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In recent years, the increasingly fierce competition among higher education institutions (HEIs), the finite resources, and the enormous influence of the COVID-19 epidemic on higher education have made it especially important to evaluate the performance of Chinese higher education institutions. This paper utilizes the DEA-BCC and Malmquist index to analyze the efficiency and productivity of 34 Chinese "985 Project” universities in the period 2017–2021. The indicator system includes three inputs and five outputs, contained in Model 1 and Model 2 for comparative analysis. The results demonstrate that the COVID-19 epidemic has had a considerable negative impact on Chinese higher education, and has induced the reduction of technical efficiency and productivity. Setting up online MOOCs is conducive to enhancing the efficiency and productivity of HEIs;in addition, the efficiency mentioned varies noticeably among different university levels, and there is no significant difference in different university types and geographical locations. © 2023 by the authors.
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For epidemic prevention and control, molecular diagnostic techniques such as field-effect transistor (FET) biosensors is developed for rapid screening of infectious agents, including Mycobacterium tuberculosis, SARS-CoV-2, rhinovirus, and others. They obtain results within a few minutes but exhibit diminished sensitivity (<75%) in unprocessed biological samples due to insufficient recognition of low-abundance analytes. Here, an electro-enhanced strategy is developed for the precise detection of trace-level infectious agents by liquid-gate graphene field-effect transistors (LG-GFETs). The applied gate bias preconcentrates analytes electrostatically at the sensing interface, contributing to a 10-fold signal enhancement and a limit of detection down to 5 × 10−16 g mL−1 MPT64 protein in serum. Of 402 participants, sensitivity in tuberculosis, COVID-19 and human rhinovirus assays reached 97.3% (181 of 186), and specificity is 98.6% (213 of 216) with a response time of <60 s. This study solves a long-standing dilemma that response speed and result accuracy of molecular diagnostics undergo trade-offs in unprocessed biological samples, holding unique promise in high-quality and population-wide screening of infectious diseases. © 2023 Wiley-VCH GmbH.
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Objective: To explore the epidemiological characteristic of a COVID-19 outbreak caused by 2019-nCoV Omicron variant BF.7 and other provinces imported in Shenzhen and analyze transmission chains and characteristics. Methods: Field epidemiological survey was conducted to identify the transmission chain, analyze the generation relationship among the cases. The 2019-nCoV nucleic acid positive samples were used for gene sequencing. Results: From 8 to 23 October, 2022, a total of 196 cases of COVID-19 were reported in Shenzhen, all the cases had epidemiological links. In the cases, 100 were men and 96 were women, with a median of age, M (Q1, Q3) was 33(25, 46) years. The outbreak was caused by traverlers initial cases infected with 2019-nCoV who returned to Shenzhen after traveling outside of Guangdong Province.There were four transmission chains, including the transmission in place of residence and neighbourhood, affecting 8 persons, transmission in social activity in the evening on 7 October, affecting 65 persons, transmission in work place on 8 October, affecting 48 persons, and transmission in a building near the work place, affecting 74 persons. The median of the incubation period of the infection, M (Q1, Q3) was 1.44 (1.11, 2.17) days. The incubation period of indoor exposure less than that of the outdoor exposure, M (Q1, Q3) was 1.38 (1.06, 1.84) and 1.95 (1.22, 2.99) days, respcetively (Wald χ2=10.27, P=0.001). With the increase of case generation, the number and probability of gene mutation increased. In the same transmission chain, the proportion of having 1-3 mutation sites was high in the cases in the first generation. Conclusions: The transmission chains were clear in this epidemic. The incubation period of Omicron variant BF.7 infection was shorter, the transmission speed was faster, and the gene mutation rate was higher. It is necessary to conduct prompt response and strict disease control when epidemic occurs.
Subject(s)
COVID-19 , Epidemics , Male , Humans , Female , SARS-CoV-2 , COVID-19/epidemiology , Disease Outbreaks , China/epidemiologyABSTRACT
In the past few years, CRISPR-based (Clustered regularly interspaced short palindromic repeats-based) detection techniques have arisen in the field of bioanalysis, molecular biology and clinical diagnosis. However, there are still some limitations, such as high cost, high technical requirements, complexity, cross contamination, lack of quantification strategies, etc. Microfluidic platforms, as emerging technol-ogies, have the potential to take CRISPR-based technology to the new level in the future. In this review, we illustrate the recent progress as well as our work and collaborative work in CRISPR-based microfluidic detection assays based on the categories of microfluidic chips, including paper-based microfluidics, centrifugal microfluidics, digital microfluidics, electrochemical microfluidics and wearable microfluidics, mainly focusing on how microfluidic platforms improve CRISPR-based detection method in the aspect of visualization, detection throughput, anti-contamination, simple operation, sensitivity, specificity and quantification, etc. And we outlook the future trends of CRISPR-based microfluidic detection methods in both innovation of CRISPR-based and design of microfluidic platforms. (c) 2022 Elsevier B.V. All rights reserved.
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Objective To understand the contaminations of SARS-CoV-2 of imported frozen food and corresponding ke;environments in Nantong Farmers’ Wholesale Market. Methods The samples of imported frozen food, surface samples of key environmental objects and throat swabs, anal swabs and blood samples from employees were collected randomly, SARS-CoV-2 nucleic acid testing as well as specific serum antibody IgM and IgG testing were implemented. Results A total of 1 685 sample: of imported frozen food in Nantong, 2 432 samples from key environment, and 1 539 samples of anal swabs and throat swabs fron key practitioners were collected, all samples were confirmed to be negative for SARS-CoV-2 nucleic acid testing. A total of 299 blood samples were collected from key employees, and virus specific serum antibodies IgM and IgG were negative. Conclusion The imported frozen food and key environment sold in the Nantong have not been contaminated with SARS-CoV-2 temporarily, and it remains being necessary to further implements surveillance and control of Corona Virus Disease 2019(COVID-19). © 2022, Editorial Department of Medical Pest Control. All rights reserved.
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Despite the introduction of vaccines and drugs for SARS-CoV-2, the COVID-19 pandemic continues to spread throughout the world. In severe COVID-19 patients, elevated levels of proinflammatory cytokines have been detected in the blood, lung cells, and bronchoalveolar lavage, which is referred to as a cytokine storm, a consequence of overactivation of the NLR family pyrin domain-containing protein 3 (NLRP3) inflammasome and resultant excessive cytokine production. The hyperinflammatory response and cytokine storm cause multiorgan impairment including the central nervous system, in addition to a detriment to the respiratory system. Hyperactive NLRP3 inflammasome, due to dysregulated immune response, is the primary cause of COVID-19 severity. The severity could be enhanced due to viral evolution leading to the emergence of mutated variants of concern, such as delta and omicron. In this review, we elaborate on the inflammatory responses associated with the NLRP3 inflammasome activation in COVID-19 pathogenesis, the mechanisms for the NLRP3 inflammasome activation and pathway involved, cytokine storm, and neurological complications as long-term consequences of SARS-CoV-2 infection. Also discussed is the therapeutic potential of NLRP3 inflammasome inhibitors for the treatment of COVID-19.
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As the COVID-19 pandemic rampages across the world, the demands of video conferencing surge. To this end, real-time portrait segmentation becomes a popular feature to replace backgrounds of conferencing participants. While feature-rich datasets, models and algorithms have been offered for segmentation that extract body postures from life scenes, portrait segmentation has yet not been well covered in a video conferencing context. To facilitate the progress in this field, we introduce an open-source solution named PP-HumanSeg. This work is the first to construct a large-scale video portrait dataset that contains 291 videos from 23 conference scenes with 14K fine-labeled frames and extensions to multi-camera teleconferencing. Furthermore, we propose a novel Self-supervised Connectivity-aware Learning (SCL) for semantic segmentation, which introduces a self-supervised connectivity-aware loss to improve the quality of segmentation results from the perspective of connectivity. And we propose an ultra-lightweight model with SCL for practical portrait segmentation, which achieves the best trade-off between IoU and the speed of inference. Extensive evaluations on our dataset demonstrate the superiority of SCL and our model.
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Mobile sensing systems have been widely used as a practical approach to collect behavioral and health-related information from individuals and to provide timely intervention to promote health and well-being, such as mental health and chronic care. As the objectives of mobile sensing could be either personalized medicine for individuals or public health for populations, in this work we review the design of these mobile sensing systems, and propose to categorize the design of these systems in two paradigms –(i) Personal Sensing and (ii) Crowd Sensing paradigms. While both sensing paradigms might incorporate common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and cloud-based data analytics to collect and process sensing data from individuals, we present two novel taxonomy systems based on the (a) Sensing Objectives (e.g., goals of mHealth sensing systems and how technologies achieve the goals), and (b) the Sensing Systems Design and Implementation (D&I) (e.g., designs of mHealth sensing systems and how technologies are implemented). With respect to the two paradigms and two taxonomy systems, this work systematically reviews this field. Specifically, we first present technical reviews on the mHealth sensing systems in eight common/popular healthcare issues, ranging from depression and anxiety to COVID-19. Through summarizing the mHealth sensing systems, we comprehensively survey the research works using the two taxonomy systems, where we systematically review the Sensing Objectives and Sensing Systems D&I while mapping the related research works onto the life-cycles of mHealth Sensing, i.e., (1) Sensing Task Creation &Participation, (2) Health Surveillance &Data Collection, and (3) Data Analysis &Knowledge Discovery. In addition to summarization, the proposed taxonomy systems also help the potential directions of mobile sensing for health from both personalized medicine and population health perspectives. Finally, we attempt to test and discuss the validity of our scientific approaches to the survey. IEEE
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While the COVID-19 epidemic expands among multiple countries, diverse measures have been exploited to halt the spread of COVID-19. In Mainland China, the containment measures can be categorized into two types, i.e., intra-city quarantine and isolation, and inter-city travel restriction. Both information acquisition and local economy play an important role while implementing the measures. In order to understand the relationship between the containment measures and pulic responses to the COVID-19 pandemic, we study the correlation among three factors, i.e., the information acquisition of containment measures, the public responses to the COVID-19 pandemic, and local economy of cities in Mainland China. We combine Markov Chain Monte Carlo (MCMC) and SIR-X to estimate the parameters related to the pandemic. Then, we investigate the correlations among multiple representative parameters including mobility, local economy, and information acquisition to understand the implementation of containment measures. We utilize the mobility data from Baidu Maps, the COVID-19 related search frequency data from Baidu Search Engine, and the data of Gross Domestic Product (GDP). From the analysis, we evidence that the the information acquisition is strongly correlated with the local economy. In addition, we find that the cities with stronger local economy have bigger inflows from Wuhan, while the citizens of the cities perform COVID-19- related searches more frequently and take the quarantine measure more strictly. © 2021 IEEE.
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Left unmitigated, climate change poses a catastrophic risk to human health, demanding an urgent and concerted response from every country. The 2015 Lancet Commission on Health and Climate Change and The Lancet Countdown: Tracking Progress on Health and Climate Change have been initiated to map out the impacts of climate change and the necessary policy responses. To meet these challenges, Tsinghua University, partnering with the University College London and 17 Chinese and international institutions, has prepared the Chinese Lancet Countdown report, which has a national focus and builds on the work of the global Lancet Countdown: Tracking Progress on Health and Climate Change. Drawing on international methodologies and frameworks, this report aims to deepen the understanding of the links between public health and climate change at the national level and track them with 23 indicators. This work is part of the Lancet's Countdown broader efforts to develop regional expertise on this topic, and coincides with the launch of the Lancet Countdown Regional Centre in Asia, based at Tsinghua University. The data and results of this report are presented at the provincial level, where possible, to facilitate targeted response strategies for local decision-makers. Based on the data and findings of the 2020 Chinese Lancet Countdown report, five recommendations are proposed to key stakeholders in health and climate change in China: (1) Enhance inter-departmental cooperation. Climate change is a challenge that demands an integrated response from all sectors, urgently requiring substantial inter-departmental cooperation among health, environment, energy, economic, financial, and education authorities. (2) Strengthen health emergency preparedness. Knowledge and findings on current and future climate-related health threats still lack the required attention and should be fully integrated into the emergency preparedness and response system. (3) Support research and raise awareness. Additional financial support should be allocated to health and climate change research in China to enhance health system adaptation, mitigation measures, and their health benefits. At the same time, media and academia should be fully motivated to raise the public and politicians' awareness of this topic. (4) Increase climate change mitigation. Speeding up the phasing out of coal is necessary to be consistent with China's pledge to be carbon neutral by 2060 and to continue to reduce air pollution. Fossil fuel subsidies must also be phased out. (5) Ensure the recovery from COVID-19 to protect health now and in the future. China's efforts to recover from COVID-19 will shape public health for years to come. Climate change should be a priority in these interventions. © 2021, Science Press. All right reserved.
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COVID-19 has changed the world fundamentally since its outbreak in January 2020. Public health experts and administrations around the world suggested and implemented various intervention strategies to slow down the transmission of the virus. To illustrate to the general public how the virus is transmitted and how different intervention strategies can check the transmission, we built an agent-based model (ABM) to simulate the transmission of the virus in the real world and demonstrate how to prevent its spread with public health strategies. © Copyright © 2021 Wang, Xiong, Liu, Jung, Stone and Chukoskie.
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The novel coronavirus disease (COVID-19) has crushed daily routines and is still rampaging through the world. Existing solution for nonpharmaceutical interventions usually needs to timely and precisely select a subset of residential urban areas for containment or even quarantine, where the spatial distribution of confirmed cases has been considered as a key criterion for the subset selection. While such containment measure has successfully stopped or slowed down the spread of COVID-19 in some countries, it is criticized for being inefficient or ineffective, as the statistics of confirmed cases are usually time-delayed and coarse-grained. To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks, where the results demonstrate the efficiency and effectiveness of C-Watcher for early detection of high-risk neighborhoods from a large number of cities.
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As the recent COVID-19 outbreak rapidly expands all over the world, various containment measures have been carried out to fight against the COVID-19 pandemic. In Mainland China, the containment measures consist of three types, i.e., Wuhan travel ban, intra-city quarantine and isolation, and intercity travel restriction. In order to carry out the measures, local economy and information acquisition play an important role. In this paper, we investigate the correlation of local economy and the information acquisition on the execution of containment measures to fight against the COVID-19 pandemic in Mainland China. First, we use a parsimonious model, i.e., SIR-X model to estimate the parameters, which represent the execution of intra-city quarantine and isolation in major cities of Mainland China. In order to understand the execution of intra-city quarantine and isolation, we analyze the correlation between the representative parameters including local economy, mobility, and information acquisition. To this end, we collect the data of Gross Domestic Product (GDP), the inflows from Wuhan and outflows, and the COVID-19 related search frequency from a widely-used Web mapping service, i.e., Baidu Maps, and Web search engine, i.e., Baidu Search Engine, in Mainland China. Based on the analysis, we confirm the strong correlation between the local economy and the execution of information acquisition in major cities of Mainland China. We further evidence that, although the cities with high GDP per capita attract more inflows from Wuhan, people are more likely to conduct the quarantine measure and to reduce travelling to other cities. Finally, the correlation analysis using search data shows that well-informed individuals are likely to carry out containment measures. © 2020 IEEE.
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Correction to: European Review for Medical and Pharmacological Sciences 2020; 24 (22): 11939-11944-DOI: 10.26355/eurrev_202011_23854-PMID: 33275267, published online 30 November, 2020. The authors state that "Figures 3 and 4 were used twice due to a careless mistake during the preparation of Figures". There are amendments to this paper. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/23854.
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OBJECTIVE: Coronavirus disease 2019 (COVID-19) has become a worldwide public health emergency; unfortunately, there is currently no treatment for improving outcomes or reducing viral-clearance times in infected patients. The aim of the present study was to evaluate the efficacy of interferon (IFN) with or without lopinavir and ritonavir as antiviral therapeutic option for treating COVID-19 infection. PATIENTS AND METHODS: The present study enrolled 148 patients that received either standard care, treatment with IFN alfa-2b, or IFN alfa-2b combined with lopinavir plus ritonavir. Viral testing was performed using Reverse-Transcription Polymerase Chain Reaction (RT-PCR). RESULTS: There was no significant difference in the viral-clearance time at 28 days after treatment between patients receiving standard care and those receiving anti-viral treatments. However, the average viral-clearance time of patients receiving standard care (14 days) was shorter than that for patients receiving IFN alfa-2b or IFN alfa-2b combined with lopinavir plus ritonavir (15.5 or 17.5 days) (p<0.05). Patients treated with IFN alfa-2b within five days or IFN alfa-2b combined with lopinavir plus ritonavir after three days of symptoms exhibited shorter viral-clearance times than the other groups (p<0.05). Moreover, viral-clearance times were significantly longer in patients receiving standard care or anti-viral treatment 5 days after symptoms appeared than those of patients who received these treatments within five days of symptom onset (p<0.05). CONCLUSIONS: Early symptomatic treatment is most critical for maximizing amelioration of COVID-19 infection. Anti-viral treatment might have complicated effect on viral-clearance.