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In 2020, the outbreak of pneumonia caused by novel coronavirus spread rapidly all over the world. In the absence of a specific drug, novel coronavirus is still pandemic all over the world. In this paper, we proposed an improved molecular activity prediction model by adding feature selection method on the basis of comparing different methods to extract molecular features and machine learning models. We first used the anti-SARS-CoV-2 compounds reported in recent literatures to construct the data set, and then constructed three machine learning models. In addition, we tried to use three methods to extract molecular features in each model. In order to further improve the performance of the model, we add three feature selection methods. Through the comparison of different models, finally, we used FCFP to extract molecular features and added lasso feature selection method to establish the SVM model. Its test set accuracy is 90.0%, and the AUC value is 0.961, which could well predict the anti-SARS-CoV-2 activity of the compound. Our model can be used to speed up the research and discovery of anti-SARS-CoV-2 drugs. © 2022 IEEE.
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Ever since late December 2019, corona virus disease 2019 (COVID -19) has been reported in China. It presents a general trend of a global pandemic. By consulting the relevant Chinese government regulations and the latest publications of COVID -19, more than 20 pediatric surgical specialists from China formulated the Expert Consensus of COVID -19 Prevention and Control Protocol. Suitable for clinical practices, it provides recommendations for children's hospitals and pediatric surgical institutions at domestic and abroad.Copyright © 2020 by the Chinese Medical Association.
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This paper addresses the pervasive absence of verbal student participation in the online class, a phenomenon observed by many lecturers and instructors expressing the frustrating and uncomfortable experiences of encountering silence from their students, particularly when it came to responding to their questions. Added to the frustration is the observed preference of students to not turn on their videos. Whilst studies on student silence in classroom discourse have been well documented in the research literature, this phenomenon has taken on new significance in the virtual classroom, the new norm in the learning context during, and most likely after, the COVID-19 situation. This study attempts to capture the perceptions of the students themselves on student silence in terms of frequency, reasons and its impact on classroom communication and meaningful learning. A questionnaire was distributed to students at a local university, followed by student focus group interviews. Data collected were then subjected to a combination of quantitative and qualitative methods of analysis. The results show that student silence is a common feature in the online classroom and that students do perceive their silence to negatively affect the flow of communication both between themselves and with their lecturers. However, the question of whether meaningful learning still occurs despite the silence is more complex and less clear, raising questions not only about what is meant by meaningful learning but also the claim by classroom discourse studies and writings that student verbal participation is key to successful learning. © 2023, Education Research Institute, Seoul National University.
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Objectives: Due to the rapid development of the Internet and Internet of Things technology, as well as the catalysis of the COVID-19 epidemic and the favorable policy environment, China's online medical services has developed rapidly. But some serious problems also emerged, such as diseases diagnosis and treatment by artificial intelligence instead of by clinicians on some online medical service platforms, drugs prescribing before prescriptions, patient information leaking, etc. The assessment of drugs using health technology assessment (HTA) methods has been matured in recent years in China, but the assessment mechanism of online diagnosis and treatment behavior is still blank. This study plans to fill this gap. Method(s): Assessment mechanism of Chinese guide for medicine comprehensive evaluation and Guidance of Deliberative process for HTA were used as references. The framework was developed based on input-throughput-output model and was subjected to two rounds of consultation using Delphi method. Members with different backgrounds, perspectives and expertise relevant to online medical services were included to ensure the representativeness of the framework. Result(s): A framework for assessment mechanism of online diagnosis and treatment behavior was developed, which consists of the assess reasons, objects, organizations, time, participants and methods of all types of online diagnosis and treatment behaviors. Conclusion(s): This framework for assessment mechanism of online diagnosis and treatment behavior clarified the dimensions and indicators of online diagnosis and treatment behavior from the perspectives of medical security, network security, service quality, economy, and appropriateness, as well as the entire process of assessment operations. it is in line with Chinese policy requirements and will provide useful tool for the governments, medical institutions and research establishments. Copyright © 2022
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In this opinion paper, we argue that global health crises are also information crises. Using as an example the coronavirus disease 2019 (COVID-19) epidemic, we (a) examine challenges associated with what we term "global information crises";(b) recommend changes needed for the field of information science to play a leading role in such crises;and (c) propose actionable items for short- and long-term research, education, and practice in information science.
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In this study, three greenhouse gases (CO2, CH4, and N2O) and one conventional gas (CO) were observed at a roadside station in Shenzhen from September, 2019 to July, 2020. The average concentration of CO2, CH4, N2O, and CO was (430.8±6.1)×10-6, (2318.5±137.9)×10-9, (332.6±1.6)×10-9, and (333.4±121.2)×10-9, respectively. Seasonal variation of CO2 and CO were high in winter and low in summer, Seasonal variation of CH4 and N2O were high in autumn and low in summer. The high concentration in autumn and winter is due to the long-distance transmission of fossil fuel emissions during the heating period, and the low concentration in summer is mainly due to the reduction of long-distance transmission sources and the enhancement of sinks such as plant photosynthesis and photochemical reactions. The diurnal variation of CO2 concentration showed a two-peak and one-valley pattern, which was mainly affected by plant photosynthesis and morning and evening traffic peak;The diurnal variation of CO concentration showed a two-peak pattern, which was mainly affected by the morning and evening traffic peaks. The diurnal variation of CH4 and N2O concentration was high at night and low at day, which was mainly affected by daytime photochemical reaction. Among them, the concentration of CO2 and CO is more sensitive to the emission of traffic sources. In addition, this study compared the COVID-19 lockdown period in 2020 with the same period in 2021, and the results showed that the concentration of CO2, CH4, N2O, and CO decreased by 3.1%, 10.6%, 0.5% and 13.9%, respectively, indicating that traffic control can play an important role in reducing urban greenhouse gas emissions. © 2022, Editorial Board of China Environmental Science. All right reserved.
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The current COVID-19 pandemic has, perhaps, expedited the move to electronic medical systems (e.g., telemedicine). However, in the digitalization of healthcare services, we have to ensure the security and privacy of (sensitive) healthcare data, often stored locally in the hospital's server or remotely within a trusted cloud server. There have been many attempts to design blockchain-based approaches to support security and privacy in medical systems, and this is the focus of this article where we systematically review the existing literature on blockchain-based medical systems. We then categorize the existing security solutions into three categories, namely, 1) decentralized authentication, 2) access control, and 3) audit, and discuss the privacy protection technologies in blockchain-based healthcare systems. Based on our analysis, we identify a number of challenges, including performance limitations and inflexible audit, as well as future research opportunities (e.g., the need for lightweight security schemes for blockchain-based medical systems).
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Background: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) since the end of 2019. As of February 15, there were 56 COVID-19 cases confirmed in Hong Kong since the first case with symptom onset on January 23, 2020. Methods: Based on the publicly available surveillance data in Hong Kong, we identified 21 transmission events as of February 15, 2020. An interval censored likelihood framework is adopted to fit three different distributions including Gamma, Weibull, and lognormal, that govern the serial interval (SI) of COVID-19. We selected the distribution according to the Akaike information criterion corrected for small sample size (AICc). Findings: We found the lognormal distribution performed slightly better than the other two distributions in terms of the AICc. Assuming a lognormal distribution model, we estimated the mean of SI at 4.9 days (95% CI: 3.6–6.2) and SD of SI at 4.4 days (95% CI: 2.9–8.3) by using the information of all 21 transmission events. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are crucially needed in combating the COVID-19 outbreak. © Copyright © 2020 Zhao, Gao, Zhuang, Chong, Cai, Ran, Cao, Wang, Lou, Wang, Yang, He and Wang.
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In December 2019, novel coronavirus disease (COVID-19) hit Wuhan, Hubei Province, China and spread to the rest of China and overseas. The emergence of this virus coincided with the Spring Festival Travel Rush in China. It is possible to estimate the total number of COVID-19 cases in Wuhan, by 23 January 2020, given the cases reported in other cities/regions and population flow data between Wuhan and these cities/regions. We built a model to estimate the total number of COVID-19 cases in Wuhan by 23 January 2020, based on the number of cases detected outside Wuhan city in China, with the assumption that cases exported from Wuhan were less likely underreported in other cities/regions. We employed population flow data from different sources between Wuhan and other cities/regions by 23 January 2020. The number of total cases in Wuhan was determined by the maximum log likelihood estimation and Akaike Information Criterion (AIC) weight. We estimated 8 679 (95% CI: 7 701, 9 732) as total COVID-19 cases in Wuhan by 23 January 2020, based on combined source of data from Tencent and Baidu. Sources of population flow data impact the estimates of the total number of COVID-19 cases in Wuhan before city lockdown. We should make a comprehensive analysis based on different sources of data to overcome the bias from different sources. © Copyright © 2020 Zhuang, Cao, Zhao, Lou, Yang, Wang, Yang and He.