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1.
Frontiers in Microbiology ; 13, 2022.
Article in English | Web of Science | ID: covidwho-2022792

ABSTRACT

BackgroundHere, a bibliometric and knowledge map analysis are used to analyze the research hot spots and development trends regarding the antibacterial effect of lactoferrin (LF). By looking for research hot spots and new topics, we provide new clues and research directions for future research. MethodsArticles and reviews regarding the antibacterial effect of LF were retrieved and from the Web of Science Core Collection (WoSCC) on 25 June 2022. CiteSpace and VOSviewer were used to conduct the bibliometric and knowledge map analysis. ResultsIn total, 8,292 authors at 2,151 institutions from 86 countries published 1,923 articles in 770 academic journals. The United States was the leader regarding research on the antibacterial effects of LF, while the Netherlands was a pioneer in conducting research in this field. The University of California system contributed the most publications. Bolscher JGM published most articles, while Wayne Bellamy had most cocitations. However, there was insufficient cooperation among the various institutions and authors. BioMetals published most LF-antibacterial activity-related articles, whereas Infection and Immunity was most commonly cocited journal. The most influential research hot spots about the antibacterial effect of LF focused on antimicrobial peptides, casein, human milk, expression, and Escherichia coli-related research. The latest hot spots and research frontier included COVID-19, antibiofilm activity, and immune defense. ConclusionsLF is a multifunctional protein with a broad spectrum of antimicrobial activities. The related field of antibacterial properties of LF will remain a research hot spot in future.

2.
Front Med (Lausanne) ; 9:988133, 2022.
Article in English | PubMed | ID: covidwho-2022785

ABSTRACT

PURPOSE: The purpose of this study was to investigate the hotspots and research trends of ophthalmology research. METHOD: Ophthalmology research literature published between 2017 and 2021 was obtained in the Web of Science Core Collection database. The bibliometric analysis and network visualization were performed with the VOSviewer and CiteSpace. Publication-related information, including publication volume, citation counts, countries, journals, keywords, subject categories, and publication time, was analyzed. RESULTS: A total of 10,469 included ophthalmology publications had been cited a total of 7,995 times during the past 5 years. The top countries and journals for the number of publications were the United States and the Ophthalmology. The top 25 global high-impact documents had been identified using the citation ranking. Keyword co-occurrence analysis showed that the hotspots in ophthalmology research were epidemiological characteristics and treatment modalities of ocular diseases, artificial intelligence and fundus imaging technology, COVID-19-related telemedicine, and screening and prevention of ocular diseases. Keyword burst analysis revealed that "neural network," "pharmacokinetics," "geographic atrophy," "implementation," "variability," "adverse events," "automated detection," and "retinal images" were the research trends of research in the field of ophthalmology through 2021. The analysis of the subject categories demonstrated the close cooperation relationships that existed between different subject categories, and collaborations with non-ophthalmology-related subject categories were increasing over time in the field of ophthalmology research. CONCLUSIONS: The hotspots in ophthalmology research were epidemiology, prevention, screening, and treatment of ocular diseases, as well as artificial intelligence and fundus imaging technology and telemedicine. Research trends in ophthalmology research were artificial intelligence, drug development, and fundus diseases. Knowledge from non-ophthalmology fields is likely to be more involved in ophthalmology research.

3.
7th International Conference on Distance Education and Learning, ICDEL 2022 ; : 246-252, 2022.
Article in English | Scopus | ID: covidwho-2020444

ABSTRACT

The sudden outbreak of COVID-19 pandemic at the beginning of 2020 poses a significant threat to the health and safety of people worldwide. Given the speed and scope of the COVID-19 pandemic, countries around the world have carried out scientific collaboration in the fight against the COVID-19 pandemic. This paper divides academic discourse power into academic discourse right and academic discourse impact. The number of published papers reflects the discourse right, and the number of cites reflects the academic discourse impact. The visualization analysis of research papers from 2019 to 2020 describes the worldwide scientific collaboration on COVID-19, and the academic discourse power of authors, institutions, and countries can be studied from the perspective of scientific collaboration. We analyze the scientific collaboration of 27,851 papers related to COVID-19 published during 2019-2020 from the perspectives of authors, institutions, and countries by using HistCite and VosViewer. Pearson correlation analysis is used to study the correlation between scientific collaboration, the number of published papers, and the number of cites. Furthermore, we find that scientific collaboration positively correlates with academic discourse right and academic discourse impact. Based on analysis of author collaboration, institutional collaboration and country collaboration, it was concluded that China has the highest total cites, reflecting its high academic discourse impact during the COVID-19 pandemic, and the USA has the highest number of international collaborators and the highest total number of published papers, reflecting its high discourse right during the COVID-19 pandemic. The number of cites and the number of published papers are significantly positively correlated with the number of collaborators in Pearson correlation of author collaboration, institutional collaboration and country collaboration.This study has presented the global collaboration on the research of COVID-19. We compared academic discourse right and academic discourse impact across different levels of authors, institutions, and countries, concluding that academic discourse right and academic discourse impact are significantly positively correlated with the number of collaborators. © 2022 ACM.

4.
J Med Chem ; 65(17):11840-11853, 2022.
Article in English | Web of Science | ID: covidwho-2016520

ABSTRACT

Site-selective lysine modification of peptides and proteins in aqueous solutions or in living cells is still a big challenge today. Here, we report a novel strategy to selectively quinolylate lysine residues of peptides and proteins under native conditions without any catalysts using our newly developed water-soluble zoliniums. The zoliniums could site-selectively quinolylate K350 of bovine serum albumin and inactivate SARS-CoV-2 3CL(pro) via covalently modifying two highly conserved lysine residues (K5 and K61). In living HepG2 cells, it was demonstrated that the simple zoliniums (5b and 5B) could quinolylate protein lysine residues mainly in the nucleus, cytosol, and cytoplasm, while the zolinium-fluorophore hybrid (8) showed specific lysosome-imaging ability. The specific chemoselectivity of the zoliniums for lysine was validated by a mixture of eight different amino acids, different peptides bearing potential reactive residues, and quantum chemistry calculations. This study offers a new way to design and develop lysine-targeted covalent ligands for specific application.

5.
Journal of chemical information and modeling ; 2022.
Article in English | MEDLINE | ID: covidwho-2008239

ABSTRACT

Five major variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged and posed challenges in controlling the pandemic. Among them, the current dominant variant, viz., Omicron, has raised serious concerns about its infectiousness and antibody neutralization. However, few studies pay attention to the effect of the mutations on the dynamic interaction network of Omicron S protein trimers binding to the host angiotensin-converting enzyme 2 (ACE2). In this study, we conducted molecular dynamics (MD) simulations and enzyme linked immunosorbent assay (ELISA) to explore the binding strength and mechanism of wild type (WT), Delta, and Omicron S protein trimers to ACE2. The results showed that the binding capacities of both the two variants' S protein trimers to ACE2 are enhanced in varying degrees, indicating possibly higher cell infectiousness. Energy decomposition and protein-protein interaction network analysis suggested that both the mutational and conserved sites make effects on the increase in the overall affinity through a variety of interactions. The experimentally determined KD values by biolayer interferometry (BLI) and the predicted binding free energies of the RBDs of Delta and Omicron to mAb HLX70 revealed that the two variants may have the high risk of immune evasion from the mAb. These results are not only helpful in understanding the binding strength and mechanism of S protein trimer-ACE2 but also beneficial for drug, especially for antibody development.

6.
Psychology Research and Behavior Management ; 15:1809-1821, 2022.
Article in English | Web of Science | ID: covidwho-1975995

ABSTRACT

Background: Medical workers have been increasingly involved in emergent public health events, which can lead to severe stress. However, no standardized, officially recognized, unified tool exists for mental distress measurement in medical workers who experienced the public health events. Purpose: In the present study, we propose the Global Health Events-Mental Stress Scale (GHE-MSS), as a revised version of the Impact of Event Scale-Revision (IES-R), for assessment of medical workers' acute mental stress responses within one month and their chronic mental stress responses within six months after major health events. Patients and methods: The IES-R was slightly modified, developed, and its reliability and validity were tested using the Delphi survey, primary survey with 115 participants, formal survey with 300 participants, and clinical evaluation with 566 participants. Results: Exploratory factor analysis and confirmatory factor analysis confirmed a promising validity of the scale. The values of Cronbach's alpha coefficient, the Spearman-Brown coefficient, and the retested Cronbach's alpha coefficient of the scale applied for the clinical evaluation were 0.88, 0.87, and 0.98, respectively, which confirmed a good internal consistency and stability. The results of the goodness-of-fit test indicated a good adaptation of the model. A correlation analysis was conducted to assess the correlation between the GHE-MSS and the PCL-C, which had a correlation coefficient of 0.68 (P < 0.01). Conclusion: GHE-MSS can be applied with a promising reliability and validity for the assessment of the acute mental stress response of medical workers experiencing public health events. This method can also be used for the screening of mental stress-associated disorders.

7.
13th International Conference on Swarm Intelligence, ICSI 2022 ; 13345 LNCS:106-117, 2022.
Article in English | Scopus | ID: covidwho-1971536

ABSTRACT

Since 2020, the Novel Coronavirus virus, which can cause upper respiratory and lung infections and even kill in severe cases, has been ravaging the globe. Rapid diagnostic tests have become one of the main challenges due to the severe shortage of test kits. This article proposes a model combining Long short-term Memory (LSTM) and Convolutional Block Attention Module for detection of COVID-19 from chest X-ray images. In this article, chest X-ray images from the COVID-19 radiology standard data set in the Kaggle repository were used to extract features by MobileNet, VGG19, VGG16 and ResNet50. CBAM and LSTM were used for classifcation detection. The simulation results showed that the experimental results showed that VGG16–CBAM–LSTM combination was the best combination to detect and classify COVID-19 from chest X-ray images. The classification accuracy of VGG-16-CBAM-LSTM combination was 95.80% for COVID-19, pneumonia and normal. The sensitivity and specificity of the combination were 96.54% and 98.21%. The F1 score was 94.11%. The CNN model proposed in this article contributes to automated screening of COVID-19 patients and reduces the burden on the healthcare delivery framework. © 2022, Springer Nature Switzerland AG.

8.
IEEE Sensors Journal ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-1961411

ABSTRACT

Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the traditional manual detection. In this paper, a tightly-coupled nonlinear optimization approach is used to augment the visual feature extraction of SLAM by the gyroscope of the IMU to obtain a high-precision visual inertial system for joint position and pose estimation. Based on the VINS-Mono frame, first, an LSD algorithm based on a conditional selection strategy is proposed to extract line features efficiently. Then, we propose recovering missing point features from line features. Moreover, we propose a strategy to recover vanishing point features from line features, and add residuals to the SLAM cost function based on optimization, which optimizes point-line features in real time to promote the tracking and matching accuracy. Second, the wavelet threshold denoising method based on the 3σcriterion is used to carry out real-time online denoising for gyroscope to improve the output precision. Our WD-PL-VINS was measured on publicly available EuRoC datasets, TUM VI datasets and evaluated and validated in lab testing with a unmanned vehicle (UV) based on the NVIDIA Jetson-TX2 development board. The results show that our method’s APE and RPE on MH 03 easy sequences are improved by 69.28% and 97.66%, respectively, compared with VINS-Mono. IEEE

9.
2nd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2021 ; : 119-125, 2021.
Article in English | Scopus | ID: covidwho-1948769

ABSTRACT

The new coronavirus (COVID-2019) epidemic outbreak has devastating impacts on people's daily lives and public healthcare systems. The chest X-ray image is an effective tool for diagnosing new coronavirus diseases. This paper proposes a new method to identify the new coronavirus from chest X-ray images to assist radiologists in fast and accurate image reading. We first enhance the contrast of X-ray images by using adaptive histogram equalization and eliminating image noise by using a median filter. Then, the X-ray image is fed to a sophisticated deep neural network (FAC-DPN-SENet) proposed by us to train a classifier, which is used to classify an X-ray image as usual or COVID-2019 or other pneumonia. Applying our method to a standard dataset, we achieve a classification accuracy of 93%, which is significantly better performance than several other state-of-the-art models, such as ResNet and DenseNet. This shows that the proposed method can be used as an effective tool to detect COVID-2019. © 2021 IEEE.

10.
Journal of Xiangya Medicine ; 5, 2020.
Article in English | Scopus | ID: covidwho-1904070
11.
47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 ; 2022-May:1376-1380, 2022.
Article in English | Scopus | ID: covidwho-1891395

ABSTRACT

Automatic segmentation of COVID-19 lesions is essential for computer-aided diagnosis. However, this task remains challenging because widely-used supervised based methods require large-scale annotated data that is difficult to obtain. Although an unsupervised method based on anomaly detection has shown promising results in [1], its performance is relatively poor. We address this problem by proposing a pixel-level and affinity-level knowledge distillation method. It obtains a pre-trained teacher network with rich semantic knowledge of CT images by constructing and training an auto-encoder at first, and then trains a student network with the same architecture as the teacher by distilling the teacher's knowledge only from normal CT images, and finally localizes COVID-19 lesions using the feature discrepancy between the teacher and the student networks. Besides, except for the traditional pixel-level distillation, we design the affinity-level distillation that takes into account the pairwise relationship of features to fully distill effective knowledge. We evaluate this method by using three different COVID-19 datasets and the experimental results show that the segmentation performance is largely improved when it is compared with the other existing unsupervised anomaly detection methods. © 2022 IEEE

12.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333583

ABSTRACT

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense. ONE SENTENCE SUMMARY: In severe COVID-19 patients, the immune system fails to generate cells that define mild disease;antibodies in their serum actively prevents the successful production of those cells.

13.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333539

ABSTRACT

While vaccine development will hopefully quell the global pandemic of COVID-19 caused by SARS-CoV-2, small molecule drugs that can effectively control SARS-CoV-2 infection are urgently needed. Here, inhibitors of spike (S) mediated cell entry were identified in a high throughput screen of an approved drugs library with SARS-S and MERS-S pseudotyped particle entry assays. We discovered six compounds (cepharanthine, abemaciclib, osimertinib, trimipramine, colforsin, and ingenol) to be broad spectrum inhibitors for spike-mediated entry. This work should contribute to the development of effective treatments against the initial stage of viral infection, thus reducing viral burden in COVID-19 patients. Abstract figure:

14.
IEEE Transactions on Intelligent Transportation Systems ; 2022.
Article in English | Scopus | ID: covidwho-1788788

ABSTRACT

With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m-2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m-1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54%under this epidemic. IEEE

15.
Non-conventional in English | National Technical Information Service, Grey literature | ID: grc-753531

ABSTRACT

The first step of SARS-CoV-2 infection is binding of the spike proteins receptor binding domain to the host cells ACE2 receptor on the plasma membrane. Here, we have generated a versatile imaging probe using recombinant Spike receptor binding domain conjugated to fluorescent quantum dots (QDs). This probe is capable of engaging in energy transfer quenching with ACE2-conjugated gold nanoparticles to enable monitoring of the binding event in solution. Neutralizing antibodies and recombinant human ACE2 blocked quenching, demonstrating a specific binding interaction. In cells transfected with ACE2-GFP, we observed immediate binding of the probe on the cell surface followed by endocytosis. Neutralizing antibodies and ACE2-Fc fully prevented binding and endocytosis with low nanomolar potency. Importantly, we will be able to use this QD nanoparticle probe to identify and validate inhibitors of the SARS-CoV-2 Spike and ACE2 receptor binding in human cells. This work enables facile, rapid, and high-throughput cell-based screening of inhibitors for coronavirus Spike-mediated cell recognition and entry.

16.
Eur Rev Med Pharmacol Sci ; 26(3): 1020-1027, 2022 02.
Article in English | MEDLINE | ID: covidwho-1699173

ABSTRACT

OBJECTIVE: Microorganisms present a global public health problem and are the leading cause of hospital-acquired infections. Therefore, it is essential to study the prevalence of microorganisms in hospital environments. The conclusion from such a study can contribute to identify the areas most likely to be contaminated in a hospital and appropriate measures that can decrease the exposure risk. MATERIALS AND METHODS: The prevalence of microorganisms in hospital air was examined in different departments by obtaining air samples with an impactor before and during the SARS-CoV-2 pandemic. A total of 2145 microorganisms were identified, and the corresponding data were jointly analyzed by area, sampling period, and concentration. RESULTS: The most frequently detected microorganisms in hospital air were Staphylococcus, Micrococcus, Neisseria, and fungi, and the more polluted departments were the hemodialysis department, respiratory department, treatment room, and toilet. Significant differences were found between the concentration of bacteria and fungi before and during the pandemic, which could be related to multiple environmental conditions. Furthermore, SARS-CoV-2 was negative in all the air samples. CONCLUSIONS: Overall, this study confirmed the existence and dynamic characteristics of airborne microorganisms in a hospital. The results contribute to the adaptation of specific measures which can decrease the exposure risk of patients, visitors, and staff.


Subject(s)
Air Microbiology , Bacteria/isolation & purification , Fungi/isolation & purification , Hospitals , Air Pollution, Indoor , Bacteria/classification , Environmental Monitoring , Epidemiological Monitoring , Fungi/classification , Hospital Departments , Pandemics , SARS-CoV-2
17.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-320524

ABSTRACT

The crisis caused by COVID-19 revealed the global unpreparedness to handle the impact of a pandemic. In this paper, we present a statistical analysis of the data related to the COVID-19 outbreak in China, specifically the infection speed, death and fatality rates in Hubei province. By fitting distributions of these quantities we design a parametric reinsurance contract whose trigger and cap are based on the probability distributions of the infection speed, death and fatality rates. In particular, fitting the distribution for the infection speed and death rates we provide a measure of the effectiveness of a state's action during an epidemic, and propose a reinsurance contract as a supplement to a state's social insurance to alleviate financial costs.

18.
ACM Transactions on Intelligent Systems and Technology ; 12(6), 2021.
Article in English | Scopus | ID: covidwho-1685720

ABSTRACT

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers). © 2021 Association for Computing Machinery.

19.
2021 International Conference on E-Commerce and E-Management, ICECEM 2021 ; : 568-571, 2021.
Article in English | Scopus | ID: covidwho-1685079

ABSTRACT

The COVID-19 pandemic has brought severe challenges and great uncertainties to the preparations and normal staging of the Beijing 2022 Winter Olympics. Based on this background, this paper analyzes the global COVID-19 for the future development of three kinds of epidemic situation, different situation the different influence to the Beijing Olympics, and finally from the games competitions held offline and use the Internet online hold the perspective of two run competitions modes, respectively, under the influence of the global COVID-19 outbreak proposed the concrete prevention and control countermeasures of the Beijing Olympics. In order to provide theoretical reference and decision-making support for the normal holding and operation of Beijing Winter Olympic Games. © 2021 IEEE.

20.
2021 International Conference on E-Commerce and E-Management, ICECEM 2021 ; : 248-251, 2021.
Article in English | Scopus | ID: covidwho-1685074

ABSTRACT

As the most influential sports event in the world, there will be many athletes and spectators from all over the world during the Olympic Games. However, the continuing severe global COVID-19 situation has a significant impact on the smooth hosting of the 2022 Beijing Winter Olympics Games (hereinafter referred to as the 'Beijing Winter Olympics'). Based on above, this paper analyzed the impact of the global COVID-19 and expounded the six risks of Beijing Winter Olympic Games. Finally, it provided corresponding suggestions and countermeasures of the preparatory work transition for the change of running mode. It is expected to provide theoretical reference and decision-making support for the Beijing Winter Olympic Games to prevent and control of the COVID-19 emergency risk. © 2021 IEEE.

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