Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. © 2023 Elsevier Ltd

Textual Research on Key Information of Ancient Classical Prescription Dayuanyin.

Dayuanyin,a representative prescription for the treatment of dampness pathogen lodging in pleurodiaphragmatic interspace syndrome,was first recorded in Treatise on Pestilence(<<>>)by Wu Youke in the Ming Dynasty for dealing with pestilence,and it still plays an important role in the treatment of coronavirus disease 2019(COVID-19)differentiated into dampness stagnating in lung syndrome. The related original ancient records were retrieved from the Chinese Classics of Traditional Chinese Medicine(Version 5.0),Full-text Database of Ancient Chinese Medicine Books,and Ancient Books of Traditional Chinese Medicine Database (http://www. gydc. ac. cn:81/),with 'Dayuanyin' and 'Dayuansan' as the search terms,followed by statistical analysis and textual research. The composition,dosage,processing of original medicinal materials,efficacy, indications, processing and administration methods, modern basic research, and clinical applications of Dayuanyin were summarized,so as to provide literature reference for its modern development and clinical application. The findings demonstrated that the composition in most medical records was identical with that of the original prescription,except that some records concerning Angelicae Dahuricae Radix and Tsaoko Fructus differed. In terms of dosage,it did not change much,with the only difference observed in Tsaoko Fructus. The processing methods of medicinal materials in Dayuanyin were not specified in historical records,so the raw medicinal materials were recommended. The processing and administration methods in the original record were basically followed in the later generations,except that some medical records chose Zingiberis Rhizoma Recens as the guide and changed the decocting amount and administration time. In terms of efficacy and indications, Dayuanyin was originally developed for dispelling pathogenic Qi away from the pleurodiaphragmatic interspace, but later employed for the treatment of such diseases as 'pestilence','epidemic malaria',and 'seasonal epidemic'. It was mainly indicated to 'epidemic diseases' with latent pathogen in pleurodiaphragmatic interspace as the pathogenesis and fever as the manifestation. In modern clinical application,ancients physicians considered 'fever' and 'powder-like tongue coating' as the important signs for this prescription. Modern physicians have utilized Dayuanyin for treating fever,diseases in the digestive,respiratory,urinary,and endocrine systems,skin diseases,pediatric diseases,as well as epidemic diseases like influenza,severe acute respiratory syndrome (SARS),and avian flu due to its good effects.Copyright © 2021, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Textual Research on Key Information of Ancient Classical Prescription Dayuanyin.

Dayuanyin,a representative prescription for the treatment of dampness pathogen lodging in pleurodiaphragmatic interspace syndrome,was first recorded in Treatise on Pestilence(<<>>)by Wu Youke in the Ming Dynasty for dealing with pestilence,and it still plays an important role in the treatment of coronavirus disease 2019(COVID-19)differentiated into dampness stagnating in lung syndrome. The related original ancient records were retrieved from the Chinese Classics of Traditional Chinese Medicine(Version 5.0),Full-text Database of Ancient Chinese Medicine Books,and Ancient Books of Traditional Chinese Medicine Database (http://www. gydc. ac. cn:81/),with 'Dayuanyin' and 'Dayuansan' as the search terms,followed by statistical analysis and textual research. The composition,dosage,processing of original medicinal materials,efficacy, indications, processing and administration methods, modern basic research, and clinical applications of Dayuanyin were summarized,so as to provide literature reference for its modern development and clinical application. The findings demonstrated that the composition in most medical records was identical with that of the original prescription,except that some records concerning Angelicae Dahuricae Radix and Tsaoko Fructus differed. In terms of dosage,it did not change much,with the only difference observed in Tsaoko Fructus. The processing methods of medicinal materials in Dayuanyin were not specified in historical records,so the raw medicinal materials were recommended. The processing and administration methods in the original record were basically followed in the later generations,except that some medical records chose Zingiberis Rhizoma Recens as the guide and changed the decocting amount and administration time. In terms of efficacy and indications, Dayuanyin was originally developed for dispelling pathogenic Qi away from the pleurodiaphragmatic interspace, but later employed for the treatment of such diseases as 'pestilence','epidemic malaria',and 'seasonal epidemic'. It was mainly indicated to 'epidemic diseases' with latent pathogen in pleurodiaphragmatic interspace as the pathogenesis and fever as the manifestation. In modern clinical application,ancients physicians considered 'fever' and 'powder-like tongue coating' as the important signs for this prescription. Modern physicians have utilized Dayuanyin for treating fever,diseases in the digestive,respiratory,urinary,and endocrine systems,skin diseases,pediatric diseases,as well as epidemic diseases like influenza,severe acute respiratory syndrome (SARS),and avian flu due to its good effects.Copyright © 2021, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a high-frequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting;the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias;and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility. © 2022 Elsevier Inc.

CAPRL Scoring System for Prediction of 30-day Mortality in 949 Patients with Coronavirus Disease 2019 in Wuhan, China: A Retrospective, Observational Study

Background:Coronavirus disease 2019 (COVID-19) is a serious and even lethal respiratory illness. The mortality of critically ill patients with COVID-19, especially short term mortality, is considerable. It is crucial and urgent to develop risk models that can predict the mortality risks of patients with COVID-19 at an early stage, which is helpful to guide clinicians in making appropriate decisions and optimizing the allocation of hospital resoureces.Methods:In this retrospective observational study, we enrolled 949 adult patients with laboratory-confirmed COVID-19 admitted to Tongji Hospital in Wuhan between January 28 and February 12, 2020. Demographic, clinical and laboratory data were collected and analyzed. A multivariable Cox proportional hazard regression analysis was performed to calculate hazard ratios and 95% confidence interval for assessing the risk factors for 30-day mortality.Results:The 30-day mortality was 11.8% (112 of 949 patients). Forty-nine point nine percent (474) patients had one or more comorbidities, with hypertension being the most common (359 [37.8%] patients), followed by diabetes (169 [17.8%] patients) and coronary heart disease (89 [9.4%] patients). Age above 50 years, respiratory rate above 30 beats per minute, white blood cell count of more than10 × 109/L, neutrophil count of more than 7 × 109/L, lymphocyte count of less than 0.8 × 109/L, platelet count of less than 100 × 109/L, lactate dehydrogenase of more than 400 U/L and high-sensitivity C-reactive protein of more than 50 mg/L were independent risk factors associated with 30-day mortality in patients with COVID-19. A predictive CAPRL score was proposed integrating independent risk factors. The 30-day mortality were 0% (0 of 156), 1.8% (8 of 434), 12.9% (26 of 201), 43.0% (55 of 128), and 76.7% (23 of 30) for patients with 0, 1, 2, 3, ≥4 points, respectively.Conclusions:We designed an easy-to-use clinically predictive tool for assessing 30-day mortality risk of COVID-19. It can accurately stratify hospitalized patients with COVID-19 into relevant risk categories and could provide guidance to make further clinical decisions. © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.

Multi-hop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and can not be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. Firstly, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Secondly, the cluster heads are selected according to the energy and location factors in the clusters, and a reasonable cluster head replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of cluster heads. Finally, a multi-hop routing mechanism between the cluster heads and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9% and 162.2% compared with IGWO, ACA-LEACH and DEAL in the monitoring area of 300m ×300m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. IEEE

Forecasting global stock market volatilities in an uncertain world

We investigate the predictive relationship between uncertainty and global stock market volatilities from a highfrequency perspective. We show that uncertainty contains information beyond fundamentals (volatility) and strongly affects stock market volatility. Using several crucial uncertainty measures (i.e., uncertainty and implied volatility indices), we prove that the CBOE volatility index (VIX) performs best in point (density) forecasting;the financial stress index (FSI) in directional forecasting. Furthermore, VIX's predictive power improved dramatically after the COVID-19 outbreak, and the VIX-based portfolio strategy enables mean-variance investors to achieve higher returns. There are two empirical properties of VIX: (i) it helps reduce significantly forecast variance rather than bias;and (ii) its forecasts encompass other uncertainty forecasts well. Overall, we highlight the importance of considering uncertainty when exploring the expected stock market volatility.

Navigating Robots in Dynamic Environment With Deep Reinforcement Learning

In the fight against COVID-19, many robots replace human employees in various tasks that involve a risk of infection. Among these tasks, the fundamental problem of navigating robots among crowds, named robot crowd navigation, remains open and challenging. Therefore, we propose HGAT-DRL, a heterogeneous GAT-based deep reinforcement learning algorithm. This algorithm encodes the constrained human-robot-coexisting environment in a heterogeneous graph consisting of four types of nodes. It also constructs an interactive agent-level representation for objects surrounding the robot, and incorporates the kinodynamic constraints from the non-holonomic motion model into the deep reinforcement learning (DRL) framework. Simulation results show that our proposed algorithm achieves a success rate of 92%, at least 6% higher than four baseline algorithms. Furthermore, the hardware experiment on a Fetch robot demonstrates our algorithm’s successful and convenient migration to real robots. IEEE

Changes in Blood Pressure and Measurement Frequency as Well as Heart Rate in Hypertensive Patients That Needed to Be Medical Observation at Home：a Cross-sectional Study in Wuhan

Background There were many hypertensive patients at non-high risk of developing COVID-19 that needed to be medical observation at home, but the changes in their blood pressure and measurement frequency as well as heart rate during the period are still unclear. Objective To perform an analysis of the changes in blood pressure and measurement frequency as well as heart rate in hypertensive patients that needed to be medical observation at home. Methods Through the iHealth cloud platform, data〔including age, sex, systolic blood pressure（SBP） and diastolic blood pressure（DBP）measured by the iHealth Clear （BPM1） at home, and heart rate〕were collected from December 1st, 2019 to March 27th, 2020, involving all hypertensive patients in Wuhan who had an ID number for consecutively uploading blood pressure readings, and were desensitized for removing the confidential information. The features of blood pressure during the period were analyzed. ARIMAX model was used to assess the association of age, sex, number of confirmed COVID-19 cases per day, cumulative confirmed COVID-19 deaths, time granularity and the traffic control with participants' blood pressure and hear rates. Results In total, blood pressure readings of 36 472 measurements by the participants using 1 232 iHealth Clear （BPM1） were collected during the 118-day period. Men demonstrated higher mean SBP, DBP and heart rate than women（P<0.05）. After January 23, 2020, the mean SBP of the participants decreased from （141±19）mm Hg to （138±18）mm Hg（P<0.05）. The analysis using the ARIMAX model revealed that after adjusting for month, week, age and number of confirmed COVID-19 cases per day, male participants showed a decrease in blood pressure（βSBP=-1.08×10-3, P=0.028, βDBP=-6.35×10-4, P=0.002）, and an increase in heart rate （βHR=2.02, P=0.003）and measurement frequency （βtimes=0.035, P=0.002）. But no significant changes were seen in females（P<0.05）. Conclusion In general, among hypertensive patients that needed to be medical observation at home, males were found with higher mean SBP, DBP, heart rate and blood pressure measurement frequency. And these hypertensive patients were found with decreased SBP and DBP after the implementation of traffic control. Using the Internet to store blood pressure data measured by the home blood pressure monitor for data assessment and treatment, is contribute to out-of-hospital management of blood pressure in hypertensive patients, which demonstrates the significance of Internet in combination with healthcare. © 2022 Chinese General Practice. All rights reserved.

DEA-UNet: a dense-edge-attention UNet architecture for medical image segmentation

Classical UNet with an encoder and decoder structure and its variants perform very well in the field of medical image segmentation. They have a key similarity of a skip-connection, which combines deep, semantic, and coarse-grained feature maps from the decoder subnetwork with shallow, low-level, and fine-grained feature maps from the encoder subnetwork. We noted that, in many cases in medical image segmentation, the boundary of the segmentation target is fuzzy and complex. Traditional UNet cannot accurately segment these details. The main purpose is to solve the fuzzy boundary problem in medical image segmentation. To solve this problem, we combine the advantages of previous models and improve them and propose a new dense edge attention U-type network (DEA-UNet) for medical image segmentation. Starting from the traditional UNet, we modified the concat and skip-connection operations in the latter part. We designed an edge guidance module that fused the features of all layers. Starting from the upsample at the deepest layer, the reverse attention module was used step by step to extract features from high to low, and the edge guidance module was combined with it, so each layer could fully extract boundary details that were difficult to be noticed by previous models, thus solving the problem of the fuzzy boundary of the lesion region. We conducted experiments on two kinds of medical datasets (chest CT and colonoscopic polyp) and compared them with the traditional network. The experimental results showed that our DEA-UNet performed better in multiple indicators. In the segmentation of coronavirus disease-19 images, the results indicate that DEA-UNet has a Dice of 74.6%, sensitivity (Sen) of 70.8%, specificity (Spe) of 96.7%, structural measure (S-alpha) of 0.766%, enhanced-alignment measure (E-phi) of 0.910%, and mean absolute error (MAE) of 0.062%. Our DEA-UNET is 31%, 16%, 3%, and 0.7 and higher than the traditional medical segmentation model UNet, UNet++, the last model Few-shot UNet, and Inf-Net in Dice. In the segmentation of colonoscopic polyp dataset Kvasir, the results indicate that DEA-UNet has a Dice of 95%, structural measure (S-alpha) of 0.953%, enhanced-alignment measure (E-phi) of 0.974%, and MAE of 0.015%. Our DEA-UNet is 13%, 13%, 23%, and 5% higher than the traditional medical segmentation model UNet, UNet++, the last model SFA, and PraNet in Dice. In other evaluation metrics, our DEA-UNet also performed better. When designing DEA-UNet, we also consider the balance between model size and prediction accuracy. Experiments show that, by proper pruning, we can greatly reduce the number of model parameters while maintaining the accuracy of prediction results with little change. This proves that our DEA-UNET has great potential in the field of medical image segmentation.

Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media

Purpose: The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage. Design/methodology/approach: This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives. Findings: In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions. Originality/value: The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication. © 2022, Emerald Publishing Limited.

Mask Waste: A Sustainable Mask-Based Epoxy Resin/SiO2 Composite for Efficient Purification of Water-in-Oil Emulsions

Since the emergence of the COVID-19 pandemic, there has been a tremendous increase in the production of masks worldwide, with more than 1.5 billion masks having been disposed of during this time. The damage caused by mask pollution is a global threat;highlighting the need to dispose of discarded masks correctly. Herein, we report a recycling approach that uses discarded masks to fabricate a superhydrophobic epoxy resin/SiO2 membrane for separating emulsions. The composite has a high flux value (2123 L. m(-2).h(-1)) and high separation efficiency (>98%). The filter maintained its excellent superhydrophobic property (WCA > 150 degrees) after tape-peel cycles, clamping cycles with tweezers, abrasion cycles with 800 grit SiC sandpaper, pressure with fingertips, and kneading cycles. This study proposes a renewable, eco-friendly, and low-cost product, which can be used for oil spill cleanup and water purification. The filter not only removes oil from oily wastewater (such as oil spills) but also solves pollution caused by discarded masks. This study provides insights for resource recovery that may contribute to the purification of oily water emulsions.

Aerosol presence reduces the diurnal temperature range: An interval when the COVID-19 pandemic reduced aerosols revealing the effect on climate

The immense reduction in aerosol levels during the COVID-19 pandemic provides an opportunity to reveal how atmospheric chemistry is regulating our climate, among which the effect of aerosols on climate is a phenomenon of great interest but still in hot debate. The Intergovernmental Panel on Climate Change (IPCC) has continually identified the effect of aerosols on climate to have the largest uncertainty among the factors contributing to global climate change. Several studies indicate an inverse relationship between aerosol presence in the atmosphere and the diurnal surface air temperature range (DTR). Herein, we test this relationship by analyzing the DTR values from in situ weather station records for periods before and during the COVID-19 epidemic in Chinawhere aerosol levels have substantially reduced, compared with the climatological mean levels for a 19 year period.Our analyses find that DTRs fromFebruary to June during the COVID-19 pandemic are greater than 3 standard deviations above the climatological mean DTR. This anomaly has never occurred before in the 21st century and is at least in part associated with the observed reduction in aerosols. © 2021 The Author(s).

The temperature difference method for screening patients with COVID-19 fever symptoms

Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, is a potentially fatal disease of global public health concern. Fever has been reported to be a common clinical symptom in COVID-19 and current CDC recommendations for mitigation of community COVID-19 transmission include temperature screening, so prompting widespread temperature screening across multiple sectors, including hospitals, office buildings and airports. The need for no-contact and rapid measurement of body temperature during the COVID-19 pandemic emergency has led to the widespread use of thermal imaging cameras. However, the body temperature measurement is also disturbed by the environment factors, including ambient temperature, background light etc. When the ambient temperature is low, the temperature of the patient will also be low. It was difficult to screen the fever patients by using the absolute temperature criteria, and it often result in missing detection. In order to solve this problem, this paper proposed a method of screening COVID-19 symptom fever patients by the body temperature difference detection. The temperature difference detection method combined the temperature measurement of the infrared imaging camera and the visible camera face recognition. This method will eliminate environmental interference and equipment errors, to reduce the probability of the fever missed detection. © Published under licence by IOP Publishing Ltd.

Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16, S73 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses. © 2021 IEEE.

The Influence of High Temperature Weather on Human Body Temperature Measurement by Infrared Thermal Imaging Thermometer

Infrared thermography thermometer is a non-contact temperature measuring equipment, which is widely used in the stage of large-scale epidemic of the covid-19 pandemic. It is used for rapid screening of human body temperature in crowded places at the entrance and exit of airports, docks, shopping malls, stations and schools. But when the outdoor temperature approaches or exceeds the body temperature in summer, can this method of measuring body surface temperature by infrared thermal imager be used as a standard for screening fever? Under the condition of high temperature in summer, the field experiment of measuring body temperature by infrared thermal imager is carried out, the experimental results are analyzed. We recommend the use of relative temperature difference for screening patients with fever. © 2021 Institute of Physics Publishing. All rights reserved.

Effect of vaccination as impediment on spread of COVID-19

The research project was conducted to probe into the vaccine's impact on the cataphoresis of COVID-19. The data involved in the project was based on official statistics from different states of the United States. The project intended to ascertain the correlations between the number of positive cases and the number of fully vaccinated populations. Also, the project includes identifying correlations between other variables like the links between the number of fully vaccinated people and the change in time. Moreover, the research project briefly studied pandemic prevention policies and outcomes in the state Connecticut. As a result of analysis, it indicated that virus spread increasingly slowed down when the fully vaccinated population reached a critical proportion with the rise in the vaccinated population. However, the necessary proportion varied from state to state. For state Connecticut, first-dose vaccination of the governor Lamont may encourage the local public to vaccinate, leading to a surge in the number of people vaccinated after Lamont's action. Therefore, it is simply inferred that vaccines play an important role in fighting against coronavirus and that the action of leaders is speculated to be influential for the public's attitude toward vaccines. © 2021 ACM.

[Preliminary clinical analysis of direct renin inhibitor aliskiren in the treatment of severe coronavirus disease 2019 patients with hypertension].

Objective: To explore the feasibility of direct renin inhibitor aliskiren for the treatment of severe or critical coronavirus disease 2019 (COVID-19) patients with hypertension. Methods: The antihypertensive effects and safety of aliskiren was retrospectively analyzed in three severe and one critical COVID-19 patients with hypertension. Results: Four patients, two males and two females, with an average age of 78 years (66-87 years), were referred to hospital mainly because of respiratory symptoms. Three were diagnosed by positive novel coronavirus 2019 (2019-nCoV) nucleic acid or antibody, and the critical patient with cardiac insufficiency was clinically determined. Two patients were treated with calcium channel antagonist (CCB), one with angiotensin converting enzyme inhibitor (ACEI), and one with angiotensin Ⅱ receptor antagonist (ARB). After admission, ACEI and ARB were discontinued, one patient with heart failure was treated by aliskiren combined with diuretic.Three patients were treated with aliskiren combined with CCB among whom two withdrew CCB due to low blood pressure after 1 to 2 weeks. Based on comprehensive treatment including antiviral and oxygenation treatment, blood pressure was satisfactorily controlled by aliskiren after three to four weeks without serious adverse events. All patients were finally discharged. Conclusion: Our preliminary clinical data shows that antihypertensive effect of aliskiren is satisfactory and safe for severe COVID-19 patients complicated with hypertension.