ABSTRACT
COVID-19 highlights the lack of interdisciplinary medical talents. The international history of medical education shows the urgent need of high-level interdisciplinary medical talents. Anchoring the goal to develop a global center of talents and highland of innovation, this article takes medical education of Zhejiang University as an example, focusing on and exploring the training mode of high-level interdisciplinary medical talents in the new era. It includes: Firstly, optimizing the training mode of eight-year program for medical doctors with non-medical bachelor degree followed with complete education for a medical doctorate that innovates the curriculum system of clinical medicine;secondly, creating the training system of postdoctor of clinical medicine and integrating medical resources that include high-quality talents and health care system, in order to build a high-quality teaching staff with a interdisciplinarity background and innovative bases. It not only strengthens the residents' competency and frontier creativity, but also ensures the sustainable development of interdisciplinary medical talents. The reform of training mode, curriculum system, teaching staff and clinical teaching bases all contribute to the goal of building a country with interdisciplinary talents that serve the frontier of science and technology in the world, the major needs of the country and people's health in the new era.Copyright © 2022, Peking Union Medical College Hospital. All rights reserved.
ABSTRACT
SARS-CoV-2, the coronavirus that causes the disease COVID- 19, was identified over three years ago, yet current small molecule therapies have limited usefulness and resistance to therapies and vaccines is inevitable. Ultra high-throughput screening (uHTS) assays for novel and repurposed inhibitors of a protein-protein interaction in the viral life cycle could be used to screen a vast number of compounds with a specific target of action. In particular, the interaction between viral SPIKE protein and human TMPRSS2 is an understudied, yet critical step in viral entry. Thus, we aim to create uHTS assays to rapidly and affordably identify inhibitors of the TMPRSS2 and SPIKE interaction for further biochemical studies and therapeutic development for SARS-CoV-2.We first sought to create a Time Resolved-Forster/Fluorescence Energy Transfer (TR-FRET) assay which uses lysates of cells with overexpressed SPIKE and TMPRSS2 and fluorescently labeled antibodies to detect interactions between these proteins. Initially, we developed and optimized this TR-FRET assay in a 384-well plate then miniaturized to a 1536-well plate. We conducted a pilot screen of compounds with known biological activity to test this assay's screening capabilities. To further narrow the hits from this TR-FRET screen, we developed an orthogonal uHTS Nanoluciferase Binary Technology (NanoBiT) assay to detect the interaction between tagged SPIKE and TMPRSS2 in live cells.With these two assays in hand, we expanded our TR-FRET screen to over 100 000 compounds and identified several that were also positive in the orthogonal NanoBiT assay. Four of these compounds were found to potentially interact with either SPIKE or TMPRSS2 from thermal shift experiments, providing support for their action as SPIKE and TMPRSS2 interaction inhibitors. Thus, we have developed TR-FRET and NanoBiT orthogonal uHTS assays which have allowed for the discovery of several possible repurposed and novel inhibitors of the SPIKE/ TMPRSS2 interaction. These uHTS assays can be employed as a model for future drug discovery efforts and the compounds identified may be used as exciting starting points for development of inhibitors of SARS-CoV-2. This research was supported in part by The Emory School of Medicine COVID Catalyst-I3 award, the NCI Emory Lung Cancer SPORE (SR, HF;P50CA217691) Career Enhancement Program (AI, P50CA217691), Emory initiative on Biological Discovery through Chemical Innovation (AI) and R01AI167356 (SS).Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.
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Surface longwave downward radiation (LWDR) is a key factor affecting the surface energy balance. The daily LWDR and the diurnal variations of LWDR are of great significance for studies of climate change and surface processes. How to obtain LWDR at an averaged temporal scale from instantaneous LWDR is one of the longstanding problems in the field of radiation budget from remote sensing. In this paper, two temporal upscaling methods are introduced, namely, a method based on the diurnal variations of LWDR (diurnal variation based, DVB) and a method based on random forest regression (RFR). The results reveal that: (1) The DVB method has a global hourly and daily LWDR root-mean-square error (RMSE) of less than 21 W/m2 and 15 W/m2, respectively, and the RMSE of the daily LWDR based on RFR is less than 7 W/m2;(2) When compared with four existing statistical interpolation methods, the DVB method can not only ensure the accuracy, but also can overcome the problem of missing samples and/or an abnormal samples during upscaling;(3) Except for directly predict daily LWDR, the DVB methods can also obtain more accurate LWDR diurnal variations such as hourly, half-hourly etc. The RFR method enables high-efficiency and accurate estimation of daily averaged LWDR from instantaneous measurements. Compared with existing methods and products, the proposed methods are not only efficient, but also have a superior applicability and reliable accuracy. The proposed strategies provide new ideas for the community in estimating LWDR at continuous temporal scales from remotely sensed measurements.
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With the development of 5G and AI technology, the infectious virus detection framework system based on the combination of 5G MEC and medical sensors can effectively assist in the intelligent detection and control of influenza viruses such as COVID-19. Employing the edge computing and 5G+MEC model, the virus AI model is trained for the collected influenza virus data. Then the virus AI model can be used to evaluate the virus patients on the local edge computing service platform. Therefore, this paper introduces an algorithm and resource allocation, which uses 5G functions (especially, low latency, high bandwidth, wide connectivity, and other functions) to achieve local chest X-ray or CT scan images to detect COVID-19. Meanwhile, this paper also compares the computational efficiency of different algorithms in the 5G edge AI-based infectious virus detection framework, in this way to select the best algorithm and resource allocation. © 2022 IEEE.
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Models in previous studies about inclusive finance often include economic data while excludes public online statements. In this paper Random Forest Regression (RFR) model is trained on the annual influencing factors and annual financial inclusion index to predict quarterly financial inclusion index by the quarterly influencing factors to expand the size of data. Then, BOW model tf-idf algorithm is used to convert COVID-19 – loan related online statements into word vectors. Lastly, these influencing factors of different lag periods are passed into the RFR model to compare their performance. Result of models shows that there is impact the epidemic has on the development of inclusive finance, and the lag period of the impact opinion texts on financial inclusion is 2 quarters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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To explore the influencing factors of perceived stress among frontline nurses during COVID-1 9, and the role of positive psychological capital in anxiety sensitivity and stress perception, so as to provide evidence for reducing the perceived stress of frontline nurses. From December 2021 to January 2022, 475 frontline nurses from 8 hospitals in X city were investigated by general data questionnaire, positive psychological capital, anxiety sensitivity index and perceived stress scale. The results showed that there were significant differences in nurses’ perceived stress in age, working years, professional title and salary. Perceived stress was positively correlated with anxiety sensitivity, negatively correlated with positive psychological capital, and anxiety sensitivity was negatively correlated with positive psychological capital. Anxiety sensitivity can not only directly predict perceived stress, but also negatively affect perceived stress through positive psychological capital. Therefore, during the epidemic prevention and control in COVID-1 9, nurses can obtain effective stress coping strategies by strengthening their positive psychological capital, thus reducing their anxiety and perceived stress, and better providing high-quality nursing services for patients. © 2022, Editorial department of Chinese Medical Ethics. All rights reserved.
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Cybercriminals often register many pornographic or gambling domains (known as abusive domains) with similar character compositions in bulk to reduce their investment in buying domains and make it easier for clients to remember and spread them. Therefore, this study combines the ideas of text similarity and text generation and proposes an abusive domain generation model based on GRU for rapidly generating new abusive domain names from known ones. Additionally, we develop a two-layer detection system for pornography and gambling domains using fastText and CNN models to obtain an abusive domain dataset for model training and validation. In the end, our detection system identifies pornographic and gambling domains with 99% precision while balancing correctness and speed. By inputting 40,000 random keywords into the abusive domain generation model, we obtained 130,220 online domains that served web pages, of which about 66% were pornographic or gambling domains. The results show that by exploiting cybercriminals' behaviors in registering abusive domain names, such as bulk registration of similar domain names, we can prospectively acquire a large number of new abusive domains based on known ones. This study demonstrates that predicting new abusive domains not only expands the domain blacklist but also allows researchers to target the generated suspicious domains and dispose of them in time before they show abusive behavior.
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The emergence of COVID-19 has reduced the opportunities for offline meetings, making people's work and study more transfer to the internet platform. However, the viewing angle and distance of the camera cannot be considered both. Therefore, machine vision is used to identify and track the presenter, and the camera pan-tilt control function of automatically tracking the presenter is realized. In many tests, the target tracking function works normally and works well. The experimental design involves relatively comprehensive disciplines, with good functional scalability and high practicability. It is an innovative experiment integrating robotics teaching, machine learning practice and embedded systems. © 2022 IEEE.
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Objective To analyze the molecular epidemiological characteristics of the Corona virus disease 2019 (COVID‑19) cases in Shijiazhuang, which can reveal the origin of the outbreak and provide a scientific basis for COVID‑19 prevention and control. Methods From January 2 to January 8, 2021, a total of 404 samples from 170 COVID‑19 cases were collected from the Shijiazhuang Fifth Hospital. The consensus sequence of 2019 novel Coronavirus(2019‑nCoV) was obtained through multiplex polymerase chain reaction‑based sequencing. The sequences of 170 COVID‑19 cases were analyzed by the PANGOLIN, and the data were statistically analyzed by T‑test. Results Among the 404 COVID‑19 samples, a total of 356 samples obtained high quality genome sequences (>95%, 100×sequencing depth). The whole genome sequences of 170 COVID‑19 cases were obtained by eliminating repeated samples. All 170 sequences were recognized as lineage B1.1 using PANGOLIN. The number of single nucleotide polymorphism arrange from 18-22 and most of the single nucleotide polymorphism were synonymous variants. All of 170 genomes could be classified into 48 sub‑groups and most of the genomes were classified into 2 sub‑groups (66 and 31, respectively). Conclusions All cases in this study are likely originated from one imported case. The viruses have spread in the community for a long time and have mutated during the community transmission.
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Since the coronavirus disease 2019 (COVID-19) pandemic, human parasitology education has been exceedingly disrupted. To deliver human parasitology knowledge, medical universities in China have employed multiple measures, some of which have had positive outcomes that have not yet been summarized. The objective of this review is to share the Chinese experience as the human parasitology teaching methods were transformed. In general, we adopted a fully online teaching model under urgent pandemic control measures based on a well-structured teaching model that integrated the course preview, live lecture, review, and assessment. Combinations were attempted of COVID-19 and parasitology teaching contents. Some active learning models, such as case-based e-learning and flipped classrooms, were proposed for offline and online blended teaching during the normalization stage of the pandemic. Meanwhile, we discuss both the strengths and flaws of online and blended teaching. Some useful assessment tools are presented for reference purposes. In conclusion, this transition to online and online-offline blended human parasitology teaching in China has boosted innovative teaching activities and may continue to catalyze the transformation of medical education.
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This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances. ©EverScience Publications
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COVID-19 highlights the lack of interdisciplinary medical talents. The international history of medical education shows the urgent need of high-level interdisciplinary medical talents. Anchoring the goal to develop a global center of talents and highland of innovation, this article takes medical education of Zhejiang University as an example, focusing on and exploring the training mode of high-level interdisciplinary medical talents in the new era. It includes: Firstly, optimizing the training mode of eight-year program for medical doctors with non-medical bachelor degree followed with complete education for a medical doctorate that innovates the curriculum system of clinical medicine;secondly, creating the training system of postdoctor of clinical medicine and integrating medical resources that include high-quality talents and health care system, in order to build a high-quality teaching staff with a interdisciplinarity background and innovative bases. It not only strengthens the residents' competency and frontier creativity, but also ensures the sustainable development of interdisciplinary medical talents. The reform of training mode, curriculum system, teaching staff and clinical teaching bases all contribute to the goal of building a country with interdisciplinary talents that serve the frontier of science and technology in the world, the major needs of the country and people's health in the new era. © 2022, Peking Union Medical College Hospital. All rights reserved.
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Coronavirus disease 2019 (COVID-19), a highly contagious respiratory disease, has rapidly become a global pandemic. Chest X-ray imaging could serve an important role in early diagnosis of the disease. Deep learning methods have recently shown promise in disease detection tasks. The aim of this study was to develop a deep learning-based approach for detection of COVID-19 in chest X-ray images. Data were extracted from an opensource COVID-19 database developed by Cohen JP. The data consisted of X-ray images of patients with COVID-19, with other pneumonias or with no findings. The 205 images were randomly partitioned into training, validation and test datasets containing 143, 32, and 30 images, respectively, using a 70%/15%/15% split. The performance of several deep convolutional neural network (CNN)-based architectures, including VGG16, ResNet50, DenseNet121, and InceptionV3, were evaluated on the disease detection task. These networks were first pretrained on the ImageNet dataset consisting of natural images and then further fine-tuned on the task of detecting COVID-19 in chest X-ray images. The networks were then evaluated on the test set by assessing overall accuracy, area under receiver operating characteristic curve (AUROC), sensitivity and specificity. The performance of the networks trained from scratch without pretraining on ImageNet was also compared to the performance of the networks that were first pretrained on ImageNet and then fine-tuned on the detection task. DenseNet121 had the best performance on the test set with an overall accuracy of 90.0% (95% confidence interval (CI): 78.6%, 100%), an AUROC of 0.95, a sensitivity of 91.3% and a specificity of 85.7%. The pretrained DenseNet121 also significantly outperformed the DenseNet121 trained from scratch with a 30.0% improvement in overall accuracy. The proposed deep learning-based approach showed significant promise for detection of COVID-19 in chest X-ray images. © 2020 IEEE
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Objective: To analyze the distribution characteristics of syndrome of TCM of the patients with COVID-19 in Kunming, China. Methods: To classify and summarize the TCM syndrome types of COVID-19 patients via Cluster analysis combining with tongue image, chest CT and clinical expertise of 36 Covid-19 patients in Kunming. Results: In the 36 cases of Kunming region confirmed COVID-19 patients, 17 cases had fever (47.2%), 18 cases had cough (50%), 16 cases felt bitter taste in the mouth (44.4%), 18 cases felt dry throat (50.0%), 17 cases had poor appetite (47.2%), 15 cases had nausea (41.7%);12 cases had diarrhea (33.3%), 15 cases had insomnia (41.7%);12 cases had chest tightness (33.3%);3 cases had dyspnea (8.3%);6 cases had nasal congestion and running nose (16.7%);15 cases had fatigue (41.7%);6 cases had headache and body pain (16.7%);5 cases had red tongue (13.9%);18 cases had pale red tongue (50%);8 cases had tongue with red edge and tip (22.2%);3 cases had dark red tongue (8.3%);2 cases had cyanosis (5.6%);3 cases had swollen tongue (8.3%);18 cases had dentate tongue (50%);4 cases had yellow tongue coating (11.1%);5 cases had yellow sticky tongue coating (13.9%);12 cases had white sticky tongue coating (33.3%);6 cases had thin white tongue coating (16.7%);2 cases had no tongue coating (5.6%). The chest CT results showed that: There were five cases without lesions. The lesions were located in the upper lobe of one lung in 13 cases located in the lower lobe of one lung in seven cases, located in the upper middle lobe in three cases, located in the lower lobe in five cases, and in the upper middle and lower lobes of double lung in 13 cases. There were 14 cases of Shaoyang syndrome, 17 cases of wet Resistance Tir-juao Syndrome. According to the time of onset, the disease was followed by Shaoyang Syndrome (1 day), the Wet blocked tri-jiao Syndrome (3 days), epidemic poison retention lung syndrome and syndrome of flaring heat in qifen and yingfen (5 days), and dampness-toxicity lung-stagnation syndrome (6 days). Conclusion: The TCM syndromes of COVID-19 in Kunming are mainly the Wet Resistance Tri-Jiao Syndrome and Shaoyang syndrome, followed by dampness-toxicity lung-stagnation syndrome, epidemic poison retention lung and syndrome of flaring heat in qifen and yingfen.
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This paper reports the impact of coronavirus disease 2019 on the practice and delivery of geotechnical and geoenvironmental engineering (GGE) education modules, including lectures, lab sessions, student assessments and research activities, based on the feedback from faculty members in 14 countries/regions around the world. Faculty members have since adopted a series of contingent measures to enhance teaching and learning experience during the pandemic, which includes facilitating active learning, exploring new teaching content related to public health, expanding e-learning resources, implementing more engaged and student-centred assessment and delivering high-impact integrated education and research. The key challenges that faculty members are facing appear to be how to maximise the flexibility of learning and meet physical distancing requirements without compromising learning outcomes, education equity and interpersonal interactions in the traditional face-to-face teaching. Despite the challenges imposed by the pandemic, this could also be a good opportunity for faculty members obliged to lecture, to rethink and revise the existing contents and approaches of professing GGE education. Three future opportunities namely, smart learning, flipped learning and interdisciplinary education, are identified. The changes could potentially provide students with a more resilient, engaged, interactive and technology-based learning environment. © 2021 ICE Publishing: All rights reserved.
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The outbreak of the coronavirus disease 2019 (Covid-19) pandemic not only has created a health crisis across the world but is also expected to impact negatively the global economy and societies at a scale that is maybe larger than that of the 2008 financial crisis. Simultaneously, it has inevitably exerted many negative consequences on the geoenvironment on which human beings depend. The current paper articulates the role of environmental geotechnics in elucidating and mitigating the effects of the current pandemic. It is the belief of all authors that the Covid-19 pandemic presents not only significant challenges but also opportunities for the development of the environmental geotechnics field. This discipline should make full use of geoenvironmental researchers' and engineers' professional skills and expertise to look for development opportunities from this crisis, to highlight the irreplaceable position of the discipline in the global fight against pandemics and to contribute to the health and prosperity of communities, to serve humankind better. In order to reach this goal while taking into account the specificity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the uncertainty of its environmental effects, it is believed that more emphasis should be placed on the following research directions: pathogen-soil interactions;isolation and remediation technologies for pathogen-contaminated sites;new materials for pathogen-contaminated soil;recycling and safe disposal of medical wastes;quantification of uncertainty in geoenvironmental and epidemiological problems;emerging technologies and adaptation strategies in civil, geotechnical and geoenvironmental infrastructures;pandemic-induced environmental risk management;and modelling of pathogen transport and fate in geoenvironment, among others. Moreover, Covid-19 has made it clear to the environmental geotechnics community the importance of urgent international co-operation and of multidisciplinary research actions that must extend to a broad range of scientific fields, including medical and public health disciplines, in order to meet the complexities posed by the Covid-19 pandemic. © 2021 ICE Publishing: All rights reserved.