Control measure implications of COVID-19 infection in healthcare facilities reconsidered from human physiological and engineering aspects.

The Wells-Riley model invokes human physiological and engineering parameters to successfully treat airborne transmission of infectious diseases. Applications of this model would have high potentiality on evaluating policy actions and interventions intended to improve public safety efforts on preventing the spread of COVID-19 in an enclosed space. Here, we constructed the interaction relationships among basic reproduction number (R0) - exposure time - indoor population number by using the Wells-Riley model to provide a robust means to assist in planning containment efforts. We quantified SARS-CoV-2 changes in a case study of two Wuhan (Fangcang and Renmin) hospitals. We conducted similar approach to develop control measures in various hospital functional units by taking all accountable factors. We showed that inhalation rates of individuals proved crucial for influencing the transmissibility of SARS-CoV-2, followed by air supply rate and exposure time. We suggest a minimum air change per hour (ACH) of 7 h-1 would be at least appropriate with current room volume requirements in healthcare buildings when indoor population number is < 10 and exposure time is < 1 h with one infector and low activity levels being considered. However, higher ACH (> 16 h-1) with optimal arranged-exposure time/people and high-efficiency air filters would be suggested if more infectors or higher activity levels are presented. Our models lay out a practical metric for evaluating the efficacy of control measures on COVID-19 infection in built environments. Our case studies further indicate that the Wells-Riley model provides a predictive and mechanistic basis for empirical COVID-19 impact reduction planning and gives a framework to treat highly transmissible but mechanically heterogeneous airborne SARS-CoV-2.

Impact of inactivated vaccines on decrease of viral RNA levels in individuals with the SARS-CoV-2 Omicron (BA.2) variant: A retrospective cohort study in Shanghai, China.

Background: SARS-CoV-2 Omicron (BA.2) has stronger infectivity and more vaccine breakthrough capability than previous variants. Few studies have examined the impact of inactivated vaccines on the decrease of viral RNA levels in individuals with the Omicron variant, based on individuals' continuous daily cycle threshold (Ct) values and associated medical information from the infection to hospital discharge on a large population. Methods: We extracted 39,811 individuals from 174,371 Omicron-infected individuals according to data inclusion and exclusion criteria. We performed the survival data analysis and Generalized Estimating Equation to calculate the adjusted relative risk (aRR) to assess the effect of inactivated vaccines on the decrease of viral RNA levels. Results: Negative conversion was achieved in 54.7 and 94.3% of all infected individuals after one and 2 weeks, respectively. aRRs were shown weak effects on turning negative associated with vaccinations in asymptomatic infections and a little effect in mild diseases. Vaccinations had a protective effect on persistent positivity over 2 and 3 weeks. aRRs, attributed to full and booster vaccinations, were both around 0.7 and had no statistical significance in asymptomatic infections, but were both around 0.6 with statistical significance in mild diseases, respectively. Trends of viral RNA levels among vaccination groups were not significant in asymptomatic infections, but were significant between unvaccinated group and three vaccination groups in mild diseases. Conclusion: Inactivated vaccines accelerate the decrease of viral RNA levels in asymptomatic and mild Omicron-infected individuals. Vaccinated individuals have lower viral RNA levels, faster negative conversion, and fewer persisting positive proportions than unvaccinated individuals. The effects are more evident and significant in mild diseases than in asymptomatic infections.

Use of YouTube by academic medical centres during the COVID-19 pandemic: an observational study in Taiwan.

OBJECTIVES: YouTube has been of immense importance in conveying essential information on COVID-19 and promoting the latest healthcare policies during the outbreak. However, there have been few studies that have focused on how healthcare organisations have used YouTube to communicate with the public and increase their awareness during the pandemic, as well as its effectiveness. DESIGN: A nationwide observational study. SETTINGS: We analysed all YouTube video posts culled from the official accounts of all medical centres in Taiwan from December 2019 to August 2021. PARTICIPANTS: All YouTube videos were categorised as either COVID-19 or non-COVID-19 related. The COVID-19-related videos were divided into five categories, and detailed metrics for each video were recorded. For comparison, we also surveyed all YouTube video posts placed by the Ministry of Health and Welfare and the Taiwan Centers for Disease Control (TCDC). RESULTS: We analysed official YouTube channels from 17 academic medical centres, involving a total of 943 videos. We found a relationship between the quantity of YouTube videos uploaded by the TCDC and the trend of confirmed cases (Pearson's correlation coefficient was 0.25, p=0.02). Data from private hospitals revealed that they posted more COVID-19 videos (103 vs 56) when compared with public hospitals. In addition, multivariate linear regression showed that more 'likes' (estimate 41.1, 95% CI 38.8 to 43.5) and longer lengths (estimate 10 800, 95% CI 6968.0 to 14 632.0) of COVID-19-related videos correlated significantly with an increased number of 'views'. CONCLUSIONS: This nationwide observational study, performed in Taiwan, demonstrates well the trend and effectiveness of academic medical centres in promoting sound healthcare advice regarding COVID-19 through YouTube due to the channel's easy accessibility and usability.

Assessing regional emissions of vehicle-based tire wear particle from macro-to micro/nano-scales with pandemic lockdowns and electromobility scenarios implications.

Despite increasing the public awareness of ubiquity of microplastics (MPs) in air, the issue on particular source of tire wear particles (TWPs) emission into atmosphere and their exposure-associated human health has not received the attention it deserves. Here we linked vehicle kilometers traveled (VKT) estimates covering demography, socio-environmental, and transportation features and emission factors to predict regional emission patterns of TWP-derived atmospheric MPs. A data-driven probabilistic approach was developed to consider variability across the datasets and uncertainty of model parameters in terms of country-level and vehicle-type emissions. We showed that country-specific VKT from billion to trillion vehicle-kilometer resulted in 103-105 metric tons of airborne TWP-derived atmospheric MPs annually in the period 2015-2019, with the highest emissions from passenger cars and heavy-duty vehicles. On average, we found that airborne TWP emissions from passenger cars by country had substantial decreased (up to ∼33%) during COVID-19 lockdowns in 2020 and pronounced increased (by a factor ∼1.9) from vehicle electrification by the next three decades. We conclude that the stunning mass of airborne TWP is a predominant source of atmospheric MP. We underscore the necessity of TWP emissions control among the United States, China, and India. Our findings can be of great use to environmental transportation planners for devising vehicle/tire-oriented decision support tools. Our data offer information to enhance TWP-exposure estimates, to examine long-term exposure trends, and subsequently to improve health risk assessment during pandemic outbreak and future electrification.

Lineage-mosaic and mutation-patched spike proteins for broad-spectrum COVID-19 vaccine.

SARS-CoV-2 spread in humans results in continuous emergence of new variants, highlighting the need for vaccines with broad-spectrum antigenic coverage. Using inter-lineage chimera and mutation-patch strategies, we engineered a recombinant monomeric spike variant (STFK1628x) that contains key regions and residues across multiple SAR-CoV-2 variants. STFK1628x demonstrated high immunogenicity and mutually complementary antigenicity to its prototypic form (STFK). In hamsters, a bivalent vaccine composed of STFK and STFK1628x elicited high titers of broad-spectrum neutralizing antibodies to 19 circulating SARS-CoV-2 variants, including Omicron sublineages BA.1, BA.1.1, BA.2, BA.2.12.1, BA.2.75, and BA.4/5. Furthermore, this vaccine conferred robust protection against intranasal challenges by either SARS-CoV-2 ancestral strain or immune-evasive Beta and Omicron BA.1. Strikingly, vaccination with the bivalent vaccine in hamsters effectively blocked within-cage virus transmission of ancestral SARS-CoV-2, Beta variant, and Omicron BA.1 to unvaccinated sentinels. Thus, our study provided insight and antigen candidates for the development of next-generation COVID-19 vaccines.

Risk Factors Surrounding an Increase in Burnout and Depression Among Health Care Professionals in Taiwan During the COVID-19 Pandemic.

OBJECTIVES: This study aimed to investigate the risk factors surrounding an increase in both burnout levels and depression among health care professionals in Taiwan through use of a longitudinal study design. DESIGN: This is a 2-year observational study that took place from January 2019 to December 2020. SETTING AND PARTICIPANTS: Data among health care professionals were extracted from the Overload Health Control System of a tertiary medical center in central Taiwan. METHODS: Burnout was measured through use of the Chinese version of the Copenhagen Burnout Inventory (C-CBI), whereas depression was ascertained by the Taiwanese Depression Questionnaire. Each participant provided both burnout and depression measurements during a nonpandemic period (2019) as well as during the COVID pandemic era (2020). Risk factors surrounding an increase in burnout levels and depression were analyzed through a multivariate logistic regression model with adjusting confounding factors. RESULTS: Two thousand nineteen participants completed the questionnaire over 2 consecutive years, including 132 visiting doctors, 105 resident doctors, 1371 nurses, and 411 medical technicians. After adjustments, sleeplessness, daily working hours >8, and stress due to one's workload were all found to be risk factors for an increase in depression levels, whereas sleeplessness, lack of exercise, and stress due to one's workload were all found to be risk factors for an increase in personal burnout level. Being a member of the nursing staff, a younger age, sleeplessness, and lack of exercise were all risk factors for an increase in work-related burnout levels. CONCLUSIONS AND IMPLICATIONS: Poor sleep, lack of exercise, long working hours, and being a member of the nursing staff were risk factors regarding an increase in personal burnout, work-related burnout levels and depression among health care professionals. Leaders within the hospital should investigate the working conditions and personal habits of all medical staff regularly and systematically during the COVID-19 pandemic and take any necessary preventive measures, such as improving resilience for nursing staff, in order to best care for their employees.

Resilience and high-quality development of tourism economy under the epidemic

In December 2021, the Central Economic Work Conference clearly pointed out that China's economic development and epidemic prevention and control will maintain a leading position in the world, the national strategic scientific and technological strength will accelerate and grow, and the resilience of the industrial chain will be improved. Tourism economic resilience is the ability of the tourism industry to cope with external disturbances and adjust its own development path after resisting shocks. Based on the analysis of the relationship between the epidemic and the resilience of the tourism economy, this article further explains the relationship between resilience and the tourism industry and how to improve the resilience of the tourism economy and help the high-quality development of the tourism industry.

The prevalence of medical adhesive-related skin injury caused by protective dressings among medical staff members during the 2019 coronavirus pandemic in China.

AIM: To explore the prevalence and risk factors for medical adhesive-related skin injury (MARSI) caused by protective dressings among medical staff members during the 2019 coronavirus disease pandemic (COVID-19) in China. MATERIALS AND METHODS: A cross-sectional survey was conducted using a questionnaire. The questionnaire was released through the Questionnaire Star website and was completed online. The prevalence of MARSI was calculated and risk factors were analyzed using a multiple regression model. RESULTS: A total of 414 front-line medical staff members treating COVID-19 patients were enrolled from 46 hospitals across four provinces and two municipalities. Overall, 83.1% used protective medical adhesive dressings applied to the head and face to prevent skin damage from personal protective equipment. The prevalence of MARSI caused by adhesive dressings was 41.9%. By multiple regression analysis, the type of dressing, duration of dressing usage, and pain score were risk factors for MARSI development. CONCLUSIONS: The high prevalence indicates MARSI is common among front-line medical staff members, especially those using hydrocolloid dressings and longer durations of dressing usage. Pain upon dressing removal can be severe and increased the risk of MARSI. We call for paying more attention to MARSI and recommend multisite studies with larger sample sizes to enhance the generalizability of these findings.

Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.

Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.

The neutralizing breadth of antibodies targeting diverse conserved epitopes between SARS-CoV and SARS-CoV-2.

Antibody therapeutics for the treatment of COVID-19 have been highly successful. However, the recent emergence of the Omicron variant has posed a challenge, as it evades detection by most existing SARS-CoV-2 neutralizing antibodies (nAbs). Here, we successfully generated a panel of SARS-CoV-2/SARS-CoV cross-neutralizing antibodies by sequential immunization of the two pseudoviruses. Of the potential candidates, we found that nAbs X01, X10, and X17 offer broad neutralizing potential against most variants of concern, with X17 further identified as a Class 5 nAb with undiminished neutralization against the Omicron variant. Cryo-electron microscopy structures of the three antibodies together in complex with each of the spike proteins of the prototypical SARS-CoV, SARS-CoV-2, and Delta and Omicron variants of SARS-CoV-2 defined three nonoverlapping conserved epitopes on the receptor-binding domain. The triple-antibody mixture exhibited enhanced resistance to viral evasion and effective protection against infection of the Beta variant in hamsters. Our findings will aid the development of antibody therapeutics and broad vaccines against SARS-CoV-2 and its emerging variants.

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning.

The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates.

Cell-based reporter assays for measurements of antibody-mediated cellular cytotoxicity and phagocytosis against SARS-CoV-2 spike protein.

The COVID-19 pandemic caused by SARS-CoV-2 infections has led to excess deaths worldwide. Neutralizing antibodies (nAbs) against viral spike protein acquired from natural infections or vaccinations contribute to protection against new- and re-infections. Besides neutralization, antibody-mediated cellular cytotoxicity (ADCC) and phagocytosis (ADCP) are also important for viral clearance. However, due to the lack of convenient methods, the ADCC and ADCP responses elicited by viral infections or vaccinations remain to be explored. Here, we developed cell-based assays using target cells stably expressing SARS-CoV-2 spikes and Jurkat-NFAT-CD16a/CD32a effector cells for ADCC/ADCP measurements of monoclonal antibodies and human convalescent COVID-19 plasmas (HCPs). In control samples (n = 190), the specificity was 99.5% (95%CI: 98.4-100%) and 97.4% (95%CI: 95.1-99.6%) for the ADCC and ADCP assays, respectively. Among 87 COVID-19 HCPs, 83 (sensitivity: 95.4%, 95%CI: 91.0-99.8%) and 81 (sensitivity: 93.1%, 95%CI: 87.8-98.4%) showed detectable ADCC (titer range: 7.4-1721.6) and ADCP activities (titer range: 4-523.2). Notably, both ADCC and ADCP antibody titers positively correlated with the nAb titers in HCPs. In summary, we developed new tools for quantitative ADCC and ADCP analysis against SARS-CoV-2, which may facilitate further evaluations of Fc-mediated effector functions in preventing and treating against SARS-CoV-2.

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources;however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates.

An orally available Mpro inhibitor is effective against wild-type SARS-CoV-2 and variants including Omicron.

Emerging SARS-CoV-2 variants continue to cause waves of new infections globally. Developing effective antivirals against SARS-CoV-2 and its variants is an urgent task. The main protease (Mpro) of SARS-CoV-2 is an attractive drug target because of its central role in viral replication and its conservation among variants. We herein report a series of potent α-ketoamide-containing Mpro inhibitors obtained using the Ugi four-component reaction. The prioritized compound, Y180, showed an IC50 of 8.1 nM against SARS-CoV-2 Mpro and had oral bioavailability of 92.9%, 31.9% and 85.7% in mice, rats and dogs, respectively. Y180 protected against wild-type SARS-CoV-2, B.1.1.7 (Alpha), B.1.617.1 (Kappa) and P.3 (Theta), with EC50 of 11.4, 20.3, 34.4 and 23.7 nM, respectively. Oral treatment with Y180 displayed a remarkable antiviral potency and substantially ameliorated the virus-induced tissue damage in both nasal turbinate and lung of B.1.1.7-infected K18-human ACE2 (K18-hACE2) transgenic mice. Therapeutic treatment with Y180 improved the survival of mice from 0 to 44.4% (P = 0.0086) upon B.1.617.1 infection in the lethal infection model. Importantly, Y180 was also highly effective against the B.1.1.529 (Omicron) variant both in vitro and in vivo. Overall, our study provides a promising lead compound for oral drug development against SARS-CoV-2.

Severe novel coronavirus pneumonia: a case report and literature review

Objective To describe the diagnosis and treatment of a patient with severe novel coronavirus pneumonia, and to improve the understanding and management of clinicians on novel coronavirus pneumonia. Methods The onset, development, treatment and outcome of a patient with severe 2019 novel coronavirus pneumonia were retrospectively analyzed and relevant literatures were reviewed. Results At the beginning of the disease, the patient presented fever and dry cough, and later the disease progressed to dyspnea. Chest CT showed bilateral exudation of the lung. Lopinavir/ritonavir, IFN-a and immunoglobulin were given to the patient according to the expert group's opinion. The pneumonia was cured and the patient was discharged two weeks later. Conclusion Appropriate management strategies are effective on diagnosis and treatment of new coronavirus pneumonia.