Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 93
Filter
1.
Nature Machine Intelligence ; 4(5):494-+, 2022.
Article in English | English Web of Science | ID: covidwho-1882770

ABSTRACT

Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight. Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method was developed that calculates a scan-specific optimal window and removes irrelevant tissues such as airways and blood vessels from images with segmentation models, so that subvisual abnormalities in lung scans become visible.

2.
Journal of Silk ; 59(1):51-57, 2022.
Article in Chinese | Scopus | ID: covidwho-1875880

ABSTRACT

As the functional textiles market expands rapidly, people's requirements and focus on functional textiles have shifted to multi-functional, cost-effective, and high standards. Meanwhile, the continuously rampant COVID-19 epidemic has greatly increased the demands for antibacterial and antiviral functional textiles. As the most widely produced and sold artificial fibre, traditional PET fabric has some disadvantages: easy to gather static electricity, poor moisture-penetrability, and poor colourability. This study aims to develop a low temperature cationic dyeable environmentally-friendly PET fabric with bacteriostatic and fast-drying functions, and to increase the product added value, thereby meeting the ever-growing consumption needs. A multi-functional polyester fibre with antibacterial, hydroscopic and fast-drying functions is developed using modified low-temperature cationic dyeable cross-sectional polyester filament yarn and antibacterialpolyester filament yarn. A series of low-temperature cationic dyeable fabrics with hydroscopic, quick-drying and antibacterial properties were prepared by cross orthogonal method. The cross-sectional polyester fibre as a profiled sectional product has multiple grooves longitudinally, greatly increasing its specific surface. The fibres and filament yarns have excellent breathability, moisture permeability, and dyeability. Adding antibacterial masterbatch to PET antibacterial polyester during spinning can achieve reliable antibacterial performance of the fibre layer, which has advantages of good laundering durability, easy production and excellent antibacterial property. In this study, cross-section polyester filament yarn is used as the warp yarn of the fabric. The weft yarn adopts alternative weft knitting between cross-sectional polyester filament yarn andantibacterial polyester filament yarn, with the weft knitting ratio of 1:0, 1:4, 1:3, 1:2, 1:1, 2:1, 3:1 and 4:1, respectively. The warp density is 650 ends/10 cm, and the weft density is 450 picks/10 cm. The satin weave structure is selected in the experiment for better hand feeling and appearance of the fabric, and to meet the requirements of product hydroscopic and antibacterial application. Its hydroscopic and fast-drying properties are evaluated by measuring its wicking height, dripping diffusion rate, water absorption and evaporation rate, moisture permeability etc. The oscillation method is used to determine the fabric antibacterial properties against Escherichia Coli and Staphylococcus Aureus. TOPSIS integrated algorithm is used to analyze the optimal process. The results show that the interwoven fabric has excellent bacteriostatic performance, and the antibacterial rate of all samples exceeds 70%. At the same time, the fabric has excellent moisture absorption and fast drying performance;the dripping diffusion time is less than 5 seconds, the average water absorption rate exceeds 150% and the average evaporation rate is more than 90% in 20 minutes. Besides, it is found that the higher weft yarn ration of the cross-sectional hydroscopic and fast-drying polyester, the better hydroscopic fast-drying performance of the fabric. The interwoven fabric obtained through TOPSIS algorithm method with cross-sectional polyester/antibacterial polyester proportion of 1:4 has the optimal comprehensive performance. Experimental results show that this process can effectively solve polyester fabric disadvantages of poor moisture-penetrability, tendeny to gather static electricity, and poor colourability and add excellent bacteriostatic performance. The alternative weft knitting between hydroscopic fast-drying polyester and antibacterial polyester, and TOPSIS comprehensive analysis method can help effectively develop multi-functional fabrics. Besides, adding other functional fibre or yarns on this basis is conductive to achieving other functions like ultraviolet protection, deodorization, skin care, etc. The research results of this study can provide a reference for the design of functional textiles. © 2022 China Silk Association. All ights reserved.

3.
Iet Information Security ; : 11, 2022.
Article in English | Web of Science | ID: covidwho-1852536

ABSTRACT

As the world is now fighting against rampant virus COVID-19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non-COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture-based feature extraction for extracting logo, symbol and for the signature we extract Crest-Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1-score, and computation time over state-of-art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201-LBP) outperforms with an accuracy of 0.94.

4.
18th IEEE International Symposium on Biomedical Imaging (ISBI) ; : 42-45, 2021.
Article in English | Web of Science | ID: covidwho-1822033

ABSTRACT

The wide spread of coronavirus disease 2019 (COVID-19) has become a global concern and millions of people have been infected. Chest Computed Tomography (CT) imaging is important for screening and diagnosis of this disease, where segmentation of the lung infections plays a critical role for quantitative assessment of the disease progression. Currently, 3D Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation tasks. However, most 3D segmentation CNNs have a large set of parameters and huge floating point operations (FLOPs), causing high command for equipments. In this work, we propose LCOV-Net, a lightweight 3D CNN for accurate segmentation of COVID-19 pneumonia lesions from CT volumes. The core component of LCOV-Net is a lightweight attention-based convolutional block (LACB), which consists of a spatiotemporal separable convolution branch to reduce parameters and a lightweight feature calibration branch to improve the learning ability. We combined our LACB module with 3D U-Net as LCOV-Net, and tested our method on a dataset of CT scans of 130 COVID-19 patients for the infection lesion segmentation. Experimental results show that: (1) our LCOV-Net outperforms existing lightweight networks for 3D segmentation and (2) compared with the widely used 3D U-Net, our LCOV-Net improved the Dice score by around 20.36% and reduced the parameter number by 90.16%, leading to 27.93% speedup. Models and code are available at https://github.com/afeizqf/LCOVNet.

5.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333821

ABSTRACT

BACKGROUND: mRNA COVID-19 vaccines are playing a key role in controlling the COVID-19 pandemic. The relationship between post-vaccination symptoms and strength of antibody responses is unclear. OBJECTIVE: To determine whether adverse effects caused by vaccination with the Pfizer/BioNTech BNT162b2 vaccine are associated with the magnitude of vaccine-induced antibody levels. DESIGN: Single center, prospective, observational cohort study. SETTING: Participants worked at Walter Reed National Military Medical Center and were seen monthly at the Naval Medical Research Center Clinical Trials Center. PARTICIPANTS: Generally healthy adults that were not severely immunocompromised, had no history of COVID-19, and were seronegative for SARS-CoV-2 spike protein prior to vaccination. MEASURES: Severity of vaccine-associated symptoms was obtained through participant completed questionnaires. Testing for IgG antibodies against SARS-CoV-2 spike protein and receptor binding domain was conducted using microsphere-based multiplex immunoassays. RESULTS: 206 participants were evaluated (69.4% female, median age 41.5 years old). We found no correlation between vaccine-associated symptom severity scores and vaccine-induced antibody titers one month after vaccination. We also observed that 1) post-vaccination symptoms were inversely correlated with age and weight and more common in women, 2) systemic symptoms were more frequent after the second vaccination, 3) high symptom scores after first vaccination were predictive of high symptom scores after second vaccination, and 4) older age was associated with lower titers. LIMITATIONS: Study only observes antibody responses and consists of healthy participants. CONCLUSIONS: Lack of post-vaccination symptoms following receipt of the BNT162b2 vaccine does not equate to lack of vaccine-induced antibodies one month after vaccination. This study also suggests that it may be possible to design future mRNA vaccines that confer robust antibody responses with lower frequencies of vaccine-associated symptoms. FUNDING: This study was executed by the Infectious Disease Clinical Research Program (IDCRP), a Department of Defense (DoD) program executed by the Uniformed Services University of the Health Sciences (USUHS) through a cooperative agreement by the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF). This project has been funded by the Defense Health Program, U.S. DoD, under award HU00012120067. Project funding for JHP was in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The funding bodies have had no role in the study design or the decision to submit the manuscript for publication.

6.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-332844

ABSTRACT

We identified a Delta-Omicron SARS-CoV-2 recombinant in an unvaccinated, immunosuppressed kidney transplant recipient who had positive COVID-19 tests in December 2021 and February 2022 and was initially treated with Sotrovimab. Viral sequencing in February 2022 revealed a 5' Delta AY.45 portion and a 3' Omicron BA.1 portion with a recombination breakpoint in the spike N-terminal domain, adjacent to the Sotrovimab quaternary binding site. The recombinant virus induced cytopathic effects with characteristics of both Delta (large cells) and Omicron (cell rounding/detachment). Monitoring of immunosuppressed COVID-19 patients treated with antiviral monoclonal antibodies is crucial to detect potential selection of recombinant variants.

8.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-330684

ABSTRACT

Neuro-inflammation signaling has been identified as an important hallmark of Alzheimer's disease (AD) in addition to amyloid beta plaques (Abeta) and neurofibrillary tangles (NFTs). However, our knowledge of neuro-inflammation is very limited;and the core signaling pathways associated with neuro-inflammation are missing. From a novel perspective, i.e., investigating weakly activated molecular signals (rather than the strongly activated molecular signals), in this study, we uncovered the core neuro-inflammation signaling pathways in AD. Our novel hypothesis is that weakly activated neuro-inflammation signaling pathways can cause neuro-degeneration in a chronic process;whereas, strongly activated neuro-inflammation often cause acute disease progression like in COVID-19. Using the two large-scale genomics datasets, i.e., Mayo Clinic (77 control and 81 AD samples) and RosMap (97 control and 260 AD samples), our analysis identified 7 categories of signaling pathways implicated on AD and related to virus infection: immune response, x-core signaling, apoptosis, lipid dysfunctional, biosynthesis and metabolism, and mineral absorption signaling pathways. More interestingly, most of genes in the virus infection, immune response and x-core signaling pathways, are associated with inflammation molecular functions. Specifically, the x-core signaling pathways were defined as a group of 9 signaling proteins: MAPK, Rap1, NF-kappa B, HIF-1, PI3K-Akt, Wnt, TGF-beta, Hippo and TNF, which indicated the core neuro-inflammation signaling pathways responding to the low-level and weakly activated inflammation and hypoxia, and leading to the chronic neuro-degeneration. The core neuro-inflammation signaling pathways can be used as novel therapeutic targets for effective AD treatment and prevention.

9.
Open Forum Infectious Diseases ; 8(SUPPL 1):S323, 2021.
Article in English | EMBASE | ID: covidwho-1746553

ABSTRACT

Background. The Prospective Assessment of SARS-CoV-2 Seroconversion (PASS) study is following over 200 healthcare workers who have received the Pfizer-BioNTech BNT162b2 COVID-19 mRNA vaccine. A major aim of the study is to determine whether baseline antibody titers against the seasonal human coronaviruses are associated with altered levels of vaccine-induced antibody responses to SARS-CoV-2. Methods. Serial serum samples obtained pre-vaccination and 1 month after the second dose were tested for IgG antibodies against the full pre-fusion spike protein and the receptor binding domain (RBD) of SARS-CoV-2, as well as the full pre-fusion spike proteins of OC43, HKU1, 229E, and NL63. Antibodies were measured using highly sensitive and specific multiplex assays based on Luminex-xMAP technology. Results. Preliminary analyses of the first 103 subjects in whom we have 1 month post-vaccination serum demonstrate development of high IgG geometric mean titers (GMT) to both the full spike protein (GMT: 13,685, 12,014-15,589, 95% CI) and the RBD (GMT: 19,448, 17,264-21,908, 95% CI) of SARS-CoV-2 after the 2nd vaccine dose. Preliminary analysis demonstrates no association between baseline antibody titers against spike protein of OC43 and antibody titers against SARS-CoV-2 spike protein (Pearson's r-value= 0.13, P-value= 0.21) or RBD (Pearson's r-value= 0.09, P-value= 0.36) one month after vaccination. Future analyses will evaluate whether there is an association with baseline seasonal coronavirus antibody titers and either SARS-CoV-2 neutralization titers or anti-SARS-CoV-2 spike protein titers at 6 months after vaccination. Conclusion. These preliminary results suggest that baseline antibody responses to seasonal coronaviruses neither boost nor impede SARS-CoV-2 vaccine-induced antibody responses. Longitudinal sampling will enable assessment of vaccine durability and determination of whether baseline seasonal coronavirus antibody levels are associated with altered duration of detectable COVID-19 vaccine-induced antibody responses.

10.
Blood ; 138:4239, 2021.
Article in English | EMBASE | ID: covidwho-1736303

ABSTRACT

Objective: To analyze the relationship between D-dimer, inflammatory markers, cytokines and disease severity, and the possibility of early identification of COVID-19 critical type patients. Methods: PubMed, EMBASE and CNKI databases were searched by computer, and references of related reviews and systematic reviews were manually searched as supplements. The retrieval deadline is February 9, 2021. According to the inclusion and exclusion criteria, the literatures were screened and the quality was evaluated, and then the data were extracted for meta-analysis. The fixed/random effects model was used to calculate the weighted mean difference (WMD) and 95% CI to evaluate whether the levels of D-dimer, hsCRP, IL-6, IL-8, IL-10 and TNF-α in critical type patients were statistically different from those in severe type patients. If there were statistical differences, logistic regression analysis was used, and establish the receiver operating characteristic curve (ROC) and area under the curve (AUC) of each index for the diagnosis of critical type patients. The best diagnostic value of COVID-19 critical type patients was calculated by Youden index. Results: A total of 3519 literatures entered the screening process. According to the inclusion and exclusion criteria, 40 articles were finally included in this study, and all of them were high-quality studies after evaluation. The results of meta-analysis showed that the levels of D-dimer, hsCRP, IL-6, IL-8 and IL-10 in critical type group were significantly higher than those in severe type group (P<0.05). Based on ROC curve, the AUC of D-dimer was 0.785 (95% CI: 0.671-0.899), AUC of hsCRP was 0.884 (95% CI: 0.632-1.000), AUC of IL-6 was 0.819 (95% CI: 0.700-0.939), which had diagnostic significance for critical type patients (P<0.05). The optimal diagnostic threshold of D-dimer was ≥2.00 mg/L (sensitivity 89.3%, specificity 64.0%);the optimal diagnostic threshold of hsCRP was ≥64.22 mg/L (sensitivity 75.0%, specificity 100%);the optimal diagnostic threshold of IL-6 was ≥33.01 ng/L (sensitivity 68.0%, specificity 92.0%). Conclusion: The levels of D-dimer, hsCRP, IL-6, IL-8 and IL-10 in COVID-19 critical type patients were significantly higher than those in severe type patients. Our results might be helpful in identify and risk reduction of mortality in critical types patients infected with COVID-19. Disclosures: No relevant conflicts of interest to declare.

11.
Journal of Theoretical and Applied Information Technology ; 100(3):743-755, 2022.
Article in English | Scopus | ID: covidwho-1727995

ABSTRACT

Analysis and design of e-office application on ministry health of Republic of Indonesia based on microservice architecture. The system is converting from monolithic architecture into microservice architecture by using domain driven design framework to breaking the domain business of the system. Microservice architecture is an important part to agile, resilience, and high-availability. By improvement the feature and the high intention to use to the system by regulation of e-government of Republic of Indonesia at Ministry bureaucracy. By using the Microservice Architecture will improve the productivity of employee, increase the public services, adaptable in current condition of pandemic covid-19 cases in Indonesia. © 2022 Little Lion Scientific

12.
IEEE/CVF International Conference on Computer Vision (ICCVW) ; : 1487-1491, 2021.
Article in English | Web of Science | ID: covidwho-1705631

ABSTRACT

In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This nearly makes conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, community visit check-in, etc. Therefore, it is very urgent to boost performance of existing face recognition systems on masked faces. Most current advanced face recognition approaches are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at: https://github.com/X-zhangyany/Real-World-Masked-Face-Dataset.

13.
13th International Conference on Education Technology and Computers, ICETC 2021 ; : 160-163, 2021.
Article in English | Scopus | ID: covidwho-1699884

ABSTRACT

Experiments play an important role in the course of automatic test for the students to master the knowledge and skills. However, the worldwide spread of Covid-19 forces the closure of universities and the learning model of face-to-face to learning online. To meet the online teaching requirement of automatic test experiment class, a teaching model is adopted based on Tencent classroom and Wechat group. Moreover, to support experiment at home a virtual experiment platform is designed which is composed of instrument simulators and VISA simulator. Practice shows the feasibility of the teaching model and the effectiveness of the developed virtual experiment platform, and lay the foundation for MOOC of the automatic test. © 2021 ACM.

14.
2nd International Conference on Computer, Big Data and Artificial Intelligence, ICCBDAI 2021 ; 2171, 2022.
Article in English | Scopus | ID: covidwho-1699820

ABSTRACT

Under the background of the novel coronavirus pneumonia outbreak in the world, unrestricted and contactless finger vein collection devices have significantly improved public health safety. However, due to the unfixed position of the finger and the open or semi-open characteristics of the acquisition device, it is inevitable to introduce plenty of factors that affect the recognition performance, such as low contrast, uneven illumination and edge disappearance. In view of these practical problems, we propose a method for ROI extraction of finger vein images that combines active contour method and morphological post-processing operations. This method starts from the local segmentation, and finally completes the acquisition of finger masks at the global level, and then combines some morphological operations to achieve precise extraction of finger masks. We designed and conducted plenty of comparison experiments on the proposed algorithm and the current mainstream finger vein image ROI extraction methods on three public available finger vein datasets. Experimental results show that our method accurately extracts the complete finger region mask and achieves the best matching accuracy on all datasets. © 2022 Institute of Physics Publishing. All rights reserved.

15.
IEEE Access ; 2022.
Article in English | Scopus | ID: covidwho-1699540

ABSTRACT

This paper proposes a new multi-kernel learning ensemble algorithm, called Ada-L1MKL-WSVR, which can be regarded as an extension of multi-kernel learning (MKL) and weighted support vector regression (WSVR). The first novelty is to add the L1 norm of the weights of the combined kernel function to the objective function of WSVR, which is used to adaptively select the optimal base models and their parameters. In addition, an accelerated method based on fast iterative shrinkage thresholding algorithm (FISTA) is developed to solve the weights of the combined kernel function. The second novelty is to propose an integrated learning framework based on AdaBoost, named Ada-L1MKL-WSVR. In this framework, we integrate FISTA into AdaBoost. At each iteration, we optimize the weights of the combined kernel function and update the weights of the training samples at the same time. Then an ensemble regression function of a set of regression functions is output. Finally, two groups of the experiments are designed to verify the performance of our algorithm. On the first group of the experiments including eight datasets from UCI machine learning repository, the MAEs and RMSEs of Ada-L1MKL-WSVR are reduced by 11.14% and 9.08% on average, respectively. Furthermore, on the second group of the experiments including the COVID-19 epidemic datasets from eight countries, the MAEs and RMSEs of Ada-L1MKL-WSVR are reduced by 31.19% and 29.98% on average, respectively. Author

16.
2021 ASEE Virtual Annual Conference, ASEE 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1696822

ABSTRACT

In this work, we describe two activities that we have designed - suitable for an introductory undergraduate course in computational and data science - that introduce rudimentary principles of graph theory. Both activities feature simple premises (yet have the potential for significant depth, depending on student interest and mathematical maturity), strengthen students' abilities in mathematical and computational reasoning, and interweave timely “out-of-the-classroom” themes that will resonate in 2021 and beyond. We also provide qualitative results from the deployment of these activities in a sophomore-level civil and environmental engineering class. © American Society for Engineering Education, 2021

17.
Embase;
Preprint in English | EMBASE | ID: ppcovidwho-326954

ABSTRACT

The rapid spread of the highly contagious Omicron variant of SARS-CoV-2 along with its high number of mutations in the spike gene has raised alarm about the effectiveness of current medical countermeasures. To address this concern, we measured neutralizing antibodies against Omicron in three important settings: (1) post-vaccination sera after two and three immunizations with the Pfizer/BNT162b2 vaccine, (2) convalescent sera from unvaccinated individuals infected by different variants, and (3) clinical-stage therapeutic antibodies. Using a pseudovirus neutralization assay, we found that titers against Omicron were low or undetectable after two immunizations and in most convalescent sera. A booster vaccination significantly increased titers against Omicron to levels comparable to those seen against the ancestral (D614G) variant after two immunizations. Neither age nor sex were associated with differences in post-vaccination antibody responses. Only three of 24 therapeutic antibodies tested retained their full potency against Omicron and high-level resistance was seen against fifteen. These findings underscore the potential benefit of booster mRNA vaccines for protection against Omicron and the need for additional therapeutic antibodies that are more robust to highly mutated variants.

18.
Embase;
Preprint in English | EMBASE | ID: ppcovidwho-326943

ABSTRACT

COVID-19 pathogen SARS-CoV-2 has infected hundreds of millions and caused over 5 million deaths to date. Although multiple vaccines are available, breakthrough infections occur especially by emerging variants. Effective therapeutic options such as monoclonal antibodies (mAbs) are still critical. Here, we report the development, cryo-EM structures, and functional analyses of mAbs that potently neutralize SARS-CoV-2 variants of concern. By high-throughput single cell sequencing of B cells from spike receptor binding domain (RBD) immunized animals, we identified two highly potent SARS-CoV-2 neutralizing mAb clones that have single-digit nanomolar affinity and low-picomolar avidity, and generated a bispecific antibody. Lead antibodies showed strong inhibitory activity against historical SARS-CoV-2 and several emerging variants of concern. We solved several cryo-EM structures at ~3 Å resolution of these neutralizing antibodies in complex with prefusion spike trimer ectodomain, and revealed distinct epitopes, binding patterns, and conformations. The lead clones also showed potent efficacy in vivo against authentic SARS-CoV-2 in both prophylactic and therapeutic settings. We also generated and characterized a humanized antibody to facilitate translation and drug development. The humanized clone also has strong potency against both the original virus and the B.1.617.2 Delta variant. These mAbs expand the repertoire of therapeutics against SARS-CoV-2 and emerging variants.

19.
International Journal of Information and Learning Technology ; ahead-of-print(ahead-of-print):25, 2022.
Article in English | Web of Science | ID: covidwho-1684982

ABSTRACT

Purpose Amid the coronavirus disease 2019 (COVID-19) pandemic, higher education institutions (HEI) all over the world have transitioned to online teaching. The purpose of this study is to examine the impact of technostress and negative emotional dissonance on online teaching exhaustion and teaching staff productivity. Design/methodology/approach Survey methodology was used to collect data from faculty members in Jordanian universities. A total of 217 responses were analyzed to test the research model. Findings The research findings reveal that technostress creators have various impact on online teaching exhaustion and teaching staff productivity. Negative emotional dissonance has positive impact on both online teaching exhaustion and teaching staff productivity. Further, online teaching exhaustion is negatively associated with teaching staff productivity. Research limitations/implications This research extends prior literature on technostress by examining the phenomenon in abnormal conditions (during a crisis). It further integrates technostress theory with emotional dissonance theory to better understand the impact of technostress creators on individual teaching staff productivity while catering for the interactional nature of teaching which is captured through emotional dissonance theory. Practical implications The research offers valuable insights for HEI and policymakers on how to support teaching staff and identifies strategies that should facilitate a smooth delivery of online education. Originality/value Unlike prior research that have examined technostress under normal operational conditions, this research examines the impact of technostress during a crisis. This study shows that technostress creators vary in their impact. Moreover, this study integrates technostress theory with emotional dissonance theory. While technostress theory captures the impact of technostress creators on individual teaching staff productivity, emotional dissonance theory captures the dynamic nature of the teaching process that involves interactions among teachers and students.

20.
Public Health ; 205: 6-13, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1648632

ABSTRACT

OBJECTIVES: Cigarette smoking is an established risk factor for illness severity and adverse outcomes in patients with COVID-19. Alcohol drinking may also be a potential risk factor for disease severity. However, the combined and interactive effects of drinking and smoking on COVID-19 have not yet been reported. This study aimed to examine the combined and interactive effects of alcohol drinking and cigarette smoking on the risk of severe illness and poor outcomes in patients with COVID-19. STUDY DESIGN: This was a multicentre retrospective cohort study. METHODS: This study retrospectively reviewed the data of 1399 consecutive hospitalised COVID-19 patients from 43 designated hospitals. Patients were grouped according to different combinations of drinking and smoking status. Multivariate mixed-effects logistic regression models were used to estimate the combined and interactive effects of drinking and smoking on the risk of severe COVID-19 and poor clinical outcomes. RESULTS: In the study population, 7.3% were drinkers/smokers, 4.3% were drinkers/non-smokers and 4.9% were non-drinkers/smokers. After controlling for potential confounders, smokers or drinkers alone did not show a significant increase in the risk of severe COVID-19 or poor clinical outcomes compared with non-drinkers/non-smokers. Moreover, this study did not observe any interactive effects of drinking and smoking on COVID-19. Drinkers/smokers had a 62% increased risk (odds ratio = 1.62, 95% confidence interval: 1.01-2.60) of severe COVID-19 but did not have a significant increase in the risk for poor clinical outcomes compared with non-drinkers/non-smokers. CONCLUSIONS: Combined exposure to drinking and smoking increases the risk of severe COVID-19, but no direct effects of drinking or smoking, or interaction effects of drinking and smoking, were detected.


Subject(s)
COVID-19 , Cigarette Smoking , Alcohol Drinking/adverse effects , Alcohol Drinking/epidemiology , COVID-19/epidemiology , Humans , Odds Ratio , Retrospective Studies
SELECTION OF CITATIONS
SEARCH DETAIL