Early autoimmunity and outcome in virus encephalitis: a retrospective study based on tissue-based assay.

To explore the autoimmune response and outcome in the central nervous system (CNS) at the onset of viral infection and correlation between autoantibodies and viruses. METHODS: A retrospective observational study was conducted in 121 patients (2016-2021) with a CNS viral infection confirmed via cerebrospinal fluid (CSF) next-generation sequencing (cohort A). Their clinical information was analysed and CSF samples were screened for autoantibodies against monkey cerebellum by tissue-based assay. In situ hybridisation was used to detect Epstein-Barr virus (EBV) in brain tissue of 8 patients with glial fibrillar acidic protein (GFAP)-IgG and nasopharyngeal carcinoma tissue of 2 patients with GFAP-IgG as control (cohort B). RESULTS: Among cohort A (male:female=79:42; median age: 42 (14-78) years old), 61 (50.4%) participants had detectable autoantibodies in CSF. Compared with other viruses, EBV increased the odds of having GFAP-IgG (OR 18.22, 95% CI 6.54 to 50.77, p<0.001). In cohort B, EBV was found in the brain tissue from two of eight (25.0%) patients with GFAP-IgG. Autoantibody-positive patients had a higher CSF protein level (median: 1126.00 (281.00-5352.00) vs 700.00 (76.70-2899.00), p<0.001), lower CSF chloride level (mean: 119.80±6.24 vs 122.84±5.26, p=0.005), lower ratios of CSF-glucose/serum-glucose (median: 0.50[0.13-0.94] vs 0.60[0.26-1.23], p=0.003), more meningitis (26/61 (42.6%) vs 12/60 (20.0%), p=0.007) and higher follow-up modified Rankin Scale scores (1 (0-6) vs 0 (0-3), p=0.037) compared with antibody-negative patients. A Kaplan-Meier analysis revealed that autoantibody-positive patients experienced significantly worse outcomes (p=0.031). CONCLUSIONS: Autoimmune responses are found at the onset of viral encephalitis. EBV in the CNS increases the risk for autoimmunity to GFAP.

The window of opportunity brought by the COVID-19 pandemic: an ill wind blows for digitalisation leapfrogging

We analyse how the COVID-19 pandemic crisis created the window of opportunity (WOP) in the digital industry and digital applications. These led to the leapfrogging of digital transformation in China. This was considered a major contribution to the pandemic recovery in early to mid-2020. By exploring this phenomenon, we examine alternatives for sustainable development during such turbulent circumstances mediating the destruction arisen from the crisis. We unveil the mechanisms that reveal the large-scale digitalisation that emerged as part of the response to managing the crisis. We find that during this crisis in China, the WOPs from policy, technology and demand perspectives facilitated the leapfrogging of the digital transformation in numerous social and economic areas. More importantly, we also discover that the WOPs are unevenly distributed among firms, time, and locations. Based on these results, we put forth that the COVID-19 pandemic brings a relative window of opportunity (RWOP) to society, impacting industries and firms. However, these impacts are disproportionately distributed. To the best of our knowledge, this is one of the first studies to illustrate how a social crisis leads to WOPs enhancing digital transformation. Furthermore, this also ultimately provided a coping mechanism to deal with the pandemic.

Impact of the COVID-19 Pandemic on Outpatient Service in Primary Healthcare Institutions: An Inspiration From Yinchuan of China.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has posed a great challenge to the healthcare system. This study evaluated the impact of the pandemic on the utilization of primary healthcare (PHC). METHODS: The outpatient data from 158 PHC institutions in Yinchuan from May 1, 2017 to April 30, 2020 were used. The difference in difference (DID) model was used to analyze the difference in the number of outpatient visits per day, total outpatient expenditure per day, and outpatient expenditure per visit between December 2019 and February 2020 compared with the same periods in two previous years. The autoregressive integrated moving average (ARIMA) modelling was used to investigate the association between the outpatient volume and the number of the last week's new COVID-19 cases in Yinchuan, Ningxia, and China. RESULTS: From December 2019 to February 2020, the decline in the number of outpatient visits per day (DID: -367.21 times, P=.004) was larger than that in two previous years, and a similar trend can be seen in the outpatient expenditure per day. However, the rise in the outpatient expenditure per visit (DID: 19.06 thousand yuan, P=.003) was larger than that in two previous years. In 2020, the outpatient visits for most types of diseases decreased from week 3 and rebounded after week 5. The decline and rebound of outpatient visits in the population aged 45 years and older were steeper than in those younger. The outpatient volume was negatively associated with the number of the last week's new COVID-19 cases. CONCLUSION: This study indicated a significant impact of the pandemic on PHC service utilization. Since PHC service is the foundation of the healthcare system in most developing countries, measures should be taken to make PHC help cope with the crisis and relieve the burden of hospital care.

Health Behaviours among Travellers Regarding Risk Compensation Following COVID-19 Vaccination in Taizhou, China.

Background: During the COVID-19 pandemic, public transport was restricted in many countries because of the transmission risk. According to the risk compensation theory, travellers post-COVID-19 vaccination may encounter higher risks; however, no real-world studies provide such evidence. Therefore, we conducted a survey to assess whether risk compensation would occur among travellers' health-related behaviours after COVID-19 vaccination, potentially aggravating the transmission of the virus. Materials and Methods: A self-administered online survey was designed and distributed over WeChat to identify the difference in health behaviours before and after COVID-19 vaccination among travellers at a train station in Taizhou, China, from 13 February to 26 April 2022. Results: A total of 602 individuals completed the questionnaire. The results revealed no statistical difference between the health behaviours reported by the vaccinated and unvaccinated groups. Participants who received the first dose of the vaccine earlier showed no statistical difference in harmful health behaviours (hand washing frequency decreased by 4.1% (P=0.145) and the duration of public transport travel increased by 3.4% (P=0.437)), but showed better protective health behaviours (mask-wearing duration increased by 24.7% (P=0.014)). Compared to those vaccinated less than three times, participants vaccinated against COVID-19 three times showed no statistical differences in harmful health behaviours mask-wearing duration decreased by 7.0% (P=0.927), their hand washing frequency decreased by 4.8% (P=0.905), and the duration of public transport travel increased by 2.5% (P=0.287). After vaccination, when compared to themselves before vaccination, participants exhibited better health behaviours (increased hand washing frequency and mask-wearing duration, and decreased duration of public transport travel) to some extent. Conclusion: In conclusion, this study found no evidence of risk compensation among travellers. After being vaccinated, health behaviours partly improved among travellers.

Achievement of learning outcomes in non-traditional (online) versus traditional (face-to-face) anatomy teaching in medical schools: A mixed method systematic review.

In recent years, the logistical challenges posed by the Covid-19 pandemic have prompted medical educators teaching gross anatomy to explore an expanded use of online instructional modalities. There is concern that this shift to online anatomy education in medical schools could affect long-term learning outcomes for future healthcare providers. In this systematic review, the educational effectiveness of online anatomy teaching is compared with traditional ("face-to-face") teaching methods, specifically in terms of students' academic performance and satisfaction. A search of four databases identified and screened 162 studies. Subsequently, 31 studies were analyzed, including both (a) teaching outcome measures (test scores) and (b) student satisfaction ratings. Authors chose studies, extracted data, assessed quality and examined risk of bias. Nineteen studies compared students' academic performances between online and face-to-face teaching methods. Twenty-six studies measured students' levels of satisfaction in both teaching methods. Both qualitative and quantitative analyses of the data revealed comparable academic performances with no statistical difference between the two teaching methods, but a higher level of satisfaction with face-to-face teaching. The findings confirm that students can learn from online teaching but are more satisfied with face-to-face teaching. The authors conclude that online teaching cannot replace traditional teaching and there is no preference for one type of modality over the other. Therefore, a multi-modal learning approach combining online with face-to-face educational modalities for medical students could be efficient and successful.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

COVID-19 vaccine uptake and vaccine hesitancy in rural-to-urban migrant workers at the first round of COVID-19 vaccination in China.

BACKGROUND: Migration can be linked to the transmission of COVID-19. COVID-19 vaccine uptake and hesitancy among rural-to-urban migrant workers in China, the largest group of internal migrants in the world, has not been characterized. OBJECTIVE: To investigate COVID-19 vaccine uptake and identify vaccine hesitancy-associated factors among rural-to-urban migrant workers in the first round of COVID-19 vaccination in China. METHODS: A cross-sectional questionnaire-based survey was conducted, including 14,917 participants. Socio-demographics, COVID-19 vaccine uptake, vaccine hesitancy and its associated factors based on Vaccine Hesitancy Determinants Matrix (VHDM) were applied for the survey. Data were principally analyzed by logistic regression analysis. RESULTS: The COVID-19 vaccine uptake and vaccine hesitancy rates were 7.1% and 57.7%, respectively. Vaccine hesitancy was strongly associated with VHDM, including individual factors (female, higher annual income and fewer medical knowledge), group factors (less family support, friend support and public opinion support), COVID-19 epidemic factors (lower fatality, infection and emotional distress) and vaccine factors (less vaccine necessity, vaccine safety, vaccine efficacy, vaccine importance and vaccine reliability). CONCLUSION: The VHDM model has the potential utility in efforts to reduce COVID-19 vaccine hesitancy. Greater efforts should be put into addressing positive predictors associated with vaccine hesitancy.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Grocery shopping in California and COVID-19: Transportation, environmental justice, and policy implications

To understand how COVID-19 changed grocery shopping and explore implications for transportation and environmental justice, we surveyed in May 2021 California members of KnowledgePanel®, the largest and oldest U.S. probability-based panel. We asked how frequently Californians grocery shopped before and during the pandemic, and how they may grocery shop afterward in-store, online with home delivery (“e-grocery”), or online with store/curbside pick-up (“click-and-pick”). We found that most Californians continued to grocery shop in-person during the pandemic, although less frequently than before. Many relied more on e-grocery (+8.9 %) and click-and-pick (+13.3 %), although older generations remained attached to in-store shopping. African American households grocery shopped in-store less than Whites pre-pandemic;post-pandemic, they may compensate with more e-grocery and click-and-pick. While higher levels of environmental injustice (based on CalEnviroScreen) were associated with less in-store shopping, we found no association with e-grocery or click-and-pick. Our results have implications for travel, food logistics, and parking management.

Family function and life satisfaction of postgraduate medical students during the COVID-19 pandemic: the mediating role of meaning in life and depression.

Recent studies have documented life satisfaction of people have changed during the COVID-19 pandemic. However, it is unknown about the influential factors and mechanisms of life satisfaction of postgraduate medical students during the COVID-19 pandemic. There is a strong link among life satisfaction and individual quality of life and achievement, so it is important to explore the influence mechanism of life satisfaction of postgraduate medical students and explore ways to improve life satisfaction for the development of postgraduate medical students. The current study was based on the Circumplex Model of Marital and Family System, The Theory of Family Functioning, The Meaning Maintenance Model, The Theory of Personal Meaning and Existential Theory to construct theoretical framework and examine whether meaning in life and depression would mediate the link between family function and postgraduate medical students' life satisfaction. By convenient sampling method, a total of 900 postgraduate medical students (Mage = 27.01 years, SD = 3.33) completed questionnaires including Family APGAR Scale, Chinese Version of Meaning In Life Questionnaire, Patient Health Questionnaire, and Satisfaction With Life Scale. In this study, SPSS 25.0 was used for correlation analysis, regression analysis and common method bias test, and AMOS 23.0 was used for structural equation modeling analysis. The results showed that (a) family function could predict life satisfaction of postgraduate medical students significantly; (b) both meaning in life and depression mediated the association between family function and life satisfaction in a parallel manner; (c) meaning in life and depression sequentially mediated the link between family function and life satisfaction of postgraduate medical students. The study illuminates the role of meaning in life and depression in improving life satisfaction and implies that it is necessary to focus on the changes of life satisfaction of postgraduate medical students during the COVID-19 pandemic, and medical educator can improve the sense of meaning in life of postgraduate medical students through improving their family function, further decreasing the risk of depression, finally improving their life satisfaction.

Communicating COVID-19: Analyzing Higher Education Institutional Responses in Canada, China, and the USA.

The study aims to examine how higher education institutions (HEIs) in three countries responded to the challenges of COVID-19 over a six-month period at the outbreak of the global pandemic. Employing document analysis, we examined 732 publicly available communications from 27 HEIs in Canada, China, and the USA. Through theoretical frameworks of crisis management and Situational Crisis Communication Theory (SCCT), we explore how HEIs respond to the pandemic and protect campus stakeholders. The study revealed common patterns in communication strategies during different stages of the pandemic that include accepting responsibility, emotional reassurance, and compensating victims. It also revealed key differences across social contexts and environments and distinct leadership styles. Findings offer insight into how HEIs communicated at the outset of the COVID-19 pandemic and inform the application of SCCT and crisis management theory to institutional behavior in the context of prolonged and intersecting disasters.

Transmission of SARS-CoV-2 on Cold-Chain Food: Precautions Can Effectively Reduce the Risk.

The COVID-19 pandemic has generated a new era in the world, also in the food safety. Up to now, there is no evidence to suggest that people can infect COVID-19 via food contaminated by SARS-CoV-2. Here, we analyzed the results of regular SARS-CoV-2 nucleic acid testing of considerable cold-chain food practitioners, cold-chain food surfaces, and their internal or external packaging as well as their associated environments, aiming to explore the risk of cold-chain food being contaminated by SARS-CoV-2 and the probability of people infecting COVID-19 through contaminated cold-chain food in the context of COVID-19 epidemic. This study found that only two batches of cold-chain food were contaminated by SARS-CoV-2, none of the cold-chain food handler were infected due to effective regulatory measures for cold-chain food. Therefore, effective supervision and preventive methods could effectively reduce the transmission risk of SARS-CoV-2 on cold-chain food.

Forecasting COVID-19 new cases using deep learning methods.

After nearly two years since the first identification of SARS-CoV-2 virus, the surge in cases because of virus mutations is a cause of grave public health concern across the globe. As a result of this health crisis, predicting the transmission pattern of the virus is one of the most vital tasks for preparing and controlling the pandemic. In addition to mathematical models, machine learning tools, especially deep learning models have been developed for forecasting the trend of the number of patients affected by SARS-CoV-2 with great success. In this paper, three deep learning models, including CNN, LSTM, and the CNN-LSTM have been developed to predict the number of COVID-19 cases for Brazil, India and Russia. We also compare the performance of our models with the previously developed deep learning models and notice significant improvements in prediction performance. Although our models have been used only for forecasting cases in these three countries, the models can be easily applied to datasets of other countries. Among the models developed in this work, the LSTM model has the highest performance when forecasting and shows an improvement in the forecasting accuracy compared with some existing models. The research will enable accurate forecasting of the COVID-19 cases and support the global fight against the pandemic.

The structural characteristics and influential factors of psychological stress of urban residents in Jiangxi province during the COVID-19 pandemic: cross sectional study.

AIMS: To explore the structural characteristics and influential factors of psychological stress of urban residents in Jiangxi province during the COVID-19 pandemic through a survey of psychological stress, personality traits, family function and life satisfaction. METHODS: By the convenient sampling, 1422 urban residents from Jiangxi province were assessed with Eysenck Personality Questionnaire Short Scale (EPQ-RSC), Psychological Questionnaires for Emergent Events of Public Health (PQEEPH), Family APGAR Scale (APGAR) and Satisfaction With Life Scale (SWLS). The relation among personality traits, psychological stress, family function and life satisfaction during the COVID-19 pandemic was analyzed by using the canonical correlation analysis and the serial mediation model. RESULTS: (1) Among the estimated correlation coefficients, the first two pairs were significant (P < 0.001 in each). (2) In the first pair of canonical variables, the loadings of neuroticism and neurasthenia were the higher (0.94, 0.70). (3) Neuroticism and life satisfaction mediated the relationship between family function and neurasthenia (ß neuroticism = -0.174; 95%CI:-0.224, -0.134; ß life satisfaction = -0.034, 95%CI:-0.012, -0.062), respectively. In addition, serial mediation analyses indicated that the association of family function and neurasthenia is mediated by neuroticism and life satisfaction in a sequential manner (ß = -0.010; 95%CI:-0.020, -0.004). CONCLUSIONS: During the COVID-19 pandemic, neuroticism was closely related to psychological stress of urban residents, especially neurasthenia. In addition, the serial mediating effect of neuroticism and life satisfaction played an important role in the process of family function influencing neurasthenia. These findings contributed to a more comprehensive understanding of the influential factors for psychological stress of urban residents during the COVID-19 pandemic.

Effect of Thymoquinone on Acute Kidney Injury Induced by Sepsis in BALB/c Mice.

Acute kidney injury (AKI) is a common complication of sepsis and has also been observed in some patients suffering from the new coronavirus pneumonia COVID-19, which is currently a major global concern. Thymoquinone (TQ) is one of the most active ingredients in Nigella sativa seeds. It has a variety of beneficial properties including anti-inflammatory and antioxidative activities. Here, we investigated the possible protective effects of TQ against kidney damage in septic BALB/c mice. Eight-week-old male BALB/c mice were divided into four groups: control, TQ, cecal ligation and puncture (CLP), and TQ+CLP. CLP was performed after 2 weeks of TQ gavage. After 48 h, we measured the histopathological alterations in the kidney tissue and the serum levels of creatinine (CRE) and blood urea nitrogen (BUN). We also evaluated pyroptosis (NLRP3, caspase-1), apoptosis (caspase-3, caspase-8), proinflammatory (TNF-α, IL-1ß, and IL-6)-related protein and gene expression levels. Our results demonstrated that TQ inhibited CLP-induced increased serum CRE and BUN levels. It also significantly inhibited the high levels of NLRP3, caspase-1, caspase-3, caspase-8, TNF-α, IL-1ß, and IL-6 induced by CLP. Furthermore, NF-κB protein level was significantly decreased in the TQ+CLP group than in the CLP group. Together, our results indicate that TQ may be a potential therapeutic agent for sepsis-induced AKI.

COVID-19 outbreak prevention by early containment in Shantou, China.

INTRODUCTION: To report about the successful outbreak containment of COVID-19 in Shantou, one of the prefectural cities of Guangdong province in the mainland China. METHODOLOGY: All patients confirmed as having COVID-19 between 23 January and 25 March 2020 by RT-PCR assay in the clinical lab of Shantou CDC were included and divided into three groups based on the source of identification: hospital diagnosis, contact tracing, and community screening. Collected data was analyzed and compared among these three groups. RESULTS: A total of 25 COVID-19 cases were identified in Shantou. The first case was identified on 14 January 2020 at one of two COVID-19 dedicated hospitals in Shantou. The majority of the cases were either imported from Wuhan or linked to Wuhan/Hubei. The median lag time for diagnosis (i.e., the time between symptom onset and case confirmation) was 2 days (IQR, 2.0-4.0) for all cases, 9 days (IQR, 7.0-10.0) for the cases diagnosed in hospitals, 2 days (IQR, 1.5-2.0) for the cases in contact tracing, and 4 days (IQR, 2.5-4.5) for cases in community screening, with a significantly longer diagnosis lag time in hospitals (p = 0.003). Multivariate linear regression models showed larger family size and severe cases as the significant predictor for increasing number of close contacts. CONCLUSIONS: The current pandemic appears to exist for an uncertain period. The early containment measures applied in Shantou, a city with insufficient healthcare resources for COVID-19, seems to be appropriate for cities or areas with similar profiles.