Reporting and literature analysis of misdiagnosis and therapy of one case with tuberculous meningoencephalitis under the coronavirus disease 2019 epidemic

Objective To improve the diagnosis and treatment level for tuberculous meningitis (TBM) under the Coronavirus disease 2019 (COVID-19) epidemic. Methods The diagnosis and treatment course of a female patient under the COVID-19 epidemic was analyzed for high fever, vomiting for 23 days, headache, talk nonsense for 10 days, inability to stand, and double vision lasting 5 days. The patient was successively misdiagnosed to suffer from viral pneumonia and acute disseminated encephalomyelitis (ADEM) in other hospitals. The patient had a history of transit at Hankou railway station (wearing a mask without departing the station throughout the process) under the COVID-19 epidemic. The patient had a history of leukopenia and long-term medical therapy. The patient was diagnosed as TBM by applying the diagnostic scheme for the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines after physical examination, cerebrospinal fluid test, magnetic resonance imaging (MRI) plain scan and enhanced examination. The analysis on reasons for extramural hospital misdiagnosis showed it was related to the lack of careful physical examination and lack of scientific analysis of laboratory test results. Results The intracranial pressure reduction, anti-tuberculosis treatment, adrenal cortex hormone treatment and symptomatic treatment were immediately administered according to the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines. Intensive anti-tuberculosis treatment (4 months) was implemented firstly and followed by the anti-tuberculosis treatment (12 months) during the consolidation phase, clinically enabling the patient to be cured. Conclusion Careful inquiry of medical history, careful physical examination, timely cerebrospinal fluid examination and MRI examination and scientific analysis on clinical data are critical to confirmation of TBM. Standard anti-tuberculosis treatment, rational use of adrenal cortex hormones and lowering intracranial pressure are critical factors for curing. © 2023, Editorial Department of Medical Pest Control. All rights reserved.

Reporting and literature analysis of misdiagnosis and therapy of one case with tuberculous meningoencephalitis under the coronavirus disease 2019 epidemic

Objective To improve the diagnosis and treatment level for tuberculous meningitis (TBM) under the Coronavirus disease 2019 (COVID-19) epidemic. Methods The diagnosis and treatment course of a female patient under the COVID-19 epidemic was analyzed for high fever, vomiting for 23 days, headache, talk nonsense for 10 days, inability to stand, and double vision lasting 5 days. The patient was successively misdiagnosed to suffer from viral pneumonia and acute disseminated encephalomyelitis (ADEM) in other hospitals. The patient had a history of transit at Hankou railway station (wearing a mask without departing the station throughout the process) under the COVID-19 epidemic. The patient had a history of leukopenia and long-term medical therapy. The patient was diagnosed as TBM by applying the diagnostic scheme for the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines after physical examination, cerebrospinal fluid test, magnetic resonance imaging (MRI) plain scan and enhanced examination. The analysis on reasons for extramural hospital misdiagnosis showed it was related to the lack of careful physical examination and lack of scientific analysis of laboratory test results. Results The intracranial pressure reduction, anti-tuberculosis treatment, adrenal cortex hormone treatment and symptomatic treatment were immediately administered according to the 2019 China Central Nervous System Tuberculosis Diagnosis and Treatment Guidelines. Intensive anti-tuberculosis treatment (4 months) was implemented firstly and followed by the anti-tuberculosis treatment (12 months) during the consolidation phase, clinically enabling the patient to be cured. Conclusion Careful inquiry of medical history, careful physical examination, timely cerebrospinal fluid examination and MRI examination and scientific analysis on clinical data are critical to confirmation of TBM. Standard anti-tuberculosis treatment, rational use of adrenal cortex hormones and lowering intracranial pressure are critical factors for curing. © 2023, Editorial Department of Medical Pest Control. All rights reserved.

The Impact of Government Behavior on the Development of Cross-Border E-Commerce B2B Export Trading Enterprises Based on Evolutionary Game in the Context of "Dual-Cycle" Policy

In 2020, the COVID-19 pandemic had a major impact on China's foreign trade. Therefore, the Chinese government has proposed a "dual cycle" policy to promote economic development. In 2021, China's cross-border e-commerce B2B exports accounted for 60 percent. Therefore, this paper studies the impact of government actions on the development of cross-border e-commerce B2B export enterprises under the background of "dual cycle" policy. First, the policies related to the cross-border e-commerce industry in the "dual circulation" policy are screened, and the LDA topic model is used to classify them, i.e., sorting by topic intensity as "fiscal policy", "tax policy", "customs clearance policy", "payment policy" and "talent policy". After that, based on the analysis results of the LDA topic model, a theoretical basis for the impact of different policies on cross-border e-commerce B2B export companies is established;then an evolutionary game model between the government and cross-border e-commerce B2B export enterprises is constructed. This article also carried out experiments to verify our analysis. The simulation results show that: (1) The government's appropriate increase in subsidies, tax incentives, infrastructure investment, talent introduction and cultivation, optimized payment system, and supervision can promote enterprises to participate in cross-border e-commerce B2B export trading;(2) excessive government supervision reduces enterprises' enthusiasm to participate in cross-border e-commerce B2B export trading;(3) the government's subsidies, tax incentives, and supervision strength have the greatest impact on whether enterprises participate in cross-border e-commerce B2B export trading, followed by the government's investment in cross-border e-commerce infrastructure, the introduction and cultivation of cross-border e-commerce talents, and the improvement of the payment system. Finally, this paper puts forward relevant policy recommendations to promote the development of cross-border e-commerce B2B export enterprises.

Antecedents and intervention mechanisms of institutional distrust of P2P accommodations during COVID-19 in China

PurposeCOVID-19 affects the peer-to-peer (P2P) accommodation industry. With regard to prospect theory, individuals' negative emotions, such as institutional distrust, are easily evoked and impede consumption intention in an environment of permeating uncertainty and risks. While existing research indicates the negative effects of institutional distrust, scant research has explored its antecedents and intervention mechanisms. This study thus aims to unveil the influencing factors and explore mitigating mechanisms of customers' institutional distrust of P2P accommodations. Design/methodology/approachOnline reviews data were used to identify the underlying critical issues. The authors developed a model to depict how institutional distrust is formed under the boundary condition of subjective norm by the results. The model was verified using a questionnaire survey. Finally, in-depth semi-structured interviews were conducted to ensure its robustness. FindingsThe external environment and internal platform effectiveness are two critical aspects affecting institutional distrust of P2P accommodations. The external environment influences institutional distrust through perceived threat, explaining the formation mechanism of customers' institutional distrust through customers' internal psychology. Furthermore, the authors found subjective norm moderating the effect of perceived threat on customers' institutional distrust. Research limitations/implicationsThis is one of the first studies, to the best of the authors' knowledge, to explore institutional distrust of P2P accommodations after COVID-19. The finding contributes to studies on P2P accommodation by uncovering the contingent role of subjective norm in influencing customers' institutional distrust. Originality/valueThis is a pioneering study that explores the antecedents and mitigating mechanisms of institutional distrust of P2P accommodations during the new normal of COVID-19.

Short-Term Power Load Forecasting Under COVID-19 Based on Graph Representation Learning With Heterogeneous Features

Since 2020, the COVID-19 has spread globally at an extremely rapid rate. The epidemic, vaccination, and quarantine policies have profoundly changed economic development and human activities worldwide. As many countries start to resume economic activities aiming at a "living with COVID " new normal, a short-term load forecasting technique incorporating the epidemic's effects is of great significance to both power system operation and a smooth transition. In this context, this paper proposes a novel short-term load forecasting method under COVID-19 based on graph representation learning with heterogeneous features. Unlike existing methods that fit power load data to time series, this study encodes heterogeneous features relevant to electricity consumption and epidemic status into a load graph so that not only the features at each time moment but also the inherent correlations between the features can be exploited;Then, a residual graph convolutional network (ResGCN) is constructed to fit the non-linear mappings from load graph to future loads. Besides, a graph concatenation method for parallel training is introduced to improve the learning efficiency. Using practical data in Houston, the annual, monthly, and daily effects of the crisis on power load are analyzed, which uncovers the strong correlation between the pandemic and the changes in regional electricity utilization. Moreover, the forecasting performance of the load graph-based ResGCN is validated by comparing with other representative methods. Its performance on MAPE and RMSE increased by 1.3264 and 15.03%, respectively. Codes related to all the simulations are available on & nbsp;https://github.com/YoungY6/ResGCN-for-Short-term-power-load-forecasting-under-COVID-19.

Are we there yet? Analyzing scientific research related to COVID-19 drug repurposing

Drug repurposing may be a pivotal means of fulfilling urgent needs for treatment of the novel coronavirus disease 2019 (COVID-19), but current studies on drug repurposing for COVID-19 seem to show a lack of consensus in their drug candidate focus. Using bibliometric methods in a non-expert perspective, in a review of 34 published articles on the COVID-19 and drug-repurposing, we investigated obvious and less obvious points of consensus on drug candidates. To establish these two types of consensus, we first implemented document clustering. Within a set of five clustered papers, we established an obvious consensus, relying solely on the occurrence of entities by using term frequency and inverse document frequency and a comparison of mentioned drugs, finding that remdesivir and chloroquine were discussed with a certain degree of agreement. For the less obvious consensus, we created a drug entity co-occurrence network to establish low-high centrality combinations to probe the crucial drugs found in article clustering that are not plainly apparent through the mere counting of the occurrence of drug entities occurrences. Lopinavir emerged as having possibly potent effects in spite of underuse, while the mainstream of studies focus more on drugs such as chloroquine that enjoy explicit consent. Using an entitymetrics perspective, we expect that our research will support investigations of drug repurposing, expediting the process of establishing treatment for COVID-19.

A machine learning platform to estimate anti-SARS-CoV-2 activities

Strategies for drug discovery and repositioning are urgently need with respect to COVID-19. Here we present REDIAL-2020, a suite of computational models for estimating small molecule activities in a range of SARS-CoV-2-related assays. Models were trained using publicly available, high-throughput screening data and by employing different descriptor types and various machine learning strategies. Here we describe the development and use of eleven models that span across the areas of viral entry, viral replication, live virus infectivity, in vitro infectivity and human cell toxicity. REDIAL-2020 is available as a web application through the DrugCentral web portal (http://drugcentral.org/Redial). The web application also provides similarity search results that display the most similar molecules to the query, as well as associated experimental data. REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

[Epidemiological characteristics of COVID-19 in Gansu province].

Objective: To understand the epidemiological characteristics of COVID-19 cases in different epidemic stages in Gansu province. Methods: Epidemiological investigation was conducted to collect the information of confirmed COVID-19 cases, including demographic, epidemiological and clinical information. Results: As of 25 February 2020, a total of 91 confirmed COVID-19 cases had been reported in Gansu. The epidemic of COVID-19 in Gansu can be divided as three different stages, i.e. imported case stage, imported-case plus indigenous case stage, and indigenous case stage. A total of 63 cases were clustered cases (69.23%), 3 cases were medical staff infected with non-occupational exposure. The initial symptoms included fever (54.95%, 50/91), cough (52.75%, 48/91), or fatigue (28.57%, 26/91), the proportion of each symptom showed a decreasing trend along with the three epidemic stages, but only the differences in proportions of fever (trend χ2=2.20, P<0.05) and fatigue (trend χ2=3.18, P<0.05) among the three epidemic stages were statistically significant. The cases with critical severe symptoms accounted for 42.85% (6/14), 23.73% (14/59) and 16.67% (3/18), respectively, in three epidemic stages, showed a decreasing trend (H=6.45, P<0.05). Also, the incubation period prolonged along with the epidemic stage (F=51.65, P<0.01), but the intervals between disease onset and hospital visit (F=5.32, P<0.01), disease onset and diagnosis (F=5.25, P<0.01) became shorter along with the epidemic stage. Additionally, the basic reproduction number (R0) had decreased from 2.61 in imported case stage to 0.66 in indigenous case stage. Conclusions: The COVID-19 epidemic in Gansu was caused by the imported cases, and about 2/3 cases were clustered ones. No medical worker was observed to be infected by occupational exposure. With the progression of COVID-19 epidemic in Gansu, the change in initial symptom and incubation period suggests, the early screening cannot only depend on body temperature monitoring.