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This paper proposes an automatic system to monitor the health status of the individuals in an estate such as their blood pressure values, their blood glucose value, their blood oxygen value, their heart rate and their respiratory rate. In particular, the system consists of an intelligent watch, a mobile application, a central server and a medical platform. The intelligent watch acquires five photoplethysmograms (PPGs) via different photo sensors with different wavelengths and transmits the PPGs to the mobile via a bluetooth transmitter. The mobile application allows the inputs of the reference values of these health indices of the individuals and displays the estimated values. Also, it sends the PPGs and these reference values to the central server. The central server estimates the health indices. The medical platform consists of a team of medical officers. They monitor the health indices of the individuals and provide the medical advices. This system can detect the occurrence of the sudden decay of the health status of the individuals. Hence, it can reduce the death rate due to the spread of the new diseases such as the COVID19. © 2022 IEEE.
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Introduction: Cangzhu, an herbal medicine used to treat symptoms of respiratory pneumonia in traditional Chinese medical system, has shown its effectiveness in combating fever, cough, and fatigue of current pandemic while no specialty drugs are available. Latest research in network pharmacology has confirmed the theoretical mechanism behind, the drug itself is commonly prescribed alone side another herb Aiye, which believed to be able to improve the effectiveness of Cangzhu. In this study, network pharmacology will be applied in search of potential mechanism behind. Method: The Traditional Chinese Medicine Systems Pharmacology (TCMSP) is used to filter the active compounds and the target of the prescription compound. The Genecard and OMIM database are applied to identify the target related to our aim symptom fever, cough, and fatigue. The STRING database is used to analyse the intercepted targets. Compound-target interaction and protein-protein interaction networks are constructed using the Cytoscape between target disease Covid and our medicine mixture Cangzhu and Aiye. The Kyoto Encyclopaedia of Genes and Genome (KEGG) pathway and Gene Ontology (GO) enrichment analysis are performed for investigation of the molecular mechanisms. Finally, the interaction probability between the targets and the active compounds can be determined by molecular docking technology. Results: A total of 14 target are identified, in which are 10 most important targets and 2 key compounds. Besides, 216 biological processes items are obtained (P<0.05). Two hundred and seventy-one pathways are obtained (P<0.05). The result of molecular docking shows a stable binding between the active compounds and the target. Copyright © 2022 Textile Bioengineering and Informatics Society.
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In the context of increasing medical resource constraints and the global pandemic of COVID-19, the acquisition and automatic diagnosis of electrocardiogram (ECG) signal at home is becoming more and more important. In this paper, we propose a dual arrhythmia classification algorithm for edge-cloud collaboration. We first design a lightweight single-lead ECG signal binary classification model incorporating RR intervals that can be deployed at the edge, which achieves lightweight ECG feature extraction by using depthwise separable convolution and positional attention, and fuses RR interval features to the fully connected layer to achieve normal or abnormal classification of ECG heartbeats. For heartbeats classified as abnormal using the above model, we design a dual-branch arrhythmia multi-classification model with channel and spatial dual attention that integrates simple convolutional neural network (CNN) modules that can be deployed in a cloud artificial intelligence (AI) server to perform accurate classification of abnormal ECG heartbeats, where the input of one branch is a heartbeat signal and the input of the other branch is an ECG segment containing adjacent R-peaks. The experimental results based on the MIT-BIH arrhythmia database demonstrate that our binary classification model achieves an average accuracy of 99.80% and the multi-classification model achieves an average accuracy of 99.71%, and our method ensures a high enough accuracy while performing dual analysis to make the analysis results more reliable. © 2022 IEEE.
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Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight. Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method was developed that calculates a scan-specific optimal window and removes irrelevant tissues such as airways and blood vessels from images with segmentation models, so that subvisual abnormalities in lung scans become visible.
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Cangzhu is a Herbal medicine used to treat symptoms of respiratory diseases in Traditional Medicine system for hundreds of years and can be a new herbal solution for the current world pandemic. By using network pharmacology and molecular docking, this study explores the possible mechanisms of Cangzhu. One target is identified in the results. Further analysis identified 1 key target and 1 key compound. Moreover, 1052 biological processes, 61 cell compositions, and 124 molecular function items are obtained (P<0.05). One hundred and thirty pathways are obtained (P<0.05). The result of molecular docking shows a stable binding between the active compounds and the target. © 2019 Textile Bioengineering and Informatics Symposium Proceedings 2021 - 14th Textile Bioengineering and Informatics Symposium, TBIS 2021. All rights reserved.
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A novel coronavirus (SARS-CoV-2) causing corona virus disease 2019 (COVID-19) has attracted global attention due to its highly infectious and pathogenic properties. Most of current studies focus on aerosols released from infected individuals, but the presence of SARS-CoV-2 in wastewater also should be examined. In this review, we used bibliometrics to statistically evaluate the importance of water-related issues in the context of COVID-19. The results show that the levels and transmission possibilities of SARS-CoV-2 in wastewater are the main concerns, followed by potential secondary pollution by the intensive use of disinfectants, sludge disposal, and the personal safety of workers. The presence of SARS-CoV-2 in wastewater requires more attention during the COVID-19 pandemic. Thus, the most effective techniques, i.e., wastewater-based epidemiology and quantitative microbial risk assessment, for virus surveillance in wastewater are systematically analyzed. We further explicitly review and analyze the successful operation of a sewage treatment plant in Huoshenshan Hospital in China as an example and reference for other sewage treatment systems to properly ensure discharge safety and tackle the COVID-19 pandemic. This review offers deeper insight into the prevention and control of SARS-CoV-2 and similar viruses in the post-COVID-19 era from a wastewater perspective.
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Objective: The objectives of this study were to measure the global impact of the pandemic on the volumes for intravenous thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with two control 4-month periods. Background: The COVID-19 pandemic led to widespread repercussions on the delivery of health care worldwide. Design/Methods: We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by ICD-10 codes and/or classifications in stroke center databases. Results: There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95%CI,-11.7 to-11.3, p<0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95%CI,-13.8 to-12.7, p<0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95%CI,-13.7 to-10.3, p=0.001). There were greater declines in primary compared to comprehensive stroke centers (CSC) for stroke hospitalizations (-17.3% vs-10.3%, p<0.0001) and IVT (-15.5% vs-12.6%, p=0.0001). Recovery of stroke hospitalization volume (9.5%, 95%CI 9.2-9.8, p<0.0001) was noted over the two later (May, June) versus the two earlier (March, April) months of the pandemic, with greater recovery in hospitals with lower COVID-19 hospitalization volume, high volume stroke center, and CSC. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. SARS-CoV-2 infection was noted in 3.3% (1,722/52,026) of all stroke admissions. Conclusions: The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months, with greater recovery in hospitals with lower COVID-19 hospitalizations, high volume stroke centers, and CSCs.
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COVID-19 pandemic has profoundly changed many aspects of Chinese society. In the education field, the alternative online education system functions not only as a timely substitution but also as a new direction. This article examines the evaluation of online education systems by using the contents of blogs about online courses that are shared on Weibo China's social media network. By exploring the blogs' emotional tendency, this article aims to describe the characteristics of alternative online education systems in the events of risks and to discuss optimized programs for future online education.
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Objective: To retrospectively analyze the clinical characteristics of coronavirus disease 2019 (COVID-19) and the impact of cardiovascular disease (CVD) on the clinical manifestations of COVID-19. Methods: A total of 128 patients diagnosed with COVID-19 in Renmin Hospital of Wuhan University from February 1 to February 29, 2020 were divided into CVD group (n=62) and non-CVD group (n=66). The general data, admission symptoms and laboratory examination results including blood routine, immunity, heart, liver and kidney function were obtained and statistically analyzed by SPSS 22.0 statistical software. The differences of various indexes between CVD group and non-CVD group were compared. Results: There was no significant difference in gender between CVD group and non-CVD group(P>0.05).The average age of CVD group was higher than that of non-CVD group (P<0.001). The proportion of fever and cough, severe and critical patients was higher than that respectively of non-CVD group (all P<0.05). There was no significant difference in the incidence of fatigue, dyspnea, and asymptomatic between the two groups (all P>0.05). The average levels of eukocyte count, neutrophil ratio, neutrophil count, monocyte count, and C-reactive protein in CVD group were higher than those in non-CVD group (all P<0.05), while the average lymphocyte proportion in CVD group was lower than that in non-CVD group (P<0.05). There was no significant difference in lymphocyte count and platelet count between the two groups (both P>0.05). The average LDH, myohemoglobin, CK-MB, NT-proBNP, TBIL, and Urea in CVD group were higher than those in non-CVD group (all P<0.05), but there was no significant difference in ALT, AST, and Cr between the two groups (all P>0.05). Conclusion: Compared with non-CVD patients with COVID-19, CVD patients with COVID-19 are older, have more obvious symptoms, with a higher risksin heart, liver, and kidney injury, but the mechanism is not clear yet. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.
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This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 dark target (DT), deep blue (DB), and multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) during the COVID-19 lockdown period (February-May 2020) compared to the pre-COVID-19 period (February-May 2019). Validation of AOD retrievals was conducted against AErosol RObotic NETwork (AERONET) Version 3 Level 1.5 AOD data obtained from three sites located in urban (Beijing_CAMS and Beijing_RADI) and suburban (XiangHe) areas of China. The results show the poor performance of the DT and DB algorithms compared to the MAIAC algorithm, which performed better during the lockdown period. Overall, all MODIS algorithms overestimated the AOD and showed higher positive bias under high aerosol loading conditions during lockdown than during prelockdown. This is mainly attributed to the overestimation of the aerosol single-scattering albedo (SSA), which was found higher during lockdown than during the same period in 2019. IEEE
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Introduction: During the COVID-19 pandemic, decreased volumes of stroke admissions and mechanical thrombectomy were reported. The objective was to examine whether subarachnoid hemorrhage(SAH) hospitalizations and ruptured aneurysm coiling interventions demonstrated similar declines. Methods: We conducted a global, retrospective, observational study across 6 continents, 37 countries, and 140 comprehensive stroke centers. Patients with diagnosis of SAH, ruptured aneurysm coiling interventions, COVID-19 were identified using ICD-10 codes or by prospectively maintained stroke databases. The 3-month cumulative volume, monthly volumes for SAH hospitalizations and ruptured aneurysm coiling procedures were compared for the period before (1- year and immediately before) and during the COVID-19 pandemic (March 1 to May 31, 2020). Results: There was a significant decline in SAH hospitalizations with 2,044 admissions in the 3 months immediately before and 1,585 admissions during the pandemic, representing a decline of 22.5% (95%CI, -24.3 to -20.7, p<0.0001). Embolization of ruptured aneurysms declined with 1,170 to 1,035 procedures, respectively, representing an 11.5%(95%CI, -13.5 to -9.8, p=0.002) drop. Hospitals with higher COVID-19 hospitalization burden demonstrated greater declines in SAH and ruptured aneurysm coiling compared to lower COVID-19 burden. A relative increase in coiling of ruptured aneurysms was noted in low coiling volume hospitals of 41.1% (95%CI, 32.3-50.6, p=0.008) despite a decrease in SAH admissions in this tertile. Conclusions: There was a global decrease in subarachnoid hemorrhage admissions and ruptured aneurysm embolizations during the COVID-19 pandemic. Among low-volume coiling SAH hospitals, there was an increase in the ruptured aneurysm coiling intervention. These findings in SAH are consistent with a global decrease in other emergencies such as stroke and myocardial infarction.
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Due to the spread of pandemic coronavirus disease (COVID-19), health care centers have been encountered an increasing number of infected patients. Therefore, the resilience of hospitals during unpredicted power outages would be a concerning issue that need to be addressed appropriately. Electricity outage can endanger patients' lives, especially those who have needed immediate special care. In this study, a hybrid microgrid (MG) including renewable energy sources (RESs), energy storage systems (ESSs), and diesel generators (DGs) is proposed to enhance the hospital's resilience during unpredicted power outages. To evaluate the resilience performance of the proposed MG, random outages are generated in different days of the year. Furthermore, a resilience index is introduced to measure the amount of unmet electrical load demand. Based on the optimization results, the resilient configuration consists of the wind turbine (WT) system, DG, ESS, and bi-directional converter. However, the optimum economic configuration is based on WT, DG, and bi-directional converter. In the resilient configuration, the WT system supplies most of the load demand over the optimization year. The surplus generated electricity by the WT system is directly sold-back to the main grid based on the predefined feed-in-tariff (FiT) rate. Simulation results show that the optimal operation of the ESS has improved the resilience of MG during unpredicted grid outages. The reliability of the hospital is also increased by considering ESS and DG in the architecture of the MG. The net present cost (NPC) and the cost of energy (COE) of the proposed system are 103,507 US$ and 272×10−6 US$/kW, respectively. Optimization results based on the grid-load system as the base case and those of hybrid energy systems indicates that the utilization of the ESS has led to an increase in NPC and COE of the resilient MG. Moreover, the proposed resilient MG produces fewer greenhouse emissions in comparison with optimum economic configuration regarding the ESS utilization. The results of sensitivity analysis on the number and duration of outages indicate the significant effect of grid outages on the configuration and economic of the system. © 2020 Elsevier Ltd