Multizone Modelling of SARS-CoV-2 Airborne Transmission in Mechanically Ventilated Buildings

This study contributes to a better understanding of the airborne transmission risks in multizone, mechanically ventilated buildings and how to reduce infection risk. A novel modeling approach combining the Wells-Riley and the US National Institute of Standards and Technology (NIST) CONTAM models was applied to a multizone whole building to simulate exposure and assess the effectiveness of different mitigation measures. A case study for the US Department of Energy large office prototype building was conducted to illustrate the approach. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Combined Therapy of Lung and Intestine in Treatment of Lung Diseases: A Review.

The theoretical origin of the combined therapy of lung and intestine can be traced back to the Inner Canon of Huangdi, which explains the physiological and pathological interaction between the lung and the large intestine. In recent years, researchers have investigated the scientific essence of the "lung- intestine axis" theory from many aspects, which enriches the relevant theoretical basis, and applied it to the treatment of COVID-19, acute lung injury, and other lung diseases. The close relation between lung and intestine in many aspects embodies the holistic conception of traditional Chinese medicine and explains the holistic theory of interrelation between organs, which correlate to each other physiologically and pathologically. Intestinal microecological disorders can affect lung immune function and cause respiratory diseases, and respiratory diseases are usually accompanied by gastrointestinal symptoms. Lung diseases can be prevented and treated by regulating intestinal flora. According to histoembryology, the epithelial tissue of the lung and intestine comes from primitive foregut. In immunology, both lung and intestine contain mucosa-associated lymphoid tissue, and the pathological changes of the respiratory tract are also closely related to intestinal microorganisms. The tissue origin of lung and large intestine, the correlation of mucosal immunity, and the synchronization of ecological changes provide a scientific basis for the combined therapy of lung and intestine. Therefore, this paper summarizes the theoretical origin, modern research mechanism, and clinical application of combined therapy of lung and intestine, in order to provide a new direction for its application in clinical and scientific research.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Speciesism and Preference of Human–Artificial Intelligence Interaction: A Study on Medical Artificial Intelligence

Artificial intelligence (AI) has revolutionized the medical industry in the decade. It is critical to integrate human–computer interaction into daily clinic service and further increase the public acceptance of medical AI. Based on self-categorization theory, our research draws on speciesism as a vital cognitive factor to examine how patients' speciesism affects their acceptance of medical AI in different roles. The study adopted a positivist research paradigm by examining 249 samples of data collected during COVID-19 in China. The results indicate that patients with higher speciesism tend to have lower acceptance of medical AI in an independent role but higher acceptance in an assistive role. Furthermore, we verified the mediating effect of human–computer trust and the positive moderating role of human uniqueness perception. This article expands the practicality of speciesism from human–animal relationships into human–AI relationships and contributes to human–computer interaction from the perspective of medical AI acceptance. © 2023 Taylor & Francis Group, LLC.

Bayesian Network Model of China's Financial Risk Under COVID-19 Based on Grey Clustering

The panic caused by COVID-19 and the stagnation of business activities induced the continuous breeding of China's financial risks. This paper considers the COVID-19 and economic indexes as nodes to establish the Bayesian topology of financial risk. The liquidity, sovereign, and stock market risks are mainly considered to evaluate the financial risk. Based on the risk characteristics, the central interval trapezoidal possibility functions are designed, then the grey clustering model is used to classify the financial risk into four different levels. The possibility distribution of financial risk levels under different COVID-19 index levels is inferenced through the Bayesian network. Finally, each node's monthly time series data from October 2019 to May 2021 is used to learn by NETICA software, and the conditional probability of each node and the possibility of financial risk are deduced. It is concluded that liquidity risk and sovereign risk are more sensitive to COVID-19, while the stock market risk is not very sensitive to it.

Antioxidating and reinforcing effect of polydopamine functionalized silica on natural rubber latex films

Since the outbreak of COVID-19, the demand for natural latex products with increased mechanical properties and aging resistance has surged. Based on the excellent adhesion and antioxidant properties of polydopamine (PDA), we employed a one-pot method to modify the surface of silica substrates using PDA containing a polyphenol structure, to prepare a reinforced silica-PDA composite latex material with antioxidant properties. As expected, the silica-PDA composite achieved both uniform dispersion and good interfacial interactions with natural rubber latex (NRL). In addition, compared with common NRL/silica films, the mechanical properties of the NRL/silica-PDA film were significantly improved;specifically, silica-PDA can highly-enhanced the mechanical property of NRL film from 24.94 to 32.18 MPa of tensile strength. Further, the antioxidant activity of the silica-PDA film exceeded that of commercially available antioxidant D. Considering the notable performance boost of silica-PDA composites on NRL films, we believe that the treatment of silica with natural polyphenols offers a convenient and facile new route for the preparation of environmentally friendly multifunctional silica additives. © 2023 Wiley Periodicals LLC.

Development of a Bayesian inference model for assessing ventilation condition based on CO2 meters in primary schools

Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO2 meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO2 generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO2 readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO2 generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO2 levels. The occupancy schedule becomes critical when the CO2 data are limited, whereas a 15-min measurement interval could capture dynamic CO2 profiles well even without the occupancy information. Hourly CO2 recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96+/-0.31 ACH for Room #1 (165 m3 and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40+/-0.08 ACH for Room #2 (236 m3 and 21 occupancies), 0.30+/-0.04 or 0.79+/-0.06 ACH depending on occupancy schedules for Room #3 (236 m3 and 19 occupancies), 0.40+/-0.32,0.48+/-0.37,0.72+/-0.39 ACH for Room #4 (231 m3 and 8-9 occupancies) for three consecutive days.

Research and development strategy of vaccine for emergent infectious diseases: Taking the COVID-19 pneumonia as an example

To block the continuously global pandemic of novel coronavirus pneumonia (COVID-19 or 2019-nCoV or SARS-CoV-2), more than 300 vaccines have been put into R&D pipelines in tens of countries within two years. At present, vaccines using different technical platforms have been fast approved to use, and widely deployed and vaccinated worldwide due to accelerated medical policies. In this article, the representative vaccines were selected from the main technical routes to analyze the strategy of vaccine R&D and management from the aspects including R&D cooperation, clinical trials, approval and access, production and circulation. We aim to provide reference for the vaccine development and supervision under the circumstance of emergency infectious diseases.

Combined Therapy of Lung and Intestine in Treatment of Lung Diseases: A Review

The theoretical origin of the combined therapy of lung and intestine can be traced back to the Inner Canon of Huangdi, which explains the physiological and pathological interaction between the lung and the large intestine. In recent years, researchers have investigated the scientific essence of the "lung- intestine axis" theory from many aspects, which enriches the relevant theoretical basis, and applied it to the treatment of COVID-19, acute lung injury, and other lung diseases. The close relation between lung and intestine in many aspects embodies the holistic conception of traditional Chinese medicine and explains the holistic theory of interrelation between organs, which correlate to each other physiologically and pathologically. Intestinal microecological disorders can affect lung immune function and cause respiratory diseases, and respiratory diseases are usually accompanied by gastrointestinal symptoms. Lung diseases can be prevented and treated by regulating intestinal flora. According to histoembryology, the epithelial tissue of the lung and intestine comes from primitive foregut. In immunology, both lung and intestine contain mucosa-associated lymphoid tissue, and the pathological changes of the respiratory tract are also closely related to intestinal microorganisms. The tissue origin of lung and large intestine, the correlation of mucosal immunity, and the synchronization of ecological changes provide a scientific basis for the combined therapy of lung and intestine. Therefore, this paper summarizes the theoretical origin, modern research mechanism, and clinical application of combined therapy of lung and intestine, in order to provide a new direction for its application in clinical and scientific research. © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Knowledge management for extreme public health events COVID-19: based on Tiktok data

Purpose Taking the COVID-19 as the background, this study aims to investigate the direct influencing factors regarding knowledge sharing behavior (KSB) on new media platforms and discuss how the characteristics of the users could enhance the KSB through moderation effect, and provide empirical evidences. Design/methodology/approach Based on the social exchange theory and after the text analysis of the data collected from the Tiktok platform in 2020, this paper uses the quantitative method to evaluate the factors influence KSB on short video social platform during the COVID-19 outbreak. Findings KSB on new media platform could be enhanced by richer knowledge content of the video posted and the attribute of the platform users directly. Platform users could affect the trustworthiness of the knowledge shared, thus influence the knowledge sharing. On the early stage of the COVID-19, the richer content of the knowledge released by users could effectively enhance the KSB. On the early stage of the emergency events, the official users could play a significant role on KS. During the mitigation stage of COVID-19, the KSB of the knowledge shared by unofficial users with richer content could be enhanced and the moderation effect is relatively stronger. Originality/value The research extends the social exchange theory to a disaster management context. The authors provide an effective reference for future governments to effectively cope with the epidemic and spread public knowledge in an emergency response context. By analyzing the influence of knowledge content and influencer characteristics, it could help the social media platform to improve content management and optimize resource allocation.

Neural network to predict probabilistically possible mutations in hemagglutinins from Eurasia H1 influenza A virus

The current COVID-19 pandemic continues with its new variants, whose mutations are unpredictable. Thus, how to predict mutations in viruses has profound meanings for vaccine and drug development as well as prevention measures. Currently the documented mutations in SARS-CoV-2 are not abundant yet, especially for making phylogenetic tree, it would be useful and easy to use the virus data with abundant mutations such as influenza A virus to build predictive model. In this study, a neural network with feedforward backpropagation algorithm is employed to predict the probabilistically possible mutation positions and mutated amino acids in hemagglutinins from Eurasia H1 influenza A virus. The study demonstrates an encouraging result and suggests the possibility to continue working along this research line. © 2021 SPIE.

Improving in-hospital stroke service utilization (MISSION) to shorten thrombolytic door-to-needle time in acute ischemic stroke patients: A cluster randomized controlled trial

Background and Aims: Rapid intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) is crucial for improving outcomes. However, randomized trials to reduce in-hospital delay are clearly limited in China. We aimed to evaluate the effect of a multi-component intervention on thrombolytic door-to needle time (DNT) of AIS patients via video teleconference based on the Behavior Change Wheel method. Methods: This trial randomly allocated 22 hospitals equally to PEITEM (Persuasion Environment reconstruction Incentivisation Training Education Modeling) intervention or routine care plus stroke registry and subsequently enrolled 1634 AIS patients who receiving IVT within 4.5 hours upon stroke onset from participant hospitals. The PEITEM group received a one-year PEITEM intervention based on the behavioral theory monthly via video teleconference. Results: A total of 1, 634 patients from the 22 hospitals were enrolled. The proportion of DNT ≤ 60 minutes was 82.0% in the PEITEM group and 73.7% in the control group (adjusted odds ratio, 1.85;95% confidence interval [CI], 1.42 to 2.42, P<0.001). The average DNT was 43 minutes in the PEITEM group and 50 minutes in the control group (β: -9.00;95% CI, -11.37 to ≤6.63, P<0.001). Favorable neurological outcomes were achieved in 55.6% patients in the PEITEM group and 50.4% patients in the control group (adjusted odds ratio, 1.34;95% CI, 1.02 to 1.75;P=0.04). Conclusions: The teleconference-delivered PEITEM intervention resulted in a moderately but clinically relevant shorter DNT and better neurological outcomes in the AIS treated with the IVT. Video teleconference may be more appropriate and easier for quality improvement in the current global COVID-19 public health crisis disrupting healthcare services.

Recurrent lymphangioleiomyomatosis in a lung allograft with COVID-19: autopsy case report and literature review

Lymphangioleiomyomatosis (LAM) is a rare neoplastic disease of the lung with a characteristic feature of diffuse cystic changes in bilateral lungs. Lung transplantation is considered to be one of the effective treatments in end stage disease. Patients with LAM who underwent lung transplant tend to have more favorable outcome compared to other end stage lung diseases. We report a case of a female patient who was diagnosed with LAM and received bilateral lung transplantation at 45 years of age. Subsequent allograft biopsies were significant for mild acute cellular rejection (Grade A2), for which the immunosuppressive regimen was adjusted accordingly. At 7 years post-transplant, she presented with shortness of breath, cough, and fatigue, and diagnosed with a viral infection. Her chest imaging was unremarkable. However, a transbronchial biopsy was performed to rule out rejection and revealed foci of spindle cells proliferation, with positive HMB-45 and smooth muscle actin immunohistochemical studies, confirming the diagnosis of recurrent LAM. After she was discharged, she was re-admitted 1 week later with severe COVID-19. Her clinical course was complicated by acute respiratory distress syndrome, respiratory failure, and gastrointestinal hemorrhage. The patient passed away on day 36 of hospital stay. Autopsy was requested and confirmed the pathology of recurrent LAM and diffuse alveolar damage from COVID-19.

The influence of the COVID-19 pandemic on stage migration and management of patients presenting to a penile cancer supra-network

Introduction: COVID-19 has had potential impact on presentation and outcome of patients to cancer services. The objective of this study was to analyse pandemic effects on patients with penile SCC by comparison with previous years within a stable 10 million population referral base. Patients and Methods: All patients referred to the penile supranetwork MDT (snMDT), 1st January 2020 to 31st December 2020 identified (COVID-19 Group A). Prospectively collated data from previous 3 years (2017- 19) referrals analysed to create a service year mean dataset (Non-COVID-19 Group B). Variables compared: (i) overall demographics, (ii) pathological stage (TNM 8), (iii) time from presentation to first treatment. Chi-squared test to evaluate the pathological stage (TNM 8) and Mann- Whitney U Test to assess time from presentation to first treatment. Results: Group A, 123 new referrals. Group B mean referrals 129 (118 - 147). Primary stage Group A, 45.4% pT1, 30.2% pT2, 24.4% pT3/pT4 vs Group B, 48.6% pT1, 38.2% pT2, 13.2% pT3/pT4 (p=0.01). Nodal stage pN0 Group A 62% vs 70% Group B, (NS p = 0.08). Median time (days) presentation to first treatment Group A, 22 (IQ 15 - 36) vs Group B, 26 (13 - 36.5). Conclusion: No. of referrals were statistically similar in COVID-19 and Non-COVID-19 years and managed within similar time frame. However, there was a statistically higher pT disease stage in the COVID-19 group but no significant difference in pN stage (although trend towards higher nodal stage). Data is not yet mature to determine an effect on cancer specific survival.

Fitting of Impact of COVID-19 Pandemic on SP 500 Index Using Random Walk

The outbreak of COVID-19 pandemic can be viewed as a surprise for its scale and impact. However, this pandemic may not be considered as a purely random event, because it is developing from a local epidemic to a global pandemic under everyone's eyes. Although COVID-19 pandemic negatively impacts the world economy, to everyone's surprise, the US stock market recovers rapidly from its initial heavy loss. For the sake of modeling, it is interesting to use a random walk model to fit SP 500 index, which is designed as the aim of current study. The results show that the random walk model can somewhat fit the SP 500 index, the shorter the time scale is, the better the fitting is. The results therefore demonstrate it impossible to use a single random seed to fit the SP 500 index from different time segments. © 2020 IEEE.

Discovery of potential SARS-CoV-2 M protease inhibitors by virtual screening, molecular dynamics, and binding free energy analyses①

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gained tremendous attention due to its high infectivity and pathogenicity. The 3-chymotrypsin-like hydrolase protease (Mpro) of SARS-CoV-2 has been proven to be an important target for anti-SARS-CoV-2 activity. To better identify the drugs with potential in treating coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 and according to the crystal structure of Mpro, we conducted a virtual screening of FDA-approved drugs and chemical agents that have entered clinical trials. As a result, 9 drug candidates with therapeutic potential for the treatment of COVID-19 and with good docking scores were identified to target SARS-CoV-2. Consequently, molecular dynamics (MD) simulation was performed to explore the dynamic interactions between the predicted drugs and Mpro. The binding mode during MD simulation showed that hydrogen bonding and hydrophobic interactions played an important role in the binding processes. Based on the binding free energy calculated by using MM/PBSA, Lopiravir, an inhibitor of human immunodeficiency virus (HIV) protease, is under investigation for the treatment of COVID-19 in combination with ritionavir, and it might inhibit Mpro effectively. Moreover, Ombitasvir, an inhibitor for non-structural protein 5A of hepatitis C virus (HCV), has good inhibitory potency for Mpro. It is notable that the GS-6620 has a binding free energy, with respect to binding Mpro, comparable to that of ombitasvir. Our study suggests that ombitasvir and lopinavir are good drug candidates for the treatment of COVID-19, and that GS-6620 has good anti-SARS-CoV-2 activity. © 2021 Fujian Institute of Research of the Structure of Matter. All rights reserved.

Simulation of Impact of COVID-19 Pandemic on Dow Jones Index Using Random Walk

The outbreak of pandemic of COVID-19 is a totally unexpected event, which impacts heavily on the world stock market, especially the US stock market. So far, mathematical, statistical and probabilistic models have been used in the simulation of stock markets, whereas the probabilistic models appear to be more suitable for current situation because of the unexpectedness of COVID-19 pandemic. In this study, the random walk model, which is based on random walk hypothesis of stock markets, was used to simulate the opens of Dow Jones Industrial Average Index for 7 months, 2 years and 7 months, 5 years and 7 months, 10 years and 7 months, and 20 years and 7 months, respectively. The unexpected events not only include the current COVID-19 pandemic but also the 9/11 terrorism attack on the World Trade Center. The simulations demonstrate that the random walk is still difficult to precisely describe the impact of COVID-19 pandemic on the Dow Jones Industrial Average Index although the general trends look similar. © 2021 ACM.

Diagnostic Value of D-Dimer in COVID-19: A Meta-Analysis and Meta-Regression

The prognostic role of hypercoagulability in COVID-19 patients is ambiguous. D-dimer, may be regarded as a global marker of hemostasis activation in COVID-19. Our study was to assess the predictive value of D-dimer for the severity, mortality and incidence of venous thromboembolism (VTE) events in COVID-19 patients. PubMed, EMBASE, Cochrane Library and Web of Science databases were searched. The pooled diagnostic value (95% confidence interval [CI]) of D-dimer was evaluated with a bivariate mixed-effects binary regression modeling framework. Sensitivity analysis and meta regression were used to determine heterogeneity and test robustness. A Spearman rank correlation tested threshold effect caused by different cut offs and units in D-dimer reports. The pooled sensitivity of the prognostic performance of D-dimer for the severity, mortality and VTE in COVID-19 were 77% (95% CI: 73%-80%), 75% (95% CI: 65%-82%) and 90% (95% CI: 90%-90%) respectively, and the specificity were 71% (95% CI: 64%-77%), 83% (95% CI: 77%-87%) and 60% (95% CI: 60%-60%). D-dimer can predict severe and fatal cases of COVID-19 with moderate accuracy. It also shows high sensitivity but relatively low specificity for detecting COVID-19-related VTE events, indicating that it can be used to screen for patients with VTE.