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The outbreak of the COVID-19 pandemic forced students to move from face-to-face learning to online learning. Online learning has high demands on students' Self-regulated Learning (SRL) skills. In this study, a questionnaire that used five-point Likert scale was administrated between international African undergraduates and Chinese undergraduate students to investigate their online learning behaviors. The questionnaire was composed of six categories: environment structuring, goal setting, time management, help-seeking, task strategies, and self-evaluation. 441 valid responses were received, 89 from international African students and 352 from Chinese undergraduates. The collected data were analyzed with SPSS Version 24.0. The results showed that there was no significant difference between Chinese student' and international African students' SRL skills in the six sub-scales. This may be due to the small sample size of African students and the similar learning environment. Larger samples are needed in future research to further verify the conclusion. The research results can be used as a reference for the future online learning design to strengthen learners' SRL skills. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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COVID-19 is a severe acute respiratory syndrome caused by a novel coronavirus, SARS-CoV- 2.COVID-19 is now a pandemic, and is not yet fully under control.As the surface spike protein (S) mediates the recognition between the virus and cell membrane and the process of cell entry, it plays an important role in the course of disease transmission.The study on the S protein not only elucidates the structure and function of virus-related proteins and explains their cellular entry mechanism, but also provides valuable information for the prevention, diagnosis and treatment of COVII)-19.Concentrated on the S protein of SARS-CoV-2, this review covers four aspects: (1 ) The structure of the S protein and its binding with angiotensin converting enzyme II (ACE2) , the specific receptor of SARS-CoV-2, is introduced in detail.Compared with SARS-CoV, the receptor binding domain (RBD) of the SARS-CoV- 2 S protein has a higher affinity with ACE2, while the affinity of the entire S protein is on the contrary.(2) Currently, the cell entry mechanism of SARS-CoV-2 meditated by the S protein is proposed to include endosomal and non-endosomal pathways.With the recognition and binding between the S protein and ACE2 or after cell entry, transmembrane protease serine 2(TMPRSS2) , lysosomal cathepsin or the furin enzyme can cleave S protein at S1/S2 cleavage site, facilitating the fusion between the virus and target membrane.(3) For the progress in SARS-CoV-2 S protein antibodies, a collection of significant antibodies are introduced and compared in the fields of the target, source and type.(4) Mechanisms of therapeutic treatments for SARS-CoV-2 varied.Though the antibody and medicine treatments related to the SARS-CoV-2 S protein are of high specificity and great efficacy, the mechanism, safety, applicability and stability of some agents are still unclear and need further assessment.Therefore, to curb the pandemic, researchers in all fields need more cooperation in the development of SARS-CoV-2 antibodies and medicines to face the great challenge.Copyright © Palaeogeography (Chinese Edition).All right reserved.
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Social distancing policies during the COVID-19 pandemic impacted offline and outdoor advertising significantly, forcing many advertising activities to move online. Native advertising, a new form of advertising, is regarded as complementary to traditional advertising. The term native advertising refers to a relationship between an advertiser and a publisher whereby the advertiser pays to distribute content on the publisher's platform, thereby taking advantage of the format and substance of the publisher's content. Most of the previous research on native advertising is based on western media platforms. In China, Tencent's WeChat is the largest social media platform. This chapter focuses on native advertising on Tencent's WeChat official accounts (WOAs). It employs qualitative discourse analysis to understand how native ads on WOAs address the pandemic while seeking to persuade consumers. Referencing Burke's theory of identification and Green's theory of narrative transportation, we argue that native ads enjoy the advantage of generating emotional resonance with consumers by talking about pandemic-related topics. However, they risk being perceived as deceptive and manipulative. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
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Wearing masks is an easy way to operate and popular measure for preventing epidemics. Although masks can slow down the spread of viruses, their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown. Therefore, we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments. This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments. The transmission of COVID-19 is simulated using the Monte Carlo simulation method. The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society. Furthermore, the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ. Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools, high schools, and hospitals. However, the use of masks alone in primary schools and hospitals cannot control outbreaks. In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse, masks can meet the need for prevention. Given the heterogeneity of individual behavior, if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing, the epidemic prevention effect of masks can be improved. Finally, asymptomatic infection has varying effects on the prevention effect of masks in different environments. The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces. However, the effect on primary schools and hospitals cannot be weakened. This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention. © 2022, Higher Education Press.
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Objective: To explore the guidance value of "treatment of disease in accordance with three conditions" theory in the prevention and treatment of corona virus disease 2019 (COVID-19) based on the differences of syndromes and traditional Chinese medicine (TCM) treatments in COVID-19 patients from Xingtai Hospital of Chinese Medicine of Hebei province and Ruili Hospital of Chinese Medicine and Dai Medicine of Yunnan province and discuss its significance in the prevention and treatment of the unexpected acute infectious diseases. Method(s): Demographics data and clinical characteristics of COVID-19 patients from the two hospitals were collected retrospectively and analyzed by SPSS 18.0. The information on formulas was obtained from the hospital information system (HIS) of the two hospitals and analyzed by the big data intelligent processing and knowledge service system of Guangdong Hospital of Chinese Medicine for frequency statistics and association rules analysis. Heat map-hierarchical clustering analysis was used to explore the correlation between clinical characteristics and formulas. Result(s): A total of 175 patients with COVID-19 were included in this study. The 70 patients in Xingtai, dominated by young and middle-aged males, had clinical symptoms of fever, abnormal sweating, and fatigue. The main pathogenesis is stagnant cold-dampness in the exterior and impaired yin by depressed heat, with manifest cold, dampness, and deficiency syndromes. The therapeutic methods highlight relieving exterior syndrome and resolving dampness, accompanied by draining depressed heat. The core Chinese medicines used are Poria, Armeniacae Semen Amarum, Gypsum Fibrosum, Citri Reticulatae Pericarpium, and Pogostemonis Herba. By contrast, the 105 patients in Ruili, dominated by young females, had atypical clinical symptoms, and most of them were asymptomatic patients or mild cases. The main pathogenesis is dampness obstructing the lung and the stomach, with obvious dampness and heat syndromes. The therapeutic methods are mainly invigorating the spleen, resolving dampness, and dispersing Qi with light drugs. The core Chinese medicines used are Poria, Atractylodis Macrocephalae Rhizoma, Glycyrrhizae Radix et Rhizoma, Coicis Semen, Platycodonis Radix, Lonicerae Japonicae Flos, and Pogostemonis Herba. Conclusion(s): The differences in clinical characteristics, TCM syndromes, and medication of COVID-19 patients from the two places may result from different regions, population characteristics, and the time point of the COVID-19 outbreak. The "treatment of disease in accordance with three conditions" theory can help to understand the internal correlation and guide the treatments.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.
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The COVID-19 pandemic has had an initial and outsized negative impact on bond exchange-traded funds (ETFs), causing concerns for financial stability. Using a large panel of US bond ETFs, we conduct a comprehensive examination of the impact of the pandemic on ETF valuation discounts. We find the change in COVID-19 deaths to be significantly related to the valuation discounts of government bond ETFs and corporate bond ETFs, with investment-grade corporate bond ETFs showing greater sensitivity. These valuation discounts reversed dramatically after the Federal Reserve announced its intentions to purchase corporate bonds and bond ETFs. Government economic policies to combat the pandemic are also negatively related to the valuation discounts of corporate bond ETFs. These findings are evidence of the efficacy of broad-based liquidity support on restoring financial stability in the bond ETF market at a time of enormously stressed market sentiment and massive pricing dislocations. Copyright 2022 With Intelligence LLC.
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Introduction: CARTITUDE-2 (NCT04133636) is a phase 2, multicohort study evaluating cilta-cel, an anti-BCMA CAR-T therapy, in several multiple myeloma (MM) patient (pt) populations. Objective(s): To report updated results with longer follow-up on cohort C pts with previous exposure to a non-cellular anti- BCMA immunotherapy. Method(s): Cohort C pts had progressive MM after treatment (tx) with a proteasome inhibitor, immunomodulatory drug, anti-CD38 antibody, and non-cellular BCMA-targeting agent. A single cilta-cel infusion (target dose 0.75x106 CAR+ viable T cells/kg) was administered 5-7 days post lymphodepletion. Primary endpoint was minimal residual disease (MRD) negativity at 10-5. Secondary endpoints included overall response rate (ORR), duration of response (DOR), and adverse events (AEs). Result(s): As of June 1, 2022, 20 pts (13 ADC exposed;7 BsAb exposed) were treated with cilta-cel;4 pts did not receive cilta-cel due to either low cellular yield (n=2, 1 in each group) or death due to progressive disease (PD) prior to dosing (n=2, 1 in each group) and 6 pts received anti-BCMA tx as their last line of therapy (n=4 ADC, n=2 BsAb). During prior anti-BCMA tx, best responses included VGPR (ADC: 2 pts;BsAb: 1 pt), sCR (ADC: 1 pt), and CR (BsAb: 1 pt);the rest had best response of stable disease or PD (1 pt not evaluable). Baseline characteristics are presented in Figure 1A. Median time from last anti- BCMA agent to cilta-cel infusion was 195 d;median administered dose of cilta-cel was 0.65x106 CAR+ viable T cells/kg. At a median follow-up of 18.0 mo, 7/10 evaluable pts (70%) were MRD negative at 10-5 (ADC: 5/7 [71.4%], BsAb: 2/3 [66.7%]). ORR: full cohort, 60%;ADC, 61.5%;BsAb, 57.1% (Figure 1B). Median DOR: full cohort, 12.8 mo;ADC, 12.8 mo;BsAb, 8.2 mo. Median PFS: full cohort, 9.1 mo;ADC, 9.5 mo;BsAb, 5.3 mo. Cilta-cel responders had a shorter median duration of last anti- BCMA agent exposure (29.5 d) compared with non-responders (63.5 d). Responders also had a longer median time from last anti-BCMA tx exposure to apheresis (161.0 d) than non-responders (56.5 d). Most common AEs were hematologic. CRS: n=12 (60%;all Gr1/2), median time to onset 7.5 d, median duration 6.0 d. ICANS: n=4 (20%, 2 Gr3/4), median time to onset 9.0 d, median duration 7.0 d. No patient had movement or neurocognitive tx emergent AE/parkinsonism. There were 12 deaths (PD: 8;COVID-19 pneumonia: 2 [not tx related];subarachnoid hemorrhage: 1 [not tx related];C. difficile colitis: 1 [tx related]). (Figure Presented)(Figure Presented)Conclusions: Pts with heavily pretreated MM and previous exposure to a non-cellular anti-BCMA therapy had favorable responses to cilta-cel. However, depth and DOR appear lower than that seen in anti-BCMA-naive pts treated with cilta-cel (at 27.7 mo, median DOR was not reached in heavily pre-treated but anti-BCMA naive CARTITUDE-1 pts). These data may inform tx plans, including sequencing and washout period between BCMA-targeting agentsCopyright © 2023 American Society for Transplantation and Cellular Therapy
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread worldwide and threatened human's health. With the passing of time, the epidemiology of coronavirus disease 2019 evolves and the knowledge of SARS-CoV-2 infection accumulates. To further improve the scientific and standardized diagnosis and treatment of maternal SARS-CoV-2 infection in China, the Chinese Society of Perinatal Medicine of Chinese Medical Association commissioned leading experts to develop the Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection under the guidance of the Maternal and Child Health Department of the National Health Commission. This recommendations includes the epidemiology, diagnosis, management, maternal care, medication treatment, care of birth and newborns, and psychological support associated with maternal SARS-CoV-2 infection. It is hoped that the recommendations will effectively help the clinical management of maternal SARS-CoV-2 infection.Copyright © Wolters Kluwer Health, Inc. All rights reserved.
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The clustering of large numbers of heterogeneous features is a hot topic in multi-view communities. Most existing multi-view clustering (MvC) methods employ matrix factorization or anchor strategies to handle large-scale datasets. The former operates on the original data and is, therefore, sensitive to noise and feature redundancy, which is reflected in the final clustering performance. The latter requires post -processing steps to generate the clustering results, which may be suboptimal owing to the isolation steps. To address the above problems, we propose one-stage multi-view subspace clustering with dictionary learning (OSMvSC). Specifically, we integrate dictionary learning, representation coefficient matrix learning, and matrix factorization as a unified learning framework, which directly learns the dictionary and representation coefficient matrix to encode the original multi-view data, and obtains the clustering results with linear time complexity without any postprocessing step. By manipulating the class centroid with the nuclear norm, a more compact and discriminative class centroid representation can be obtained to further improve clustering performance. An effective optimization algorithm with guaranteed convergence is designed to solve the proposed method. Substantial experiments on various real-world multi-view datasets demonstrate the effectiveness and superiority of the proposed method. The source code is available at https://github.com/justcallmewilliam/OSMvSC.(c) 2022 Elsevier B.V. All rights reserved.
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Background The transmission dynamics of influenza were affected by public health and social measures (PHSMs) implemented globally since early 2020 to mitigate the COVID-19 pandemic. We aimed to assess the effect of COVID-19 PHSMs on the transmissibility of influenza viruses and to predict upcoming influenza epidemics. Methods For this modelling study, we used surveillance data on influenza virus activity for 11 different locations and countries in 2017-22. We implemented a data-driven mechanistic predictive modelling framework to predict future influenza seasons on the basis of pre-COVID-19 dynamics and the effect of PHSMs during the COVID-19 pandemic. We simulated the potential excess burden of upcoming influenza epidemics in terms of fold rise in peak magnitude and epidemic size compared with pre-COVID-19 levels. We also examined how a proactive influenza vaccination programme could mitigate this effect. Findings We estimated that COVID-19 PHSMs reduced influenza transmissibility by a maximum of 17.3% (95% CI 13.3-21.4) to 40.6% (35.2-45.9) and attack rate by 5.1% (1.5-7.2) to 24.8% (20.8-27.5) in the 2019-20 influenza season. We estimated a 10-60% increase in the population susceptibility for influenza, which might lead to a maximum of 1-5-fold rise in peak magnitude and 1-4-fold rise in epidemic size for the upcoming 2022-23 influenza season across locations, with a significantly higher fold rise in Singapore and Taiwan. The infection burden could be mitigated by additional proactive one-off influenza vaccination programmes. Interpretation Our results suggest the potential for substantial increases in infection burden in upcoming influenza seasons across the globe. Strengthening influenza vaccination programmes is the best preventive measure to reduce the effect of influenza virus infections in the community. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.
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The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound popula-tion flows originating from Wuhan;however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.
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It has been more than two years since the outbreak of COVID-19, which has spread to almost every corner of the world and killed a great number of people. Rapid detection and screening have become an important means of controlling the spread of COVID-19. Segmentation of COVID-19 infected tissue from computed tomography (CT) images of a patient’s lungs can provide clinicians with important information to quantify and diagnose COVID-19. However, the accuracy of medical image segmentation is seriously affected by such factors as the low contrast between the infected tissue and the edge of the surrounding environment, the large variation of the infected tissue and the lack of labeling data. Therefore, a deep learning model called CdcSegNet to accurately segment lung lesions from CT images infected by COVID-19 is proposed. In our method, transfer learning is introduced to solve the problem of lack of annotation data, and three modules, i.e., continuous dilated convolution module (CDC), parallel dual attention module (PDA) and additional multi-core pooling layer (AMP) are innovatively proposed to solve the problem of fuzzy segmentation boundary and to segment effectively infected tissues. Extensive experiments and comparison studies are made, and demonstrate that our model CdcSegNet has high accuracy in COVID-19 segmentation, and is superior to the state-of-the-art models in terms of DICE, SEN, SPE, PPV, and VOE. IEEE
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The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient's utterance. To this end, we create a high-quality annotated Chinese Medical query (utterance) dataset, CMedQ (about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model Conco-ERNIE, using concept co-occurrence patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on CMedQ demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot, MedicalBot. To provide knowledge support for MedicalBot, we also build a Chinese medical graph, CMedKG (about 45k entities and 283k relationships). © 2023 Association for Computing Machinery.
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The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay. © 2022 IEEE.
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Objective: To compare the efficacy of the combination of abidol, lopinavir/ritonavir plus recombinant interferon alpha-2b (rIFNalpha-2b) and the combination of lopinavir/ritonavir plus rIFNalpha-2b for patients with COVID-19 in Zhejiang province. Method(s): A multicenter prospective study was carried out to compare the efficacy of triple combination antiviral therapy and dual combination antiviral therapy in 15 medical institutions of Zhejiang province during January 22 to February 16, 2020. All patients were treated with rIFNalpha-2b (5 million U, 2 times/d) aerosol inhalation, in addition 196 patients were treated with abidol (200 mg, 3 times/d) + lopinavir/ritonavir (2 tablets, 1 time/12 h) (triple combination group) and 41 patients were treated with lopinavir/ritonavir (2 tablets, 1 time/12 h) (dual combination group). The patients who received triple combination antiviral therapy were further divided into three subgroups: <48 h, 3-5 d and >5 d according the time from the symptom onset to medication starting. The therapeutic efficacy was compared between triple combination group and dual combination group, and compared among 3 subgroups of patients receiving triple combination antiviral therapy. SPSS 17.0 software was used to analyze the data. Result(s): The virus nucleic acid-negative conversion time in respiratory tract specimens was (12.2+/-4.7) d in the triple combination group, which was shorter than that in the dual combination group [(15.0+/-5.0) d] (t=6.159, P<0.01). The length of hospital stay in the triple combination group [12.0 (9.0, 17.0) d] was also shorter than that in the dual combination group [15.0 (10.0, 18.0) d] (H=2.073, P<0.05). Compared with the antiviral treatment which was started within after the symptom onset of in the triple combination group, the time from the symptom onset to the viral negative conversion was 13.0 (10.0, 17.0), 17.0 (13.0, 22.0) and 21.0 (18.0, 24.0) d in subgroups of 48 h, 3-5 d and >5 d, respectively (Z=32.983, P<0.01), while the time from antiviral therapy to viral negative conversion was (11.8+/-3.9), (13.5+/-5.1) and (11.2+/-4.3) d, respectively(Z=6.722, P<0.05). Conclusion(s): The triple combination antiviral therapy of abidol, lopinavir/litonavir and rIFNalpha-2b shows shorter viral shedding time and shorter hospitalization time, compared with the dual combination antiviral therapy;and the earlier starting triple combination antiviral therapy will result in better antiviral efficacy.Copyright © 2020 by the Chinese Medical Association.
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Several synthetic routes of nirmatrelvir (the ingredient of a new drug to treat COVID-19 made by Pfizer) have been reported. We focused on a second route to improve the synthetic method of nirmatrelvir with a methodology that included different steps. The first step was an analysis of reaction byproducts using acetonitrile as a solvent of the condensation reaction to improve the inversion rate. Then, we used isobutyl acetate as a crystalline solvent to obtain the key intermediate as a solvate, which was a stable crystal product with high purity. Complementarily, we also used trifluoroacetic anhydride as the primary-amide dehydrating agent, and 2-methyl tetrahydrofuran as the solvent to prepare nirmatrelvir, which led to an overall yield of 48% via four steps and a purity of 99.5% according to high-performance liquid chromatography. We also investigated the crystal form of nirmatrelvir: the single-crystal features and transformation from a crystal form to nirmatrelvir were dependent upon temperature. Our data have great value for study of the synthetic method and crystal stability of nirmatrelvir. © 2023 The Royal Society of Chemistry.
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Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method is based on the assumption that similar drugs share similar targets and vice versa. However, one major disadvantage is that only one similarity metric is used in MF models, which is not enough to represent the similarity between drugs or targets. In this work, we develop a similarity fusion enhanced MF model to incorporate different types of similarity for novel drug-target link prediction. We apply the proposed model on a drug-virus association dataset for anti-COVID drug prioritization, and compare the performance with other existing MF models developed for COVID. The results show that the similarity fusion method can provide more useful information for drug-drug and virus-virus similarity and hence improve the performance of MF models. The top 10 drugs as prioritized by our model are provided, together with supporting evidence from literature. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.