Association of interleukin-6 and CD4+ T cells and two-week prognosis of patients with COVID-19: a predictive role.

OBJECTIVE:  The aim of this study was to determine the association of inflammation and immune responses with the outcomes of patients at various stages, and to develop risk stratification for improving clinical practice and reducing mortality. PATIENTS AND METHODS: We included 77 patients with primary outcomes of either death or survival. Demographics, clinical features, comorbidities, and laboratory tests were compared. Linear, logistic, and Cox regression analyses were performed to determine prognostic factors. RESULTS: The average age was 59 years (35-87 years). There were 12 moderate cases (16.2%), 42 severe cases (54.5%), and 23 critical cases (29.9%); and 41 were male (53.2%). Until March 20, 68 cases were discharged (88.3%), and nine critically ill males (11.7%) died. Interleukin-6 (IL-6) levels on the 1st day were compared with IL-6 values on the 14th day in the severe and the critically ill surviving patients (F=4.90, p=0.034, ß=0.35, 95% CI: 0.00-0.10), and predicted death in the critically ill patients (p=0.028, ß=0.05, OR: 1.05, 95% CI: 1.01-1.10). CD4+ T-cell counts at admission decreased the hazard ratio of death (p=0.039, ß=-0.01, hazard ratio=0.99, 95% CI: 0.98-1.00, and median survival time 13.5 days). CONCLUSIONS: The present study demonstrated that IL-6 levels and CD4+ T-cell count at admission played key roles of predictors in the prognosis, especially for critically ill patients. High levels of IL-6 and impaired CD4+t cells are seen in severe and critically ill patients with COVID-19.

The Realities Facing Graduate Students: Before, During, and after the 2020 COVID-19 Pandemic

Committees from the American Political Science Association (APSA) on the status of graduate students in political science conducted digital surveys in 2018, 2020, and 2022. Distributed using listservs from APSA, the surveys asked about a range of realities facing graduate students including employment opportunities, industry or academic support, and overall well-being. Analysis of the data pre-, during-, and post-pandemic revealed high anxiety in 2018 as part of students' experience looking for jobs. By 2020 and 2022, anxiety worsened, such that the well-being of graduate students in political science should be addressed. We recommend a change in the structure of graduate academic programs to include stronger institutional support and an emphasis on alternative paths for work that does not entail teaching at an academic institution. © The Author(s), 2023. Published by Cambridge University Press on behalf of the American Political Science Association.

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. Note to Practitioners—This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3). IEEE

Perceived Risk of Chinese Outbound Tourists in the Risky World

The unprecedented COVID-19 pandemic and Ukraine-Russian conflict have posed a huge threat to international tourists. Adopting protection motivation theory, this study investigated how mainland Chinese outbound tourists perceived three significant travel risks, namely, natural disasters, infectious diseases, and social instability. Confronted with travel risks, Chinese outbound tourists are very likely to change their travel plans. Notably, infectious diseases and social instability are the main concerns. A comparison of workers and non-workers, students perceived a higher magnitude of threat and response efficacy. Assistance from the Chinese government can significantly enhance tourists' confidence to travel abroad. Implications are provided accordingly. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Levels of peripheral IL-6 and CD4+ and CD8+ T cells and their prognostic significance in COVID-19.

OBJECTIVE: The aim of this study was to discuss the prognostic significance of peripheral interleukin-6 (IL-6) and CD4+ and CD8+ T cells in COVID-19. PATIENTS AND METHODS: Eighty-four COVID-19 patients were retrospectively analyzed and classified into three groups, including the moderate group (15 cases), the serious group (45 cases), and the critical group (24 cases). The levels of peripheral IL-6, CD4+, and CD8+ T cells and CD4+/CD8+ were determined for each group. It was assessed whether these indicators were correlated to the prognosis and death risks of COVID-19 patients. RESULTS: The three groups of COVID-19 patients differed significantly in the levels of peripheral IL-6 and CD4+ and CD8+ cells. The IL-6 levels in the critical, moderate, and serious groups were increased successively, but the changed levels of CD4+ and CD8+ T cells were just opposite to that of IL-6 (p<0.05). The peripheral IL-6 level increased dramatically in the death group, while the levels of CD4+ and CD8+ T cells decreased significantly (p<0.05). The peripheral IL-6 level was significantly correlated with the level of CD8+ T cells and CD4+/CD8+ ratio in the critical group (p<0.05). The logistic regression analysis indicated a dramatic increase in the peripheral IL-6 level in the death group (p=0.025). CONCLUSIONS: The aggressiveness and survival of COVID-19 were highly correlated with the increases in IL-6 and CD4+/CD8+ T cells. The fatalities of COVID-19 individuals remained at increased incidence due to elevated peripheral IL-6 levels.

Path Planning Algorithms for Mobile Robots in Hospital Environment during Covid-19

Since 2019, the COVID-19 virus has spread worldwide, posing a significant health and safety concern. The application of mobile robots in the medical field has gradually demonstrated their unique advantages. Therefore, we focus on the application of mobile robots inwards. By collating and summarizing some of the most popular existing path planning algorithms, this paper illustrates that different algorithms can produce varying outcomes depending on different environments and hardware used. MATLAB is used in this study to simulate four algorithms: To determine the most efficient path, A∗, RRT, RRT∗, and PRM in a specific hospital map are compared, as well as parameters including path length, average execution time, and resource consumption. Modelling a single-layer hospital map makes it possible for mobile robots in the medical field to execute tasks more efficiently between entry and ward in the COVID-19 hospital environment. Based on a comparison and comprehensive consideration of the data derived from the simulations, it is found that the A∗algorithm is superior in terms of optimality, completeness, time complexity, and spatial complexity. Therefore, the A∗algorithm is more valuable in finding the best path for a mobile robot in a hospital environment. © 2022 ACM.

Robustness of evidence reported in preprints during peer review

Scientists have expressed concern that the risk of flawed decision making is increased through the use of preprint data that might change after undergoing peer review. This Health Policy paper assesses how COVID-19 evidence presented in preprints changes after review. We quantified attrition dynamics of more than 1000 epidemiological estimates first reported in 100 preprints matched to their subsequent peer-reviewed journal publication. Point estimate values changed an average of 6% during review;the correlation between estimate values before and after review was high (0 & BULL;99) and there was no systematic trend. Expert peer-review scores of preprint quality were not related to eventual publication in a peer-reviewed journal. Uncertainty was reduced during peer review, with CIs reducing by 7% on average. These results support the use of preprints, a component of biomedical research literature, in decision making. These results can also help inform the use of preprints during the ongoing COVID-19 pandemic and future disease outbreaks.

Bird's-eye View Social Distancing Analysis System

Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. Hence, in this paper, we propose and evaluate a real-time privacy-preserving social distancing analysis system (B-SDA), which uses bird's-eye view video recordings of pedestrians who cross traffic intersections. We devise algorithms for video pre-processing, object detection, and tracking which are rooted in the known computer-vision and deep learning techniques, but modified to address the problem of detecting very small objects/pedestrians captured by a highly elevated camera. We propose a method for incorporating pedestrian grouping for detection of social distancing violations, which achieves 0.92 F1 score. B-SDA is used to compare pedestrian behavior in pre-pandemic and during-pandemic videos in uptown Manhattan, showing that the social distancing violation rate of 15.6% during the pandemic is notably lower than 31.4% prepandemic baseline.

Survival in Pandemic Times: Managing Energy Efficiency, Food Diversity, and Sustainable Practices of Nutrient Intake Amid COVID-19 Crisis

The COVID-19 pandemic led to an economic crisis and health emergency, threatening energy efficiency consumption, sustainable food diversity, and households’ nutrition security. The literature documented that environmental threats can divert attention from renewable energy and food security challenges that affect humans’ environmental behaviors. The COVID-19 crisis has consistently influenced environmental behaviors, as it primarily decreased income and disrupted food systems worldwide. This study investigated the COVID-19 consequences on household income, sustainable food diversity, sustainable energy consumption, and nutritional security challenges. The study used a self-structured online survey due to non-pharmaceutical restrictions and collected data from 728 households. The investigators applied t-test and logit regression to analyze the data for drawing results. Descriptive statistics show that COVID-19 has adversely affected the income of more than two-thirds (67%) of households. The pandemic has influenced households’ food consumption, energy, and dietary patterns to safeguard their income. The t-test analysis indicated that households’ food diversity and energy consumption significantly declined during the pandemic, and households consumed low-diversified food to meet their dietary needs more than twofold compared to pre-pandemic levels. The results showed that all nutrient consumption remained considerably lower in the COVID-19. Cereals are the primary source of daily dietary needs, accounting for over two-thirds of total energy and half of the nutrient consumption amid COVID-19. The share of vegetables and fruits in household energy consumption dropped by 40 and 30%. Results exhibited that increasing monthly income was inversely associated with worsening food diversity and intake with energy efficiency. Compared with farmers and salaried employment, wage earners were 0.15 and 0.28 times more likely to experience a decline in consuming food diversity. Medium and large households were 1.95 times and 2.64 times more likely than small, to experience decreased food diversity consumption. Launching a nutrition-sensitive program will help minimize the COVID-19 impacts on energy consumption, food diversity, and nutritional security for low-income individuals. This survey relied on the recall ability of the households for the consumed quantities of food commodities, which may lack accuracy. Longitudinal studies employing probability sampling with larger samples can verify this study’s insightful results. Copyright © 2022 Geng, Haq, Abbas, Ye, Shahbaz, Abbas and Cai.

Comparison of Online and face-to-face Learning Experience: A Case Study of Higher Vocational Hospitality Education in China

Online learning has attracted attention from academics and educators for several decades. Online learning plays a significant role in many educational institutions, including higher vocational hospitality colleges, especially during the COVID-19 outbreak. However, students' learning experience of online and face-to-face higher vocational hospitality courses is scarcely understood. To fill this research gap, a higher vocational college in China is selected as a case study to conduct a comparative study. Results show that students scored higher in face-to-face hospitality course learning experience than in online courses. Theoretical and practical implications are provided accordingly.

Exploring the Influence of Teacher-Student Interaction Strength, Interaction Time, Interaction Distance and Interaction Content on International Student Satisfaction with Online Courses

Since the outbreak of COVID-19 in January 2020, international online courses in universities in China have begun to develop on a large scale. This study explores the related influencing factors of teacher-student interaction on international student satisfaction with online courses in Chinese universities. It reveals which aspects of teacher-student interaction in online classes positively correlate with international students' satisfaction. This study is of a quantitative nature with four (independent variables (IVs) and one dependent variable (DV). The four IVs are the four dimensions of teacher-student interaction, namely interaction strength (IS), interaction time (IT), interaction content (IC), and interaction distance (ID). The DV is international student satisfaction (ISS) with online courses. This study was conducted in a university in Zhejiang Province, China. To answer the questionnaire, one hundred international students who were unable to enter China during COVID-19 were selected by stratified random sampling. The study used SPSS 21 to conduct descriptive and multiple linear regression analysis on the collected quantitative data. A total of 93 valid questionnaire data was collected. The analysis results showed that both IVs (IC & ID) have a positive correlation with the DV (ISS). Therefore, under the condition of limited equal resources, online teachers may give priority to the teacher-student interaction factors that have the greatest impact on the satisfaction of international students, carefully design teacher-student interaction activities, and maximise the satisfaction of international students. © 2022 Society for Research and Knowledge Management. All rights reserved.

Adaptive Self-Paced Transfer Learning for COVID-19 Diagnosis with CXR Images

Coronavirus Disease 2019 (COVID-19) has become an unprecedented public health crisis since December of 2019. Compared with real-time reverse transcription polymerase chain reaction (rRT-PCR), the computer-aided diagnosis machine learning algorithm based on medical images can vastly ease the burden on clinicians. Even so, despite existing hundreds of millions of confirmed cases worldwide, there has not been a mature, large scale, high quality, single standard shared image data set yet, which can lead to some problems. For instance, 1) Because the sources of medical images and the collection standards are not guaranteed, features extracted by the neural network may not be very ideal. 2) Due to the small number of samples, some outliers (e.g., blurry medical images, inconspicuous symptoms) may significantly descend the performance of the model. To address these problems, we propose an adaptive self-paced transfer learning (ASPTL) algorithm in this paper. Specifically, inspired by the process of human learning from easy to difficult, we also evaluated the learning difficulty of the samples. Samples with no obvious disease features or wrong labels are relatively difficult to diagnose, and the samples that are easy to diagnose are selected adaptively in the iterative process. In addition, we adopt transfer learning to select easy to learn samples on the pre-trained network by self-paced learning, and gradually fine-tune the pre-trained model in an iterative way. We designed two experiments to validate the ASPTL algorithm's performance on COVID-19. The reult prove the effectiveness on solving mentioned problems. © 2021 IEEE.

Numerical Simulation Study of Information Digitization Establishment and Convergence in Triangle System for Epidemic Prevention

Outbreak of coronavirus brings the greatest challenge ever for government management and social administration. In this thesis, D Community in ZQ city as the research subject. The concept of establishing and convergence activates the cooperation among various organizations and individuals to form a complete chain against the Coronavirus. The operation of that chain against the virus in D Community is summarized as the triangle system with the neighborhood committee as the center, and three subordinate triangle systems as three pivot points. To be specific, the three pivot points include the subordinate triangle system composed of neighborhood committee, village committee and villagers, the subordinate system composed of neighborhood committee, enterprises and employees, as well as the subordinate system that consists of neighborhood committee, landlords and tenants. In this thesis, in accordance with Triangle System Establishment and Convergence Theory as the framework, various phenomena during the war against coronavirus in D Community are analyzed to summarize the experience of resisting the epidemic, which is important references for regional governance and epidemic control in communities. © 2021 ACM.

Operation Status Comparison Monitoring of China’s Southeast Asian Industrial Parks before and after COVID-19 Using Nighttime Lights Data

COVID-19 has had a huge impact on many industries around the world. Internationallyfunded enterprises have been greatly affected by COVID-19 prevention and control measures, such as border controls. However, few studies have examined the impact of COVID-19 on internationally-funded enterprises. To this end, this paper considered 12 of China’s industrial parks situated in Southeast Asia, while comparing the operation status before and after the outbreak of COVID-19 based on remote sensing of nighttime lights (NTL). The NTL is generally used as a proxy for economic activity. First, six parameters were proposed to quantify and monitor the operation status based on NTL data. Subsequently, these parameters were calculated for the parks and for 10 km buffer zones surrounding them to analyze the differences in operating conditions. The results showed that (1) despite the negative impact of COVID-19, 9 out of the 12 parks had a mean NTL greater than 1, indicating that these parks are in better operating condition in 2020 than 2019;(2) 7 out of the 10 km buffer zones around the parks showed a decline in mean NTL. Only three parks showed a decline in mean NTL. The impact of COVID-19 on surrounding areas was greater than the impact on parks. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

When and whom should retailers offer price reductions in times of crisis?

Purpose With a decrease in consumer spending during the coronavirus disease 2019 (COVID-19) pandemic, many retailers are offering price reductions to stimulate demand. However, little is known about how consumers perceive such price reductions executed during turbulent times. The authors examine whether the timing of price reductions and individual differences impact consumers' evaluations of the retailers offering such reductions. Design/methodology/approach Using a longitudinal design, the authors inquire into four retailers' motives that consumers may infer from a price decrease at two different times during the COVID-19 crisis. Findings The authors find that the timing of price reductions plays a key role in shaping consumers' inference of retailers' motives. The authors also uncover individual characteristics that affect consumers' inferences. Originality/value This research advances the literature by demonstrating the critical role of timing and individual characteristics in consumers' perceptions of price reductions during times of crisis. The authors findings also provide retailers with actionable insights for their pricing strategies. The findings may be generalizable to other types of crises that may arise in the future.

Malnutrition Prolongs the Hospitalization of Patients with COVID-19 Infection: A Clinical Epidemiological Analysis (vol 25, pg 369, 2021)

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Highly Resolved Dynamic Emissions of Air Pollutants and Greenhouse Gas CO2during COVID-19 Pandemic in East China

The unintentional emission reductions caused by the COVID-19 pandemic provides an opportunity to investigate the impact of energy, industry, and transportation activities on air pollutants and CO2 emissions and their synergy. Here, we constructed an approach to estimate city-level high resolution dynamic emissions of both anthropogenic air pollutants and CO2 by introducing dynamic temporal allocation coefficients based on real-time multisource activity data. We first apply this approach to estimate the spatiotemporal evolution of sectoral emissions in eastern China, focusing on the period around the COVID-19 lockdown. Comparisons with observational data show that our approach can well capture the spatiotemporal changes of both short-lived precursors (NOx and NMVOCs) and CO2 emissions. Our results show that air pollutants (SO2, NOx, and NMVOCs) were reduced by up to 31%-53% during the lockdown period accompanied by simultaneous changes of 40% CO2 emissions. The declines in power and heavy industry sectors dominated regional SO2 and CO2 reductions. NOx reductions were mainly attributed to mobile sources, while NMVOCs emission reductions were mainly from light industry sectors. Our findings suggest that differentiated emission control strategies should be implemented for different source categories to achieve coordinated reduction goals. © 2021 American Chemical Society.

Early Recognition and Discrimination of COVID-19 Severity using Slime Mould Support Vector Machine for Medical Decision-making

COVID-19 has spread rapidly across the world, leading to the insufficiency of medical resources in many regions. Early detection and identification of high-risk COVID-19 patients will contribute to early intervention and optimize medical resource allocation. Using the clinical data from the Affiliated Yueqing Hospital of Wenzhou Medical University (Yueqing, China), an evolutionary support vector machine model is designed to recognize and discriminate the severity of the COVID-19 by patients basic information and hematological indexes. The support vector machine is a frequently used pattern classification tool affected by both the kernel parameter setting and feature selection for its classification accuracy. This study recommends an enhanced Slime Mould Algorithm (ESMA), mixing a new movement strategy of white holes, black holes, and wormholes, to perform parameter optimization and feature selection simultaneously for SVM. Therefore, the proposed SVM framework (ESMA-SVM) can also obtain high-quality classification results, and it is less prone to stagnation in the classification process. To verify the capabilities of the proposed methodology, first, the performance of the ESMA is thoroughly verified by using IEEE CEC2017 benchmark functions and the diversity and compared with other similar methods experimentally using these standard benchmark functions. Moreover, the balance between diversification and intensification capability of the enhanced ESMA and the original SMA is also investigated statistically. Finally, the designed model ESMA-SVM and other competitive SVM models based on other optimization algorithms are applied to early recognition and discrimination of COVID-19 severity. Through the analysis of experimental results, the core compensations of ESMA are confirmed, and the ESMA-SVM can obtain strong performance in terms of several performance evaluation indexes on discrimination of COVID-19 severity. Author

Risk factors analysisi for COVID‑19 in the elderly

Objective: To analyze the clinical characteristics and prognosis of the coronavirus disease 2019 (COVID‑19) in the elderly(aged 60 or above), and to explore the high risk factors of severe disease progression for early identification and prevention. Methods: Novel coronavirus pneumonia patients aged 60 or above diagnosed in Hubei Veterans Hospital from January 20 to February 29 in year 2020 were collected. According to the degrees of disease, the patients were divided into mild and severe groups, and their clinical features, laboratory examination, chest CT features, treatment, and outcome were compared. Results: A total of 108 patients were included, including 69 in mild group and 39 in severe group. The average age of the severe group was higher than that of the mild group ( P <0.001). The clinical symptoms of fever, expectoration, dyspnea, fatigue and diarrhea in the severe group were severer and more common than those in the mild group (all P <0.001). The proportion of hypertension ( P <0.05), respiratory system diseases (such as chronic bronchitis and COPD) ( P <0.05), and hypoproteinemia ( P <0.001) combined with COVID⁃19 were higher in severe groupthe severe group. Leukocyte count (WBC), neutrophil count (NEUT), CRP and SAA in the severe group were significantly higher ( P <0.05), while lymphocyte count (LY) and eosinophil count (EOS) were lower than those in the mild group ( P <0.05). Lung CT images showed that patients in the severe group had more bilateral lung involvements and pleural effusion than those in the mild group ( P <0.05). Among the 108 cases, 96 (88.9%) were cured and improved, 12 (11.1%) died. Conclusion: Age, basic comorbidities, decreasing in lymphocytes and acidophilic granulocytes, and multiple bacterial infections are risk factors for severe COVID‑19. Hypoalbuminemia may be a potential and independent adverse prognostic indicator for the elderly COVID‑19. Symptoms of dyspnea and diarrhea, bilateral lung involvements, the pleural effusion are high risk signs for the elderly COVID‑19 patients progressing to severe. These findings are valuable for the early recognition, early diagnosis and treatment for COVID⁃19. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.