Diagnostic value and characteristic analysis of serum nucleocapsid antigen in COVID-19 patients.

Background: To date, several types of laboratory tests for coronavirus disease 2019 (COVID-19) diagnosis have been developed. However, the clinical importance of serum severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid antigen (N-Ag) remains to be fully elucidated. In this study, we sought to investigate the value of serum SARS-CoV-2 N-Ag for COVID-19 diagnosis and to analyze N-Ag characteristics in COVID-19 individuals. Methods: Serum samples collected from 215 COVID-19 patients and 65 non-COVID-19 individuals were used to quantitatively detect N-Ag via chemiluminescent immunoassay according to the manufacturer's instructions. Results: The sensitivity and specificity of the N-Ag assay were 64.75% (95% confidence interval (95% CI) [55.94-72.66%]) and 100% (95% CI [93.05-100.00%]), respectively, according to the cut-off value recommended by the manufacturer. The receiver operating characteristic (ROC) curve showed a sensitivity of 100.00% (95% CI [94.42-100.00%]) and a specificity of 71.31% (95% CI [62.73-78.59%]). The positive rates and levels of serum SARS-CoV-2 N-Ag were not related to sex, comorbidity status or disease severity of COVID-19 (all P < 0.001). Compared with RT‒PCR, there was a lower positive rate of serum N-Ag for acute COVID-19 patients (P < 0.001). The positive rate and levels of serum SARS-CoV-2 N-Ag in acute patients were significantly higher than those in convalescent patients (all P < 0.001). In addition, the positive rate of serum SARS-CoV-2 N-Ag in acute COVID-19 patients was higher than that of serum antibodies (IgM, IgG, IgA and neutralizing antibodies (Nab)) against SARS-CoV-2 (all P < 0.001). However, the positive rate of serum SARS-CoV-2 N-Ag in convalescent COVID-19 patients was significantly lower than that of antibodies (all P < 0.001). Conclusion: Serum N-Ag can be used as a biomarker for early COVID-19 diagnosis based on appropriate cut-off values. In addition, our study also demonstrated the relationship between serum N-Ag and clinical characteristics.

OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series.

Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining methods to discover interesting patterns in time series. The discretization will not only cause the loss of some important information, which partially destroys the continuity of time series, but also ignore the order relations between time-series values. Inspired by order-preserving matching, this article explores a new method called order-preserving sequential pattern (OPP) mining, which does not need to discretize time series into symbolic sequences and represents patterns based on the order relations of time series. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath time series. We propose an OPP-Miner algorithm to mine frequent patterns in time series with the same relative order. OPP-Miner employs the filtration and verification strategies to calculate the support and uses the pattern fusion strategy to generate candidate patterns. To compress the result set, we also study to find the maximal OPPs. Experimental results validate that OPP-Miner is not only efficient but can also discover similar subsequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/OPP-Miner.

Robust crisis communication in turbulent times: Conceptualization and empirical evidence from the United States

Drawing on recent research on robust governance, we conceptualize robust crisis communication as a dynamic process centered on evolving public communication demands. We propose a three‐dimensional measurement for empirically examining the robustness of government crisis communication in the context of the COVID‐19 pandemic. We collected 43,642 Twitter messages posted by 50 state governors in the United States from January 1 to June 30, 2020. We applied machine learning algorithms to code the voluminous Twitter data based on messaging topics, sentiments, and interactions. This study found an overall low level of robustness in the governors' crisis communication. Governors most frequently posted reputation management tweets, followed by tweets about the government's handling of the pandemic. This research presents empirical evidence for the heavy influence of politics on governors' crisis communication strategies and highlights the need to understand and build robust crisis communication.摘要本文借鉴稳健性治理的最新相关研究，提出稳健性危机沟通的概念，将其界定为聚焦于满足危机中持续变化的公众沟通需求的动态沟通过程与模式。基于此概念，本文以新型冠状病毒肺炎疫情中的危机沟通为例，构建三维测量方法，用以检验政府危机沟通在内容主题、情感表达和对话互动三方面的稳健程度。本文收集美国50个州长在2020年1月1日至2020年6月30日期间发布的43,642条推特信息，并应用机器学习对所有信息的主题、情感和互动特征进行编码分析。本文发现美国州长危机沟通的稳健性程度总体偏低。美国州长最经常发布与声誉管理相关的推文，其次是政府的疫情防控措施。文章进一步阐释与例证政治因素对州长危机沟通策略的深刻影响，并强调政府部门理解与践行稳健性风险沟通的必要性与重要性。

How does ai use drive individual digital resilience? A conservation of resources (cor) theory perspective

Artificial intelligence (AI) serves as a useful resource for replacing, supporting, and augmenting individuals in responding to external difficulties and enhancing individual resilience. However, little is known about the underlying laws of how AI can heighten individual resilience. This research examines the formation of individual resilience based on the consequences of different AI usage behaviours. Study 1 uses text mining to detect individual resilience based on the experience with AI. We identify not only individual resilience but also family resilience. Study 2, based on the context of the COVID-19 pandemic, collects online survey data from personal intelligent assistant users to investigate the transformation mechanism of AI usage behaviours to individual resilience. Drawing upon the conservation of resources theory, routine and infusion use are considered two levels of resource investments to strengthen the different degrees of individual resilience by coping responses (task-focused, emotion-focused, and avoidance coping). The findings confirm the differences between routine and infusion use in the formation of individual resilience, mediated by both task-focused and emotion-focused coping, without the mediating role of avoidance coping. Our research provides enlightenment for researchers and practitioners on building resilience and improving performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

On the value of apricot kernel in modern medicine and its future development

Apricot kernels are one of the most regularly used traditional Chinese medicinal ingredients in Asia. The medical significance of apricot kernels is highlighted since Traditional Chinese Medicine (TCM) demonstrated its favourable impact when apricot kernels were used in the prevention and treatment of Corona Virus Disease 2019 (COVID-19). Furthermore, apricot kernels are high in fat, protein, dietary fibre, and specific amygdalin, making them a new form of dried fruit in comparison to almond kernels, with a bigger market opportunity. This paper systematically reviewed the active components of apricot kernels and their application in medicine, especially for molecular mechanisms of anti-tumors of amygdalin, providing scientific theoretical foundations for modern medicine treatment with COVID-19-induced lung disease, and for the development of high value-added apricot kernels.

Network Governance for Coordinated Disaster Response

The paper addresses the important role of interorganizational coordination and network governance in implementing disaster response policies facing complex environments of emergencies and crises. The paper analyzes the National Response Framework (NRF) in the U.S. and its role in disaster response coordination. A network perspective is utilized to understand functional coordination based on the emergency support functions (ESFs), and to evaluate the role of the NRF in coordinating disaster response at the federal level. Based on the roles and coordination structures defined by the ESFs, interorganizational networks and affiliation networks are presented in the paper. In the context of the COVID-19 pandemic, this paper illustrates how the ESFs, especially ESF #7 - Logistics and ESF #8 - Public Health and Medical Services, operated in response to the crisis.

How does AI use drive individual digital resilience? a conservation of resources (COR) theory perspective

Artificial intelligence (AI) serves as a useful resource for replacing, supporting, and augmenting individuals in responding to external difficulties and enhancing individual resilience. However, little is known about the underlying laws of how AI can heighten individual resilience. This research examines the formation of individual resilience based on the consequences of different AI usage behaviours. Study 1 uses text mining to detect individual resilience based on the experience with AI. We identify not only individual resilience but also family resilience. Study 2, based on the context of the COVID-19 pandemic, collects online survey data from personal intelligent assistant users to investigate the transformation mechanism of AI usage behaviours to individual resilience. Drawing upon the conservation of resources theory, routine and infusion use are considered two levels of resource investments to strengthen the different degrees of individual resilience by coping responses (task-focused, emotion-focused, and avoidance coping). The findings confirm the differences between routine and infusion use in the formation of individual resilience, mediated by both task-focused and emotion-focused coping, without the mediating role of avoidance coping. Our research provides enlightenment for researchers and practitioners on building resilience and improving performance. [ FROM AUTHOR]

Long-Term Changes of HIV/AIDS Incidence Rate in China and the U.S. Population From 1994 to 2019: A Join-Point and Age-Period-Cohort Analysis.

Although HIV caused one of the worst epidemics since the late twentieth century, China and the U.S. has made substantial progress to control the spread of HIV/AIDS. However, the trends of HIV/AIDS incidence remain unclear in both countries. Therefore, this study aimed to highlight the long-term trends of HIV/AIDS incidence by gender in China and the U.S. population. The data were retrieved from the Global Burden of Disease (GBD) database since it would be helpful to assess the impact/role of designed policies in the control of HIV/AIDS incidence in both countries. The age-period-cohort (APC) model and join-point regression analysis were employed to estimate the age-period-cohort effect and the average annual percentage change (AAPC) on HIV incidence. Between 1994 and 2019, we observed an oscillating trend of the age-standardized incidence rate (ASIR) in China and an increasing ASIR trend in the U.S. Despite the period effect in China declined for both genders after peaked in 2004, the age effect in China grew among the young (from 15-19 to 25-29) and the old age groups (from 65-69 to 75-79). Similarly, the cohort effect increased among those born in the early (from 1924-1928 to 1934-1938) and the latest birth groups (from 1979-1983 to 2004-2009). In the case of the U.S., the age effect declined after it peaked in the 25-29 age group. People born in recent birth groups had a higher cohort effect than those born in early groups. In both countries, women were less infected by HIV than men. Therefore, besides effective strategies and awareness essential to protect the young age groups from HIV risk factors, the Chinese government should pay attention to the elderly who lacked family support and were exposed to HIV risk factors.

Crisis leadership during COVID-19: the role of governors in the United States

Purpose The purpose of this viewpoint article is to understand crisis leadership during COVID-19 by examining the decision-making with respect to implementing COVID-19 mitigation measures, collaboration with stakeholders, and communication strategies of the governors of the States of California, Texas, Florida, and New York in the United States. Design/methodology/approach This viewpoint article employs information from the extant literature on crisis leadership and secondary sources to understand the four governor's crisis leadership strategies during COVID-19. Findings Governors Gavin Newsom of California and Andrew Cuomo of New York made quicker decisions regarding implementing COVID-19 mitigation measures (e.g. shutting down the economy, mandating physical distancing, issuing stay-at-home orders, mandating wearing face covering in public and issuing a state of emergency) compared to Governors Greg Abbott of Texas and Ron DeSantis of Florida. In addition, all four governors collaborated with state and local governments, private entities, and nonprofits to address COVID-19. Finally, all four governors held some form of briefing on COVID-19 on a regular basis and used different media to get their information out to the public. Originality/value This viewpoint article uses decision-making regarding implementation of mitigation strategies, collaboration with stakeholders, and crisis communication strategy to understand governors' crisis leadership during COVID-19.

SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model.

MOTIVATION: The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. RESULTS: We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. AVAILABILITYAND IMPLEMENTATION: SARS2020 is available at http://design.rxnfinder.org/sars2020/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[Effect of new simple breathing apparatus on oxygen therapy in patients with severe and critical coronavirus disease 2019].

OBJECTIVE: To make a new simple respirator and observe the oxygen therapy effect of the respirator on patients with severe and critical coronavirus disease 2019 (COVID-19). METHODS: Based on the infectivity and hospital requirements of COVID-19, a new simple respirator was designed by the medical staff of the Department of Anesthesiology of the Second Affiliated Hospital of Nanchang University, which was applied on the 22 patients with severe and critical COVID-19 who needed oxygen therapy admitted to the Cancer Center of Tongji Medical College of Huazhong University of Science and Technology from February 15th to March 15th in 2020. The new simple respirator contained two National Utility Model Patents (a respirator: ZL 2015 2 0410623.6, a fluid switch and oxygen suction device: ZL 2017 2 0873509.6), which was mainly composed of anesthesia mask and filter, L-shaped connecting tube, soft breathing bladder, connecting tube and elastic fixing belt. When in use, the anesthesia mask was fixed to the patient's mouth and nose with elastic straps, the connecting tube was inserted into the oxygen meter interface, the oxygen flow was adjusted to 6-10 L/min, and the L-shaped connecting tube was opened immediately after the soft breathing bag was full. The carbon dioxide and excess oxygen in the body was discharged from exhaust port. The oxygen flow was lowered to 2-3 L/min, the patient's respiratory rate (RR) was observed through the soft breathing bag fluctuations, and the oxygen flow was adjusted at any time. The changes of pulse oxygen saturation (SpO2), RR and heart rate (HR) before and after application of new simple respirator were observed, and the blood gas test results of part of the patients were collected. RESULTS: Twenty-two patients with severe and critical COVID-19 had significantly higher SpO2 at 10 minutes after application of the new simple ventilator than before application (0.994±0.007 vs. 0.952±0.017, P < 0.01), and RR was significantly lower than that before application (times/min: 27.59±3.63 vs. 29.64±3.81, P < 0.01); after 1 day of application, each index was further improved. All 13 patients who received blood gas analysis indicated no carbon dioxide accumulation. CONCLUSIONS: The new simple respirator can significantly improve the oxygen therapy effect of patients with severe and critical COVID-19. At the same time, 2019 novel coronavirus (2019-nCoV) can be filtered through the filter to reduce the formation of aerosol and protect the medical staff and patients.

Hybrid Coordination for Coping with the Medical Surge from the COVID-19 Pandemic: Paired Assistance Programs in China.

The COVID-19 pandemic poses unprecedented challenges to public health care systems and demands intergovernmental coordination to cope with the resulting medical surge. This essay analyzes the operation of Paired Assistance Programs (PAPs) in China, offering a timely comparative case for researchers and practitioners to examine when reflecting on the classic debate over the hierarchical versus network approaches to coordination in emergency management. PAPs highlight the importance of network management and necessity of institutionalizing mechanisms of governance to facilitate coordination within multilevel response systems.