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Intro The coronavirus disease 2019 (COVID-19) pandemic is still raging around the world. Therefore, an efficient, rapid, and low-cost nucleic acid detection method for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is urgently needed. There is a miniaturized ultra-fast real-time PCR instrument, and we will verify and evaluate its 30-minute rapid detection of SARS-CoV-2. Methods A rapid PCR temperature change mode was explored by moving the reaction tube between the independent temperature modules with large temperature differences and a portable ultra-fast real-time PCR instrument were developed. We established a rapid SARS-CoV-2 test method using the instrument, a China Food and Drug Administration-certified SARS-CoV-2 reagent and optimized reaction condition. The analytical and clinical performances of the rapid tests were evaluated by comparing with the standard SARS-CoV-2 tests. Findings The new temperature change mode can effectively shorten the amplification reaction time. The rapid SARS-CoV-2 test method was established and the time to yield results were greatly shortened from 81 min of the standard test to 31 min. Specificity of the rapid test was assessed and no non-specific amplification (0/63) was observed. Clinical performance was evaluated using 184 respiratory specimens from patients with suspected SARS-CoV-2 infection. The positive agreement between the rapid and standard tests was 100% (67/67), the negative agreement was 97.4% (114/117), and the kappa statistic was 0.965 (P<0.001). No significant differences in the Ct values for each target gene were observed between the rapid test and the standard test (P>0.05). Conclusion A 30-minute detection method for SARS-CoV-2 nucleic acid using a novel ultra-fast real-time PCR instrument was developed. The waiting time for test results in cases of suspected infection may be reduced greatly by using the ultra-fast real-time PCR instrument
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A growing number of investors in recent years has focused on environmental, social and governance (ESG) factors in carrying out investment activities and the COVID-19 pandemic has only driven such trends of ESG investing at an accelerated rate. Many studies have examined the relationship between ESG scores and corporate financial performance, along with the effectiveness of ESG portfolios. This paper discusses various approaches to incorporate ESG factors into a portfolio optimisation and critically compares and contrasts the efficacy of these approaches on the Dow Jones Industrial Average constituents. It finds that thematic investing appears to be the best performer. In addition, it is also found that there is no evidence that ESG portfolios underperform the market. © Henry Stewart Publications 1752-8887 (2023).
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Research has proven that counselling services are essential to solving the troubles in the mental health of international Chinese students in the post-epidemic stage. Online questionnaires were implemented for about 1000 international Chinese college students from three universities in Thailand. Results showed that female junior and senior students who stayed in post-pandemic Thailand longer than others were likely to suffer from mental health disorders. In addition, in Thailand, counselling has a significant positive association with the mental health status of the students. Therefore, it is recommended in this study that Thai universities should provide more counselling services to support students in focusing on education and adjusting or adapting to the environment abroad.
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Objective: To explore the potential action mechanism of Bupleuri Radix and Puerariae Lobatae Radix herb-pair in the treatment of Corona Virus Disease 2019(COVID-19)caused by 2019-nCoV virus through network pharmacology method, so as to provide theoretical guidance for further mechanism and clinical translational research. Method(s): The potential active ingredients and their respective related action targets of Bupleuri Radix and Puerariae Lobatae Radix were obtained through the TCMSP, and the COVID-19-related disease targets were searched by GeneCards database.The common targets from the both screenings were input into the STRING protein interaction online database to construct the interaction network of potential targets.The potential core targets were further screened by MCODE plug-ins.The "ingredients-targets-diseases" network and PPI network were constructed by Cytoscape 3.2.1 software, and GO function enrichment analysis and KEGG signal pathway enrichment analysis were carried out by DAVID v6.8 online software. Result(s): A total of 22 active ingredients and 226 drug targets were screened from Bupleuri Radix and Puerariae Lobatae Radix herb-pair, among which 47 were co-acting targets with COVID-19 and 21 were potential core targets.The results of GO functional enrichment analysis showed that it was mainly related to gene transcriptional expression, inflammatory response and immune system response, while KEGG signal pathway enrichment analysis showed that it was mainly associated with influenza A virus, TNF pathway, Toll-like receptor signal pathway and others. Conclusion(s): Bupleuri Radix and Puerariae Lobatae Radix herb-pair can exert an anti-2019-nCoV effect through its regulatory role in inflammatory reaction and immune system with a multi-ingredients, multi-targets and multi-pathways pharmacological characteristics. Copyright © 2021, Central Station of Chinese Medicinal Materials Information, National Medical Products Administration. All right reserved.
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This paper has approached the investment method by alpha value as the excess return to compensate for risks other than the market risk with the data sample of filtered stocks from three major exchanges of the Vietnam stock market HOSE, HNX, and UPCOM from January 2016 to December 2020. Then, we compare the performance of the portfolio through 2021, the year Vietnam fell into the 4th wave of Covid and was the hardest hit. The results of the paper have shown that the portfolio selected by the alpha method has eliminated the beta market risk of the portfolio and has the actual portfolio return higher than the general rate of return of the stock market index, thereby reinforcing and proving the effectiveness of the alpha investment model. © 2022 The authors and IOS Press.
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Robots have been used extensively in the battle against the COVID-19 pandemic since its outbreak. One prominent direction is the use of robots for swab sampling, which not only solves the shortage of medical staffs, but also prevents them from being infected during face-to-face sampling. However, a massive deployment of sampling robots is still not achievable due to their high costs, safety concerns, deployment complexity, and so on. In this letter, we propose a flexible, safe, and easy-to-deploy swab robot in a compact bench-top system. The robot can perform nasal/throat swab sampling tasks as dexterous as a human manual operation. The bio-mimetic rigid interior and soft exterior design guarantee the sampling robot with both flexibility and safety. Furthermore, the integration of 3-D fiber Bragg grating (FBG) based shape sensor and multi-axis force sensor may enhance the control performance. A dedicated constrained compliance control (CCC) algorithm was developed to tackle the unexpected interactions during sampling, which ensures the validity and safety of the sampling under disturbance. Various experiments are conducted to validate our system and prove its feasibility, flexibility, high safety, and efficiency for both nasal/throat swab sampling tasks. The proposed system is promising to be massive duplicated for robotic swab sampling.
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With the COVID-19 outbreak, there is an increasing focus on sports and fitness needs. Due to the need of epidemic prevention and control, fitness at home has gradually become a popular lifestyle. With the rapid development of smart phone application technology, artificial intelligence and Internet of Things, many enterprises have developed sports and fitness apps. These apps provide training courses for users, and customizes personalized training plans for them in combination with smart wearable devices. Keep App, a sports and fitness app developed by Keep Company, has the most users in China at present. It has created a technology platform for users to experience smart sports at home from multiple aspects, and has gained considerable income in operation. Taking Keep company as an example, this paper analyzes the marketing environment of the sports and fitness industry by using PEST model and Porter's Five Forces model, and puts forward optimization suggestions for Keep company's marketing strategy from the aspects of customer's need strategy, customer's cost strategy, convenience strategy and communication strategy by using 4C marketing theory. © 2022 ACM.
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INTRODUCTION The purpose of surveillance after resection of colorectal liver metastases (CLM) is to detect and treat recurrence using axial imaging, biomarker measurement, and a history/physical examination. In response to COVID-19 pandemic, telemedicine was used as a risk mitigation strategy to replace in-person visits, including for cancer surveillance. The objective of the study was to measure the uptake of telemedicine for cancer surveillance and outcomes following telemedicine surveillance after resection of CLM. METHODS Data from a prospective database was combined with real world data obtained from electronic health records using a cloud-based, data integration tool (Palantir Foundry) to identify patients in active surveillance following first surgical resection for CLM between April 2017 and April 2021. Telemedicine surveillance visit was defined as a follow-up visit >90 days following surgery using video or telephone. Recurrence was defined as detection of a new lesion. Bivariate statistical testing was performed using Student's t-test or chi-squared test. Retrospective chart review was used to validate identification of recurrence using the Foundry platform (100% interobserver agreement). RESULTS A total of 1,057 surveillance visits (306 patients) met our inclusion criteria. Prior to April 2020, 0% (0/686) visits utilized telemedicine. After April 2020, an average of 47.3% of visits per month utilized telemedicine (range 33.0 – 69.0%). The overall rate of identifying a recurrence during surveillance visit was 18.1% (191/1,057). There was no difference when comparing detection of recurrence using in-person (17.6%, 154/872) versus telemedicine visits (20.0%, 37/185, P=.371). The management of recurrence did not differ whether it was identified with an in-person or telemedicine visit;surgery, 36 (23%) vs. 10 (27%);ablation, 26 (17%) vs. 8 (22%);systemic therapy, 83 (54%) vs. 16 (43%);other, 9 (6%) vs. 3 (8%), respectively (P=.699). CONCLUSION Telemedicine was used in almost half of surveillance visits for CLM during the COVID- 19 pandemic. Detection and treatment of recurrence was similar for both telemedicine and in-person visits. Telemedicine-based follow-up is a safe and effective approach for surveillance after resection of CLM, supporting continued utilization beyond the pandemic.
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In recent years, the COVID-19 pandemic has negatively impacted all aspects of social life. Due to ease in the infected method, i.e., through small liquid particles from the mouth or the nose when people cough, sneeze, speak, sing, or breathe, the virus can quickly spread and create severe problems for people???s health. According to some research as well as World Health Organization (WHO) recommendation, one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space. A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection. This simple method can reduce up to 95% of the spread of the particles. However, this solution depends heavily on social consciousness, which is sometimes unstable. In order to improve the effectiveness of wearing face masks in public spaces, this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask. The approach uses the deep learning technique that relies on GoogleNet, AlexNet, and VGG16 models. The results are synthesized by an ensemble method, i.e., the bagging technique. From the experimental results, the approach represents a more than 95% accuracy of face mask recognition.
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Objective To analyze the utilization of HIV testing services and related influencing factors among men who have sex with men (MSM) on COVID-19. Methods From September to November 2020, an electronic questionnaire survey was conducted on MSM in the AIDS Vct of Longhua CDC and the Third People's Hospital of Shenzhen, The rank sum test was used to compare the changes in the utilization of HIV testing services in different situations of various factors, and ordinal multinomial logistic regression model was established to analyze the influencing factors of the utilization of HIV testing services. Results A total of 30.4% MSM were reported reduction in the use of HIV testing services. Logistic regression analysis showed that highly panic of COVID-19 reducing the frequency of anal sex (OR=0.056, 95% CI: 0.021-0.150, P < 0.001), being advised not going to testing agency (OR=0.538, 95% CI: 0.297-0.975, P=0.041), and being infected of COVID-19 (OR=21.979, 95% CI: 4.369-110.559, P < 0.001) had higher chance of reduction in the used of HIV testing services. Conclusion The HIV testing service utilization is decreased in MSM during COVID-19. It is necessary to pay more attention to this convenience when formulating and implementing epidemic prevention and control measures.
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In order to explore the emotions of people during this pandemic by using the Baidu Post Bar, we propose a neural network to classify text based on emotions. The data for training the proposed model and performing experiments is obtained from various online forums. The results of the proposed model are compared with other models presented in literature. The experimental results show that the proposed CNN_BiLSTM model has good results in classifying the data samples and it satisfies the requirements of emotion classification of forum Chinese texts. The proposed method accurately classifies the emotion of each sentence and provides necessary technical support for subsequent emotion analysis. © 2022 IEEE.
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The aim of this study is to explore the changes in the attention of the public and the trend of emotional changes in the face of the COVID-19 pandemic, based on web crawlers and big data statistical analysis technology. Public opinion information related to COVID-19 was especially obtained from Chinese online forums. The data was cleaned and filtered through data preprocessing operations, and statistical analysis was performed on multiple data characteristics to grasp the public opinion trends on the pandemic. The results of this study are of great significance for maintaining the social stability of China, the stable development of the economy, and the implementation of regular epidemic prevention and control measures and fully advancing work resumption. © 2022 IEEE.
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In situ measurements have suggested vehicle emissions may dominate agricultural sources of NH3 in many cities, which is alarming given the potential for urban NH3 to significantly increase human exposure to ambient particulate matter. However, confirmation of the prevalence of vehicle NH3 throughout a city has been challenging because of mixing with agricultural sources, and the latter are thus routinely assumed to dominate. Here we report vehicle NH3 emissions based on TROPOMI NO2 and CrIS NH3 (0.152 kg s(-1)) that are consistent with a model-based estimate (0.178 kg s(-1)) and show that COVID-19 lockdowns provide a unique opportunity for making the first satellite-based constraints on vehicle NH3 emissions for an entire urban region (western Los Angeles), which we find make up 60-95% of total NH3 emissions, substantially higher than the values of 13-22% in state and national inventories. This provides a new means of constraining a component of transportation emissions whose impacts may rival those of NOx yet which has been largely under-recognized and uncontrolled.
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Since 2013, China has launched the “Belt and Road” Initiative adhering to “The Five-Connectivity Program”, which aims at achieving win-win results with the countries along the road. Meantime, “17+1” mechanism is an important regional platform that promotes cooperation between China and Central and Eastern European countries. In this context, the Sino-Bulgarian economic relationships are constantly expanding and increasingly diversified in terms of foreign direct investment, trade, and cooperation of companies. There are increasing research interests and results of these two policies but there are still a lot of gaps to fill. The most pressing one is that more researches are needed about the impact of the Belt and Road Initiative and “17+1” mechanism on Central and Eastern Europe Countries, especially an individual country such as Bulgaria. Bulgaria was the second country that recognized and established diplomatic relations with China in 1949. Since then, Bulgaria and China have established a very good and strong relationship in different ways. However, the outbreak of COVID-19 in 2020 has brought many challenges for both Bulgaria and China, which has a deep impact on the bilateral economic relations. Therefore, by searching the past ten years of SSCI literature in the database, this paper systematically sorts out the existing research on Sino-Bulgarian economic relations and makes a comprehensive review, reflecting the latest and changing landscape of the Sino-Bulgarian economic relations. Then by collecting, screening and analyzing the latest statistical data in multiple sources, this paper identifies the characteristics of FDI, the bilateral trade respectively. The purpose is to provide a guidance of the prospect of Sino-Bulgarian economic relations in the future. The paper establishes that the economic relations between the two countries have made great progress under this initiative and new mechanism but still remain unstable and far below their potential. Finally, the paper concludes that there is a very promising future for the bilateral economic relations even facing many challenges and risks in the post pandemic world. © 2021, University of National and World Economy. All rights reserved.
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A significant cause of morbidity in COVID-19 infected patients admitted to the hospital is a severe dysregulated inflammatory response characterized as a cytokine storm, a key component of acute respiratory distress syndrome (ARDS). Here we have assembled a basic immune regulatory model from a list of 19 immune mediators with reported involvement in cytokine storm. Automated text-mining of over 2,500 full text journal publications using the MedScan natural language processing (NLP) engine identified 112 documented regulatory interactions coordinating the dynamic response of this network. This same text mining highlighted reported bi-directional associations between Coronavirus infection and a broad set of immune mediators producing a complex feedback pattern of host- pathogen interaction. Decisional logic parameters supporting the network's dynamic response were identified such that observed responses to SARS-CoV infection in an in vitro system of Calu3 human lung adenocarcinoma cells could be accurately predicted. Of the 19 competing models, 2 supported a dominant inactive immune resting state, with a predicted onset of cytokine storm in 63% and 26% of simulated infections respectively. Discrete event simulation based on the latter suggest that some repurposing strategies might outperform popular use of hydroxychloroquine as a companion to anti-viral therapy.
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Background: Remdesivir (RDV) has been shown to shorten recovery time and was well tolerated in patients with severe COVID-19. Here we report safety of RDV in patients with moderate COVID-19. Methods: We conducted an open-label, phase 3 trial (NCT04252664) in hospitalized patients with confirmed SARS-CoV-2 infection, evidence of pulmonary infiltrates, and oxygen saturation >94% on room air. Patients were randomly assigned to receive RDV (5 or 10 days) or standard of care (SOC). RDV was dosed intravenously at 200 mg on day 1, 100 mg daily thereafter. Adverse events (AEs) and laboratory abnormalities were evaluated through the day 11 data cut;safety data through day 28 will be presented at the meeting. Results: 584 patients were randomized and treated (5d RDV: n=191;10d RDV, n=193;SOC: n=200). Baseline characteristics were balanced among groups;median (range) age was 57y (12-95y), 39% were female and 19% Black, 39% had arterial hypertension, 15% hyperlipidemia, 11% asthma. Briefly, across both the 5d and 10d arms, RDV was well tolerated with a similar rate of Grade 3 or 4 AEs and fewer SAEs compared to SOC (Table). AEs more common with RDV vs SOC included nausea, headache, and hypokalemia. Overall, across the 3 arms, incidence of AEs leading to discontinuation and death were low and no clinically relevant changes in laboratory parameters were observed. In addition, median changes in renal and liver function tests from baseline were not statistically significant between the RDV 5d and RDV 10d groups compared to the SOC only group at d14 (Table 1). Conclusion: RDV given for 5d or 10d was well tolerated in patients with moderate COVID-19. No clinically significant safety signals were observed with RDV vs SOC. (Figure Presented).
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Background: Remdesivir (RDV) has been shown to shorten recovery time and was well tolerated in patients with severe COVID-19. Here we report baseline characteristics associated with clinical improvement at day (d) 14. Methods: We enrolled hospitalized patients with confirmed SARS-CoV-2 infection, oxygen saturation >94% on room air, and radiological evidence of pneumonia. Patients were randomized 1:1:1 to receive 5d or 10d of intravenous RDV once daily plus standard of care (SoC), or SoC only. For this analysis, patients were followed through discharge, d14, or death. Baseline demographic and disease characteristics associated with clinical improvement in oxygen support (≥2-point improvement on a 7-category ordinal scale ranging from discharge to death) were evaluated using multivariable logistic regression methods. Results: 584 patients were randomized and treated (5/10d RDV, n=384;SoC: n=200). 159 (27%) were ≥65y, 227 (39%) female, 328 (61%) white, 102 (19%) Asian, and 99 (19%) Black. 252 participants (43%) were enrolled in Europe, 260 (45%) North America (NA), and 72 (12%) in Asia. Most patients (483 [83%]) were not on supplemental oxygen but required medical care at baseline. In a multivariable model, 5/10d RDV was significantly positively associated with clinical improvement (adjusted odds ratio [OR] 1.69, 95% CI: 1.08, 2.65;p=0.0226). Significant covariables positively associated with clinical improvement included age < 65y (p< 0.0001) and region of treatment (Europe and NA vs Asia, p< 0.0001 each;Table);other examined factors were not significantly associated with clinical improvement, including gender, race, ethnicity, baseline oxygen support, duration of symptoms and hospitalization, obesity, and baseline transaminase levels. Conclusion: In moderate COVID-19 patients, after adjusting for treatment arm, age < 65y and region (NA vs Asia;Europe vs Asia) were associated with higher rates of clinical improvement. These observations recapitulate younger age as positive prognostic factor, and highlight the differences in the impact of the pandemic globally.
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Background: Remdesivir (RDV) shortens time to recovery time in patients with severe COVID-19. Its effect in patients with moderate COVID-19 remains unclear. Methods: We conducted an open-label, phase 3 trial (NCT04252664) involving hospitalized patients with confirmed SARS-CoV-2 infection, evidence of pulmonary infiltrates, and oxygen saturation >94% on room air. Patients were randomly assigned 1:1:1 to receive up to 5d or 10d of RDV with standard of care (SoC), or SoC alone;patients could be discharged prior to completing per-protocol assigned treatment duration. RDV was dosed intravenously at 200 mg on d1, 100 mg daily thereafter. Patients were evaluated daily while hospitalized, and via telephone if discharged. The primary endpoint was clinical status on d11 assessed on a 7-point ordinal scale. Results regarding the primary endpoint are expected to be published before IDWeek 2020;we plan to present d28 results at the meeting. Results: In total, 584 patients underwent randomization and started their assigned treatment (191, 5d RDV;193, 10d RDV;200, SoC). By d11, 3 2 point improvement on the ordinal scale occurred in 70% of patients in the 5d arm, 65% in the 10d arm, and 61% in the SoC arm. Patients in the 5d RDV arm were significantly more likely to have an improvement in clinical status than those receiving SoC (odds ratio [OR], 1.65;95% confidence interval [CI], 1.09-2.48;P=0.017);OR of improvement for the 10d RDV arm compared to SoC was 1.31 (95% CI, 0.88-1.95];p=0.183). This improvement in the 5-day arm over the SOC arm was noted from d6 through d11. We observed a peak of discharges corresponding with the assigned treatment duration of RDV, with increased discharges at d6 in the 5-day arm and at d11 in the 10-day arm. A worsening of clinical status of ≥ 1 point in the ordinal scale was observed more commonly in the SoC am (n=19, 10%) versus the 5d RDV (n=7, 4%) and 10d RDV (n=9, 5%). Conclusion: RDV for up to 5 days was superior to SoC in improving the clinical status of patients with moderate COVID-19 by d11. We will report d28 outcomes at the meeting. (Table Presented).
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Purpose: COVID-19 hits every country’s health-care system and economy. There is a trend toward using automation technology in response to the COVID-19 crisis not only in developed countries but also in those with lower levels of technology development. However, current studies mainly focus on the world level, and only a few ones report deployments at the country level. The purpose of this paper is to investigate the use of automation solutions in Vietnam with locally available materials mainly in the first wave from January to July 2020. Design/methodology/approach: The authors collected COVID-related automation solutions during the first wave of COVID-19 in Vietnam from January to July 2020 through a search process. The analysis and insights of a panel consisting of various disciplines (i.e. academia, health care, government, entrepreneur and media) aim at providing a clear picture of how and to what extent these solutions have been deployed. Findings: The authors found seven groups of solutions from low to high research and development (R&D) levels deployed across the country with various funding sources. Low R&D solutions were widely spread owing to simplicity and affordability. High R&D solutions were mainly deployed in big cities. Most of the solutions were deployed during the first phases when international supply chains were limited with a significant contribution of the media. Higher R&D solutions have opportunities to be deployed in the reopening phase. However, challenges can be listed as limited interdisciplinary research teams, market demand, the local supporting industry, end-user validation and social-ethical issues. Originality/value: To the authors’ best knowledge, this is the first study analyzing the use of automation technology in response to COVID-19 in Vietnam and also in a country in Southeast Asia. Lessons learned from these current deployments are useful for future emerging infectious diseases. The reality of Vietnam’s automation solutions in response to COVID-19 might be a reference for other developing countries with similar social-economic circumstances and contributes to the global picture of how different countries adopt technology to combat COVID-19. © 2020, Emerald Publishing Limited.
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Graph databases witness the rise of Graph Query Language (GQL) in recent years, which enables non-programmers to express a graph query. However, the current solution does not support motif-related queries on knowledge graphs, which are proven important in many real-world scenarios. In this paper, we propose a GQL framework for mining knowledge graphs, named M-Cypher. It supports motif-related graph queries in an effective, efficient and user-friendly manner. We demonstrate the usage of the system by the emerging Covid-19 knowledge graph analytic tasks. © 2020 ACM.