〔OR=0.143，95%CI（0.034，0.594），P=0.007〕. Conclusion Theinternet-based intervention can effectively improve the KAP level toward rehabilitation，PA level，and exercise adherence in patients after PCI. © 2023 Chinese General Practice. All rights reserved.
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Objective To analyze the epidemiological characteristics of a Human rhinovirus outbreak in a primary school in northern Shaanxi, and to provide scientific evidence for the prevention and control. Methods On - site epidemiological investigation of an unexplained febrile aggregated outbreak reported in a primary school in northern Shaanxi on May 22, 2020. Nasopharyngeal swabs were collected from typical cases, and nucleic acid testing was performed to test for SARS COV 2, and 16 respiratory pathogens. Results A total of 37 cases were reported, including 1 adult teacher and 36 students, with the overall incidence rate of 1.75%, a male and female ratio of 3:1, and the incidence age mainly concentrated in 6 to 12 years old. The cases were mainly concentrated in 3 first-grade classes and 7 second-grade classes on the same floor, and the first grade cases accounted for 75.68% of the total number of cases. There was a statistically significant difference in the incidence rate of the cases in the classes (χ2 = 49.29, P<0.01). The clinical features of the cases were mainly fever (body temperature between 37.3 and 38.8°C), sore throat, runny nose, nasal congestion and cough, and some of which were accompanied by diarrhea and vomiting, and other gastrointestinal symptoms. Of the 33 nasopharyngeal swabs detected by laboratory, 14 were positive for Rhinovirus, and the positive rate was 42.42%. Conclusion This aggregated outbreak is caused by Rhinovirus infection. Primary and secondary schools in northern Shaanxi should be alert for aggregated unexplained fever due to Rhinovirus outbreaks during the epidemic season of respiratory infectious diseases. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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Objective To understand the impact of Coronavirus disease 2019 (COVID-19) epidemic and mass emergency vaccination on parents' perception and experience of immunization. Methods From May 6, 2021 to June 20, 202l, an online questionnaire survey was conducted among 4 171 parents of children using the mobile APP of vaccination service in Guangzhou. Results Of all the respondents, 1 911 of them (45.8%) agreed with the suspension of routine immunization measures during the COVID-19 epidemic, and 1 508 respondents (36.2%) would actively postpone child immunization even if the vaccination clinic was not stopped during the COVID-19 epidemic. 2 959 (70.9%), 2 558 (61. 3%) and 2 399 (57. 5%)respondents were satisfied with the protective measures, on-site order and service quality a ter the resumption of vaccination, respectively. 3 437 respondents (82. 4%) indicated that the COVID-19 epidemic had enhanced their attention to vaccination. A total of 1 415 (33.9%) parents of children said that the discontinuation of vaccination clinics weakened their attention to the timeliness of vaccination, and 1 380 (33.1%) parents agreed that "the postponement of vaccination will not affect the vaccination effect”. Compare to parents with higher education (university or above), parents with young children, parents with secondary education (below university), and parents with older children who were older in age themselves were relatively satisfied with the various protective measures taken by vaccination units during the period of suspension of vaccination clinics and the resumption of vaccination. They believed that the field order and the quality of vaccination service were improved. They were more sensitive to the COVID-19 epidemic and tend to actively delay vaccination. They pay more attention to the importance and timeliness of vaccination, and were vulnerable to the impact of COVID-19 epidemic and medical suspension. Due to the COVID-19 epidemic and the control measures after the resumption of vaccination, 1 882 (45. 1%) children missed routine vaccination. The top three reasons were that the outpatient clinic only had the appointment number but could not make an appointment, the outpatient clinic reduced the daily dose of vaccination, and the outpatient discontinuation. Conclusion The satisfaction of parents of children in Guangzhou with the prevention and control measures of vaccination clinics during the COVID-19 epidemic and after the resumption of vaccination is above the medium level. The COVID-19 epidemic and the suspension of vaccination clinics have a two-way impact on the immunization concept and behavior of parents of children in Guangzhou, and some parents increase their attention to immunization. A small number of parents weakened their emphasis on the timeliness of vaccination, suggesting that vaccination units need to arrange staff and vaccination time reasonably, relieve the pressure on vaccination caused by the backlog of COVID-19 epidemic, carry out targeted positive publicity and guidance, and spread the correct knowledge of vaccination, so as to eliminate the doubts of children's parents. © 2023, Editorial Department of Medical Pest Control. All rights reserved.
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Recently, mounting evidence documented an increased morbidity and mortality of COVID-19 in individuals with pre-existing cardiovascular diseases (CVDs). To better understand the pathogenesis of COVID-19 and its impact in CVDs, we designed a text-mining analysis project to evaluate the molecular interfaces between COVID-19 and several known CVDs. We assembled our data corpus from publicly accessible databases and applied text mining to COVID-19 symptoms, comorbidities, and human proteins impacted by COVID-19. Our exploration includes a statistical overview of unstructured text datasets with associated biomedical entities where the information extraction was assisted by data indexing and entity search methodologies. Using 333 human COVID-19-interacting proteins as entities and 8 CVDs classified by MeSH as categories, we examined and computed their Context Aware Semantic Analytic Processing (CaseOLAP) scores. Using this dataset, we determined associations of COVID-19 symptoms with a variety of major and minor comorbidities. Then, we further explored proteins at the interface of COVID-19 and 8 categories of CVD, evaluating relationships between the proteins and CVD categories to determine the proteins' significance in each disease. We then performed pathway analyses on those proteins of significance and their presence in each of the CVD categories. For the first time, our cluster analyses determined which COVID-19-interacting proteins are most relevant for each CVD category. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Biomarker detection has attracted increasing interest in recent years due to the minimally or non-invasive sampling process. Single entity analysis of biomarkers is expected to provide real-time and accurate biological information for early disease diagnosis and prognosis, which is critical to the effective disease treatment and is also important in personalized medicine. As an innovative single entity analysis method, nanopore sensing is a pioneering single-molecule detection technique that is widely used in analytical bioanalytical fields. In this review, we overview the recent progress of nanopore biomarker detection as new approaches to disease diagnosis. In highlighted studies, nanopore was focusing on detecting biomarkers of different categories of communicable and noncommunicable diseases, such as pandemic COVID-19, AIDS, cancers, neurologic diseases, etc. Various sensitive and selective nanopore detecting strategies for different types of biomarkers are summarized. In addition, the challenges, opportunities, and direction for future development of nanopore-based biomarker sensors are also discussed.Copyright © 2023 Elsevier B.V.
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In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines. © 2022 IEEE.
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This paper focuses on an important problem of early misinformation detection in an emergent health domain on social media. Current misinformation detection solutions often suffer from the lack of resources (e.g., labeled datasets, sufficient medical knowledge) in the emerging health domain to accurately identify online misinformation at an early stage. To address such a limitation, we develop a knowledge-driven domain adaptive approach that explores a good set of annotated data and reliable knowledge facts in a source domain (e.g., COVID-19) to learn the domain-invariant features that can be adapted to detect misinformation in the emergent target domain with little ground truth labels (e.g., Monkeypox). Two critical challenges exist in developing our solution: i) how to leverage the noisy knowledge facts in the source domain to obtain the medical knowledge related to the target domain? ii) How to adapt the domain discrepancy between the source and target domains to accurately assess the truthfulness of the social media posts in the target domain? To address the above challenges, we develop KAdapt, a knowledge-driven domain adaptive early misinformation detection framework that explicitly extracts rel-evant knowledge facts from the source domain and jointly learns the domain-invariant representation of the social media posts and their relevant knowledge facts to accurately identify misleading posts in the target domain. Evaluation results on five real-world datasets demonstrate that KAdapt significantly outperforms state-of-the-art baselines in terms of accurately detecting misleading Monkeypox posts on social media. © 2022 IEEE.
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Carbon emissions from human activities are the main cause of climate warming. Under the background of economic and social digital transformation, accurately assessing the carbon emission reduction effect of the development of the digital economy is of great significance for countries to deal with climate warming in the post-COVID-19 era. This paper constructs a dynamic evaluation model of orthogonal projection to measure the level of digital economy development at the provincial level in China from 2007 to 2019. On this basis, the panel fixed effects model and mediation model are used to empirically test the impact of digital economy development on carbon emission intensity and its mechanism. The results indicate that: (1) The development of China's digital economy is unbalanced among regions, showing a geospatial pattern of decreasing from east to west. (2) China's carbon emission intensity has a trend of decreasing year by year, and there are geospatial differences of "high in the west and low in the east” and "high in the north and low in the south.” (3) The digital economy development can effectively reduce regional carbon emission intensity through industrial structure optimization effect and resource allocation effect, and the industrial structure optimization effect can suppress carbon emission intensity more obviously. (4) The development of digital economy in different regions has different degrees of reducing carbon emission intensity. The development of digital economy in the eastern region has a stronger inhibitory effect on carbon emission intensity than that in the middle and western regions, and the development of digital economy in economically developed regions can suppress carbon emission intensity more. This paper provides enlightenment for policy makers to deal with climate warming. Copyright © 2023 Lyu, Zhang and Wang.
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Accurate power load forecasting is an important guarantee for normal operation of a power system. There have been problems of large fluctuations in load demand and difficulty in modeling historical reference load during the COVID-19 outbreak. Thus this paper proposes a short-term load forecasting method based on machine learning, silent index and rolling anxiety index. First, Google mobility data and epidemic data are used to construct the silent index and rolling anxiety index to quantify the impact of the economic and epidemic developments on the power load. Then, the maximal information coefficient is used to analyze the strong correlation factors of power load during the epidemic and introduce epidemic load correlation characteristics. Finally, meteorological data, historical load and the constructed epidemic correlation features are combined as the input variables of the prediction model, and the prediction algorithm is analyzed by multiple machine learning models. The results show that the load forecasting model with the introduction of the epidemic correlation features can effectively improve the accuracy of load forecasting during the epidemic. © 2022 Power System Protection and Control Press. All rights reserved.
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In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. Note to Practitioners—This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3). IEEE
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Our innate immune systems are evolved to provide the first line of immune defense against microbial infections. A key effector component is the adenosine deaminase acting on the RNA-1 (ADAR-1)/ interferon (IFN) pathway of the innate cytoplasmic immunity that mounts rapid responses to many viral pathogens. As an RNA-editing enzyme, ADAR-1 targets viral RNA intermediates in the cytoplasmic compartment to interfere with the infection. However, ADAR-1 may also edit characteristic RNA structures of certain host genes, notably, the 5-hydroxytryptamine (serotonin) receptor 2C (5HT2CR). Dysfunction of 5-HT2CR has been linked to the pathology of several human mental conditions, such as Schizophrenia, anxiety, bipolar disorder, major depression, and the mental illnesses of substance use disorders (SUD). Thus, the ADAR-1mediated RNA editing may be either beneficial or harmful;these effects need to be tightly modulated to sustain innate antiviral immunity while restricting undesired off-target self-reactivity. In this communication, we discuss ideas and tools to identify the orphan drug candidates, including small molecules and biologics that may serve as effective modulators of the ADAR-1/IFN innate immunity and are thereby promising for use in treating or preventing SUD-and/or viral infection-associated mental illnesses.Copyright © 2023, Research Trends (P) LTD.. All rights reserved.
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The emergence of the novel severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) Coronavirus resulted in a global pandemic due to its nature of rapid transmission and variable severities that facilitated its spread worldwide. Correspondingly, owing to advances in molecular technologies, information on this virus is generated at an unprecedented pace. Since the onset of the pandemic, multiple highthroughput "omics" analyses - including transcriptomics and proteomics of different viral infection models - have been made readily available to the research and wider community. The availability and ability to rapidly generate these data facilitate the deciphering of virus–host interactions during SARS-CoV-2 infection - thus enhancing understanding of the viral transmission, host susceptibility, pathogenesis, viral evolution, and disease complications. Such information is vital for eventual applications towards biomarker and treatment discovery against Coronavirus disease 2019 (COVID-19), and can serve as useful models for future pandemic responses. © 2023 by World Scientific Publishing Co. Pte. Ltd.
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The COVID-19 pandemic has provided a unique circumstance for the study of resilience, and clergy resilience has garnered increased research attention due to greater recognition that religious/spiritual leaders are at risk for elevated levels of anxiety and burnout. We examined longitudinal patterns of change during the pandemic in a sample of emerging leaders (N = 751;Mage = 32.82;SD 11.37;49.9% female;59.8% White). In doing so, we offered a conceptual and methodological approach based on historical and critical evaluations of the study of resilience. Results revealed a subgroup that exhibited resilience over three waves of data. The labeling of this trajectory was based on established criteria for determining resilience: (a) significant adversity in the form of COVID-19 stress at time 1, which included the highest levels of the subjective appraisal of stress;(b) risk in the form of low religiousness/spirituality and greater likelihood of reporting marginalized identifications, relative to those who were flourishing;(c) a protective influence for transformative experiences to promote positive adaptation;and (d) interruption to the trajectory in the form of improvement in levels of symptoms and well-being. Practical implications center on the potential for transformative experiences to clarify emotional experience and construct new meaning. © 2023 by the authors.
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Purpose: Under uncertain circumstances, digital technologies are taken as digital transformation enablers and driving forces to integrate with medical, healthcare and emergency management research for effective epidemic prevention and control. This study aims to adapt complex systems in emergency management. Thus, a digital transformation-driven and systematic circulation framework is proposed in this study that can utilize the advantages of digital technologies to generate innovative and systematic governance. Design/methodology/approach: Aiming at adapting complex systems in emergency management, a systematic circulation framework based on the interpretive research is proposed in this study that can utilize the advantages of digital technologies to generate innovative and systematic governance. The framework consists of four phases: (1) analysis of emergency management stages, (2) risk identification in the emergency management stages, (3) digital-enabled response model design for emergency management, and (4) strategy generation for digital emergency governance. A case study in China was illustrated in this study. Findings: This paper examines the role those digital technologies can play in responding to pandemics and outlines a framework based on four phases of digital technologies for pandemic responses. After the phase-by-phase analysis, a digital technology-enabled emergency management framework, titled "Expected digital-enabled emergency management framework (EDEM framework)” was adapted and proposed. Moreover, the social risks of emergency management phases are identified. Then, three strategies for emergency governance and digital governance from the three perspectives, namely "Strengthening weaknesses for emergency response,” "Enhancing integration for collaborative governance,” and "Engaging foundations for emergency management” that the government can adopt them in the future, fight for public health emergency events. Originality/value: The novel digital transformation-driven systematic circulation framework for public health risk response and governance was proposed. Meanwhile, an "Expected digital-enabled emergency management framework (EDEM model)” was also proposed to achieve a more effective empirical response for public health risk response and governance and contribute to studies about the government facing the COVID-19 pandemic effectively. © 2022, Emerald Publishing Limited.
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SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (Span). Span was designed by analyzing the homology of 2675 SARS-CoV-2 S protein sequences from the NCBI database before the Delta variant emerged. The refined Span protein harbors high-frequency residues at given positions that reflect cross-clade generality in sequence evolution. Compared with a prototype wild-type (Swt) vaccine, which, when administered to mice, induced serum with decreased neutralization activity against emerging variants, Span vaccination of mice elicited broad immunity to a wide range of variants, including those that emerged after our design. Moreover, vaccinating mice with a heterologous Span booster conferred complete protection against lethal infection with the Omicron variant. Our results highlight the importance and feasibility of a universal vaccine to fight against SARS-CoV-2 antigenic drift.
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Accurate power load forecasting is an important guarantee for normal operation of a power system. There have been problems of large fluctuations in load demand and difficulty in modeling historical reference load during the COVID-19 outbreak. Thus this paper proposes a short-term load forecasting method based on machine learning, silent index and rolling anxiety index. First, Google mobility data and epidemic data are used to construct the silent index and rolling anxiety index to quantify the impact of the economic and epidemic developments on the power load. Then, the maximal information coefficient is used to analyze the strong correlation factors of power load during the epidemic and introduce epidemic load correlation characteristics. Finally, meteorological data, historical load and the constructed epidemic correlation features are combined as the input variables of the prediction model, and the prediction algorithm is analyzed by multiple machine learning models. The results show that the load forecasting model with the introduction of the epidemic correlation features can effectively improve the accuracy of load forecasting during the epidemic. © 2022 Power System Protection and Control Press. All rights reserved.