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Background: The increasing burden of mental health problems continues in the post-COVID-19 era, and nursing interns were particularly likely to experience negative emotions during the pandemic. Both psychological resilience and social support affect negative emotion, but the relationship among the three has not been explored in nursing interns in the postpandemic era. Objectives: To explore the current prevalence of negative emotions among nursing interns and the role of psychological resilience in mediating the relationship between social support and negative emotions in the postpandemic era. Methods: A cross-sectional survey of 788 nursing interns was conducted. The instruments included Psychological Resilience Scale, Social Support Scale, Beck Anxiety Scale and Beck Depression Scale. Structural equation modeling was applied to analyze the mediating role of psychological resilience. Results: The prevalence of anxiety disorder among nursing interns was 24.7%, while that of depression was 10.5%. Pearson correlation analysis showed that both social support and psychological resilience negatively correlated with negative emotions, while psychological resilience positively correlated with social support. Psychological resilience showed a partial mediating effect (53.9%) between social support and negative emotion, with an effect value of −0.1456. Conclusion: Psychological resilience and social support protect nursing students from negative emotions, and psychological resilience partially mediates the relationship between social support and negative emotion in the postpandemic era. © 2023 Wiley Periodicals LLC.
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Network pharmacology is a method to study the mechanism of a Traditional Chinese Medicine (TCM) prescription on a disease. However, most articles using network pharmacology to study the mechanism did not combine the weight information of herbs, the weight information of targets of disease, and the interaction information between targets together. We propose a method, network pharmacology combined with two iterations of PageRank algorithm, to make use of these information. It takes prescription-disease system as a whole, calculates PageRank score of targets in the prescription-disease system, which means an importance in the system, and the score is used to rank the analysis results of GO and KEGG pathway which help us to analyze the mechanism of a prescription on a disease. At last, we use two prescription-disease pairs which have been proved effectiveness in clinical trials: Qingfei Paidu Decoction on COVID-19, and FuFang DanShen Diwan on Coronary Heart Disease, and find that the results of our method are consistent with some results of clinical trials.Copyright © 2021
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As countries enter the endemic phase of COVID-19, people's risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowdsourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.
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The COVID-19 pandemic has revealed several limitations of existing healthcare systems. Thus, there is a surge in healthcare innovation and new business models using computer-mediated virtual environments to provide an alternative healthcare system. Today, digital transformation is not limited to virtual communication alone but encompasses digitalizing the network of social connections in the healthcare industry using metaverse technology. The metaverse is a universal and immersive virtual world facilitated by virtual reality (VR) and augmented reality (AR). This paper presents the first effort to offer a comprehensive survey that examines the latest metaverse developments in the healthcare industry, which covers seven domains: telemedicine, clinical care, education, mental health, physical fitness, veterinary, and pharmaceuticals. We review metaverse applications and deeply discuss technical issues and available solutions in each domain that can help develop a self-sustaining, persistent, and future-proof solution for medical healthcare systems. Finally, we highlight the challenges that must be tackled before fully embracing the metaverse for the healthcare industry.
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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.
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The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission. © 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.
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The COVID-19 pandemic is one of the most severe challenges the world faces today. In order to contain the transmission of COVID-19, people around the world have been advised to practise social distancing. However, maintaining social distance is a challenging problem, as we often do not know beforehand how crowded the places we intend to visit are. In this paper, we demonstrate crowded.sg, an AI-empowered platform that leverages on Unmanned Aerial Vehicles (UAVs), crowdsourced images, and computer vision techniques to provide social distancing decision support.
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Purpose: Through the short-term rapid prediction and evaluation of the COVID-19 epidemic in the United States, explore the development trend of the COVID-19 epidemic in the United States in the short term in the future, and provide an effective basis for the prediction, prevention and control of subsequent epidemic spread;Method: Using the feature selection method based on stepwise regression is to process theCOVID-19 epidemic data set fromJanuary 13,2020 to January 16,2021 in the United States, and data mining is carried out through computer programs for a large number of indicators that reflect the situation of the epidemic. After statistical testing, the ARIMA model and the improved ARIMAX model based on feature selection quickly solves the development trend of the COVID-19 epidemic in the United States in the short-term;Result: The implementation of the computer program shows that the traditional ARIMAmodel cannot predict the cumulative number of COVID-19 diagnoses in the United States well, and the improved ARIMAX model using features based on stepwise regression can accurately predict the scale of the COVID-19 epidemic in the United States under the 95% confidence interval. The U.S. epidemic will show a clear upward trend in the next 60 days, and in mid-March the cumulative number of confirmed diagnoses in the country is about 3,724,000, and the cumulative death toll is about 476,000, and the number of people in the ICU ward is about 22,118. © 2021 Association for Computing Machinery. All rights reserved.
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Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we propose EDL-COVID, an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%.
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The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significantly from the pattern as urban events. However, few studies have considered the long temporal dependency of sentiment strength in geotagged social media data, and thus it is difficult to further improve the reliability of detection results. In this paper, we combined a sentiment analysis method and long short-term memory neural network for detecting urban events with geotagged social media data. We first applied a dictionary-based method to evaluate the positive and negative sentiment strength. Based on long short-term memory neural network, the long temporal dependency of sentiment strength in geotagged social media data was constructed. By considering the long temporal dependency, daily positive and negative sentiment strength are predicted. We extracted anomalies that deviated significantly from the prediction as urban events. For each event, event-related information was obtained by analyzing social media texts. Our results indicate that the proposed approach is a cost-effective way to detect urban events, such as festivals, COVID-19-related events and traffic jams. In addition, compared to existing methods, we found that accounting for a long temporal dependency of sentiment strength can significantly improve the reliability of event detection. © 2021 by the authors.
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Aggregation of >104 beads due to specific protein molecules is quantified using microfluidic chips, fast lens-free microscopes, and image processing algorithms. The limit of detection, cost, and size are appropriate for COVID-19 point-of-care testing. © OSA 2020 © 2020 The Author(s)
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Objective: To explore the clinical application value of routine indicators such as blood routine and liver and kidney function in auxiliary diagnosis and prognosis of COVID-19 patients. Methods: SNK-q and other methods were used to retrospectively analyzed the differences of blood routine test, liver and kidney function and other inflammatory indexes of 30 patients with covid-19, 29 patients with other viral pneumonia, 35 patients with influenza A/B and 25 healthy persons from January 28 to February 14, 2020 in Xiangya Hospital of Central South University. Results: The neutrophils count increased gradually in COVID-19 group, influenza A/B group and other types of viral pneumonia group, and the difference between COVID-19 group and other viral pneumonia groups was statistically significant(H=-19.064,P<0.05); The lymphocyte count decreased gradually in the control group, influenza A/B group, other viral pneumonia group and COVID-19 group. In addition, DB, UA and GLU were also different among groups. Subgroup analysis showed that there were statistically significant differences in N(F=9.581,t=-0.152,P<0.05), N%(F=5.723,t=-0.600, P<0.05), NLR(F=4.773, t=-1.161, P<0.05), PCT(F=17.464, t=-1.477, P<0.05)and CRP(F=7.656, t=-1.973, P<0.05) between patients with lung involvement +-++ and patients with lung involvement +++-++++. There were statistically significant differences in NLR(F=63.931, t=-2.815, P<0.01), AST(F=15.704, t=-1.930, P<0.01), ALT(F=35.551, t=-2.199, P<0.01), LDH(F=7.715, t=-2.703, P<0.05) and GLU(F=6.306, t=-5.116, P<0.05) between the light+common subgroup and the heavy+critical subgroup of COVID-19 clinical classification. Correlation analysis showed that clinical stage and imaging credit period were significantly correlated with NLR (r=0.406, P=0.026; r=0.397, P=0.030), ALT (r=0.403, P=0.049; r=0.418, P=0.047), LDH (r=0.543, P<0.01; r=0.643, P<0.01) and GLU(r=0.750, P<0.01; r=0.471, P=0.042). A total of 5 principal components were extracted from all the included indicators, and the comprehensive information extraction rate was 82.86%. Indicators of a large load included Ur, PCT and CRP in PC1; ALT, AST and GLU in PC2; N%, L%, L and NLR in PC3. It indicated that the indicators of acute infection, liver function and blood routine had certein warning effect on disease surveillance. The results of ROC curve analysis showed that the combined detection of N+TB+Urea was the best practice to distinguish COVID-19 and other viral pneumonia, while the combined detection of N+L+UA was the most effective solution to make a distinction between COVID-19 and influenza A/B patients. In the aspect of disease evaluation, NL+LDH+GLU+ALT combined detection represent the best diagnostic performance to distinguish the clinical stage of light+common type and heavy+critical type, achieving the AUC (ROC) to 0.904, with the sensitivity 75% and the specificity 100% at the cut-off value of 0.477. Conclusion: In addition to etiology and imaging examination, doctors can also improve the routine laboratory tests such as blood routine test, liver and kidney function to assist diagnosis and disease prediction of patients with respiratory tract infection.
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COVID-19 , Humans , Kidney Function Tests , Liver , ROC Curve , Retrospective Studies , SARS-CoV-2ABSTRACT
Objective: To provide reference for prevention and control of SARS-CoV-2 infection through analysis of related factors of patients diagnosed and suspected with COVID-19. Methods: Data of 40 confirmed cases and 24 suspected cases of COVID-19 admitted from January to February 2020 in the Second People's Hospital of Jingzhou City were collected, and the differences in indicators and related factors between the confirmed and suspected groups were compared. Results: There was no significant difference in patients age and APACHEⅡ score between the two groups (P> 0.05). Compared with the suspected group, WBC and Neut decreased in the diagnosed group, and the difference was statistically significant (p <0.05). PCT, Lymph, hs-CRP, ALT, IL-6, LDH, CK and other indicators including;gender, fever, dry cough, limb soreness, fatigue, underlying disease, were not statistically significant (p> 0.05). There was no significant difference in the factors such as single lung lobe lesions and multiple lobe lesions (P> 0.05). Conclusion: There is no significant difference between the common COVID-19 patients and the suspected patients in terms of population characteristics, clinical manifestations and most laboratory tests.
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The sources and components of hospital sewage are complex, including pathogenic microorganisms, drugs, metabolites, antibiotic resistant genes, heavy metals, contrast agents, etc. It will become an important way of epidemic spread and a serious source of environmental pollution without effective treatment. Moreover, the emerging contaminants, such as drugs, have become a hotspot in water environment and water pollution control, and the hospital sewage is one of important sources of these contaminants. Based on the impact of COVID-19 on the prevention and control system of the medical system during the current epidemic period, this review illustrated the distribution of drugs and pathogenic microorganisms in hospital sewage, summarized the progress and problems of domestic and foreign hospital sewage treatment technology, and also proposed the future development direction of hospital sewage treatment technology. More importantly, under the special circumstances of COVID-19 epidemic, higher requirements and standards are needed for the construction of hospital sewage control system. Specifically, the simultaneous degradation of drugs and disinfection of pathogenic microorganisms may be the "hotspot" for the development of hospital sewage treatment technology and equipment in the future. © 2020, Editorial Department of JCEE. All right reserved.
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The unprecedented global economic and social crisis caused by the coronavirus outbreak has not spared the energy sector. Using a dynamic model, we investigated the effect of COVID-19 cases on investor sentiments and stock returns of clean energy in the Asian-Pacific region. The results show that coronavirus cases negatively affect stock returns using investor sentiments as a transmission channel. We also find a negative effect of air pollution on stock returns. Since COVID-19 restricted trade and plummeted the oil prices, economies relied on non-renewable sources to meet energy demands. Nevertheless, the investor’s optimism and high sentiment level may deteriorate this link. On the other hand, we do not find any significant effect of low-high temperature on either investor sentiments or clean energy stock returns. Clean energy stocks were viewed as more sustainable and less vulnerable to external shocks, however, the fear and pessimism among investors induced by coronavirus are spilled over the renewable energy sector. © 2020, Politechnika Lubelska. All rights reserved.
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Background: Acute kidney injury (AKI) was found in some patients with COVID-19 pneumonia and accompanied with poor outcomes. The objective of this study was to investigate the association of AKI with clinical outcomes in COVID-19 patients. Methods: In this cohort study, we reviewed electronic medical data from patients with COVID-19 in Shenzhen from January 11 to February 19, 2020. Clinical features and clinical outcomes in COVID-19 patients with and without AKI were analyzed. Further, we evaluated the association between AKI development and clinical outcomes. Results: In this study, 9.6% patients developed AKI during hospitalization. Those with AKI presented older age, severer pneumonia, more comorbidity and lower lymphocyte count. Totally, more patients (77.5%) had primary composite outcomes (intensive care unit (ICU) admission, use of high-flow nasal cannula (HFNC) and mechanical ventilation) in AKI group compared to non-AKI group (2.9%) during the observation period. The median length of stay (LOS) and ICU stay were longer among those with AKI. After adjusted for related covariates, AKI development was independently correlated with LOS (β (95% CI): 9.16 (3.87-14.46)), rather than primary outcomes (HR (95% CI): 1.34 (0.56-3.21)) in COVID-19 patients. Conclusions: The development of AKI was not one of the reasons for ICU admission, use of HFNC and mechanical ventilation, but a kind of manifestation of severe illness in COVID-19 hospitalized patients.
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Background: It was difficult for primary care to take emergency actions to contain COVID-19 at the beginning of its outbreak, and the management of screening and triage procedure was disorderly. To address the situation, it is critical to make a sound management method for appropriately screening, triage, treat and transfer the patients. Objective: To evaluate the effect of an approach for unified management of COVID-19 screening and triage in members within a regional medical consortium. Methods: This study was carried out in a regional medical consortium with five unified features including unified goal, legal person, information platform, service team and salary scheme. An approach developed by our research group, has been used for unified management(of relevant goals, healthcare workers and procedure) of COVID-19 screening and triage in this consortium led by a tertiary hospital, together with 18 stations of a community health center, and the implementation results between January 25 and April 4, 2020 were analyzed. Results: During this period, of the 173 841 cases screened and triaged in the tertiary hospital, 440 were triaged to the COVID-19 fever clinic, 2 051 to the general fever clinic, 271 to the quarantine ward due to suspected COVID-19 symptoms, and 4 were confirmed and referred to the designated hospital. The 18 stations of community heath centers screened and triaged 52 525 cases, including 25 who were triaged to the COVID-19 fever clinic, and 122 to the general fever clinic. There were no missed screening of suspected COVID-19 cases and cross infections within the medical consortium. Conclusion: Our unified management approach has effectively facilitated the development of COVID-19 screening and triage from disorderly to orderly within the regional medical consortium, demonstrating good effects in containing the COVID-19 pandemic. Copyright © 2020 by the Chinese General Practice.
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Objective: To construct and evaluate a diagnosis pathway (Xiangya pathway) for Corona Virus Disease 2019 (COVID-19). Methods: Consecutive subjects aged ≥12 years old who were screened for COVID-19 were included in Xiangya Hospital of Central South University from January 23 to February 3, 2020, and the subjects were further divided into the inception cohort and the validation cohort. The gender, age, onset time of disease of the subjects were recorded. The information of epidemiological history, fever, and the declined blood lymphocytes were collected as clinical indicators, CT scan was used to evaluate the possibility of COVID-19 and range of lung involvement. According to the current Chinese national standards, throat swabs of suspected cases were collected and the nucleic acid of COVID-19 was detected by reverse transcription-polymerase chain reaction (RT-PCR). The Xiangya pathway was constructed with multi-indexes, compared with clinical indicators, CT results and Chinese national standards, their effectiveness of detecting confirmed cases were verified in the inception and validation cohort. Results: A total of 382 consecutive adults who was screened for COVID-19 were included, and 261 cases were in the inception cohort and 121 cases were in the validation cohort. Among the 382 cases, 192 were males (50.3%) and 190 were females (49.7%), with a median age of 35 years (range: 15-92 years). There were 183 cases (47.9%) with epidemiological history, 275 cases (72.0%) with fever, 212 cases (55.5%) with decreased peripheral blood lymphocytes, 114 cases (29.8%) with positive CT findings, 43 cases (11.3%) with positive CT-COVID-19, and 30 cases (7.9%) with positive virus nucleic acid by throat swab. Compared with clinical indicators, the sensitivity and specificity of CT were 0.950 and 0.704, respectively. The accuracy of CT to make a definite diagnosis was higher than that of epidemiological history, fever, and declined blood lymphocyte count (0.809 vs 0.660, 0.532, 0.596, P=0.001, 0.002, 0.003, respectively). The sensitivity of this pathway and the pathway recommended by the Health Commission of China were both high (all were 1.000), while the specificity and accuracy of the Xiangya pathway were higher than the one recommended by the Health Commission (0.872 vs 0.765, 0.778 vs 0.592, both P<0.001). The CT-COVID-19 reduced the missed diagnosis rate caused by false negative of nucleic acid test (31 vs 64), with difference rate of 51.6%, and the positive rate of nucleic acid test was 64.5% (20/31). In validation cohort, the specificity and accuracy of the Xiangya pathway was 0.967, the positive rate of nucleic acid test was 76.9%(10/13). Conclusions: The Xiangya pathway can predict the nucleic acid test results of COVID-19, and can be applied as a reliable strategy to screen patients with suspected COVID-19 among people aged ≥12 years in areas other than Hubei during the epidemic period of COVID-19. The cohort size needs to be increased for further validation.