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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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Pandemics often cause dramatic losses of human lives and impact our societies in many aspects such as public health, tourism, and economy. To contain the spread of an epidemic like COVID-19, efficient and effective contact tracing is important, especially in indoor venues where the risk of infection is higher. In this work, we formulate and study a novel query called Indoor Contact Query (<sc>ICQ</sc>) over raw, uncertain indoor positioning data that digitalizes people's movements indoors. Given a query object <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula>, e.g., a person confirmed to be a virus carrier, an <sc>ICQ</sc> analyzes uncertain indoor positioning data to find objects that most likely had close contact with <inline-formula><tex-math notation="LaTeX">$o$</tex-math></inline-formula> for a long period of time. To process <sc>ICQ</sc>, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for <sc>ICQ</sc>. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and the acceleration strategies. We conduct extensive experiments on synthetic and real datasets to evaluate our proposals. The results demonstrate the efficiency and effectiveness of our proposals. IEEE
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Background/Aims: The clinical course of hepatitis B virus (HBV) infection in individuals with HIV-1 coinfection is marked by accelerated disease progression. A tenofovir-containing antiretroviral regimen is recommended in most people with HIV-1/HBV-coinfection, but there have not been randomized studies of tenofovir disoproxil fumarate (TDF) vs tenofovir alafenamide (TAF) in treatment- naive HIV-1/HBV-coinfected individuals. We report primary endpoint results from a Phase 3 study comparing bictegravir/emtricitabine/ TAF (B/F/TAF) vs dolutegravir + emtricitabine/TDF (DTG + F/TDF) at Week (W)48 in participants initiating treatment for both viruses. Method(s): Adults with HIV-1/HBV coinfection were randomized 1:1 to initiate blinded treatment with B/F/TAF or DTG + F/TDF (with placebo). Primary endpoints were the proportion of participants with HIV-1 RNA<50 copies/mL (FDA Snapshot) and plasma HBV DNA<29 IU/mL (missing = failure) at W48. Noninferiority was assessed with 95% CI (12% margin). Secondary and other endpoints included change from baseline cluster of differentiation 4 (CD4) count, proportion with hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg) loss/seroconversion, and alanine transaminase (ALT) normalization (AASLD criteria). Result(s): Participants (N = 243) were randomized and treated (B/F/ TAF [n = 121], DTG + F/TDF [n = 122]) from 11 countries in Asia, Europe, North, and Latin America. Baseline characteristics were median age of 32 years, 4.5% female, 88% Asian, 30% HIV-1 RNA>100,000 c/mL, 40% CD4<200 cells/lL, median HBV DNA 8.1 log10 IU/mL, 78% HBeAg+. At W48, B/F/TAF was noninferior to DTG + F/TDF at achieving HIV-1 RNA<50 copies/mL (95% vs 91%, difference 4.1%;95% CI -2.5%-10.8%;P = 0.21), with mean CD4 gains of + 200 and + 175 cells/lL, respectively. B/F/TAF was superior to DTG + F/TDF at achieving HBV DNA<29 IU/mL (63% vs 43%, difference 16.6%;95% CI 5.9%-27.3%;P = 0.0023). Participants treated with B/F/TAF vs DTG + F/TDF had numerically higher HBsAg loss (13% vs 6%;P = 0.059), HBeAg loss (26% vs 14%;P = 0.055), HBeAg seroconversion (23% vs 11%;P = 0.031), and ALT normalization (73% vs 55%;P = 0.066). The most frequent adverse events among participants treated with B/F/TAF vs DTG + F/TDF were upper respiratory tract infection (17% vs 11%), COVID- 19 (13% vs 11%), pyrexia (9% vs 12%), ALT increase (7% vs 11%), and nasopharyngitis (11% vs 4%). ALT flares (elevations at >= 2 consecutive postbaseline visits) occurred in 11 participants (7 B/F/ TAF, 4 DTG + F/TDF), and all resolved. Conclusion(s): Among adults with HIV-1/HBV-coinfection starting antiviral therapy, both B/F/TAF and DTG + F/TDF had high HIV-1 suppression at year 1, with B/F/TAF resulting in superior HBV DNA suppression and significantly more HBeAg seroconversion. Safety findings were similar between groups.
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This study is situated within corpus-based discourse analysis and provides a critical discussion on China in the Russian mainstream media RIA Novosti during the COVID-19 epidemic. The paper analyzes RIA Novosti's reports on China during the pandemic COVID-19. The authors use Fairclough's Three-Dimensional Model to explore the discourse representations of RIA Novosti's reports on China during the epidemic and thus uncover the attitudes and stances of the Russian media and social cognition. The authors come to conclusion that RIA Novosti shows great concern about China during the pandemic by focusing on the epidemic itself and its impact. Additionally, Russian reports reflect the stages of China's fight against the pandemic objectively, truthfully, and comprehensively. RIA Novosti holds a positive attitude towards China's efforts to fight the epidemic. The study broadens the perspective of academic study of COVID-19 pandemic coverage in China from foreign media and enriches empirical research in Russian.
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During the COVID-19 pandemic era that began in 2020, there has been a growing trend in the literature to tackle the problem of health stress (HS) for promoting a sense of public health. In turn, this developing area of research has a high level of relevancy linked to business and economic recovery (Cvirik, 2020). Since HS has increased sharply during the COVID-19 pandemic era, there has been a need to further investigate the balance between coping with HS and the positive continuous intention to use mobile health applications (mHealth apps) among the public. This is the first study that takes the Asia-Pacific region as its case study and empirically investigates the validity of extensions based on the theories of expectation confirmation theory (ECT) (Bhattacherjee, 2001) on user continuous behavior relating to mHealth apps during the COVID-19 pandemic. Results reveal that HS as an emotion can positively affect perceived usefulness and satisfaction in relation to the continuous intention to use mHealth apps. The differences between new and frequent users are confirmed. Discussion and implications for practices are provided in the end. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of College of Management, National Cheng Kung University.
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COVID-19 is currently a major global public health challenge. In the battle against the outbreak of COVID-19, how to manage and share the COVID-19 Electric Medical Records (CEMRs) safely and effectively in the world, prevent malicious users from tampering with CEMRs, and protect the privacy of patients are very worthy of attention. In particular, the semi-trusted medical cloud platform has become the primary means of hospital medical data management and information services. Security and privacy issues in the medical cloud platform are more prominent and should be addressed with priority. To address these issues, on the basis of ciphertext policy attribute-based encryption, we propose a blockchain-empowered security and privacy protection scheme with traceable and direct revocation for COVID-19 medical records. In this scheme, we perform the blockchain for uniform identity authentication and all public keys, revocation lists, etc are stored on a blockchain. The system manager server is responsible for generating the system parameters and publishes the private keys for the COVID-19 medical practitioners and users. The cloud service provider (CSP) stores the CEMRs and generates the intermediate decryption parameters using policy matching. The user can calculate the decryption key if the user has private keys and intermediate decrypt parameters. Only when attributes are satisfied access policy and the user's identity is out of the revocation list, the user can get the intermediate parameters by CSP. The malicious users may track according to the tracking list and can be directly revoked. The security analysis demonstrates that the proposed scheme is indicated to be safe under the Decision Bilinear Diffie-Hellman (DBDH) assumption and can resist many attacks. The simulation experiment demonstrates that the communication and storage overhead is less than other schemes in the public-private key generation, CEMRs encryption, and decryption stages. Besides, we also verify that the proposed scheme works well in the blockchain in terms of both throughput and delay.
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COVID-19 is the most widespread infectious disease in the world. There is an incubation period in the early stage of infection. At present, there are some difficulties in the diagnosis of COVID-19. Medical image analysis based on computed tomography (CT) images is an important tool for clinical diagnosis. However, the lesion size of COVID-19 is smaller, and the lesion shape of COVID-19 is more complex. The effect of the aided diagnosis model is not good. To solve this problem, an aided diagnostic model of COVID-ResNet was proposed based on CT images. Firstly, an improved attention ResNet model was designed based on CT images to focus on the focal lesion area. Secondly, the SE-Res block was constructed. The squeeze excitation mechanism with the residual connection was introduced into the ResNet. The SE-Res block can enhance the correlation degree among different channels and improve the overall accuracy of the model. Thirdly, MFCA (multi-layer feature converge attention) blocks were proposed, which extract multi-layer features. In this model, coordinated attention was used to focus on the direction information of the lesion area. Different layer features were concatenated so that the shallow layer and deep layer features were fused. The experimental results showed that the model could significantly improve the recognition accuracy of COVID-19. Compared with similar models, COVID-ResNet has better performance. On the COVID-19 CT dataset, the accuracy, recall rate, F1 score, and AUC value could reach 96.89%, 98.15%,96.96%, and 99.04%, respectively. Compared with the ResNet model, the accuracy, recall rate, F1 score, and AUC value were higher by 3.1%, 2.46%, 3.0%, and 1.16%, respectively. In ablation experiments, the experimental results showed that the SE-Res block and MFCA model proposed by us were effective. COVID-ResNet transfers the shallow features to the deep, gathers the features, and makes the information complementary. COVID-ResNet can improve the work efficiency of doctors and reduce the misdiagnosis rate. It has a positive significance for the computer-aided diagnosis of COVID-19. © 2023 by the authors.
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The outbreak of COVID-19 occurred in January 2020, the epidemic is still rampant around the world. The Shanghai Expert Consensus on Comprehensive Treatment and Management of Corona Virus Disease 2019 was issued in early March 2020, which provided the guidance of the standardized treatment and rational medication for COVID-19. The administration of " four agents (glucocorticoids, heparin, high-dose vitamin C, Interferon-kappa) and one peptide (thymic peptide)" recommended by the consensus is the key to successfully block and treat critical illness.Copyright © 2021 Chinese Medical Association
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Signal detection in a label-based immunoassay is performed normally when the antigen/antibody binding reaction reaches the equilibrium state during the incubation period of an assay process. Shortening the incubation period in an assay helps reduce the turnaround time and is particularly valuable for point-of-care testing, but the cost is the reduction of signal level and, possibly, measurement precision as well. This work demonstrates that the signal loss could be offset by the stronger emission of an electronically neutral ruthenium(II) complex label, Ru(2, 2′-bipyridine) (bathophenanthroline disulfonate)[4-(2, 2′-bipyridin-4-yl)butanoic acid], used in the electrochemiluminescence (ECL) immunoassay. Combined with the uniquely well-established flow-through washing process in the automated ECL analyzers and the precise control over liquid handling, the assays performed with a 5-minute incubation period showed the same signal level and measurement precision as those of conventional ECL assays. Additionally, the absence of biotin and streptavidin components in the reagent formulation avoids the biotin-streptavidin interaction during assay incubation and fundamentally eliminates the interference of biotin, especially when used in some high-dose therapies. The results obtained from the procalcitonin prototype kit and the supporting evidence from other preliminary reagents (for SARS-CoV-2 N protein and troponin T) are general. The nonequilibrium detection, along with the downsized instrument design, makes the enhanced ECL (EECL) technology a fast high-performance POCT platform that provides the same high-quality data as those generated from the widely deployed [Ru(bpy)3]2+ based laboratorial ECL systems. The anticipated regulatory approval and follow-up clinical implementation will be a significant stride in the decade-long pursuit of novel ECL labels. © 2023 The Author(s)
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Critical cross-border issues have emerged during the COVID-19 pandemic, especially pertaining to security, supply chain, and education, which has led to several new challenges for management. The balance between potential risks and economic benefits has attracted the attention of both industry and academia. Hence, we invited three panelists to participate in the 2021 Association for Information Systems (AIS) Special Interest Group (SIG) on Information Systems in Asia Pacific (ISAP) workshop. The suggested solutions include the right Internet approach, multi-national cooperation to develop flexible global operations, and people's education (especially refugees) to mitigate risks. These solutions encompass three levels, i.e., technology, management, and society. © 2023 Xi'an Jiaotong University
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Post - COVID - 19 condition in children refers to the presence of physical symptoms or syndromes lasting more than 12 weeks after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which can be manifested as a combination of various signs and symptoms. Girls,school - aged,adolescents,and children with severe illness in the acute phase of COVID - 19 ,with underlying allergic diseases and poor baseline physical and mental health are high - risk factors for post - COVID - 19 condition in children. Pathogenesis may be related to viral persistence, autoimmunity, chronic inflammatory responses, chronic inflammation of vascular endothelial cells, and microthrom-bosis. A comprehensive treatment scheme including symptoms relieving treatment, rehabilitation, and psychological support. This article reviews the epidemiology, clinical manifestations, pathogenesis, diagnosis, treatment, and management of the post - COVID - 19 condition in children to improve clinicians' comprehension of the disease.Copyright © 2022 Chinese Journal of Applied Clinical Pediatrics. All rights reserved.
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COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association
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COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association
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COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association
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With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods. IEEE
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AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) are widely used in sterilization, sensing, water purification, medical treatment, non-line of sight (NLOS) communication and many other fields. Especially it has been reported that the global novel coronavirus (COVID-19) can be effectively inactivated by the DUV light with a wavelength below 280 nm (UVC) within a few seconds, which has also attracted great attention. However, the external quantum efficiency (EQE) of UVC LED is still at a low level, generally not more than 10%. As an important component of EQE, internal quantum efficiency (IQE) plays a crucial role in realizing high-performance DUV-LED. In order to improve the IQE of AlGaN-based DUV-LED, this work adopts an electron blocking layer (EBL) structure based on InAlGaN/AlGaN superlattice. The results show that the superlattice EBL structure can effectively improve the IQE compared with the traditional single-layer and double-layer EBL structure for the DUV-LED. On this basis, the optimization method based on JAYA intelligent algorithm for LED structure design is proposed in this work. Using the proposed design method, the InAlGaN/AlGaN superlattice EBL structure is further optimized to maximize the LED' s IQE. It is demonstrated that the optimized superlattice EBL structure is beneficial to not only the suppression of electron leakage but also the improvement of hole injection, leading to the increase of carrier recombination in the active region. As a result, the IQE of the DUV-LED at 200 mA injection current is 41.2% higher than that of the single-layer EBL structure. In addition, the optimized structure reduces IQE at high current from 25% to 4%. The optimization method based on intelligent algorithm can break through the limitation of the current LED structure design and provide a new method to improve the efficiency of AlGaN-based DUV-LED. © 2023 Chinese Physical Society.
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Many transit providers changed their schedules and route configurations during the COVID-19 pandemic, providing more frequent bus service on major routes and curtailing other routes, to reduce the risk of COVID-19 exposure. This research first assessed the changes in Metropolitan Atlanta Rapid Transit Authority (MARTA) service configurations by reviewing the prepandemic versus during-pandemic General Transit Feed Specification (GTFS) files. Energy use per route for a typical week was calculated for pre-pandemic, during-closure, and post-closure periods by integrating GTFS data with MOVES-Matrix transit energy and emission rates (MOVES signifying MOtor Vehicle Emission Simulator). MARTA automated passenger counter data were appended to the routes, and energy use per passenger-mile was compared across routes for the three periods. The results showed that the coupled effect of transit frequency shift and ridership decrease from 2019 to 2020 increased route-level energy use for over 87% of the routes and per-passenger-mile energy use for over 98% of the routes. In 2021, although MARTA service had largely returned to pre-pandemic conditions, ridership remained in an early stage of recovery. Total energy use decreased to about pre-pandemic levels, but per-passenger energy use remained higher for more than 91% of routes. The results confirm that while total energy use is more closely associated with trip schedules and routes, perpassenger energy use depends on both trip service and ridership. The results also indicate a need for data-based transit planning, to help avoid inefficiency associated with over-provision of service or inadequate social distancing protection caused by under-provision of service. © National Academy of Sciences: Transportation Research Board 2022.
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COVID-19 (coronavirus disease 2019) is a new viral infection disease that is widely spread worldwide. Deep learning plays an important role in COVID-19 images diagnosis. This paper reviews the recent progress of deep learning in COVID-19 images applications from five aspects;Firstly, 33 COVID-19 datasets and data enhancement methods are introduced;Secondly, COVID-19 classification methods based on supervised learning are summarized from four aspects of VGG, ResNet, DenseNet and Lightweight Networks. The COVID-19 segmentation methods based on supervised learning are summarized from four aspects of attention mechanism, multiscale mechanism, residual connectivity mechanism, and dense connectivity mechanism;Thirdly, the application of deep learning in semi-supervised COVID-19 images diagnosis in terms of consistency regularization methods and self-training methods. Fourthly, the application of deep learning in unsupervised COVID-19 diagnosis in terms of autoencoder methods and unsupervised generative adversarial methods. Moreover, the challenges and future work of COVID-19 images diagnostic methods in the field of deep learning are summarized. This paper reviews the latest research status of COVID-19 images diagnosis in deep learning, which is of positive significance to the detection of COVID-19. © 2023 by the authors.
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The outbreak of COVID-19 occurred in January 2020, the epidemic is still rampant around the world. The Shanghai Expert Consensus on Comprehensive Treatment and Management of Corona Virus Disease 2019 was issued in early March 2020, which provided the guidance of the standardized treatment and rational medication for COVID-19. The administration of " four agents (glucocorticoids, heparin, high-dose vitamin C, Interferon-kappa) and one peptide (thymic peptide)" recommended by the consensus is the key to successfully block and treat critical illness.Copyright © 2021 Chinese Medical Association
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Objective: To develop a SARS-CoV-2 antigen detection management system for Chinese residents under community grid management, which is supported by "health information technology" and "neural network image recognition", so as to give full play to the advantages of "grid management". This system is applied to the normalized prevention and control of COVID-19 epidemic. Method(s): The model of image recognition algorithm was built based on deep learning and convolution neural network (CNN) artificial intelligence algorithm. The improved Canny edge detection algorithm was used to monitor and locate the image edge, and then the image segmentation and judgment value calculation were completed according to projection method. The system construction was completed combing with the grid number design. Result(s): The proposed method had been tested and showed the accuracy of the algorithm. With a certain robustness, the algorithm error was proved to be small. Based on the image recognition algorithm model, the development of SARS-CoV-2 antigen detection management system covering user login, paper-strip test image upload, paper-strip test management, grid management, grid warning and regional traffic management was completed. Conclusion(s): Antigen detection is an important supplementary means of COVID-19 epidemic prevention and control in the new stage. The SARS-CoV-2 antigen detection management system for Chinese residents under community grid managemen based on image recognition enables mobile communication devices to recognize the image of SARS-CoV-2 antigen detection results, which is helpful to form a grid management mode for the epidemic and improve the management framework of epidemic monitoring, detection, early warning and prevention and control.Copyright © 2023 The Authors