Clinical features, diagnosis, and management of COVID-19 vaccine-associated Vogt-Koyanagi-Harada disease.

Vogt-Koyanagi-Harada (VKH) disease is a rare and serious ocular adverse reaction following COVID-19 vaccination. This study aimed to evaluate the clinical features, diagnosis and management of COVID-19 vaccine-associated VKH disease. Case reports of VKH disease after COVID-19 vaccination were collected up to February 11, 2023 for retrospective analysis. Twenty-one patients (9 males and 12 females) were included, with a median age of 45 years (range 19-78), from three main regions, Asia (12/21), the Mediterranean region (4/21), and South America (5/21). Fourteen patients developed symptoms after the first dose of the vaccine, and 8 after the second dose. Vaccines included mRNA vaccine (10 cases), virus vector vaccine (6 cases), and inactivated vaccine (5 cases). The average time interval from vaccination to onset of symptoms was 7.5 days (range 12 hours to 4 weeks). All 21 patients experienced visual impairment after vaccination, with 20 cases involving both eyes. Sixteen patients showed symptoms of meningitis. Serous retinal detachment was observed in 16 patients, choroidal thickening was observed in 14, aqueous cell in 9, and subretinal fluid in 6. CSF pleocytosis was detected in 7 patients and skin symptoms were found in 3 patients. All patients received corticosteroid therapy, and 8 also received immunosuppressive agents. All patients recovered well, with a mean recovery time of 2 months. Early diagnosis and early treatment are crucial to the prognosis of patients with VKH after vaccination with COVID-19 vaccine. The risk of vaccination against COVID-19 in patients with a history of VKH disease should be evaluated clinically.

Dendritic Deep Residual Learning for COVID‐19 Prediction

Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch‐Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID‐19 prediction problem show the superiority of the proposed method in comparison with other state‐of‐the‐art ones. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

The relationship between dimensions of empathy and symptoms of depression among university students during the COVID-19 pandemic: A network analysis.

Background: The relationship between different dimensions of empathy and individual symptoms of depression during the COVID-19 pandemic remains unclear, despite the established link between empathy and depression. The network analysis offers a novel framework for visualizing the association between empathy and depression as a complex system consisting of interacting nodes. In this study, we investigated the nuanced associations between different dimensions of empathy and individual symptoms of depression using a network model during the pandemic. Methods: 1,177 students completed the Chinese version of the Interpersonal Reactivity Index (IRI), measuring dimensions of empathy, and the Chinese version of the Patient Health Questionnaire-9 (PHQ-9), measuring symptoms of depression. First, we investigated the nuanced associations between different dimensions of empathy and individual depressive symptoms. Then, we calculated the bridge expected influence to examine how different dimensions of empathy may activate or deactivate the symptoms of depression cluster. Finally, we conducted a network comparison test to explore whether network characteristics such as empathy-depression edges and bridge nodes differed between genders. Results: First, our findings showed that personal distress was positively linked to symptoms of depression. These symptoms involved psychomotor agitation or retardation (edge weight = 0.18), sad mood (edge weight = 0.12), trouble with concentrating (edge weight = 0.11), and guilt (edge weight = 0.10). Perspective-taking was found to be negatively correlated with trouble with concentrating (edge weight = -0.11). Empathic concern was negatively associated with suicidal thoughts (edge weight = -0.10) and psychomotor agitation or retardation (edge weight = -0.08). Fantasy was not connected with any symptoms of depression. Second, personal distress and empathic concern were the most positive and negative influential nodes that bridged empathy and depression (values of bridge expected influence were 0.51 and -0.19 and values of predictability were 0.24 and 0.24, respectively). The estimates of the bridge expected influence on the nodes were adequately stable (correlation stability coefficient = 0.75). Finally, no sex differences in the studied network characteristics were observed. Conclusions: This study applied network analysis to reveal potential pathways between different dimensions of empathy and individual symptoms of depression. The findings supported the existing theoretical system and contribute to the theoretical mechanism. We have also made efforts to suggest interventions and preventions based on personal distress and empathic concern, the two most important dimensions of empathy for depressive symptoms. These efforts may help Chinese university students to adopt better practical methods to overcome symptoms of depression during the COVID-19 pandemic.

Dendritic Deep Residual Learning for COVID‐19 Prediction

Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch‐Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID‐19 prediction problem show the superiority of the proposed method in comparison with other state‐of‐the‐art ones. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ FROM AUTHOR] Copyright of IEEJ Transactions on Electrical & Electronic Engineering is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Analysis of SARS-CoV-2 variants B.1.617: host tropism, proteolytic activation, cell-cell fusion, and neutralization sensitivity.

SARS-CoV-2 has caused the COVID-19 pandemic. B.1.617 variants (including Kappa and Delta) have been transmitted rapidly in India. The transmissibility, pathogenicity, and neutralization characteristics of these variants have received considerable interest. In this study, 22 pseudotyped viruses were constructed for B.1.617 variants and their corresponding single amino acid mutations. B.1.617 variants did not exhibit significant enhanced infectivity in human cells, but mutations T478K and E484Q in the receptor binding domain led to enhanced infectivity in mouse ACE2-overexpressing cells. Furin activities were slightly increased against B.1.617 variants and cell-cell fusion after infection of B.1.617 variants were enhanced. Furthermore, B.1.617 variants escaped neutralization by several mAbs, mainly because of mutations L452R, T478K, and E484Q in the receptor binding domain. The neutralization activities of sera from convalescent patients, inactivated vaccine-immunized volunteers, adenovirus vaccine-immunized volunteers, and SARS-CoV-2 immunized animals against pseudotyped B.1.617 variants were reduced by approximately twofold, compared with the D614G variant.

Identifying the Subtypes and Characteristics of Mental Workload Among Chinese Physicians in Outpatient Practice: A Latent Profile Analysis

Objective: The purpose of this study is to investigate the mental workload level of physicians in outpatient practice since the normalization of prevention and control of the COVID-19 pandemic in China and explore the subtypes of physicians regarding their mental workload. Methods: A cross-sectional survey of 1,934 physicians primarily in 24 hospitals in 6 provinces in Eastern, Central, and Western China was conducted from November 2020 to February 2021. A latent profile analysis was performed to identify clusters based on the six subscales of the Chinese version of physician mental workload scale developed by our research team. Chi-square tests were performed to explore the differences in demographic characteristics of the subtypes among the subgroups, and multinomial logistic regression analysis was further conducted to identify the determinants of the subtypes of physicians. Results: Overall, the participating physicians reported high levels of task load but with high self-assessed performance (68.01 ± 14.25) while performing communication work tasks characterized by direct patient interaction in outpatient clinics. About 33.8% of the participating physicians were identified as “high workload and high self-assessment” subtype, compared to 49.7% “medium workload and medium self-assessment” subtype and 16.4% “low workload and low self-assessment” subtype. Physicians in “high workload and high self-assessment” subtype had the highest mean mental workload score. Physicians who were female, younger, married, worse health status, those who had lower educational level and an average monthly income of 5,001–10,000 RMB, those who worked in tertiary A hospitals, more hours per week and more than 40 h per week in outpatient clinics, and those who saw more outpatients per day, and spent more time per patient but with higher outpatient satisfaction were more likely to belong to “high workload and high self-assessment” subtype. Conclusion: Our findings can help provide a solid foundation for developing targeted interventions for individual differences across physicians regarding their mental workload. We suggest the hospital managers should pay more attention to those physicians with characteristics of the “high workload and high self-assessment” subtype and strengthen the management of the workload of this subtype of physicians to reduce the risks of their mental health, and to maintain their high work performance in outpatient clinics.

Risk and protective factors for anxiety during COVID-19 pandemic.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is a global pandemic and an anxiety-provoking event. There are few studies to identify potential risk and protective factors related to anxiety during COVID-19 pandemic. METHODS: We collected information on demographic data and lifestyles by a web-based survey of 19,802 participants from 34 provinces in China during COVID-19 pandemic. Level of anxiety was evaluated using the Self-Rating Anxiety Scale. We used ordinal multivariable logistic regression to estimate the associations of anxiety level with potential risk and protective factors. We further developed a new score to simplify the assessment of anxiety during COVID-19 crisis. RESULTS: Among 19,802 participants, we found that those who were front-line medical personnel, suffered from chronic disease, with present symptoms of SARS-CoV-2 infection or contact history had 112, 93, 40 and 15% increased risk of higher anxiety level; while those with knowledge about personal protective measures or wore masks had 75 and 29% lower risk of higher anxiety level respectively. We developed a risk score by calculating the sum of single score of 17 factors. Each one increase of the risk score was associated with a 297% increase in anxiety index score. In categorical analysis, low risk (the risk score between 1 to 2), the moderate risk group (the risk score of 3) and high risk group (the risk score ≥ 4) had - 0.40 (95% CI: - 1.55, 0.76), 1.44 (95% CI: 0.27, 2.61) and 9.18 (95% CI: 8.04, 10.33) increase in anxiety index score, and 26% (95% CI: - 7, 72%), 172% (95% CI: 100, 270%), and 733% (95% CI: 516, 1026%) higher risk of anxiety respectively, when compared with the very low risk group (the risk score of 0). The AUC was 0.73 (95% CI, 0.72, 0.74) for the model fitted the developed risk score, with the cut-off point of 3.5. CONCLUSIONS: These findings revealed protective and risk factors associated with anxiety, and developed a simple method of identifying people who are at an increased risk of anxiety during COVID-19 pandemic.

Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19.

This study explores the dynamics of energy use patterns, climate change issues and the relationship between social-psychological factors, with residents' acceptance of and willingness to pay (WTP) for home energy management systems (HEMS) during the COVID-19 pandemic in New York. The results of our survey suggest that there were no longer morning or evening usage peaks on weekdays, and a significant portion of respondents are experiencing higher or much higher electricity use than average. Most residents' perception of climate change issues during COVID-19 remained unchanged. Attitude, perceived behavioral control, and social norms are overall the strongest predictors of adoption intention and WTP for HEMS. Regarding WTP for specific well-being features, attitude was the strongest positive predictor of telemedical and home security features, and social norms are the strongest positive predictor of elderly assistance and job search. Technology anxiety, surprisingly, positively influences WTP for the well-being features. Trust in utilities is not related to adoption intention, but is positively associated with WTP for the well-being features. Although cybersecurity concerns are positively associated with HEMS adoption intention for energy and well-being features, this relationship is not significant in WTP. Residents who had moderate perceived risk of getting COVID-19 are willing to pay more than the high- and low-risk groups. This paper addresses the interactions among technology attributes, and users' social-psychological and demographics factors. Additionally, this study provides insights for further research in examining technology adoption and energy dynamics during times of crises, such as the COVID-19.