Fast de novo antibody structure prediction with atomic accuracy

Background: In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2 (AF2), a breakthrough in the field of structural biology, provides a solution to this protein structure prediction problem by learning a deep learning model. However, the computational efficiency and undesirable prediction accuracy on antibody, especially on the complementarity-determining regions limit its applications in de novo antibody design. Method(s): To learn informative representation of antibodies, we trained a deep antibody language model (ALM) on curated sequences from observed antibody space database via a well-designed transformer model. We also developed a novel model named xTrimoABFold++ to predict antibody structure from antibody sequence only based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame aligned point loss. Result(s): xTrimoABFold++ outperforms AF2 and OmegaFold, HelixFold-Single with 30+% improvement on RMSD. Also, it is 151 times faster than AF2 and predicts antibody structure in atomic accuracy within 20 seconds. In recently released antibodies, for example, cemiplimab of PD1 (PDB: 7WVM) and cross-neutralizing antibody 6D6 of SARS-CoV-2 (PDB: 7EAN), the RMSD of xTrimoABFold++ are 0.344 and 0.389 respectively. Conclusion(s): To the best of our knowledge, xTrimoABFold++ achieved the state-of-the-art in antibody structure prediction. Its improvement on both accuracy and efficiency makes it a valuable tool for de novo antibody design, and could make further improvement in immuno-theory.

Pyroptosis in the lung and spleen of patients died from COVID-19

The purpose of this study was to investigate the expression of pyroptosis-related factors (NLRP3, IL-18, NF-kappa B, HMGB-1, and GSDMD) in patients who died of COVID-19. The expression levels of NLRP3, IL-18, NF-kappa B, HMGB-1, and GSDMD in lung and spleen tissues of the COVID-19 group and the control group were detected by tissue immunofluorescence. The control group includes lung tissues and spleen tissues of two patients who died unexpectedly without SARS-CoV-2 infection, and the COVID-19 group includes the lung and spleen tissues of three patients who died of SARS-CoV-2 virus infection. The positive rates of NF-kappa B, NLRP3, IL-18, and GSDMD in the lung tissues from the control group and COVID-19 group were 9.8% vs 73.4% (p = 0.000), 5.5% vs 63.6% (p = 0.000), 24.4% vs 76.2% (p = 0.000), and 17.5% and 46.8% (p = 0.000) respectively. The positive rates of NF-kappa B, NLRP3, IL-18, HMGB-1, and GSDMD in the spleen tissues from the control group and COVID-19 group were 20.6% vs 71.2% (p = 0.000), 18.9% vs 72.0% (p = 0.000), 15.2% vs 64.8% (p = 0.000), 27.6% vs 69.2% (p = 0.000), and 23% and 48.8% (p = 0.000), respectively. The positive rates of SARS-CoV-2 spike protein in the CD68 positive cells of the lung and spleen in the control group and COVID-19 group were 2.5% vs 56.8% (p = 0.000);3.0% vs 64.9% (p = 0.000) respectively. The rates of NF-kappa B positive nuclei in the control group and COVID-19 group were 13.4% vs 51.4% (p = 0.000) in the lung and 38.2% vs 59.3% (p = 0.000) in the spleen. The rates of HMGB-1 positive cytoplasm in the control and the COVID-19 group were 19.7% vs 50.3% (p = 0.000) in the lung and 12.3% vs 45.2% (p = 0.000) in the spleen. The targets of SARS-CoV-2 are the lung and spleen, where increased macrophages could be involved in the up-regulation of pyroptosis-related inflammatory factors such as NF-kappa B, HMGB-1, NLRP3, IL-18, and GSDMD.

Study on pathogen spectrum of 1,046 hospitalized children with respiratory tract infections during COVID-19

Objectives: This study aimed to describe the pathogen spectrum of bacteria and viruses of RTIs in hospitalized children during the Coronavirus disease 2019 (COVID-19) epidemic in Shenzhen. Method(s): From October 2020 to October 2021, the results of pathogenic tests causing RTIs were retrospectively analyzed in hospitalized children in Shenzhen Luohu Hospital Group. Result(s): 829 sputum samples for bacterial isolation and 1,037 nasopharyngeal swabs for virus detection in total. The positive detection rate (PDR) of bacteria was 42.1%. Staphylococcus aureus (18.8%) was the predominant bacteria detected in positive cases, with Moraxella catarrhalis (10.9%), Streptococcus pneumoniae (9.5%) following. The PDR of the virus was 65.6%. The viruses ranking first to third were Human Rhinovirus (HRV), Respiratory syncytial virus (RSV), and Human Parainfluenza (HPIV), with rates of 28.0, 18.1, and 13.5%, respectively. Children under 3 years were the most susceptible population to RTIs. The pathogens of S. aureus, M. catarrhalis, S. pneumoniae, HRV, and HPIV were more prevalent in autumn. Meanwhile, RSV had a high rate of infection in summer and autumn. S. aureus and HRV had higher co-infection rates. Conclusion(s): Our findings demonstrate the pathogen spectrum of 1,046 hospitalized children with RTIs in Shenzhen, China, during the COVID-19 outbreak.Copyright © 2022 the author(s), published by De Gruyter, Berlin/Boston.

Analyzing pure contagion between crude oil and agricultural futures markets

Crude oil and agricultural product prices are important factors affecting a country's economic and social stability. The pure contagion between these two markets may lead to excessive price linkage, increasing the fragility of the financial system. This paper uses the CEEMDAN method, fine-to-coarse reconstruction method, and TVP-VAR model to study the pure contagion between crude oil and agricultural futures markets. The empirical results show that there always is significant pure contagion between agricultural futures markets. However, pure contagion between crude oil and agricultural futures markets only exists in some specific periods. The crude oil futures market has obvious pure contagion to the agricultural futures markets in most periods. Only a few periods the agricultural futures have pure contagion to the crude oil futures. It is worth noting that the COVID-19 epidemic aggravates the pure contagion between crude oil and the agricultural futures markets. Based on the research conclusions, this paper puts forward corresponding policy recommendations, hoping to provide a reference and theoretical basis for the government to formulate corresponding policies. © 2023 Elsevier Ltd

Ambient fine particulate matter exposures and oxidative protein damage in early pregnant women

The association between oxidative protein damage in early pregnant women and ambient fine particulate matter (PM2.5) is unknown. We estimated the effect of PM2.5 exposures within seven days before blood collection on serum 3-nitrotyrosine (3-NT) and advanced oxidation protein products (AOPP) in 100 women with normal early pregnancy (NEP) and 100 women with clinically recognized early pregnancy loss (CREPL). Temporally-adjusted land use regression model was applied for estimation of maternal daily PM2.5 exposure. Daily nitrogen dioxide (NO2) exposure of each participant was estimated using city-level concentrations of NO2. Single-day lag effect of PM2.5 was analyzed using multivariable linear regression model. Net cumulative effect and distributed lag effect of PM2.5 and NO2 within seven days were analyzed using distributed lag non-linear model. In all 200 subjects, the serum 3-NT were significantly increased with the single-day lag effects (4.72%–8.04% increased at lag 0–2), distributed lag effects (2.32%–3.49% increased at lag 0–2), and cumulative effect within seven days (16.91% increased). The single-day lag effects (7.41%–10.48% increased at lag 0–1), distributed lag effects (3.42%–5.52% increased at lag 0–2), and cumulative effect within seven days (24.51% increased) of PM2.5 significantly increased serum 3-NT in CREPL group but not in NEP group. The distributed lag effects (2.62%–4.54% increased at lag 0–2) and cumulative effect within seven days (20.25% increased) of PM2.5 significantly increased serum AOPP in early pregnant women before the coronavirus disease (COVID-19) pandemic but not after that, similarly to the effects of NO2 exposures. In conclusion, PM2.5 exposures were associated with oxidative stress to protein in pregnant women in the first trimester, especially in CREPL women. Analysis of NO2 exposures suggested that combustion PM2.5 was the crucial PM2.5 component. Wearing masks may be potentially preventive in PM2.5 exposure and its related oxidative protein damage. © 2022 Elsevier Ltd

Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market

In this study, we construct an investor sentiment indicator (SsPCA) to predict stock volatility in the Chinese stock market by applying the scaled principal component analysis (sPCA). As a new dimension reduction technique for supervised learning, sPCA is employed to extract useful information from six individual sentiment proxies and obtain the common variations to characterize the investor sentiment (SsPCA). The empirical results indicate that SsPCA is a significant and powerful volatility predictor both in and out of sample. We also employ the partial least squares (PLS)-based investor sentiment index, three extra sentiment measures in past studies, and six individual sentiment proxies for comparison, and find SsPCA outperforms them on predicting stock volatility in the Chinese stock market. More importantly, the predictability of SsPCA remains significant before and after the famous financial crises (the sub-prime mortgage crisis and Chinese stock market turbulence) and the spread of the pandemic (COVID-19). Additionally, our findings imply that SsPCA still plays an essential role in predicting sock volatility after considering the leverage effect. The robustness of SsPCA in volatility forecasting is further verified in various industry indices of the Chinese stock market. Finally, we state that the strong predictability of SsPCA is highly related to its dimensionality reduction. Our results indicate that SsPCA is a robust volatility predictor from various aspects and performs better compared with existing sentiment indicators. © 2022 Elsevier Inc.

Understanding Oligonucleotide Hybridization and the Role of Anchoring on the Single-Walled Carbon Nanotube Corona Phase for Viral Sensing Applications

Semiconducting single-walled carbon nanotubes (SWCNTs) with tailored corona phases (CPs), or surface-adsorbed molecules, have emerged as a promising interface for sensing applications. The adsorption of an analyte can be specifically transduced as a modulation of their band-gap near-infrared (nIR) photoluminescence (PL). One such CP ideal for this purpose is single-stranded DNA (ssDNA), where subsequent sequence-dependent hybridization can result in PL emission wavelength shifts. Due to ssDNA adsorption to the SWCNT surface, the resultant noncanonical hybridization and its effect on SWCNT photophysical properties are not well understood. In this work, we study 20- and 21-mer DNA and RNA hybridization on the complementary ssDNA-SWCNT CP in the context of nucleic acid sensing for SARS-CoV-2 sequences as model analytes. We found that the van't Hoff transition enthalpy of hybridization on SWCNT CP was −11.9 kJ mol-1, much lower than that of hybridization in solution (−707 kJ mol-1). We used SWCNT solvatochromism to calculate the solvent-exposed surface area to indicate successful hybridization. We found that having a 30-mer anchor region in addition to the complementary region significantly improved PL response sensitivity and selectivity, with a (GT)15 anchor preferred for RNA targets. Coincubation of ssDNA-SWCNTs with an analyte at 37 °C resulted in faster hybridization kinetics without sacrificing specificity. Other methods aimed to improve CP rearrangement kinetics such as bath sonication and surfactant additions were ineffective. We also determined that the target sequence choice is important as secondary structure formation in the target is negatively correlated with hybridization. Best-performing CPs showed detection limits of 11 and 13 nM for DNA and RNA targets, respectively. Finally, we simulated sensing conditions using the saliva environment, showing sensor compatibility in biofluids. In total, this work elucidates key design features and processing to enable sequence-specific hybridization on ssDNA-SWCNT CPs. © 2022 American Chemical Society.

Effective Analysis and Investigation in COVID-19 Prevention Policies Based on Population Dynamics Modeling and Simulation

The paper proposes a population dynamics model to simulate the COVID-19 pandemic and analyze the effectiveness of prevention policies in the early stage. The model is designed to aid the decision-making process of policy-making in the early stage. The model is formulated based on the SEIR model to simulate the spread of COVID19 from human to human. By implementing the data in the U.S., the model is first fitted to the data first. Then, the model simulates the number of infected people with the change of time under different levels of social distancing and mask-wearing. © 2022 IEEE.

Sustainable Market Environment and Consumer Confidence in Food Safety in China after Covid-19: Urban Consumer Perspectives

Previously, several food safety incidents have lowered Chinese consumer confidence regarding the purchase of meat and domestically-produced infant formulas ( DIF). Recently, the COVID-19 pandemic was caused by a food safety issue, and it is, therefore, important to investigate sustainable consumer confidence and its influence on consumption behavior by first estimating consumer confidence in meat and DIF products. The purpose of this study is thus to estimate the sustainable market environment and Chinese consumer confidence behavior post- COVID-19 regarding the purchase of meat and DIF products by applying a choice experiment. An online survey was conducted with 704 consumers residing in Nanjing. The survey obtained information on the features of meat and DIF products that affected consumer purchase consideration and determined the perceived estimated value of the product (price 15.24%, quality certification 31.57%, organic 23.69%, traceability 18.51%). These findings provide insight into consumer perceived premium pay behavior concerning the value estimation of product features according to the market environment (price 191.6 CNY, quality certification 285.3 CNY, organic product 189.1 CNY, and traceability 143.7 CNY). Chinese consumers' low confidence in the safety of meat and DIF products appears less sustainable. Quality certification is the product feature that is most valued, followed by organic origin and production-to-consumption traceability. Apart from these, price is seen as an indication of high quality by consumers with low confidence, which results in a positive image of DIF products. In addition, consumer preferences for production-toconsumption traceability are highly affected by consumer confidence.

DeepLPI: A Novel Drug Repurposing Model based on Ligand-Protein Interaction Using Deep Learning

Background. Drug repurposing has gained increased attention because it proposes to find effective cures for new diseases from approved drugs to lower development costs and time, and computational prediction of protein-ligand interactions (PLI) can provide accurate and fast drug screening. The shortcomings of existing machine learning-based methods for PLI prediction include 1) using human-selected features leads to loss of information and therefore lower accuracy and 2) using limited 3D structure data for input leads to lower generalizability. Methods. To address the shortcomings, I proposed DeepLPI, a novel deep learning-based model that takes as input the raw sequences of drug molecules and proteins. DeepLPI applied pre-trained embedding models to encode the raw sequences into dense vector representations, which were then fed into 1D-CNN and biLSTM to obtain predictions. BindingDB dataset was used for model training and performance evaluation that is compared with a start-of-the-art method DeepCDA. Conclusion. The high performance of DeepLPI suggests that our model has the potential to identify new COVID-19 drugs when applied to approved drugs. The generalizability of the model also promises applications to diseases in a wider scope.

Understanding Oligonucleotide Hybridization and the Role of Anchoring on the Single-Walled Carbon Nanotube Corona Phase for Viral Sensing Applications

Semiconducting single-walled carbon nanotubes (SWCNTs) with tailored corona phases (CPs), or surface adsorbed molecules, have emerged as a promising interface for sensing applications. The adsorption of an analyte can be specifically transduced as a modulation of their band-gap near infrared (nIR) photoluminescence (PL). One such CP ideal for this purpose is single-stranded DNA (ssDNA), where subsequent sequence-dependent hybridization can result in PL emission wavelength shifts. Due to ssDNA adsorption to the SWCNT surface, the resultant noncanonical hybridization and its effect on SWCNT photophysical properties are not well understood. In this work, we study 20-and 21-mer DNA and RNA hybridization on the complementary ssDNA-SWCNT CP in the context of nucleic acid sensing for SARS-CoV-2 sequences as model analytes. We found that the van't Hoff transition enthalpy of hybridization on SWCNT CP was -11.9 kJ mol-1, much lower than that of hybridization in solution (-707 kJ mol-1). We used SWCNT solvatochromism to calculate the solvent-exposed surface area to indicate successful hybridization. We found that having a 30-mer anchor region in addition to the complementary region significantly improved PL response sensitivity and selectivity, with a (GT)15 anchor preferred for RNA targets. Coincubation of ssDNA-SWCNTs with an analyte at 37 degrees C resulted in faster hybridization kinetics without sacrificing specificity. Other methods aimed to improve CP rearrangement kinetics such as bath sonication and surfactant additions were ineffective. We also determined that the target sequence choice is important as secondary structure formation in the target is negatively correlated with hybridization. Best performing CPs showed detection limits of 11 and 13 nM for DNA and RNA targets, respectively. Finally, we simulated sensing conditions using the saliva environment, showing sensor compatibility in biofluids. In total, this work elucidates key design features and processing to enable sequence-specific hybridization on ssDNA-SWCNT CPs.

[Impact of COVID-19 epidemic on syphilis case reporting in China].

Objective: To analyze the impact of COVID-19 epidemic on syphilis case reporting in China, and provide evidence to evaluate the epidemic situation of syphilis and strengthen the prevention and control of syphilis during COVID-19 epidemic. Methods: The data were collected from the National Notifiable Infectious Disease Reporting System of China Information System for Disease Control and Prevention, National STD Management Information System, and the "nCov2019" R package of github website. The changes of reported cases of syphilis before and during COVID-19 epidemic in China were analyzed. Joinpoint regression model was established by using the reported case number of syphilis from 2010 to 2018, the data in 2019 was used for validation, and the number of syphilis cases in 2020 and 2021 was predicted. The impact of COVID-19 epidemic on the number of syphilis cases was evaluated with calculating the percentage error (PE) between actual number and predicted number of syphilis cases reported.The correlation between reported cases of syphilis and COVID-19 was analyzed by Spearman's correlation analysis. The softwares of Joinpoint 4.9.1.0 and SPSS 18.0 were used for statistical analysis. Results: In 2020 and 2021, the reported cases of syphilis in China decreased significantly by 13.32% and 10.41%, respectively, compared with 2019 (before COVID-19 epidemic), and the reported cases of syphilis in 2021 increased by 3.36% compared with 2020. The reported cases of syphilis in 2020 and 2021 decreased by 17.95% and 20.41%, respectively, compared with predicted numbers. From January to March 2020, the reported monthly case number of syphilis was completely negatively correlated with the confirmed case number of COVID-19 (rs=-1.00, P<0.001). In the provinces with different scales of COVID-19 epidemic, there was also a negative correlation between the monthly reported case number of syphilis and confirmed case number of COVID-19 (all P<0.05). Conclusions: In China, the change of reported cases of syphilis was closely associated with COVID-19 epidemic in 2020 and 2021. Due to the influence of COVID-19 epidemic, the number of reported cases of syphilis decreased significantly, but it should not be thought that syphilis incidence will become a decline trend in the future. It is necessary to carefully and scientifically assess the changes in syphilis epidemic.

Exploring effective components and molecular targets of Guizhi decoction for treatment of COVID-19 combined with allergic rhinitis based on network pharmacology and molecular docking

Aim To explore the effeetive components and molecular targets of Guizhi decoetion in treating COVID-19 combined with allergic rhinitis.Methods The potential targets assoeiated with Guizhi deeoetion, allergie rhinitis and COVID-19 were sereened from TC- MSP and Gene Cards databases.Draw Venn Diagram website, String database, and Cytoscape software were used to obtain the common targets of drugs and diseases, followed by generation of PPI network and " herbal-active component-target" network as well as screening of core targets and key components based on the degree value.Metascape and KEGG databases were used for GO and KEGG enrichment analysis.Molecular docking was utilized to validate the affinity between the core targets and the key components.Results A total of 127 effective components of Guizhi decoction were screened, of which 108 components could combine with 52 common targets to exert the therapeutic effects.Common targets were mainly enriched in 1523 (X) terms and 145 KEGG signaling pathways.Molecular docking confirmed that the core targets could spontaneously combine with key components.Conclusions Guizhi decoction is mainly involved in the regulation of viral, immune and inflammation-related signaling pathways and biological cellular processes through the binding of active components such as flavonoids, phy- tosterols and phenols to common targets ( IL-6, TNF, MAPK3, etc.) , ultimately achieving the goal of treating COVID-19 and allergic rhinitis. Copyright © 2022 Publication Centre of Anhui Medical University. All rights reserved.

Mental health status of frontline clinical nurses fighting the Omicron variant of COVID-19 - a cross-sectionalsurvey in China

Introduction and Objective. From March 2022, China has been in the rapid progressing stage of the Omicron outbreak. However, the mental status of clinical nurses against infection by the Omicron variant of COVID-19 has been not explored. Therefore, a nationwide online investigation with a larger sample size was conducted to explore the mental status of Chinese frontline clinical nurses, and its influencing factors using sound validated and reliable measurements. Materials and method. A cross-sectional study was conducted, and 1,204 clinical nurses fighting the Omicron outbreak were recruited across various provinces of China. Results. The mean age of the nurses was 30.43 (SD=6.59) years. The majority were female. The rates of these nurses with depression, anxiety, stress, and insomnia symptoms were 29.2%, 37.2%, 19.1%, and 48.8%, respectively. The variables of department, insomnia, fear with COVID-19, turnover intention, job burnout, work coping style, and public recognition of nursing, were significant factors influencing the mental status of clinical nurses. Conclusions. The Chinese clinical nurses fighting the Omicron variant of COVID-19 had a similar level of depression and anxiety, a higher level of insomnia, and a lower level of stress, in comparison with nurses globally who experienced the initial period of the pandemic. Targeted investigation and interventions are urgently needed for Chinese frontline clinical nurses with high levels of anxiety, depression and insomnia, who fought against infection by Omicron. Much more professional advocacy is strongly recommended during control of the pandemic and recovery to consolidate the role and influence of nurses. The contribution and visibility of nursing should be recognized not only by the medical professionals, but by the public in general. Copyright © 2022, Institute of Agricultural Medicine. All rights reserved.

Inflammatory Response Inhibition and Improvement of COVID-19 Symptoms in COVID-19 Patients by Combination of Antiviral Regimen and Tanreqing Injection

The present study explored the inflammatory response and clinical efficacy of Tanreqing injection in combination with antiviral therapy in patients with coronavirus disease (COVID-19). The results demonstrated that the levels of C-reactive protein (CRP) and interleukin-6 (IL-6) in the treatment group were significantly lower than those in the control group (p < 0.05). Clinical efficacy assessment revealed a significant improvement in the time necessary for image absorption improvement in the treatment group (p < 0.05), while the time taken for fever and muscle soreness symptoms to resolve significantly reduced (p < 0.05). Furthermore, the time taken to obtain a negative COVID-19 test result was significantly shortened (p < 0.05). Tanreqing injection combined with antiviral treatment improved clinical symptoms of COVID-19 faster than when the anti-viral treatments were used alone and this may be related to the reduction in inflammatory response. Copyright © 2022, Colegio de Farmaceuticos de la Provincia de Buenos Aires. All rights reserved.

Time-varying pure contagion effect between energy and nonenergy commodity markets

This paper combines the Kalman filtering technique and the time-varying parameter vector autoregression model with stochastic volatility model to explore pure contagion effects between energy and nonenergy (i.e., industrial metals, precious metals, and agricultural) commodity markets. Empirical results show the significant pure contagion effects between energy and industrial metals markets in most periods, while pure contagion effects between energy and precious metals and agricultural markets occur only in a few specific periods. Comparing the level of pure contagion effects between different commodity markets, energy is still the main price transmitter. In addition, with the acceleration of the global commodity market financialization process, the frequency and harm of pure contagion effects are gradually increasing. Notably, the COVID-19 pandemic is emerging as another major crisis after the global financial crisis, exacerbating the pure contagion effects between energy and precious metals and agricultural markets. © 2022 Wiley Periodicals LLC.

HEALTHCARE QUALITY OF TIMEDEPENDENT DISEASES DURING COVID-19 EPIDEMIC: THE EXPERIENCE IN STROKE

Background: The study aimed to analyze the impact of the coronavirus disease 2019 (COVID-19) pandemic on the healthcare quality of acute ischemic stroke (AIS) patients and summarize the potential experience in response to the COVID-19 outbreak. Methods: We continuously included AIS patients registered in the CASE-II before and during the COVID-19 pandemic. We investigated the changes of 15 key performance indicators (KPIs),the composite score developed from these KPIs, and in-hospital outcomes of AIS during the COVID-19 outbreak and analyzed potential causes of these changes. Results: There were 5,854 AIS patients admitted in pandemic period (January 23 to April 30, 2020) and 6,807 patients in pre-pandemic period (November 1, 2019 to January 22, 2020) among 52 hospitals. The median onset-to-door time was 28 minutes longer in pandemic than in prepandemic period (189 [90-585] versus 161 [87-417] minutes;P=0.001), but the proportion of patients receiving reperfusion treatment and the door-to-treatment time did not differ between the two groups. The composite measure (92.5% versus 91.4%;P=0.165) and all-or-none measure (54.4% versus 50.6%;P=0.893) did not significantly differ between the two periods. The rate of antithrombotic at discharge, smoking cessation and stroke education increased by 1.2% (P=0.025) and 1.5% (P=0.001), respectively during pandemic. There was also no significant difference between the two groups in mRS score at discharge (P=0.532) and new clinical vascular events (P=0.069). Conclusions: Although AIS inpatients decreased during COVID-19 pandemic, the healthcare quality for hospitalized stroke patients was not significantly affected. This might be attributed to the health information technology and the effective prevention measures of COVID-19.

Technology Behavior Model-Impact of Extended Reality on Patient Surgery

Smart surgery is a new way to utilize smart devices to change existing surgeries. Smart glasses can enhance the surgical procedure so that the patient can understand the procedure more intuitively. Surgery is for patients, and patient acceptance of extended reality surgery is the purpose of this study. This study uses the technology behavior model, which is more in line with the user's assessment of the acceptance behavior of the new technology. A triangulated research approach was used, which applies to this study for a specific patient population. Primary data were collected from hospitals through questionnaires and were statistically analyzed by CB&PLS-SEM multimodel using SmartPLS software. It was concluded that patients were influenced by operational emotional factors in undergoing extended reality surgery. The study provides a basis for future research related to the practical application of smart surgery from the patient's perspective in viewing and accepting surgery.

A MEASURE MODEL FOR THE SPREAD OF VIRAL INFECTIONS WITH MUTATIONS

Genetic variations in the COVID-19 virus are one of the main causes of the COVID-19 pandemic outbreak in 2020 and 2021. In this article, we aim to introduce a new type of model, a system coupled with ordinary differential equations (ODEs) and measure differential equation (MDE), stemming from the classical SIR model for the variants distribution. Specifically, we model the evolution of susceptible S and removed R populations by ODEs and the infected I population by a MDE comprised of a probability vector field (PVF) and a source term. In addition, the ODEs for S and R contains terms that are related to the measure I. We establish analytically the well-posedness of the coupled ODE-MDE system by using generalized Wasserstein distance. We give two examples to show that the proposed ODE-MDE model coincides with the classical SIR model in case of constant or time-dependent parameters as special cases.