Cutaneous Adverse Drug Reactions of COVID-19 Vaccines: A Cross-Sectional Study of the Largest Healthcare System in Taiwan

Objectives: Potential cutaneous adverse drug reactions (cADRs) associated with COVID-19 vaccinations are well-known. However, comprehensive evaluation including detailed patient characteristics, vaccine types, signs and symptoms, treatments and outcomes from such cADRs are still lacking in Taiwan. Method(s): A cross-sectional study was conducted from December 2019 to October 2022 to analyze spontaneous ADR reporting data from Taiwan's largest multi-institutional healthcare system. Physicians and pharmacists initially ensured the data quality and completeness of the reported ADR records. Subsequently, we applied descriptive statistics to analyze the patient cohort based on demographic characteristics, administered COVID-19 vaccines, clinical manifestations, and patient management. Result(s): We identified 242 cADRs from 759 reported COVID-19 vaccine-related ADRs, 88.3% of which were judged as "possible" using the Naranjo Scale. The mean age of patients with cADRs was 48.1+/-17.5 years, with the majority (44.2%) of cADRs reported in the 40-64yr old age group. cADRs were more common in women (68.2%) and most of the patients had no history of allergy to vaccines (99.6%). Oxford/AstraZeneca (58.6%) accounted for the most reported brand of COVID-19 vaccines. Patients developed cADRs within 1 to 198 days (median = 5.5 days), and mostly after first-dose vaccination (77.8%). The most frequently reported cADR was rash/eruption (18.7%), followed by itchiness/pruritus (11.7%) and urticaria (9.2%), mainly affecting the lower limbs (23.8%) and upper limbs (22.6%). Medications were prescribed for 65.1% of the cADRs, and signs and symptoms were resolved within 1 to 167 days (median = 7 days) after treatment with oral antihistamines (23.0%), topical corticosteroids (14.6%) or oral corticosteroids (14.4%). Conclusion(s): Our findings provide comprehensive details regarding COVID-19 vaccine-related cADRs in Taiwan. Certain groups, especially women and the middle-aged, who reported a relatively higher rate of cADRs, may benefit from pre-vaccination counseling about the risks of cADRs and the use of appropriate medications.Copyright © 2023

Comparison of disinfection quality in nurseries of Nanjing during 2019 - 2021

Objective To analyze the disinfection quality and influencing factors of nurseries in Nanjing during 2019-2021 so as to provide a scientific basis for optimizing preventive disinfection strategies and measures in nurseries. Methods Environmental samples from 389 nurseries in Nanjing from January 2019 to December 2021 were tested and the change of disinfection quality qualification rate was compared. Results The overall disinfection qualification rate of nurseries of year 2019-2021 were 96.32% 95.85% and 94.60% respectively showing a downward trend χ2trend = 8.67 P<0.05 . Specifically disinfection qualification rate of object surfaces staff hands and tableware showed a downward trend while the disinfection qualification rate of dynamic air showed an upward trend and the differences were statistically significant χ2trend = 23.17 12.32 5.37 21.48 P<0.05 . The total qualification rate of disinfection in Jiangning and Liuhe districts increased during 2019-2021 χ2trend = 21.46 24.05 P<0.05 . Conclusion Disinfection quality of nurseries in Nanjing has declined by year during 2019-2021 especially the object surfaces and staff hands. It is urgent to optimize and refine the strategies and measures for preventive disinfection in nurseries strengthen the training of personnel on disinfection knowledge and ensure the quality of disinfection in nurseries. © 2023 Chinese Journal of General Surgery. All rights reserved.

Authentic leadership and employees' job performance: mediation effect of positive employee health

In light of global competition and the COVID-19 pandemic, organizations are encountering an increasingly challenging and unpredictable environment. Consequently, employees are experiencing heightened levels of job strain. This study aims to explore the impact of various organizational mechanisms on promoting positive employee health within the organization, ultimately affecting employees' job performance. The findings of this study indicate that authentic leadership and the absence of organizational politics are significant predictors of positive employee health. Moreover, positive employee health has a positive influence on supervisor-rated job performance through its effect on job engagement. This study serves as a valuable resource for organizations, shedding light on the fundamental factors that contribute to positive employee health. It also raises managers' awareness of the importance of nurturing and sustaining employees' emotional and physical well-being to maintain competitiveness in the market. © 2023 Antai College of Economics and Management, Shanghai Jiao Tong University.

Research on impacts of COVID-19 on students' clinical social work career determination in Taiwan

A quantitative method is adopted to survey 197 students at the department of social work at a university in Taiwan from April to May in 2020. The study aims to explore the impact of the new coronavirus on social work students' career determination. The result presents the participants with higher social loneliness have lower "Career Determination of Clinical Medical Social Work (CDCMSW)", and the mental burden feeling, and family relationship are predictive of the CDCMSW. © 2023 Author(s).

Lessons Learned from the COVID-19 Pandemic: A Multigroup Structural Equation Modelling of Underground Space Environment and Users' Health

Due to the inherent limitations of underground spaces, such as the lack of natural ventilation and sunlight, underground space users tend to face more health risks compared with their aboveground counterparts. However, little is known about how the underground environment, users' health, and their associations were impacted by the outbreak of the pandemic. In this study, we investigated and compared the impacts of the general underground environment on regular users' physical and psychological health before and after the pandemic. To achieve this aim, the data from 525 surveys were collected from eleven underground sites, followed by an objective field measurement study conducted at five underground sites in Hong Kong pre- and post-outbreak of the pandemic. The multigroup structural equation modelling results indicated that: (i) surprisingly, the users' satisfaction towards almost all underground environment factors, including greenery, connectivity with the aboveground environment, thermal comfort, ventilation, indoor air quality, acoustic comfort, and lighting, excluding wayfinding, were significantly higher in the post-outbreak period;(ii) the users' health, both physical and psychological, was significantly better in the post-outbreak period;(iii) the impacts of visual comfort on the users' physical and psychological health were significantly greater in the post-outbreak period (critical difference ratio (;CDR;) > 1.96);(iv) the impacts of wayfinding, greenery, and acoustic and thermal comfort on the users' physical or psychological health were significant only in the pre-outbreak period (;CDR;> 1.96);(v) the impacts of connectivity on the users' physical and psychological health were significant in both the pre- and post-outbreak periods (;CDR;< 1.96). The findings were further cross-validated using the objective measurement results. With an increasing need to develop healthy underground spaces, the study contributes to the development, design, and management of the underground environment to enhance the users' health in the post-outbreak era.

Delayed Care Seeking and Outcomes of Testicular Torsion among Children during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis

Introduction Since the onset of coronavirus disease 2019 (COVID-19), stay-at-home orders and fear caused by the pandemic have had a significant effect on the timing and outcomes of testicular torsion. However, the evidence was limited since the study results were inconsistent. This study aims to examine the hospitalization rates, timing, and outcomes of testicular torsion in children before and during the pandemic. Materials and Methods Using PubMed, Embase, and Google Scholar databases, we conducted a systematic search and meta-analysis of studies reporting the timing and outcomes of children admitted with testicular torsion before and during the COVID-19 pandemic. Subgroup analyses were conducted to explore possible sources of heterogeneity. Result The outcomes of 899 testicular torsion patients from eight studies were evaluated. Our study found an increased hospitalization rate for patients with testicular torsion (incidence rate ratio = 1.60, 95% confidence interval [CI]: 1.27-2.03;p = 0.001). Despite a significant increase in the duration of symptoms during the COVID-19 pandemic (weighted mean difference = 11.04, 95% CI: 2.75-19.33;p = 0.009), orchiectomy rates did not increase (odds ratio = 1.33, 95% CI: 0.85-2.10;p = 0.147). Conclusion During the COVID-19 pandemic, hospitalization rates for testicular torsion and the duration of symptoms among children increased significantly. Moreover, the rate of orchiectomy did not increase during the pandemic, indicating that pediatric emergency services have remained efficient and have prevented an increase in the number of orchiectomies performed despite pandemic-related closures and delays in transporting patients to medical care.

Design and Construction of the New Web Learning System of Tsinghua University

Online learning platforms play an important role in supporting colleges and universities in integrating online and offline learning. The project team designed and developed the Web Learning System of Tsinghua University, which has become a basic supporting platform for teachers and students to carry out teaching and learning activities. The Web Learning System possesses functionalities such as course announcement management, courseware management, assignment management, discussion, Q & A, and online timetables, providing supporting services for course teaching, teacher-student interaction, and especially online and offline integrated learning during the COVID-19 pandemic. © 2022 IEEE.

Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance. © Wolters Kluwer Health, Inc. All rights reserved.

Antithrombotic strategies in COVID-19 patients: a systematic review and Bayesian network meta-analysis

Introduction: COVID-19 is a public health emergency of international concern. Clinicians are likely to adopt various antithrombotic strategies to prevent embolic events, but the optimal antithrombotic strategy remains uncertain. We performed a Bayesian network meta-analysis to evaluate various antithrombotic strategies comprehensively. Method(s): We systematically searched PubMed, Cochrane Library, Web of Science, EMBASE and Clinical trials. gov to screen trials comparing different antithrombotic strategies. The primary outcome is 28-day mortality, and the secondary outcomes include major thrombotic event, major bleeding and in-hospital mortality, etc. We assessed the risk of bias using the Cochrane Collaboration's tool and the quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. We successively performed traditional pairwise and Bayesian network meta-analysis using R v4.2.1 software. Result(s): Twenty-six eligible randomized controlled trials were included, giving a total of 35 paired comparisons with 32,041 patients randomized to 7 antithrombotic strategies. In comparison to standard of care (SoC) strategy, therapeutic anticoagulation (TA) (RR 0.36, 95% CrI 0.13-0.86) and prophylactic anticoagulation (PA) (RR 0.35, 95% CrI 0.12-0.85) strategy significantly reduced the mortality of COVID-19 patients (Fig. 1). The antiplatelet (AP) strategy was associated with high risk of major bleeding when compared with SoC strategy (RR 2.5, 95% CrI 1.1-8.9), and the TA (RR 0.43, 95% CrI 0.17-0.98), PA (RR 0.27, 95% CrI 0.10-0.63) and PA with Fibrinolytic agents (FA) strategy (RR 0.12, 95% CrI 0.01-0.81) was associated with low risk of major thrombotic event. Conclusion(s): This network meta-analysis indicates that the TA and PA strategies probably reduce mortality and confer other important benefits in COVID-19 patients. These findings provide guidance on how to choose optimal antithrombotic strategies for COVID-19 patients.

Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation

Lupus nephritis (LN) is one of the most common and serious clinical manifestations of systemic lupus erythe-matosus (SLE), which causes serious damage to the kidneys of patients. To effectively assist the pathological diagnosis of LN, many researchers utilize a scheme combining multi-threshold image segmentation (MIS) with metaheuristic algorithms (MAs) to classify LN. However, traditional MAs-based MIS methods tend to fall into local optima in the segmentation process and find it difficult to obtain the optimal threshold set. Aiming at this problem, this paper proposes an improved water cycle algorithm (SCWCA) and applies it to the MIS method to generate an SCWCA-based MIS method. Besides, this MIS method uses a non-local means 2D histogram to represent the image information and utilizes Renyi's entropy as the fitness function. First, SCWCA adds a sine initialization mechanism (SS) in the initial stage of the original WCA to generate the initial solution to improve the population quality. Second, the covariance matrix adaptation evolution strategy (CMA-ES) is applied in the population location update stage of WCA to mine high-quality population information. To validate the excellent performance of the SCWCA-based MIS method, the comparative experiment between some peers and SCWCA was carried out first. The experimental results show that the solution of SCWCA was closer to the global optimal solution and can effectively deal with the local optimal problems. In addition, the segmentation experiments of the SCWCA-based MIS method and other equivalent methods on LN images showed that the former can obtain higher-quality segmented LN images.

Risk of autoimmune diseases in patients with COVID-19 a retrospective cohort study

Background There are a growing number of case reports of various autoimmune diseases occurring after COVID-19, yet there is no large-scale population-based evidence to support this potential association. This study provides a closer insight into the association between COVID-19 and autoimmune diseases and reveals discrepancies across sex, age, and race of participants.Methods This is a retrospective cohort study based on the TriNetX U.S. Collaborative Network. In the test-negative design, cases were participants with positive polymerase chain reaction (PCR) test results for SARS-CoV-2, while controls were participants who tested negative and were not diagnosed with COVID-19 throughout the follow-up period. Patients with COVID-19 and controls were propensity score-matched (1: 1) for age, sex, race, adverse socioeconomic status, lifestyle-related variables, and comorbidities. The primary endpoint is the incidence of newly recorded autoimmune diseases. Adjusted hazard ratios (aHRs) and 95% confident intervals (CIs) of autoimmune diseases were calculated between propensity score-matched groups with the use of Cox proportional-hazards regression models.Findings Between January 1st, 2020 and December 31st, 2021, 3,814,479 participants were included in the study (888,463 cases and 2,926,016 controls). After matching, the COVID-19 cohort exhibited significantly higher risks of rheumatoid arthritis (aHR:2.98, 95% CI:2.78-3.20), ankylosing spondylitis (aHR:3.21, 95% CI:2.50-4.13), systemic lupus erythematosus (aHR:2.99, 95% CI:2.68-3.34), dermatopolymyositis (aHR:1.96, 95% CI:1.47-2.61), systemic sclerosis (aHR:2.58, 95% CI:2.02-3.28), Sjogren's syndrome (aHR:2.62, 95% CI:2.29-3.00), mixed connective tissue disease (aHR:3.14, 95% CI:2.26-4.36), Behcet's disease (aHR:2.32, 95% CI:1.38-3.89), polymyalgia rheumatica (aHR:2.90, 95% CI:2.36-3.57), vasculitis (aHR:1.96, 95% CI:1.74-2.20), psoriasis (aHR:2.91, 95% CI:2.67-3.17), inflammatory bowel disease (aHR:1.78, 95%CI:1.72-1.84), celiac disease (aHR:2.68, 95% CI:2.51-2.85), type 1 diabetes mellitus (aHR:2.68, 95%CI:2.51-2.85) and mortality (aHR:1.20, 95% CI:1.16-1.24).Interpretation COVID-19 is associated with a different degree of risk for various autoimmune diseases. Given the large sample size and relatively modest effects these findings should be replicated in an independent dataset. Further research is needed to better understand the underlying mechanisms.Funding Kaohsiung Veterans General Hospital (KSVGH111-113).

Impact of Digital Industrialization on the Energy Industry Supply Chain: Evidence from the Natural Gas Industry in China

The global economy is moving into a new era characterized by digital and green development. To examine the impact of digital industrialization development on the energy supply chain, in relation to the sustainable development of China's energy security, we discuss the nonlinear impact and transmission mechanism of digital industrialization on the supply chain of the energy industry using a panel threshold regression model based on sample data on the development of the provincial natural gas industry in China from 2006 to 2020. We found that there are multiple threshold effects of digital industrialization level development on energy supply chain length, and the results are statistically significant, i.e., digital industrialization development positively contributes to natural gas supply chain length after digital industrialization is raised to or crosses the critical threshold. Meanwhile, the heterogeneity analysis results show that there are differences in the impact of digital industrialization on the energy supply chain from sub-sectors, regional development differences, and different development periods. Therefore, we provide some factual support and experience for achieving the construction goal of "Digital China" and accelerating the digital reform of the energy supply chain as well as transforming and upgrading the economic structure.

A Study on the Online VS Traditional Classroom Teaching Styles in Sino-Foreign Cooperative Institutions at Times of COVID 19

The outbreak of Corona Virus Disease 2019 (henceforth COVID-19) has put restrictions on travel and educational exchange. In terms of Sino-Foreign Cooperative Institutions, this pandemic has imposed a strain on normal operation of education process, as foreign teachers hardly have the access to the mainland of China for prevention and control. Thus, these institutions have to rely on online teaching as the only way to maintain teaching work. However, some courses taught online are not effective and cannot fully replace traditional classroom teaching. Therefore, this study target at the largest Sino-Australian cooperative school in Zhejiang Province of China as the research object, and conduct a survey on English teaching delivered by foreign teachers via online and face-to-face methods. Specifically, listening and reading modules are taught online while speaking and writing ones are taught face-to-face. It is found that the combination of these ways performs better from feedback students provide and can almost reach the standard of traditional classroom teaching. © 2023 The authors and IOS Press.

A more accurate mask detection algorithm based on Nao robot platform and YOLOv7

With the global outbreak of Corona Virus Disease 2019(COVID-19), many countries had made it mandatory for people to wear masks in public places. This paper proposed a novel mask detection algorithm RMPC (Restructing the Maxpool layer and the Convolution layer)-YOLOv7 based on YOLOv7 for detecting whether people wear masks in public places. The RMPC-YOLOv7 algorithm reconstructed the downsampling structure in the original YOLOv7 algorithm. We changed the stacking of the maxpooling layer and the convolutional layer. This enabled the feature information to be fully integrated to achieve the accuracy improvement of the new model. Through comparison experiments, our proposed RMPC-YOLOv7 had was improved 0.9% and 1.2% for mAP0.5 and mAP0.5:0.95, respectively. The experimental results demonstrated the feasibility of RMPC-YOLOv7. © 2023 IEEE.

Time-frequency connectedness and cross-quantile dependence between carbon emission trading and commodity markets: Evidence from China

This paper aims to investigate the dynamic connectedness and the cross-quantile dependence structure between carbon emission trading and commodity markets in China. We employ both the Baruník and Křehlík (2018) connectedness method and the Baruník and Kley (2019) cross-quantile dependence method to provide time-frequency-quantile evidence. In addition, we use a daily dataset from September 2, 2013, to September 30, 2022, to gauge the macroeconomic effects of the COVID-19 pandemic. We find that Petrochemical is the biggest contributor and recipient in the carbon-commodities system, and the results show that carbon markets are more influenced by other commodity markets than the reverse. Furthermore, the total connectedness is stronger in the short term but can increase over the long term, especially during the onset of COVID-19. The dynamic pair-wise results show that the carbon market can impact other commodity markets, but the effects are diverse and varied. The quantile-varying dependence between the carbon market and commodities is detected, and the cross-quantile dependence gradually strengthens as the trading days increase. This paper concludes with fruitful policy implications for resource decision-makers. © 2023 Elsevier Ltd

MentalNet: Heterogeneous Graph Representation for Early Depression Detection

Depression is one of the leading factors in global disability and a top driver for suicides. Studies have shown that depression has an effect on language usage. In recent years, especially during the COVID pandemic, social media platforms have become the de facto platform for many individuals to self-disclose or discuss mental health issues like depression. This trend presents a unique opportunity for researchers and healthcare professionals to detect potential mental illnesses for early intervention or treatment by taking advantage of the recent advances in machine learning approaches. Existing depression detection methods on social media, however, suffer from two major limitations. First, these solutions heavily rely on the amount, quality, and type of user-posted content. Second, the overlooked social circle impact should be leveraged to enhance the prediction capabilities. In this paper, we propose a depression detection framework, MentalNet, based on heterogeneous graph convolution by capturing users' interactions (replies, mentions, and quotetiveets) with their friends on social media and differentiating the intimacy of users' social circles (e.g., family, friends, or acquaintances). Specifically, we formulate the problem of depression detection on social media as a graph classification problem by representing users' social circles in the format of heterogeneous graphs. MentalNet embraces three modules, (1) extraction of ego-network node features, (2) construction of user interaction graphs, and (3) depression detection based on heterogeneous graph classification. The extensive experiments on Twitter data demonstrate that MentalNet consistently and significantly outperforms the state-of-the-art methods in terms of all the effectiveness metrics. Compared to the baseline methods, MentalNet is able to effectively predict early depression in Twitter users with up to 24% improvement on F1 score. © 2022 IEEE.