The 17th National Annual Conference on Neurological Infectious Diseases and Cerebrospinal Fluid Cytology of the Chinese Medical Association Neurology Branch was successfully held

The 17th National Annual Conference on Neurological Infectious Diseases and Cerebrospinal Fluid Cytology of the Chinese Medical Association Neurology Branch was successfully held in Wuhan, Hubei, China. During the conference, many well-known neurology experts in China conducted in-depth and detailed discussions on the research progress of infectious diseases of the nervous system and cerebrospinal fluid cytology, which benefited the participants a lot.Copyright © 2021

Early Prediction of COVID-19 Infection with IoT and Machine Learning

The deadly virus COVID-19 has heavily impacted all countries and brought a dramatic loss of human life. It is an unprecedented scenario and poses an extreme challenge to the healthcare sector. The disruption to society and the economy is devastating, causing millions of people to live in poverty. Most citizens live in exceptional hardship and are exposed to the contagious virus while being vulnerable due to the inaccessibility of quality healthcare services. This study introduces ubiquitous computing as a state-of-The-Art method to mitigate the spread of COVID-19 and spare more ICU beds for those truly needed. Ubiquitous computing offers a great solution with the concept of being accessible anywhere and anytime. As COVID-19 is highly complicated and unpredictable, people infected with COVID-19 may be unaware and still live on with their life. This resulted in the spread of COVID-19 being uncontrollable. Therefore, it is essential to identify the COVID-19 infection early, not only because of the mitigation of spread but also for optimal treatment. This way, the concept of wearable sensors to collect health information and use it as an input to feed into machine learning to determine COVID-19 infection or COVID-19 status monitoring is introduced in this study. © 2023 IEEE.

Knowledge mapping of resilience and human rights in supply chains: A roadmapping taxonomy for twin green and digital transition design

Supply chain sustainability (SCS) has gone beyond the sustainability-performance approach, towards the increasing adoption of the sustainability-practice approach. The use of digital technologies in this approach can enhance resilience and human rights, particularly in the context of the green and digital twin transition post-COVID-19 pandemic. To enrich the sustainability-practice approach, this paper aims to produce a roadmapping taxonomy, based on knowledge mapping of a dataset collected in late December 2022 from the Web of Science Core Collection. As the knowledge map reveals the dimensions of resilience, human rights, and digital technologies, the proposed taxonomy highlights the importance of dynamic capabilities in facing supply chain disruptions, especially their ripple effects, along with the corresponding digital technologies to enhance human social dynamics in facing such disruptions. The proposed taxonomy provides a knowledge-based framework for professionals and researchers to enhance their understanding of supply chain resilience in designing and implementing digital solutions. The proposed roadmapping taxonomy features a people- and community-centric perspective and several managerial insights, contributing to the wider discussions on the green and digital transformation of the supply chain, by shaping actions and interactions in networked, digitized, and datafied forms to enhance supply chain sustainability. Copyright © 2023 Pan, Liao and Zhang.

Real-time Control of the Air Volume in Ventilation Facilities by Limiting Concentration with Cluster Algorithms

Ventilation improves indoor air quality and reduces airborne infections. It is particularly important at present because of the COVID-19 pandemic. Commercially available ventilation facilities can only be instantly turned on/off or at a set time with adjustable air volumes (high, middle, and low). However, maintaining the indoor carbon dioxide concentration while reducing the energy consumption of these facilities is challenging. Hence, this study developed clustering algorithms to determine the carbon dioxide concentration limit thus enabling real-time air volume adjustment. These limit values were set using the existing energy recovery ventilation (ERV) controller. In the experiment, dual estimation was adopted, and the constructing building energy models from data were sampled at a low rate to compare that the ventilation facilities are only turned on/off. In addition, switching control is closely related to fuzzy control;that is, fuzzy control can be considered a smooth version of switching control. The experimental results indicated that the limits of 600 and 700 ppm were suitable to effectively control the real-time air volume based on the ERV operation. An ERV-based carbon dioxide concentration limit reduced the energy consumption of ventilation facilities by 11%implications of this study. IEEE

Using the big data analysis and basic information from lecture Halls to predict air change rate

School lecture halls are often designed as confined spaces. During the period of COVID-19, indoor ventilation has played an even more important role. Considering the economic reasons and the immediacy of the effect, the natural ventilation mechanism becomes the primary issue to be evaluated. However, the commonly used CO2 tracer gas concentration decay method consumes a lot of time and cost. To evaluate the ventilation rate fast and effectively, we use the common methods of big data analysis - Principal Component Analysis (PCA), K-means and linear regression to analyze the basic information of the lecture hall to explore the relation between variables and air change rate. The analysis results show that the target 37 lecture halls are divided into two clusters, and the measured 11 lecture halls contributed 64.65%. When analyzing the two clusters separately, there is a linear relation between the opening area and air change rate (ACH), and the model error is between 6% and 12%, which proves the feasibility of the basic information of the lecture hall by calculating the air change rate. © 2023 Elsevier Ltd

Online learning-related screen use and associated factors in school-aged children in Shanghai during the COVID-19 epi-demic

Objective To understand the online learning-related screen use duration and screen types in school-aged children in Shanghai during the COVID-19 epidemic. Methods Random clustering sampling was used to select 5 591 parents of students from 8 primary and junior schools that are in the sampling pool of the national myopia survey in districts of Jiading, Pudong and Baoshan in Shanghai in April 2020. Electronic questionnaire was administered to parents regarding their child's online learning-related screen use. Results On average, the median weekly duration of online learning-related screen use was 13.33 hours, the curricular and extracurricular parts of which were 10(8.75, 16.67) and 0(0, 3.33) hours, respectively. About 29.44% of investigated school-aged children only used small-size screen for online learning. Children in higher grades, being myopic and parents neither being myopic were associated with reporting higher weekly duration(P<0.05);children in higher grades of primary school and parents neither being myopic were associated with a higher likelihood of using small-size screen for online learning(P<0.05). Conclusion At the early stage of the COVID-19 epidemic, the burden associated with online-learning-related screen use was high in school-aged children in Shanghai. Health education regarding online learning-related screen use should be addressed in parents to guide their children to use screen appropriately. © 2022 Ｃｈｉｎ Ｊ Ｓｃｈ Ｈｅａｌｔｈ. All rights reserved.

Present therapeutic and diagnostic approaches for SARS-CoV-2 infection

The novel Coronavirus (nCoV), severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2), has shaken the whole world and posed significant challenges to the global healthcare system for more than a year. The scientific community across the globe is trying to combat this virus by developing a safe vaccine that can provide long-term immunity against the virus. The other means of overcoming its pathogenicity is to treat the infected people with available drugs and/or novel therapeutic strategies. The available drugs were previously designed to combat viral infections and come with tested safety. This appears to be the most practical approach as a quick response to the highly infectious pandemic with high morbidity and mortality. Although many repurposed drugs like favipiravir and hydroxychloroquine have been tried, they have been proven toxic and/or less efficacious. This has led the world to find urgent therapeutic interventions (traditional and novel), to help decrease the severity of COVID-19 infection and aim towards recovery. This chapter of the book will discuss the most up-to-date published data with respect to prevention and treatment of COVID-19 infection. Diagnosis also plays an important part in controlling the pandemic caused by the virus. A cheap, accurate and fast identification test for the virus is the need of the hour. This chapter will also throw light on the various diagnostic procedures available for the identification of SARS-CoV-2, till date, along with their advantages and disadvantages. © 2022 Elsevier Inc. All rights reserved.

The optimal anticoagulation strategy for COVID-19, prophylactic or therapeutic?: a meta-analysis, trial sequential analysis, and meta-regression of more than 27,000 participants

Background: Anticoagulants are promising regimens for treating coronavirus disease 2019 (COVID-19). However, whether prophylactic or intermediate-to-therapeutic dosage is optimal remains under active discussion. Methods: We comprehensively searched PubMed, Embase, Scopus, Web of Science, Cochrane Library, ClinicalTrials, and MedRxiv databases on April 26, 2022. Two independent researchers conducted literature selection and data extraction separately according to predetermined criteria. Notably, this is the first meta-analysis on COVID-19, taking serious consideration regarding the dosage overlap between the 2 comparison groups of prophylactic anticoagulation (PA) and intermediate-to-therapeutic anticoagulation (I-TA). Results: We included 11 randomized controlled trials (RCTs) and 36 cohort studies with 27,051 COVID-19 patients. By analyzing all the RCTs, there was no significant difference in mortality between the PA and I-TA groups, which was further confirmed by trial sequential analysis (TSA) (odds ratio [OR]: 0.93;95% confidence interval [CI]: 0.71–1.22;P = 0.61;TSA adjusted CI: 0.71–1.26). The rate of major bleeding was remarkably higher in the I-TA group than in the PA group, despite adjusting for TSA (OR: 1.73;95% CI: 1.15–2.60;P = 0.009;TSA adjusted CI: 1.09–2.58). RCTs have supported the beneficial effect of I-TA in reducing thrombotic events. After including all studies, mortality in the I-TA group was significantly higher than in the PA group (OR: 1.38;95% CI: 1.15–1.66;P = 0.0005). The rate of major bleeding was similar to the analysis from RCTs (OR: 2.24;95% CI: 1.86–2.69;P < 0.00001). There was no distinct difference in the rate of thrombotic events between the 2 regimen groups. In addition, in both critical and noncritical subgroups, I-TA failed to reduce mortality but increased major bleeding rate compared with PA, as shown in meta-analysis of all studies, as well as RCTs only. Meta-regression of all studies suggested that there was no relationship between the treatment effect and the overall risk of mortality or major bleeding (P = 0.14, P = 0.09, respectively). Conclusion: I-TA is not superior to PA for treating COVID-19 because it fails to lower the mortality rate but increases the major bleeding rate in both critical and noncritical patients. Copyright © 2022 Shandong University, published by Wolters Kluwer, Inc.

SPATIAL MODELLING OF COVID-19 INCIDENCE RATE IN CANADA

The COVID-19 was first declared by World Health Organization (WHO) as global pandemic on March 11th 2020. While most of COVID-related studies have focused on epidemiological perspective, the spatial analysis of disease outbreak is also important to provide perceptions of transmission rates. Therefore, this paper attempts to identify the potential factors contributing to the COVID-19 incidence rate at provincial-level in Canada. Three statistical regression models, ordinary least squares (OLS), spatial error model, and spatial lag model (SLM) were applied to 14 independent variables including socio-demographic, economic, weather, health and facilities related factors. The results indicated that three factors including median income, diabetes and unemployment significantly affected the COVID-19 rates in Canada. Among three global models, the SLM performed the best to explain the key variables and spatial variability of disease incidence with a R2 value of 61%. However, in this study, the application of local regression models such as geographically weighted regression (GWR) and multiscale GWR (MGWR) have not been considered and this could be a scope for the future research.

Smart Customer Experience of ESG Education Capability Building Roadmap for Smart Consulting, Reporting and Engaging for Carbon Peak and Carbon Neutrality Goals

Cultivating talents for executing environmental, social, and governance (ESG) activities is increasingly essential for the education sector. It is because the social and ecological resilience of the human society depends on guiding human development towards sustainability. To prepare future professionals to manage climate change and the Covid-19 pandemic recovery challenges, the article proposes several ESG educational models, based on and design workshop outcomes using stakeholder analysis and the ESG and carbon neutrality research. Three partner maps for ESG consulting, reporting, and engaging have been developed, with special attention on the use of digital technologies to manage carbon emission for green and just transitions. Based on these maps, a model 'Smart Customer System on Carbon Information' is proposed. The model highlights the importance of collaboration across professions such as management, accounting, public administration, public relations, communications, business intelligence, and data sciences, resulting in an integrated capability building roadmap among stakeholders. Future education practices and research must advance and facilitate the overall 'smart customer journey' of carbon emission reporting in order to account for ESG records for a net-zero carbon-neutral future, with the help of information and communication technologies. © 2021 IEEE.

Casirivimab and Imdevimab for Treatment of Hospitalized Patients with COVID-19 Receiving Low Flow or No Supplemental Oxygen

Background. Casirivimab and imdevimab (CAS/IMDEV) is authorized for emergency use in the US for outpatients with COVID-19. We present results from patient cohorts receiving low flow or no supplemental oxygen at baseline from a phase 1/2/3, randomized, double-blinded, placebo (PBO)-controlled trial of CAS/IMDEV in hospitalized patients (pts) with COVID-19. Methods. Hospitalized COVID-19 pts were randomized 1:1:1 to 2.4 g or 8.0 g of IV CAS/IMDEV (co-administered) or PBO. Primary endpoints were time-weighted average (TWA) change in viral load from baseline (Day 1) to Day 7;proportion of pts who died or went on mechanical ventilation (MV) through Day 29. Safety was evaluated through Day 57. The study was terminated early due to low enrollment (no safety concerns). Results. Analysis was performed in pooled cohorts (low flow or no supplemental oxygen) as well as combined treatment doses (2.4 g and 8.0 g). The prespecified primary virologic analysis was in seronegative (seroneg) pts (combined dose group n=360;PBO n=160), where treatment with CAS/IMDEV led to a significant reduction in viral load from Day 1-7 (TWA change: LS mean (SE): -0.28 (0.12);95% CI: -0.51, -0.05;P=0.0172;Fig. 1). The primary clinical analysis had a strong positive trend, though it did not reach statistical significance (P=0.2048), and 4/6 clinical endpoints prespecified for hypothesis testing were nominally significant (Table 1). In seroneg pts, there was a 47.0% relative risk reduction (RRR) in the proportion of pts who died or went on MV from Day 1-29 (10.3% treated vs 19.4% PBO;nominal P=0.0061;Fig. 2). There was a 55.6% (6.7% treated vs 15.0% PBO;nominal P=0.0032) and 35.9% (7.3% treated vs 11.5% PBO;nominal P=0.0178) RRR in the prespecified secondary endpoint of mortality by Day 29 in seroneg pts and the overall population, respectively (Fig. 2). No harm was seen in seropositive patients, and no safety events of concern were identified. Conclusion. Co-administration of CAS/IMDEV led to a significant reduction in viral load in hospitalized, seroneg pts requiring low flow or no supplemental oxygen. In seroneg pts and the overall population, treatment also demonstrated clinically meaningful, nominally significant reductions in 28-day mortality and proportion of pts dying or requiring MV.

Special issue: highly pathogenic microbes causing respiratory infection. (Special Issue: Highly pathogenic microbes causing respiratory infection.)

This special issue contains 7 articles that discuss COVID-19, influenza A, hantaviruses, and Mycobacterium tuberculosis. Aspects on vaccines, vaccine development, diagnostics, antivirals, monoclonal antibodies, and pneumonia are also tackled.

[Therapy of coronavirus disease 2019 induced acute respiratory distress syndrome is different from traditional acute respiratory distress syndrome].

Coronavirus disease 2019 (COVID-19) has become a global public health emergency threatening people's lives around the world. Although the acute respiratory distress syndrome (ARDS) induced by COVID-19 is similar to the ARDS caused by other diseases in terms of pathophysiological basis and clinical manifestations, they are also different in many aspects, which lead to different clinical therapies. Therefore, understanding the differences and similarities of ARDS induced by COVID-19 and other diseases currently are the basis for clinicians to make decisions for the treatment of COVID-19 induced ARDS.

The prevalence and risk factors for anxiety in frontline nurses under COVID-19 pandemic based on a large cross-sectional study using the propensity score-matched method

Introduction: We determined the prevalence of anxiety and the associated risk factors in frontline nurses under COVID-19 pandemic. Methods: This cross-sectional study was conducted from February 20, 2020, to March 20, 2020, and involved 562 frontline nurses. The effective response rate was 87.68%. After propensity score matched, there were 532 participants left. Extensive characteristics, including demographics, dietary habits, life-related factors, work-related factors, and psychological factors were collected based on a self-reported questionnaire. Specific scales measured the levels of sleep quality, physical activity, anxiety, perceived organization support and psychological capital. Adjusted odds ratios and 95% confidence intervals were determined by binary paired logistic regression. Results: Of the nurses enrolled in the study, 33.60% had anxiety. Five independent risk factors were identified for anxiety: poor sleep quality (OR=1.235), experienced major events (OR=1.653), lower resilience and optimism of psychological capital (OR=0.906, and OR=0.909) and no visiting friend constantly (OR=0.629). Conclusions: This study revealed a considerable high prevalence of anxiety in frontline nurses during the COVID-19 outbreak, and identified five risk factors, which were poor sleep quality, experienced major events, lower resilience and optimism of psychological capital, and no visiting friend constantly. Protecting mental health of nurses is important for COVID-19 pandemic control and their wellbeing. These findings enrich the existing theoretical model of anxiety and demonstrated a critical need for additional strategies that could address the mental health in frontline nurses for policymakers.