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Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.
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IntroductionDue to the rapid spread of the COVID-19 pandemic and the disruption of education systems worldwide, secondary schools in Malaysia have shifted to online classes to ensure educational continuity. Therefore, it was necessary to investigate the various effects of the COVID-19 pandemic on secondary school students. MethodsA self-reported survey with closed and open-ended questions was used to collect data involving 1,067 secondary school students from eight schools in Sabah, Malaysia. The participants were mostly male (53.4%), with a mean age of 14.8 (SD = 1.64). The study involved students from various levels/grades, including transition class, forms 1-5, lower six, and upper six. ResultsStudents faced a variety of school-related stressors, including academic failure due to a poor online course;general mental health issues;a poor internet connection;a lack of in-person interaction;a SOP restriction;an inability to focus;too many homework assignments;burnout;becoming lazier;home conditions;and financial difficulties. DiscussionThe implications for classroom practice, policy formulation, and future research are examined.
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Objectives: To provide an overview of trends in the current evidence landscape of products and services in development that support remote patient monitoring (RPM) and remote therapeutic monitoring (RTM), given the release of new billing codes for RPM and RTM by Centers for Medicare and Medicaid Services (CMS) in 2019. Method(s): A focused literature review was conducted in PubMed. Articles published between January 1, 2013 and January 1, 2023 were eligible for inclusion if reported technologies were classified as RPM (defined as the collection and interpretation of physiologic data digitally stored and/or transmitted by patients and/or caregivers to qualified health care professionals) or RTM (defined as the use of medical devices to monitor a patient's health or response to treatment using non-physiological data), following CMS definitions. RPM and RTM technologies included hardware, software, telehealth, and blockchain applications. Articles were then categorized using a semi-automated software platform (AutoLit, Nested Knowledge, St. Paul, MN) based on disease area, study design, intervention, and outcomes studied. Result(s): Of the 673 records screened, 245 articles were included. Observational studies (19.6%) were the most common study design, followed by systematic or focused literature reviews (11.0%) and narrative reviews (10.6%). The most common disease areas included cardiology (25.7%), coronavirus disease of 2019 (COVID-19;13.9%), and diabetes (9.4%). The most frequent clinical, non-clinical, and patient-reported outcomes were symptom monitoring (20.8%), all cause readmission and hospitalization rates (both 7.3%), and patient experience (7.8%), respectively. Conclusion(s): CMS policy and coding practices for RPM and RTM are evolving, and this trend is likely to continue into the future. This review provides details on the current evidence trends associated with RPM/RTM technologies. Evidence development of RPM and RTM should be assessed as evidence needs for coverage and reimbursement may receive increased payer management.Copyright © 2023
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Background The outbreak of COVID-19 in Xi'an between 2021 and 2022 was a large-scale local epidemic in a large city with a huge number of cases. It is necessary to analyze and summarize the contents of this outbreak. Objective To analyze the disease characteristics of patients with COVID-19，and to explore the risk factors as well as predictors of serious cases. Methods General data and laboratory parameters were retrospectively collected from patients diagnosed with a new coronavirus pneumonia who were admitted to the Fourth People's Hospital of Xi'an between December 2021 and January 2022. Based on the the ratios of total IgG to lymphocyte percentage （IgG∶L%），total IgM to lymphocyte percentage （IgM∶L%），total IgG to lymphocyte count ratio （IgG∶L#），and total IgM to lymphocyte count ratio （IgM∶L#），patients were divided into three groups: mild and common，severe and critical. Multivariate Logistic regression analysis was used to explore the risk factors of developing severe and critically new coronavirus；then the ROC curve was drawn to analyze the predictive indexes and predictive value of severe and critical COVID-19，the area under the ROC curve （AUC） was calculated，and the AUC of each index was compared using the Delong test. Results A total of 699 patients with identified COVID-19 were finally included，and divided into two groups: the mild and common（n=678） and the severe and critical （n=21） forms，with the mild and common forms having younger age，and less underlying disease，D-dimer，IgM ∶ L%，IgM ∶ L#，and higher lymphocyte percentage and lymphocyte count than the severe and critical forms （P<0.05）. Multivariate Logistic regression analysis showed that age〔OR=1.068，95%CI（1.031，1.105），P<0.001〕，D-dimer 〔OR=1.612，95%CI（1.026，2.533），P=0.038〕as well as IgM ∶L#〔OR=1.034，95%CI（1.006，1.063），P=0.018〕 were risk factors for the development of severe and dangerous new coronavirus，and lymphocyte percentage 〔OR=0.918，95%CI（0.844，0.997），P=0.043〕was a protective factor for the development of severe and critical new coronavirus. To establish a joint prediction model for severe and critical novel coronavirus infection，P=-5.031+0.065×age-0.086× lymphocyte percentage +0.738× lymphocyte count +0.477× D-dimer +0.034×IgM∶L#，and the cutoff value for combined detection to predict severe and critical COVID-19 was 0.04，with a sensitivity of 90.00%，a specificity of 83.18%，and its AUC of 0.912〔95%CI（0.858，0.965）〕，which was greater than that for age （Z=5.314，P<0.001），lymphocyte percentage （Z=-1.987，P=0.047），D-dimer （Z=2.273，P=0.023），and IgM∶L# （Z=0.161，P<0.001），with statistically significant differences. Conclusion In the acute phase of COVID-19，there is an imbalance between inflammatory response and cellular immune function，and this imbalance，along with age and D-dimer，are all risk factors for severe COVID-19. Combined indicators including age，D-dimer，lymphocyte percentage and IgM∶L# can effectively predict severe and critical COVID-19. © 2023 Chinese General Practice. All rights reserved.
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Astaxanthin was established to conserve kidney function and subcellular structure through anti-oxidation and/or the free radical scavenging system, yet little research linked a new protective effect to autophagy or lysosomes. We pre-fed Wistar rats with natural astaxanthin, β-carotene, or placebo and induced acute kidney injury using gentamicin, before examining renal tissues and measuring physiological indices. Qualitative evidence from histopathological and subcellular images, along with quantitative evidence showing treatment effects on blood urea nitrogen and serum creatinine (p < 0.01), indicated that esterified Haematococcus astaxanthin surpassed β-carotene at effectively counteracting chemical damage and protecting the kidneys from injury. Proliferation of enlarged lysosomes and mediation analysis results revealing enhanced lysosomal acid phosphatase activity were consistent with the hypothesized autophagy-lysosomal pathway being up-regulated by astaxanthin intake (p < 0.05). In conclusion, the protective effect of astaxanthin against acute kidney injury exerted through the autophagy-lysosomal detoxification pathway, which totally different from the anti-oxidation and/or conventional SOD-dependent free radical scavenging system, was demonstrated with strong evidence. In light of the pandemic outbreak of novel coronavirus pneumonia associated with a virus preferentially targeting the renal tubular cells, dietary astaxanthin may help bring down incidence rate of coronavirus disease, cases of acute kidney injury secondary to the disease, and mortality rate from acute kidney injury, especially when a standard of care treatment for the infectious disease is pending. © 2023 Elsevier B.V.
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Objective: To demonstrate the value of Internet of things (IoT)-based diagnosis-treatment model in improving medical service quality during the novel coronavirus pneumonia (COVID-19) outbreak. Methods: In this retrospective analysis, 483 patients with chronic diseases treated between January 2020 and March 2021 were selected and grouped as follows based on different intervention methods: a research group (the Res group) with 229 patients that were given IoT-based diagnosis and treatment, and a control group (the Con group) with 254 patients that were treated with routine diagnosis and treatment. The qualified rate of medical records, the missing rate of medical records, and the incidence of doctor-patient disputes were compared between the two groups. In addition, investigations were made regarding patients' daily living ability, psychological state, health behavior, self-care ability, quality of life, as well as treatment satisfaction. Results: There was no difference in the qualified rate of medical records between the Res group and the Con group (P>0.05), but the missing rate of medical records and the incidence of doctor-patient disputes were lower in the Res group (both P<0.05). An obviously improved living ability was observed in both groups after the treatment (both P<0.05), with no statistical significance between groups (P>0.05). Besides, the Res group presented lower scores of SAS and SDS but higher scores of SRAHP, ES-CA and SF-36 than the Con group after treatment (all P<0.05). Finally, according to the satisfaction survey, more patients in the Res group were very satisfied but fewer cases were dissatisfied with the medical service they received as compared with the Con group (both P<0.05). Conclusions: The IoT-based diagnosis-treatment model can effectively improve the quality of medical services and patients' self-care ability, which is extremely important and promising for addressing the current medical limitations during the COVID-19 epidemic.
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Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has spread globally, causing a pandemic of respiratory illness designated coronavirus disease 2019 (COVID-19) and is likely to lead to complexities in treating thoracic malignancies. Patients with lung cancer are at an increased risk of becoming infected with the SARS-CoV-2 virus and experience higher morbidity and mortality than the general population. However, little is known about the host tissue and cellular responses associated with SARS-CoV-2 infection, symptoms, and disease severity. Methods Here, we use the Nanostring GeoMX Digital Spatial Profiler (DSP) and CoxMX Spatial Molecular Imager (SMI) technology to determine tissue signatures, and spatially resolved quantitative single-cell proteogenomic changes driven by SARS-CoV-2 infection. This dual approach was used to generate an in-depth picture of the pumonary transcriptional and proteomic landscape of COVID-19, pandemic H1N1 and uninfected control patients.1 Rapid autopsy COVID-19 lung samples were collected across two independent cohorts of patients, and tissue microarrays (TMAs) were prepared. For GeoMx, n=10 COVID-19, n=10 pH1N1 and n=5 normal control tissues were compared. For CosMx, n=19 COVID-19 cores in technical replicates, and n=20 normal control tissues were compared. Tissue-based gene signatures were subsequently tested in the peripheral samples from COVID-19 patients. Results SARS-CoV-2 viral presence was confirmed by RNAscope and integrated to inform region of interest and cell types involved in infection. Analysis of the Nanostring GeoMx data revealed tissue signatures associated with SARS-CoV-2 infection, including Type 1 IFN, blood coagulation, hypoxia and angiogenesis. Analysis of the Nanostring CosMx data enabled single cell typing and mapping of tissue-specific signatures to cellular compartments of interest (e.g. macrophages, fibroblasts) and investigation of complex cell population heterogeneity and interactions. All these while preserving spatial context and highlighted differential cell type distribution in the lungs of COVID-19 patients compared to non-infected controls. Our tissue-based Type 1 IFN signatures, when tested in the blood, were found to be predictive of disease severity in COVID-19 patients when measured within the first few days of symptom onset. Conclusions Here, we've used innovative, cutting-edge spatial transcriptomics approaches to delineate tissue signatures and cellular profiles unique to COVID-19 and common across acute respiratory distress syndrome. These data will aid in understanding the proteogenomic landscape of SARS-CoV-2 infected lung tissues and form new knowledge for the impact on thoracic malignancies, and treatments such as immunotherapy. Moreover, the study demonstrates how tissue-based findings can be rapidly developed into signatures tested in noninvasive samples.
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Purpose: Financial technology (FinTech) is undergoing a transformation as a result of robotics and artificial intelligence. FinTech service providers are embracing contactless technology, including the development and widespread adoption of innovative payment service. Among the many types of contactless payment services, facial recognition payment (FRP) has gained in popularity. To capitalize on this rising popularity, comprehending the mechanisms underlying continuous usage intention toward FRP is essential. Drawing from the stimulus–organism–response (S-O-R) model, this study investigates how FRP attributes facilitate continuous usage intention. Design/methodology/approach: In total, 321 Chinese FRP users completed an online survey. Partial least squares structural equation modeling analyzed the results of the survey. Findings: The results reveal that relative advantage and compatibility, user-interface attractiveness and perceived security (stimuli) promote performance expectancy, effort expectancy and positive emotion (organism), which in turn foster FRP continuous usage intention (response). Originality/value: This research presents an S-O-R model that incorporates several attributes from DOI theory, the UTAUT model and the AIDUA framework to elucidate the antecedents of consumers' continuous usage intention toward FRP. The findings corroborate the significance of the S-O-R mechanism in FRP, setting the groundwork for the acceptance and development of biometric authentication technologies in service contacts and banks. In addition, the study highlights opportunities and essential aspects for FinTech service developers and providers to consider in terms of their practical significance. © 2022, Emerald Publishing Limited.
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The Corona Virus Disease 2019 (COVID-19) epidemic is a sudden public health crisis, known as an "International Emergency of Public Health Event". This study uses the bottom-up characteristics of multi-agents to construct multi-agent simulation models for COVID-19 prevention and control. The development trend of the epidemic situation under the condition that the government adopts different prevention and control measures is studied, and on this basis, the influence of temperature on the spread of the virus is discussed. The simulation results show that the multi-agent modeling method can effectively capture the emergence of complex systems. The evaluation of the effects of single measures and multiple interventions will help determine key prevention and control strategies and provide important experience and scientific basis for future epidemic prevention and control. © 2022 IEEE.
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Jiedu Huoxue Decoction (JHD), a recommended traditional prescription for patients with severe COVID-19, has appeared in the treatment protocols in China. Based on bioinformatics and computational chemistry methods, including molecular docking, molecular dynamics (MD) simulation, and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) calculation, we aimed to reveal the mechanism of JHD in treating severe COVID-19. The compounds in JHD were obtained and screened on TCMSP, SwissADME, and ADMETLab platforms. The compound targets were obtained from TCMSP and STITCH, while COVID-19 targets were obtained from Genecards and NCBI. The protein-protein interaction network was constructed by using STRING. Gene Ontology (GO) and KEGG enrichment were performed with ClueGO and R language. AutoDock vina was employed for molecular docking. 100 ns MD simulation of the optimal docking complex was carried out with AmberTools 20. A total of 84 compounds and 29 potential targets of JHD for COVID-19 were collected. The key phytochemicals included quercetin, luteolin, β-sitosterol, puerarin, stigmasterol, kaempferol, and wogonin, which could regulate the immune system. The hub genes included IL6, IL10, VEGFA, IL1B, CCL2, HMOX1, DPP4, and ACE2. ACE2 and DPP4 were related to SARS-CoV-2 entering cells. GO and KEGG analysis showed that JHD could intervene in cytokine storm and endothelial proliferation and migration related to thrombosis. The molecular docking, 100 ns MD simulation, and MM/GBSA calculation confirmed that targets enriched in the COVID-19 pathway had high affinities with related compounds, and the conformations of the puerarin-ACE2, quercetin-EGFR, luteolin-EGFR, and quercetin-IL1B complexes were stable. In a word, JHD could treat COVID-19 by intervening in cytokine storm, thrombosis, and the entry of SARS-CoV-2, while regulating the immune system. These mechanisms were consistent with JHD's therapeutic concept of “detoxification” and “promoting blood circulation and removing blood stasis” in treating COVID-19. The research provides a theoretical basis for the development and application of JHD. © The Author(s) 2022.
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Multi-Agent System (MAS) is an important branch of artificial intelligence research. This study uses the bottom-up characteristics of multi-agents to construct multi-agent simulation models for Corona Virus Disease 2019 (COVID-19) virus prevention and control based on different age groups. The development of the epidemic and the infection of residents of all ages under different prevention and control measures issued by the government were studied. The simulation results proved that the multi-agent modeling method could effectively capture the emergence of complex systems. Its experimental conclusions provided a basis for predicting the development of the epidemic and provide scientific support for government decision-making. © 2021 IEEE.
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A subset of hospitalized COVID-19 patients, particularly the aged and those with comorbidities, develop the most severe form of the disease, characterized by acute respiratory disease syndrome (ARDS), coincident with experiencing a "cytokine storm." Here, we demonstrate that cytokines which activate the NF-kappa B pathway can induce activin A. Patients with elevated activin A, activin B, and FLRG at hospital admission were associated with the most severe outcomes of COVID-19, including the requirement for mechanical ventilation, and all-cause mortality. A prior study showed that activin A could decrease viral load, which indicated there might be a risk to giving COVID-19 patients an inhibitor of activin. To evaluate this, the role for activin A was examined in a hamster model of SARS-CoV-2 infection, via blockade of activin A signaling. The hamster model demonstrated that use of an anti-activin A antibody did not worsen the disease and there was no evidence for increase in lung viral load and pathology. The study indicates blockade of activin signaling may be beneficial in treating COVID-19 patients experiencing ARDS.
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In order to solve the problem of low intensity contrast between infected areas and normal tissues, A corona virus disease 2019 (COVID-19) segmentation model TMNet is proposed based on triple attention mechanism (TAM), and applied to conditional generative adversarial network in this paper. The MultiConv module in TM-Net can automatically extract rich features of infected areas in lung slices. These features contain different types of lesion information. The designed TAM, which integrates spatial, channel and positional attention modules, can accurately locate lesions in the infected area. By composing of three types of loss functions, the loss function of TM-Net can minimize the differences between prediction graphs and real labels, thus optimizing the TM-Net. Experiment and evaluations conducted on COVID-19 data sets show that the average dice similarity coefficient (DSC) of ground glass opacities (GGO) and consolidation of TM-Net are 1.4% and 0.5% higher than the results of attention U-Net and R2U-Net, respectively, proving the accuracy improvement of TM-Net in COVID-19 lesions segmentation. © 2022, Editorial Office of Journal of Applied Sciences. All right reserved.
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The outbreak of COVID-19 has had an immeasurable impact on the global economy. It has damaged parts of the real economy, but also provided new opportunities for China’s green development. Both the system and foreign direct investment (FDI) have an important impact on China’s green recovery path. Based on the provincial panel data of China from 2007 to 2016, this paper uses a slacks-based measure (SBM) model and Malmquist–Luenberger (ML) index to measure the green total factor productivity (GTFP), and empirically analyzes the regulatory role of system in the influencing mechanism of FDI on GTFP. The results show that the overall level of FDI significantly inhibits the improvement of GTFP, and the interaction between system and FDI makes it shift from inhibition to promotion, but the promotion would be weakened with the improvement of the system. FDI in the eastern region shows a positive effect on GTFP, which will be weakened with the improvement of the system. FDI in central and western regions shows a negative effect on GTFP, and the negative effect in western regions will be increased with the improvement of the system. Then this article puts forward targeted policy suggestions for further improving the level of regional systems and introducing FDI of high quality. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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With the vast applications of massive online learning platforms during the coVID-19 outbreak, the personalized exercise recommendation methods play an import role on computer aided instruction(CAI). Most existing methods generates the exercises according to the contents and knowledge system structure, lacking semantic relationships between exercises and its knowledge. Knowledge graph is widely used to represent the semi-structured and schemaless information (nodes) and their relation (edges), and indicate the sentence embedding grammatical structure and semantic relations, thus it can be applied on computer aided instruction to automatically generate the personalized exercises. Aiming to improve the efficiency of exercise recommendation, this paper studies the feature information of computer network course, and proposes a content and knowledge graph based personalized exercise recommendation method. More specifically, knowledge graph is firstly constructed from entities and relations of computer network course, and the information vectors of exercises are generated by combining the knowledge with the exercises content. And then the learner's historical log data is analyzed, and the semantic similarity between exercises and their knowledge are generated for the wrong answers. According the semantic similarity of knowledge, the final exercises are recommended for the learners. Experimental results show that the proposed method can improve the efficiency of exercises recommendation. © 2020 IEEE.
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The prevention and control of the novel coronavirus (COVID-19) is the priority work to maintain the public health security of the world nowadays. The COVID-19 prevention and control model using multi-agent modeling and simulation technology is proposed. The model can simulate the different dynamic development trend of the epidemic under different prevention and control measures. Taking Taiyuan as an example, according to the researched COVID-19 transmission rules, the prevention and control simulation of COVID-19 has been achieved under the designing rule of the interactive infection process and status transition process between various resident agents. Multi-scenario simulation experiments are realized under different policy measures of hospital and government. The experimental results show that the multi-agent modeling method is effective in analyzing the spread of COVID-19 and can provide decision support for city epidemic prevention and control. © 2020, The Editorial Board of Journal of System Simulation. All right reserved.
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The duplicate US1 genes of duck enteritis virus (DEV) encode a protein with a conserved Herpes_IE68 domain, which was found to be closely related to the herpes virus immediate early regulatory protein family and is highly conserved among counterparts encoded by Herpes_IE68 genes. Previous studies found the homologous proteins HSV-1 ICP22 and VZV ORF63/ORF70 to be critical for virus transcription and replication. However, little is known about the DEV ICP22 protein. In this paper, we describe the characteristics of this protein based on pharmacological experiments, real-time quantitative Polymerase Chain Reaction, Western blot, and immunofluorescence assays. We also investigate the role of the protein in DEV replication via mutation of US1. As a result, we found that the DEV ICP22 protein is a non-essential immediate early protein predominantly located in the nucleus of infected DEF cells and that DEV replication is impaired by US1 deletion. We also found that ICP22 contains a classical nuclear localization signal (NLS) at 305-312AA, and ICP22 cannot enter the nucleus by itself after mutating residue 309. © Copyright © 2020 Li, Wu, Wang, Ma, Jia, Chen, Zhu, Liu, Yang, Zhao, Zhang, Huang, Ou, Mao, Zhang, Liu, Yu, Pan, Tian, Rehman, Chen and Cheng.
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This text elaborates some reflections regarding the responses that have been undertaken by the sectors: health, social security and social assistance, which make up social security in Brazil. It assumes its centrality, to face the pandemic caused by SARS-CoV-2. Considering this context and assuming a professional action for social participation with autonomy, we share experiences in Social Occupational Therapy with young people who live in urban peripheries, certainly poor for the market/consumption, but rich in life, in the Covid-19 pandemic. The aim was to produce care that is consistent with social protection and is directed, in defense of the non-negotiable value of each life and of its pulse, towards the promotion of an emancipatory circulation, an issue that existed prior to the pandemic, albeit now aggravated, and always present among this group of young people.