Effect of WeChat group management on blood pressure control rate and drug compliance of hypertension patients during the epidemic of coronavirus disease 2019.

Objective To explore the effect of WeChat group management on blood pressure control rate and drug compliance of hypertension patients during the epidemic of coronavirus disease 2019 (COVID-19) . Methods A total of 428 consecutive patients with essential hypertension in our outpatient department from Jan. 2020 to Dec. 2020 were enrolled and randomly divided into experimental group and control group with a ratio of 1 : 1. There were 214 patients in the experimental group, 110 males and 104 females, with an average age of (55.48+/-6.11) years. There were 214 cases in the control group, 108 males and 106 females, with an average age of (56.52+/-5.19) years. WeChat groups were established for the 2 groups separately. Information on education, supervised medication and lifestyle of hypertension was provided to the patients in the experimental group through WeChat, while no active intervention was given to the control group. The blood pressure control rate and medication possession ratio (MPR) were calculated at 1, 3, 6 and 12 months of intervention, and the differences between the 2 groups were compared. Results There were no significant differences in the blood pressure control rate (91.12%195/214 vs 90.65% 194/214, 86.67%182/210vs 89.62%190/212or MPR (0.90+/-0.03 vs 0.90+/-0.05, 0.85+/-0.04 vs 0.88+/-0.03) between the 2 groups at 1 or 3 months of intervention (all P>0.05). At 6 and 12 months, the blood pressure control rate (81.73%170/208vs 88.57%186/210,75.12%154/205vs 85.99%178/207) and MPR (0.74+/-0.04 vs 0.87+/-0.05, 0.58+/-0.05 vs 0.85+/-0.03) of patients in the experimental group were significantly higher than those in the control group (all P<0.05). Conclusion During the COVID-19 epidemic, WeChat group management of hypertension patients by doctors could improve patients' blood pressure control rate and drug compliance and strengthen patients' self-management ability.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

Investigation on the relationship between the number of coronavirus disease 2019 cases at the beginning of the epidemic and the decrease of PM2.5 in Hubei, China: The role of temperature changes

In response to the coronavirus disease 2019 pandemic, the Chinese government implemented blockade measures in Hubei, which largely affected the emission of pollutants. This work is aimed to explore the effects of epidemics on pollutants at different temperatures in Hubei, China. We applied for a panel nonlinear model with autonomous search thresholds to explore this, using daily average temperature as a threshold variable, and PM2.5 set as the explained variable, and the cumulative number of confirmed coronavirus disease 2019 cases set as the explanatory variable. An empirical analysis was conducted by running the proposed model and using nine cities in China most impacted by the pandemic. The results show that there was a non-linear negative relationship between the cumulative number of confirmed coronavirus disease 2019 cases and PM2.5. A more detailed non-linear relationship between the two was uncovered by the proposed panel threshold regression model. When the temperature crosses the threshold value (12.5 degrees C and 20.5 degrees C) in sequence, the estimated value was -0.0688, -0.0934, and -0.1520 in that order. This means that this negative non-linear relationship increased with increasing temperature. This work helps to explore the effect of coronavirus disease 2019 on pollutions at different temperatures and provides a methodological reference to study their nonlinear relationship.

Effect of WeChat group management on blood pressure control rate and drug compliance of hypertension patients during the epidemic of coronavirus disease 2019

Objective To explore the effect of WeChat group management on blood pressure control rate and drug compliance of hypertension patients during the epidemic of coronavirus disease 2019 (COVID-19) . Methods A total of 428 consecutive patients with essential hypertension in our outpatient department from Jan. 2020 to Dec. 2020 were enrolled and randomly divided into experimental group and control group with a ratio of 1 : 1. There were 214 patients in the experimental group, 110 males and 104 females, with an average age of (55.48+/-6.11) years. There were 214 cases in the control group, 108 males and 106 females, with an average age of (56.52+/-5.19) years. WeChat groups were established for the 2 groups separately. Information on education, supervised medication and lifestyle of hypertension was provided to the patients in the experimental group through WeChat, while no active intervention was given to the control group. The blood pressure control rate and medication possession ratio (MPR) were calculated at 1, 3, 6 and 12 months of intervention, and the differences between the 2 groups were compared. Results There were no significant differences in the blood pressure control rate (91.12%[195/214] vs 90.65% [194/214], 86.67%[182/210]vs 89.62%[190/212])or MPR (0.90+/-0.03 vs 0.90+/-0.05, 0.85+/-0.04 vs 0.88+/-0.03) between the 2 groups at 1 or 3 months of intervention (all P>0.05). At 6 and 12 months, the blood pressure control rate (81.73%[170/208]vs 88.57%[186/210],75.12%[154/205]vs 85.99%[178/207]) and MPR (0.74+/-0.04 vs 0.87+/-0.05, 0.58+/-0.05 vs 0.85+/-0.03) of patients in the experimental group were significantly higher than those in the control group (all P<0.05). Conclusion During the COVID-19 epidemic, WeChat group management of hypertension patients by doctors could improve patients' blood pressure control rate and drug compliance and strengthen patients' self-management ability.Copyright © 2022, Second Military Medical University Press. All rights reserved.

SIREN - smart ventilation for infection risk mitigation and HVAC energy efficiency: a case study amid the COVID-19 pandemic

The COVID-19 pandemic has underscored the need for effective ventilation control in public buildings. This study develops and evaluates a smart ventilation control algorithm (SIREN) that dynamically adjusts zone and system-level HVAC operation to maintain an acceptable COVID-19 infection risk and HVAC energy efficiency. SIREN uses real-time building operation data and Trim & Respond control logic to determine zone primary and system outdoor airflow rates. An EnergyPlus and CONTAM co-simulation framework was developed to assess its performance across various control scenarios and US climate zones. Results show that SIREN can flexibly control infection risk within a customized threshold (e.g. 3%) for every zone, while traditional controls cannot. At the building level, SIREN's HVAC energy consumption is comparable to a fixed 70% outdoor airflow fraction scenario, while its infection risk is lower than the 100% outdoor airflow scenario, illustrating its potential for safe and energy-efficient HVAC operation during pandemics.

The role of supply chain diversification in mitigating the negative effects of supply chain disruptions in COVID-19

Purpose: This study examines the firm-level financial consequences caused by supply chain disruptions during COVID-19 and explores how firms' supply chain diversification strategies, including diversified suppliers, customers and products, moderate the negative effect on firm performance. Design/methodology/approach: Based on data drawn from 222 publicly traded firms in China, the authors use event study methodology to estimate the effects of supply chain disruptions on the financial performance of affected firms. Regression analyses are conducted to examine the moderating effects of supply chain diversification. Findings: Firms affected by supply chain disruptions during COVID-19 experienced a significant decline in shareholder value in two weeks and a subsequent decrease in operating performance in one year. Diversified suppliers, customers and products act as shock absorbers to alleviate the negative effects. Further regression shows a substitution effect between customer and product diversification. Cross-industry comparisons reveal that service firms experienced more loss than manufacturing firms. Customer diversification mitigates the adverse effects of supply chain disruptions for both manufacturing and service firms. Supplier diversification exerts a noteworthy role in manufacturing firms, while product diversification is beneficial for service firms. Originality/value: The study provides empirical evidence on the magnitude of financial consequences of supply chain disruptions during COVID-19 in both the short term and long term and enriches the current understanding of how to build resilience from the supply chain diversification perspective. © 2023, Emerald Publishing Limited.

Examining gains and pains of a new virtual internship design

This study investigated how student effort and the course design influenced an online internship in China. A cohort of 95 postgraduate students became distance learners in a credit-bearing internship course due to COVID-19. The course leader applied the action learning framework to prompt student online collaboration and group inquiry. The framework assumes the importance of self-reliant learner autonomy in virtual internships. After the course, researchers analyzed the effects of self-directed learning with technology on a multidimensional community of inquiry in a virtual environment. The study also identified students' narratives that explain how self-directed learning with technology interacted with three elements of virtual communities of inquiry: social, cognitive, and teaching. Findings explain how virtual internships can be facilitated through a community of inquiry model. Educators and practitioners may consider the model to demonstrate student-staff partnerships (Fitzgerald et al., 2020) to achieve quality transformation of internships from face-to-face mode to distance education. © 2023 Open and Distance Learning Association of Australia, Inc.

Sotrovimab Pharmacokinetics and Covariate Effects in Hematopoietic Stem Cell Transplant Recipients

Hematopoietic cell transplant (HCT) recipients are at increased risk of morbidity and mortality from COVID-19. They may have lower SARS-CoV-2-directed antibody levels due to protein loss from the gastrointestinal (GI) tract as a result of preparative regimen-related toxicity and graft-vs.-host disease (GVHD). In fact, previous studies suggested that GI GVHD or diarrhea from other etiologies were associated with a reduction in the half-life of monoclonal antibodies (mAbs). Hence, understanding the pharmacokinetic (PK) profile of mAbs targeting SARS-CoV-2 in this vulnerable population is critical for dose-selection and predicting the duration of protection against COVID-19. This analysis aims to use a population pharmacokinetics (popPK) approach to evaluate the PK of sotrovimab and the effect of covariates in HCT recipients. In a Phase I trial (COVIDMAB), all participants received 500 mg sotrovimab IV prophylactically within one week prior to starting transplant conditioning. Sotrovimab serum concentrations were determined weekly for up to 12 weeks in autologous (n=5) and allogeneic (n=15) HCT recipients (129 observations). Sotrovimb PK and the effect of covariates were analyzed using popPK modeling in NONMEM (version 7.4). GVHD and diarrhea severity data were collected weekly via survey and included as time-dependent covariates during the covariate screening process. The final PK model with covariates was validated using simulation-based validation and goodness of fit plots. PK data were compared to non-transplant patients from 1891 patients with COVID-19 in COMET-ICE, COMET-PEAK, BLAZE-4, and COMET-TAIL and 38 healthy individuals enrolled in GlaxoSmithKline Pharma Study 217653. A two-compartment model best described sotrovimab PK in HCT recipients. In comparison to non-transplant patients, sotrovimab clearance (CL) was 14.0% higher in HCT recipients. Weight was a significant covariate on sotrovimab CL and (Figure Presented) volume of distribution in the central compartment (V2). With every 10 kg increase in body weight, sotrovimab CL and V2 were estimated to increase by 9.5% and 5.5%, respectively. Diarrhea severity was also a significant covariate on sotrovimab CL. HCT recipients with grade 3 diarrhea showed an increase in CL by 1.5-fold compared to those without diarrhea. Based on popPK analyses, sotrovimab CL was higher in HCT recipients compared to non-transplant patients. Higher bodyweight as well as diarrhea resulted in increased sotrovimab CL. There were only 3 patients with GI GVHD, and larger studies are needed to determine whether diarrhea due to GI GVHD or conditioning toxicity was responsible for the observed increase in sotrovimab CL. Further validation of these findings in a larger number of HCT recipients is also warranted to help optimize mAb dosing for COVID-19 prophylaxis and determine whether presence of large-volume diarrhea may require intensified dosing strategiesCopyright © 2023 American Society for Transplantation and Cellular Therapy

What is the trend after Covid-19? A Political-Economic Analysis of the Pension Systems in Hong Kong and MACAU

The aging trend of the population in Hong Kong and Macau is evident, so the pension system is especially significant. This research paper uses document analysis and a double-case study as the research method. It uses path dependence and critical moments in historical institutionalism theory as the theoretical tools for political economy analysis. The discussion argues that "the social culture shaped by local politics," "the combination of local economic development and economic structure," and "influence from social structure" are the three main factors that influence the pension systems in Hong Kong and Macau, and are the fundamental reasons for the differences between the pension systems in Hong Kong and Macau. We also conclude that the outbreak of COVID-19 is causing the evolution of the pension systems in both regions to be converging.

Quantifying Mobility Perturbation in America's Cities during COVID-19: A Network-Based Approach

The pandemic of COVID-19 has caused severe disruptions in urban lives. Understanding and quantifying these disruptions is important to inform the development of targeted and effective measures to control the pandemic and its impact. One way of achieving this object is to measure the urban mobility perturbation caused by the pandemic. In this study, we built mobility-based networks for seven major metropolitan statistical areas (MSAs) across the United States in the years of 2019 and 2020, respectively. We quantified the disruptions of urban mobility by computing and comparing a set of network-based metrics before and during the pandemic. The proposed approach is able to uncover the impact of COVID-19 in cities and provides new insights into the resilience of cities when facing large-scale disasters.

dbSCI: A manually curated database of SARS-CoV-2 inhibitors forCOVID-19 br

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen of the ongoing coronavirusdisease 2019 (COVID-19) global pandemic. Here, by centralizing published cell-based experiments, clinical trials, andvirtual drug screening data from the NCBI PubMed database, we developed a database of SARS-CoV-2 inhibitors forCOVID-19, dbSCI, which includes 234 SARS-CoV-2 inhibitors collected from publications based on cell-basedexperiments, 81 drugs of COVID-19 in clinical trials and 1305 potential SARS-CoV-2 inhibitors from bioinformaticsanalyses. dbSCI provides four major functions: (1) search the drug target or its inhibitor for SARS-CoV-2, (2) browsetarget/inhibitor information collected from cell experiments, clinical trials, and virtual drug screenings, (3) download,and (4) submit data. Each entry in dbSCI contains 18 types of information, including inhibitor/drug name, targetingprotein, mechanism of inhibition, experimental technique, experimental sample type, and reference information. Insummary, dbSCI provides a relatively comprehensive, credible repository for inhibitors/drugs against SARS-CoV-2and their potential targeting mechanisms and it will be valuable for further studies to control COVID-19

Detection of Pneumonia Based on ResNet Improved by Attention Mechanism

In recent years, Covid-19 is one of the major health challenges facing the human population. Due to the highly infectious nature of Covid-19 and the difficulty of detecting symptoms in the early stages, it is definitely necessary to combine X-ray for the diagnosis of pneumonia. Using traditional neural networks such as VGG, ResNet, and DenseNet to diagnose pneumonia based on X-ray images faces a number of difficulties. These models have insufficient spatial information extraction capability and are prone to overfitting on the training set. The attention mechanism is a means to improve model performance by helping the model better extract channel and spatial features from the feature maps. To identify pneumonia more accurately, we combined the ResNet network and CBAM attention mechanism to design the ResNet101-cbam model with a series of data augmentation methods as well as training strategies. We used the same approach to add attention mechanisms to ResNet50, ResNet101 and ResNet152 and tested their performance. The results show that ResNet101-cbam is the best performing model overall. It achieved a recall of 0.8205, a precision of 0.822, and an accuracy of 0.8285 on the test set, while the original pretrained ResNet101 had a precision of 0.7280 and an accuracy of 0.7644. Its performance were better than the more complex model: ResNet152-cbam, a little bit, but the training speed is improved by more than 25%. More importantly, the model with the added attention mechanism effectively overcomes the effects of positive and negative sample imbalance. The ResNet101-cbam model can be used as a medical aid, which can improve diagnostic efficiency and help us better deal with large-scale pneumonia epidemics. © 2023 IEEE.

FedUSC: Collaborative Unsupervised Representation Learning from Decentralized Data for Internet of Things

Federated Learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this paper, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally non-IID, leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 datasets and outperform several federated unsupervised learning methods under non-IID settings. IEEE

Early antiviral therapy of abidol combined with lopinavir/ritonavir and recombinant interferon alpha-2b for patients with COVID-19 in Zhejiang: A multicenter prospective study.

Objective: To compare the efficacy of the combination of abidol, lopinavir/ritonavir plus recombinant interferon alpha-2b (rIFNalpha-2b) and the combination of lopinavir/ritonavir plus rIFNalpha-2b for patients with COVID-19 in Zhejiang province. Method(s): A multicenter prospective study was carried out to compare the efficacy of triple combination antiviral therapy and dual combination antiviral therapy in 15 medical institutions of Zhejiang province during January 22 to February 16, 2020. All patients were treated with rIFNalpha-2b (5 million U, 2 times/d) aerosol inhalation, in addition 196 patients were treated with abidol (200 mg, 3 times/d) + lopinavir/ritonavir (2 tablets, 1 time/12 h) (triple combination group) and 41 patients were treated with lopinavir/ritonavir (2 tablets, 1 time/12 h) (dual combination group). The patients who received triple combination antiviral therapy were further divided into three subgroups: <48 h, 3-5 d and >5 d according the time from the symptom onset to medication starting. The therapeutic efficacy was compared between triple combination group and dual combination group, and compared among 3 subgroups of patients receiving triple combination antiviral therapy. SPSS 17.0 software was used to analyze the data. Result(s): The virus nucleic acid-negative conversion time in respiratory tract specimens was (12.2+/-4.7) d in the triple combination group, which was shorter than that in the dual combination group [(15.0+/-5.0) d] (t=6.159, P<0.01). The length of hospital stay in the triple combination group [12.0 (9.0, 17.0) d] was also shorter than that in the dual combination group [15.0 (10.0, 18.0) d] (H=2.073, P<0.05). Compared with the antiviral treatment which was started within after the symptom onset of in the triple combination group, the time from the symptom onset to the viral negative conversion was 13.0 (10.0, 17.0), 17.0 (13.0, 22.0) and 21.0 (18.0, 24.0) d in subgroups of 48 h, 3-5 d and >5 d, respectively (Z=32.983, P<0.01), while the time from antiviral therapy to viral negative conversion was (11.8+/-3.9), (13.5+/-5.1) and (11.2+/-4.3) d, respectively(Z=6.722, P<0.05). Conclusion(s): The triple combination antiviral therapy of abidol, lopinavir/litonavir and rIFNalpha-2b shows shorter viral shedding time and shorter hospitalization time, compared with the dual combination antiviral therapy;and the earlier starting triple combination antiviral therapy will result in better antiviral efficacy.Copyright © 2020 by the Chinese Medical Association.

Perfectionism, Goal Orientation, and Emerging Adults' Identity Processing Styles

The current study explored the direct and interactive contributions of multidimensional measures of perfectionism and goal orientation in predicting patterns of identity-related self-processing for pre-COVID-19 and during-COVID-19 samples of traditional age (18–22 year old) emerging adult college students (N = 722). Regression models controlled for age, binary gender, and race, then tested the unique conditional effects and interactions between perfectionism and goal orientation in explaining variability in each of three identity processing styles. After controlling for multiple covariates and hypothesis tests, only a few effects were repeated between the two samples. Those results indicated that a growth-seeking goal orientation was predictive of an informational identity style whereas validation-seeking goal orientation was a significant predictor of diffuse-avoidant and normative identity processing styles. The overall findings suggested that fruitful targets for future intervention studies promoting healthy identity development during the college years might include reducing validation-seeking while strengthening growth-seeking motives. © 2023 Society for the Study of Emerging Adulthood and SAGE Publishing.

A Smart CO2-Based Ventilation Control Framework to Minimize the Infection Risk of COVID-19 In Public Buildings

This study aims to present a smart ventilation control framework to reduce the infection risk of COVID-19 in indoor spaces of public buildings. To achieve this goal, an artificial neural network (ANN) was trained based on the results from a parametric computational fluid dynamics (CFD) simulation to predict the COVID-19 infection risk according to the zone carbon dioxide (CO2) concentration and other information (e.g., zone dimension). Four sample cases were analyzed to reveal how the CO2 concentration setpoint was varied for a given risk level under different scenarios. A framework of smart ventilation control was briefly discussed based on the ANN model. This framework could automatically adjust the system outdoor airflow rate and variable air volume (VAV) terminal box supply airflow rate to meet the needs of reducing infection risk and achieving a good energy performance. © International Building Performance Simulation Association, 2022

Emergency medical service planning considering dynamic and stochastic demands of infected and non-infected patients during epidemics

During a large-scale epidemic, a local healthcare system can be overwhelmed by a large number of infected and non-infected patients. To serve the infected and non-infected patients well with limited medical resources, effective emergency medical service planning should be conducted before the epidemic. In this study, we propose a two-stage stochastic programming model, which integrally deploys various types of emergency healthcare facilities before an epidemic and serves infected and non-infected patients dynamically at the deployed healthcare facilities during the epidemic. With the service equity of infected patients and various practical requirements of emergency medical services being explicitly considered, our model minimizes a weighted sum of the expected operation cost and the equity cost. We develop two comparison models and conduct a case study on Chengdu, a Chinese city influenced by the COVID-19 epidemic, to show the effectiveness and benefits of our proposed model. Sensitivity analyses are conducted to generate managerial insights and suggestions. Our study not only extends the existing emergency supply planning models but also can facilitate better practices of emergency medical service planning for large-scale epidemics. © Operational Research Society 2023.

Expert consensus on diagnosis and treatment of adult mental stress-induced hypertension in China (2022 Revision): Part B

Mental stress has been recognized as an essential risk factor for hypertension. Therefore, experts specializing in cardiology, psychiatry, and Traditional Chinese Medicine organized by the Psycho-cardiology Group, College of Cardiovascular Physicians of Chinese Medical Doctor Association, and Hypertension Group of the Chinese Society of Cardiology proposed the expert consensus on the diagnosis and treatment of adult mental stress-induced hypertension in March 2021, which includes the epidemiology, etiology, diagnosis, and treatment of the mental stress-induced hypertension. This consensus will hopefully facilitate the clinical practice of this disorder. In addition, the COVID-19 pandemic has become one of the primary global sources of psychosocial stressors since the beginning of 2020, and the revision of this expert consensus in 2022 has increased the relevant content. This consensus consists of two parts. The sections of Part A include (I) Background and epidemiological characteristics, (II) Pathogenesis, and (III) Diagnosis. The sections of Part B contain (IV) Treatment recommendations, and (V) Prospects. This article presents Part B of the consensus. © 2022 Heart and Mind ;Published by Wolters Kluwer - Medknow.