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1.
Communications in Information and Systems ; 22(3):339-361, 2022.
Article in English | Web of Science | ID: covidwho-1995150

ABSTRACT

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective for variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.

2.
Industrial Management & Data Systems ; : 37, 2022.
Article in English | Web of Science | ID: covidwho-1927496

ABSTRACT

Purpose Considering both online and offline service scenarios, this study aims to explore the factors affecting doctors' intention to offer consulting services in eHealth and compare the factors between the free- and paid-service doctors. The theory of reasoned action and social exchange theory are integrated to develop the research model that conceptualizes the role of extrinsic motivations, intrinsic motivations, costs, and attitudes in doctors' behavioral intentions. Design/methodology/approach Partial least square structural equation modeling (PLS-SEM) was leveraged to analyze 326 valid sample data. To provide robust results, three non-parametric multigroup analysis (MGA) methods, including the PLS-MGA, confidence set, and permutation test approaches, were applied to detect the potential heterogeneity between the free- and paid-service doctors. Findings The results with overall samples reveal that anticipated rewards, anticipated associations, anticipated contribution, and perceived fee are all positively related to attitude, which in turn positively influences behavioral intention, and that perceived fee positively moderates the relationship between attitude and behavioral intention. Attitude's full mediation is also confirmed. However, results vary between the two groups of doctors. The three MGA approaches return relatively convergent results, indicating that the effects of anticipated associations and perceived fee on attitude are significantly larger for the paid-service doctors, while that of anticipated rewards is found to be significantly larger for the free-service doctors. Originality/value eHealth, as a potential contactless alternative to face-to-face diagnoses, has recently attracted widespread attention, especially during the continued spread of COVID-19. Most existing studies have neglected the underlying heterogeneity between free- and paid-service doctors regarding their motivations to engage in online healthcare activities. This study advances the understanding of doctors' participation in eHealth by emphasizing their motivations derived from both online and offline service scenarios and comparing the differences between free- and paid-service doctors. Besides, horizontally comparing the results by applying diverse MGA approaches enriches empirical evidence for the selection of MGA approaches in PLS-SEM.

5.
J Hosp Infect ; 123: 52-60, 2022 May.
Article in English | MEDLINE | ID: covidwho-1757533

ABSTRACT

BACKGROUND: Meticillin-resistant Staphylococcus aureus (MRSA) infections are rampant in hospitals and residential care homes for the elderly (RCHEs). AIM: To analyse the prevalence of MRSA colonization among residents and staff, and degree of environmental contamination and air dispersal of MRSA in RCHEs. METHODS: Epidemiological and genetic analysis by whole-genome sequencing (WGS) in 12 RCHEs in Hong Kong. FINDINGS: During the COVID-19 pandemic (from September to October 2021), 48.7% (380/781) of RCHE residents were found to harbour MRSA at any body site, and 8.5% (8/213) of staff were nasal MRSA carriers. Among 239 environmental samples, MRSA was found in 39.0% (16/41) of randomly selected resident rooms and 31.3% (62/198) of common areas. The common areas accessible by residents had significantly higher MRSA contamination rates than those that were not accessible by residents (37.2%, 46/121 vs. 22.1%, 17/177, P=0.028). Of 124 air samples, nine (7.3%) were MRSA-positive from four RCHEs. Air dispersal of MRSA was significantly associated with operating indoor fans in RCHEs (100%, 4/4 vs. 0%, 0/8, P=0.002). WGS of MRSA isolates collected from residents, staff and environmental and air samples showed that ST 1047 (CC1) lineage 1 constituted 43.1% (66/153) of all MRSA isolates. A distinctive predominant genetic lineage of MRSA in each RCHE was observed, suggestive of intra-RCHE transmission rather than clonal acquisition from the catchment hospital. CONCLUSION: MRSA control in RCHEs is no less important than in hospitals. Air dispersal of MRSA may be an important mechanism of dissemination in RCHEs with operating indoor fans.


Subject(s)
COVID-19 , Methicillin-Resistant Staphylococcus aureus , Staphylococcal Infections , Aged , COVID-19/epidemiology , Carrier State/epidemiology , Humans , Methicillin , Methicillin-Resistant Staphylococcus aureus/genetics , Pandemics , Staphylococcal Infections/epidemiology
7.
Psychology and Marketing ; 2022.
Article in English | Scopus | ID: covidwho-1729174

ABSTRACT

With the advancement of technology and the widespread of coronavirus disease 2019 pandemic, catering operators have favored electronic ordering due to its convenience and safety. However, little research has examined whether the change from traditional waiter ordering to electronic device ordering would affect consumers' healthy eating. Based on previous research of self-control, this article explores whether ordering by electronic device or waiter prompts healthier food choices. Through four experimental studies conducted in China, our findings demonstrated that whether ordering by electronic device or waiter is also one determinant of healthy eating. Compared to waiter ordering, consumers would make healthier food choices through electronic ordering, because it relives the time pressure brought on by the interpersonal waiter interaction. Whereas electronic ordering may be effective only if there is no waiting line or only for consumers who have a relatively low degree of trait self-control. The findings advance the understanding of determinants of healthy eating, as well as enrich the literature that explores the difference between human and electronic service. © 2022 Wiley Periodicals LLC.

8.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing ; 27:223-230, 2022.
Article in English | Scopus | ID: covidwho-1624071

ABSTRACT

The continued generation of large amounts of data within healthcare-from imaging to electronic medical health records to genomics and multi-omics -necessitates tools and methods to parse and interpret these data to improve healthcare outcomes. Artificial intelligence, and in particular deep learning, has enabled researchers to gain new insights from large scale and multimodal data. At the 2022 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare", we showcase the latest research, influenced and inspired by the idea of using technology to build a more fair, tailored, and cost-effective healthcare system after the COVID-19 pandemic.

9.
Pacific Symposium on Biocomputing ; 27:223-230, 2022.
Article in English | MEDLINE | ID: covidwho-1564034

ABSTRACT

The continued generation of large amounts of data within healthcare-from imaging to electronic medical health records to genomics and multi-omics -necessitates tools and methods to parse and interpret these data to improve healthcare outcomes. Artificial intelligence, and in particular deep learning, has enabled researchers to gain new insights from large scale and multimodal data. At the 2022 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare", we showcase the latest research, influenced and inspired by the idea of using technology to build a more fair, tailored, and cost-effective healthcare system after the COVID-19 pandemic.

10.
Huanjing Kexue/Environmental Science ; 42(12):5594-5601, 2021.
Article in Chinese | Scopus | ID: covidwho-1551910

ABSTRACT

Based on the pollution emission survey and the observation data of air quality and component stations, the WRF/SMOKE/CMAQ model system was adopted to analyze the impact of pollution sources and changes in meteorological conditions on air quality during the COVID-19 outbreak. The results showed that during the COVID-19 outbreak in 2020, in addition to the year-on-year increase in ρ(O3) in the Sichuan Basin, ρ(SO2), ρ(NO2), ρ(CO), ρ(PM2.5), and ρ(PM10) all decreased year-on-year, with decreases ranging from 8% to 41%. Compared with levels from the same period in 2019, ρ(Cl-), ρ(K+), ρ(Si), ρ(Al), ρ(Ca), and ρ(EC) in Chengdu decreased year-on-year, indicating that the emission reduction of construction sites, motor vehicles, industrial coal burned, and biomass combustion were the main reasons for the decrease in PM2.5 concentration. During the COVID-19 outbreak, the SO2, NOx, PM10, PM2.5, and VOCs industrial emissions decreased by 32%, 31%, 40%, 39%, and 41%, respectively. The traffic volume of motor vehicles in Chengdu was only 40.3% of that during the normal period, and the speed of traffic increased by 19.7%. The daily emissions of NOx, VOCs, and CO were reduced by 44.7%, 49.6%, and 38.0%, respectively. The non-equal decrease in pollutants made the atmospheric oxidability contributed by motor vehicle emissions relatively further enhanced. The unfavorable weather conditions in the Sichuan Basin caused ρ(PM2.5), ρ(NO2), ρ(SO2), ρ(O3), and ρ(PM10) to rise by 2%, 4%, 23%, 6%, and 8%, respectively. After deducting the influence of changes in weather conditions, the concentrations of ρ(PM2.5), ρ(NO2), ρ(SO2), and PM10 decreased by 21%, 45%, 31%, and 30%, respectively, and ρ(O3) increased by 12%. © 2021, Science Press. All right reserved.

11.
Acs Es&T Water ; 1(6):1352-1362, 2021.
Article in English | Web of Science | ID: covidwho-1531982

ABSTRACT

SARS-CoV-2 is shed by COVID-19 patients and can be detected in wastewater. Thus, testing wastewater for the virus provides a depiction of disease prevalence in a community. Virus concentration data can be utilized to monitor infection trends, identify hot spots, and inform decision makers regarding reopening efforts and directing resources. This perspective aims to shed light on the current situation relating to SARS-CoV-2 in the wastewater system and the opportunity to utilize wastewater to collect useful epidemiological data. First, the survivability of SARS-CoV-2 in different water matrices is examined through the lens of surrogate viruses. Second, the effect of wastewater treatment processes on SARS-CoV-2 is investigated. Current standards for sufficient reduction of the virus and the risk of exposure that arises at each stage in the wastewater treatment process are discussed. Third, the immense potential of wastewater-based epidemiology (WBE) for managing the ongoing COVID-19 pandemic is analyzed. Studies that have tested wastewater or sludge for SARS-CoV-2 are discussed, and results are tabulated. Lastly, the current limitations of WBE and opportunities of future research are explored. Using the wealth of knowledge that the scientific community now has about WBE, wastewater testing should be considered by regional governments and private institutions.

12.
Natural Product Communications ; 16(9), 2021.
Article in English | EMBASE | ID: covidwho-1458448

ABSTRACT

Purpose: Prescriptions of Han-Shi-Yu-Fei (HSYF), Han-Shi-Zu-Fei (HSZF), and Yi-Du-Bi-Fei (YDBF) were effective in treating COVID-19. Based on network pharmacology and molecular docking, overlapping Traditional Chinese medicines (TCMs), their active components, and core targets were explored in this study. Methods: First, the overlapping TCMs and their active components were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) by evaluating Oral Bioactivity (OB) and Drug Likeness (DL). The overlapping targets of potential components and COVID-19 were collected by SwissTargetPrediction, Gene Cards, and Venn 2.1.0 databases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed via DAVID6.8.1 database. Through comprehensive analysis of the “prescriptions-TCMs-components” (P-T-C), “components-targets-pathways” (C-T-P) and “protein–protein interaction” (PPI) networks constructed by Cytoscape 3.7.1 software, the active components and core targets were obtained. Finally, the binding energies of these components with ACE2 and SARS-CoV-2 3CL were analyzed by AutDockTools-1.5.6 and PyMOL software. Results: In all, five overlapping TCMs, 40 potential active components, and 47 candidate targets were obtained and analyzed in these prescriptions. There were 288 GO entries (P < 0.05), including 211 biological process (BP), 40 cell composition (CC), and 37 molecular function (MF) entries. Most of the 105 KEGG pathways (P < 0.05) were involved with viral infection and inflammation. Through “PPI” and “C-T-P” networks, the core targets (EGFR, PTGS2, CDK2, GSK3B, PIK3R1, and MAPK3) and active components (Q27134551, acanthoside B, neohesperidin, and irisolidone) with high degrees were obtained. Molecular docking results showed that the above-mentioned four components could inhibit the binding of ACE2 and SARS-COV-2 3CL to protect against COVID-19. Conclusion: In this study, the active components and core targets of three prescriptions in the treatment of COVID-19 were elaborated by network pharmacology and molecular docking, providing a reference for their applications.

13.
J Hosp Infect ; 116: 78-86, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1404776

ABSTRACT

AIM: To describe the nosocomial transmission of Air, multidrug-resistant, Acinetobacter baumannii, nosocomial, COVID-19 Acinetobacter baumannii (MRAB) in an open-cubicle neurology ward with low ceiling height, where MRAB isolates collected from air, commonly shared items, non-reachable high-level surfaces and patients were analysed epidemiologically and genetically by whole-genome sequencing. This is the first study to understand the genetic relatedness of air, environmental and clinical isolates of MRAB in the outbreak setting. FINDINGS: Of 11 highly care-dependent patients with 363 MRAB colonization days during COVID-19 pandemic, 10 (90.9%) and nine (81.8%) had cutaneous and gastrointestinal colonization, respectively. Of 160 environmental and air samples, 31 (19.4%) were MRAB-positive. The proportion of MRAB-contaminated commonly shared items was significantly lower in cohort than in non-cohort patient care (0/10, 0% vs 12/18, 66.7%; P<0.001). Air dispersal of MRAB was consistently detected during but not before diaper change in the cohort cubicle by 25-min air sampling (4/4,100% vs 0/4, 0%; P=0.029). The settle plate method revealed MRAB in two samples during diaper change. The proportion of MRAB-contaminated exhaust air grills was significantly higher when the cohort cubicle was occupied by six MRAB patients than when fewer than six patients were cared for in the cubicle (5/9, 55.6% vs 0/18, 0%; P=0.002). The proportion of MRAB-contaminated non-reachable high-level surfaces was also significantly higher when there were three or more MRAB patients in the cohort cubicle (8/31, 25.8% vs 0/24, 0%; P=0.016). Whole-genome sequencing revealed clonality of air, environment, and patients' isolates, suggestive of air dispersal of MRAB. CONCLUSIONS: Our findings support the view that patient cohorting in enclosed cubicles with partitions and a closed door is preferred if single rooms are not available.


Subject(s)
Acinetobacter Infections , Acinetobacter baumannii , COVID-19 , Cross Infection , Acinetobacter Infections/drug therapy , Acinetobacter Infections/epidemiology , Acinetobacter baumannii/genetics , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , Cross Infection/drug therapy , Cross Infection/epidemiology , Drug Resistance, Multiple, Bacterial , Humans , Microbial Sensitivity Tests , Pandemics , SARS-CoV-2
14.
Communications in Information and Systems ; 21(1):31-36, 2021.
Article in English | Web of Science | ID: covidwho-1124178

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused by coronavirus disease 2019 (COVID-19) has led to a tremendous human fatality and economic loss. SARS-CoV-2 infectivity is a key reason for the widespread viral transmission, but its rigorous experimental measurement is essentially impossible due to the ongoing genome evolution around the world. We show that artificial intelligence (AI) and algebraic topology (AT) offer an accurate and efficient alternative to the experimental determination of viral infectivity. AI and AT analysis indicates that the on-going mutations make SARS-CoV-2 more infectious.

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