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1.
BMC Med Imaging ; 22(1): 110, 2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1879227

ABSTRACT

BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. METHODS: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann-Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. RESULTS: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. CONCLUSION: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.


Subject(s)
COVID-19 , Deep Learning , Artificial Intelligence , Bayes Theorem , COVID-19/diagnostic imaging , Critical Care , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
2.
Independent Journal of Management & Production ; 13(3):S107-S122, 2022.
Article in English | ProQuest Central | ID: covidwho-1879674

ABSTRACT

The purpose of this study was in the research of prospects for simultaneous use of 6G generation cellular communications for the purposes of automatization of cost accounting of the activity of enterprises of various branches and cybersecurity of accounting information. The theoretical and methodological aspects of the use of 6G cellular network technologies for accounting and cybersecurity purposes have been studied on the basis of general research methods - institutional and innovative;economic and mathematical methods of analysis using Excel spreadsheets were used to predict the pace of implementation of cellular communication of new generations;to determine perspective areas of use of 6G technology - methods of bibliographic and comparative analysis using the information resource "ResearchGate". The methods of permanent collection and transmission of accounting data about the production process and the procedure for monitoring the stay of employees or outsiders at the workplace using production equipment connected to the 6G cellular network has been developed. The procedure for combining the functional abilities of Global Positioning System (GPS) and cellular positioning (mobile subscribers)for accounting of transport costs and control over the movement and economic use of vehicles has been proposed.The procedure for combining unmanned aerial vehicles in a cluster on the basis of 6G communication with the purpose of aerovisual surveillance of agricultural and construction activities for automated accounting of production costs and prevention of unauthorized getting into an enterprise ofpersons (drones). The methods for determining the cost of rental space from the lessor based on counting the popularity among visitors and identifying offenders (thieves of information and material resources) through automated monitoring of the location of 6G cellular subscribers. The practical implementation of the developments presented in the article on the use of 6G cellular technologies will contribute to reliable costing and accounting of production costs of production, agricultural, construction, trade activities in combination with effective cyber protection of enterprises in preventing and detecting violators of information and territorial security. Further research is needed on the methods of management of business entities on the basis of accounting information obtained with the use of 6G cellular network technology.

3.
Indian Journal of Anesthesia ; 66(5):368-374, 2022.
Article in English | ProQuest Central | ID: covidwho-1879556

ABSTRACT

Background and Aims: The incorporation of artificial intelligence (AI) in point-of-care ultrasound (POCUS) has become a very useful tool to quickly assess cardiorespiratory function in coronavirus disease (COVID)-19 patients. The objective of this study was to test the agreement between manual and automated B-lines counting, left ventricular outflow tract velocity time integral (LVOT-VTI) and inferior vena cava collapsibility index (IVC-CI) in suspected or confirmed COVID-19 patients using AI integrated POCUS. In addition, we investigated the inter-observer, intra-observer variability and reliability of assessment of echocardiographic parameters using AI by a novice. Methods: Two experienced sonographers in POCUS and one novice learner independently and consecutively performed ultrasound assessment of B-lines counting, LVOT-VTI and IVC-CI in 83 suspected and confirmed COVID-19 cases which included both manual and AI methods. Results: Agreement between automated and manual assessment of LVOT-VTI, and IVC-CI were excellent [intraclass correlation coefficient (ICC) 0.98, P < 0.001]. Intra-observer reliability and inter-observer reliability of these parameters were excellent [ICC 0.96-0.99, P < 0.001]. Moreover, agreement between novice and experts using AI for LVOT-VTI and IVC-CI assessment was also excellent [ICC 0.95-0.97, P < 0.001]. However, correlation and intra-observer reliability between automated and manual B-lines counting was moderate [(ICC) 0.52-0.53, P < 0.001] and [ICC 0.56-0.69, P < 0.001], respectively. Inter-observer reliability was good [ICC 0.79-0.87, P < 0.001]. Agreement of B-lines counting between novice and experts using AI was weak [ICC 0.18, P < 0.001]. Conclusion: AI-guided assessment of LVOT-VTI, IVC-CI and B-lines counting is reliable and consistent with manual assessment in COVID-19 patients. Novices can reliably estimate LVOT-VTI and IVC-CI using AI software in COVID-19 patients.

4.
Security and Communication Networks ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-1879156

ABSTRACT

The Corona Virus Disease 2019 epidemic broke out in 2020, and digital technologies pervaded all aspects of people’s lives, resulting in a significant shift in how education is delivered. The importance and role of digital technologies and online learning are highlighted in this paper, which examines the challenges posed by the sudden epidemic crisis to higher education institutions, analyses the factors that universities must consider in order to effectively create flexible learning pathways, and examines the challenges posed by the sudden epidemic crisis to higher education institutions. In the postepidemic era, the use of the Internet and online teaching platforms by university faculty to integrate online and offline teaching has not only facilitated the construction of “golden courses” but also added impetus to teaching reform, and digital technology-based teaching models have provided higher education practitioners with the opportunity to rethink scholarship and innovative teaching. In this paper, we propose a personalized learning resource recommendation system that includes user profiles to fully explore and analyze users’ learning behaviors and cognitive characteristics and enhance the depth and breadth of personalized education with the help of the Internet and artificial intelligence technologies in order to provide meaningful information and thoughts for higher education institutions to discuss and adapt to the education model in the postepidemic era. The goal is to provide useful information and ideas for higher education institutions to discuss and adapt to the postepidemic education paradigm.

5.
Strategic HR Review ; 21(3):74-77, 2022.
Article in English | ProQuest Central | ID: covidwho-1878953

ABSTRACT

Purpose>The purpose of the paper is to highlight the area on how can we bridge the skills gap in post-coronavirus Britain through role-relevant qualifications.Design/methodology/approach>The paper draws on the author’s personal observations and opinions learned through experience in the field.Findings>The paper explores a number of potential solutions to the skills gap, including post-pandemic digital transformation creating a bigger rift between supply and demand;addressing the digital skills vacuum, which intensifies the war on talent;upskilling;and plugging the skills gap with industry-focused education and training.Originality/value>The paper offers original insight based on the author’s unique perspective.

6.
Strategic HR Review ; 21(3):83-86, 2022.
Article in English | ProQuest Central | ID: covidwho-1878951

ABSTRACT

Purpose>This paper aims to review and explore the adoption of Metaverse as a training ecosystem. The paper focuses on the current debate on the relevance of Metaverse for training.Design/methodology/approach>This paper reviews the recent articles and interviews and then critically discusses the future of training in Metaverse.Findings>Recent reports, studies and developments show that immersive training holds much potential. The future for training in Metaverse seems to be bright with benefits for both employers and employees.Originality/value>This viewpoint paper is easy to read and comprehensive.

7.
Journal of Hospitality and Tourism Technology ; 13(3):349-355, 2022.
Article in English | ProQuest Central | ID: covidwho-1878915

ABSTRACT

[...]these technologies can provide efficient resource management in all sub-sectors that make up the travel and hospitality industry (e.g. transportation, accommodation facilities and events). [...]the development of information technologies facilitates information sharing on a global scale.

8.
Journal of Hospitality and Tourism Technology ; 13(3):500-526, 2022.
Article in English | ProQuest Central | ID: covidwho-1878914

ABSTRACT

Purpose>Concerning the emergence of Industry 4.0 and the concept of “smartness”, the technology competence of hospitality practitioners that was previously neglected and overlooked should be explored. Therefore, this study aims to explore previous hospitality technology competence through a literature review and then to extend, strengthen and build a new framework of the required technology competencies for hospitality practitioners in terms of facing smartness.Design/methodology/approach>To investigate the previous research on the characteristics of the required technology competencies for hospitality practitioners, this study carried out a systematic literature review (SLR) on works published from 2011 to 2020. Then, based on the SLR results, the required technology competencies for hospitality practitioners in terms of facing smartness was explored with 26 experts from the government, industry and academia. The data were analysed through thematic analysis based on the perspectives of task–technology fit, and then, the framework was constructed.Findings>This study reconfirmed that technology competence has been neglected in the previous hospitality competence literature and that the current methods and ways of thinking cannot succeed in this smart era. Moreover, based on fundamental technology competence, a new framework with ten dimensions of technology competencies required for hospitality practitioners in terms of facing smartness was created.Originality/value>This study identified the required technology competencies for hospitality practitioners, an area that has rarely been addressed in the previous literature. Moreover, specific competencies, especially those needed to face this smart era, are urgent and novel in the academic hospitality field.

9.
European Journal of Marketing ; 56(6):1721-1747, 2022.
Article in English | ProQuest Central | ID: covidwho-1878876

ABSTRACT

Purpose>This paper aims to examine how consumers respond to social media influencers that are created through artificial intelligence (AI) and compares effects to traditional (human) influencers.Design/methodology/approach>Across two empirical studies, the authors examine the efficacy of AI social media influencers. With Study 1, the authors establish baseline effects for AI influencers and investigate how social-psychological distance impacts consumer perceptions. The authors also investigate the role of an influencer’s agency – being autonomous or externally managed – to test the boundaries of the results and determine the interactive effects between influencer type and influencer agency. Study 2 acts as an extension and validation of Study 1, whereby the authors provide generalisability and overlay the role of need for uniqueness as a moderated mediator.Findings>The authors show that there are similarities and differences in the ways in which consumers view AI and human influencers. Importantly, the authors find no difference in terms of intention to follow or personalisation. This suggests that consumers are equally open to follow an AI or human influencer, and they perceive the level of personalisation provided by either influencer type as similar. Furthermore, while an AI influencer is generally perceived as having lower source trust, they are more likely to evoke word-of-mouth intentions. In understanding these effects, the authors show that social distance mediates the relationship between influencer type and the outcomes the authors investigate. Results also show that AI influencers can have a greater effect on consumers who have a high need for uniqueness. Finally, the authors find that a lack of influencer agency has a detrimental effect.Research limitations/implications>The studies investigate consumers’ general response to AI influencers within the context of Instagram, however, future research might examine consumers’ response to posts promoting specific products across a variety of category contexts and within different social media platforms.Practical implications>The authors find that in some ways, an AI influencer can be as effective as a human influencer. Indeed, the authors suggest that there may be a spill-over effect from consumer experiences with other AI recommendation systems, meaning that consumers are open to AI influencer recommendations. However, the authors find consistent evidence that AI influencers are trusted less than traditional influencers, hence the authors caution brands from rushing to replace human influencers with their AI counterparts.Originality/value>This paper offers novel insight into the increasingly prominent phenomenon of the AI influencer. Specifically, it takes initial steps towards developing understanding as to how consumers respond to AI influencers and contrast these effects with human influencers.

10.
Construction Innovation ; 22(3):405-411, 2022.
Article in English | ProQuest Central | ID: covidwho-1878873

ABSTRACT

[...]they proposed a framework focusing on facilitating the information exchange and interoperability for existing buildings. [...]semantic Web technologies and standards, such as Web Ontology Language and existing AEC domain ontologies, were used to enhance and improve the proposed framework. [...]four levels of awareness were developed based on Endsley’s situation awareness model. Furthermore, they addressed the lack of an organised digital content asset dedicated to producing VR site scenarios that emerged as one of the most limiting factors for implementing BIM and VR for construction workers’ safety training. [...]a dedicated site object library was proposed to improve this critically time-consuming process.

11.
British Food Journal ; 124(7):2096-2113, 2022.
Article in English | ProQuest Central | ID: covidwho-1878867

ABSTRACT

Purpose>This paper investigates the relationship between agricultural entrepreneurship (AE) and new technologies using academic and practitioners' perspectives to understand how new technologies such as artificial intelligence (AI), machine learning and augmented reality can promote agri-businesses.Design/methodology/approach>The paper adopts a content and thematic analysis of 325 academic sources extracted from the Scopus database and 683 patents retrieved from the European Patent Office (EPO) dataset. Additionally, the research applies the Kruskal–Wallis test as a non-parametric test for evaluating differences in the main concepts discussed in the two sources.Findings>The academic and practitioners' debate highlights a trading zone among the two streams. patents' analysis from the EPO reveals four main common themes as a new business that benefits from AI in weather predictions, new smart and intelligent ways to monitor crops, new businesses that use clouds to control plant's humidity. The analysis of Scopus's sources demonstrates theoretical approaches related to the technology acceptance model (TAM) and practical strategies in terms of entrepreneurial skills to support the agricultural sector. However, barriers among the two streams of sources exist in innovation management and scale-up entrepreneurial initiatives.Research limitations/implications>Regarding implications, the authors aim to connect academic and practitioners' views by understanding the new potential innovation applications and the connected new research avenues. Limitations might arise from the sources used to develop our analysis.Originality/value>The paper is novel because it investigates the issues arising from the relationship between AE and new technologies by examining original validated patents released by practitioners and approved by the EPO, rather than reviewing blogs or the financial press. This leads to a holistic understanding of the impact of tangible practices among agricultural entrepreneurs. The results support the view that new trading zones and case studies are needed to highlight and show the positive impact of technologies in this field. The authors argue that practitioners require scholars to reduce the ambiguity between AE and its expected results, leading to investments to boost new agricultural business ideas.

12.
Journal of Decision Systems ; 2022.
Article in English | Scopus | ID: covidwho-1878657

ABSTRACT

Over 85% of Cameroonians use building informal sector mechanisms which involve a disorganized and varied workforce, types and qualities of materials from various origins with unclear supply networks, supported by a wide range of funding sources. Although previous work enabled us to master these mechanisms, their complexity is accentuated by sustainable development requirements and sanitary measures. Sustainability concept deals with fields to respond to social, economic and environmental challenges but its operationality in building encounters many difficulties due to informal mechanisms complexity. Dealing with this environment recommend taking advantage of Building Information Modeling, especially the assets of artificial intelligence (AI) to get appropriate, rapid, and diversified assistance. In this paper, we propose a concept of intelligent building sites management combining knowledge base, information system capitalizing on previous best practices and achievements to organize several construction sites in real-time with all requirements including those of SD goals and covid-19. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

13.
Int J Inf Technol ; 14(4): 2049-2056, 2022.
Article in English | MEDLINE | ID: covidwho-1878015

ABSTRACT

Corona Virus Disease (COVID) 19 has shaken the earth at its root and the devastation has increased the diagnostic burden of radiologists by large. At this crucial juncture, Artificial Intelligence (AI) will go a long way in decreasing the workload of physicians working in the outbreak zone, aiding them to accurately diagnose the new disease. In this work, a hybrid Particle Swarm Optimization-Support Vector Machine based AI algorithm is deployed to analyze the Computed Tomography images automatically providing a high probability in determining the presence of pneumonia due to COVID19. This paper presents a model for training the system to segregate and classify the presence of pneumonia which will in turn save around 50% of the time frame for physicians. This will be especially useful in places of outbreaks where a team of people are working together with the aid of artificial intelligence and/or medical background. The AI incorporated system was distributed in all areas of across the globe. It has been observed that challenges such as data security, testing time effectiveness of model, data discrepancy etc. were positively handled using the deployed system. Moreover, since the AI integrated system identifies the infected patients immediately physicians can confirm the infection and segregate the patients at the right period. A total of 200 training cases have been observed of which 150 were identified to be infected. The proposed work shows specificity of 0.85, a sensitivity of 0.956 and an accuracy of 95.78%.

14.
Med Biol Eng Comput ; 60(6): 1647-1658, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1877935

ABSTRACT

The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their hospital stay. Data included more than 400 patients hospitalized due to COVID-19 during the first wave in France (spring of 2020) with clinical and biological features. Machine learning and explainability methods were used to construct an aggravation risk score and analyzed feature effects. The model had a robust AUC ROC Score of 81%. Most important features were age, chest CT Severity and biological variables such as CRP, O2 Saturation and Eosinophils. Several features showed strong non-linear effects, especially for CT Severity. Interaction effects were also detected between age and gender as well as age and Eosinophils. Clustering techniques stratified inpatients in three main subgroups (low aggravation risk with no risk factor, medium risk due to their high age, and high risk mainly due to high CT Severity and abnormal biological values). This in-depth analysis determined significantly distinct typologies of inpatients, which facilitated definition of medical protocols to deliver the most appropriate cares for each profile. Graphical Abstract Graphical abstract represents main methods used and results found with a focus on feature impact on aggravation risk and identified groups of patients.


Subject(s)
COVID-19 , Communicable Disease Control , Humans , Inpatients , Pandemics , Retrospective Studies , SARS-CoV-2
15.
Future Foods: Global Trends, Opportunities, and Sustainability Challenges ; : 81-105, 2021.
Article in English | Scopus | ID: covidwho-1878023

ABSTRACT

Climate change has had devastating effects on agriculture, industry, and food security. The recent outbreak of the Covid-19 pandemic has exacerbated this situation as tons of crops have had to be destroyed due to the global closure of retail outlets. This raises questions on the world’s preparedness to deal with pandemics without ceasing food production and distribution. Most staple foods comprise grain crops;therefore, feeding the ever-increasing global population means increasing production of these crops. There is a need for drought, pest, and disease-resilient crops that are nutritionally superior and health-promoting. Future grain crops need to be produced in a cost-effective, sustainable, and environmentally conscious way. Therefore, some innovative farming methods need to be explored. The future of agriculture, therefore, conceivably lies in the use of artificial intelligence, less reliance on agricultural chemicals as well as carbon-emitting fuels, hydroponics, and short-season cultivars, among others. © 2022 Elsevier Inc. All rights reserved.

16.
Roadmap to Successful Digital Health Ecosystems: A Global Perspective ; : 363-374, 2022.
Article in English | Scopus | ID: covidwho-1878020

ABSTRACT

This chapter describes a model for healthcare information technology (HIT) support resulting from genetically identified viruses or diseases/tissues. The biotechnology begins with the genetic identification of the virus;the integration of healthcare systems and standards from home to hospital to doctors’ offices and nursing homes;the surveillance of individuals and the community;and the telehealth in the hospital and in primary care to home are magnified with a population health pandemic such as the COVID-19 virus. The role of artificial intelligence (AI) and machine learning (ML) conducted on large data repositories and clouds makes the analysis of risks, outcome of treatment, and vaccine development possible. The chapter concludes with recommendations for continued research and education for healthcare professionals in informatics. The next generation of documentation required for monitoring vaccines and outcomes of chronic diseases resulting from the pandemic is described. © 2022 Elsevier Inc. All rights reserved.

17.
Lecture Notes on Data Engineering and Communications Technologies ; 117:397-407, 2022.
Article in English | Scopus | ID: covidwho-1877785

ABSTRACT

Artificial intelligence (AI) is showing a paradigm shift in all spheres of the world by mimicking human cognitive behavior. The application of AI in healthcare is noteworthy because of availability of voluminous data and mushrooming analytics techniques. The various applications of AI, especially, machine learning and neural networks are used across different areas in the healthcare industry. Healthcare disruptors are leveraging this opportunity and are innovating in various fields such as drug discovery, robotic surgery, medical imaging, and the like. The authors have discussed the application of AI techniques in a few areas like diagnosis, prediction, personal care, and surgeries. Usage of AI is noteworthy in this COVID-19 pandemic situation too where it assists physicians in resource allocation, predicting death rate, patient tracing, and life expectancy of patients. The other side of the coin is the ethical issues faced while using this technology like data transparency, bias, security, and privacy of data becomes unanswered. This can be handled better if strict policy measures are imposed for safe handling of data and educating the public about how treatment can be improved by using this technology which will tend to build trust factor in near future. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

18.
19th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2022 ; 13238 LNCS:131-140, 2022.
Article in English | Scopus | ID: covidwho-1877761

ABSTRACT

It is known that the transmissibility of COVID-19 is higher in indoor space than outdoor. The fact that the indoor space is usually closed and has less factors to take into account than outdoor may facilitate the analysis of COVID-19 infection. However, few works have been done on the analysis on COVID-19 transmissibility in indoor space. In this paper, we discuss simulation methods to analyze the transmissibility in indoor space, particularly a simulation environment consisting of three components;indoor maps, positions and trajectories of persons in indoor space, and infection models of COVID-19 in indoor space. And we analyze the requirements and design issues of each component. Among three COVID-19 infection models, we developed a simulation tool for indoor person-person infection model. While only the person-person infection model has been implemented for the simulation, the other two models of COVID-19 are planned to be designed and implemented in the future. © 2022, Springer Nature Switzerland AG.

19.
IAENG International Journal of Computer Science ; 49(2), 2022.
Article in English | Scopus | ID: covidwho-1877466

ABSTRACT

The world has experienced the spread of a dangerous virus, Coronavirus (COVID-19), that has caused the death of millions of people worldwide at an extremely rapid rate, many studies have confirmed that the virus can be detected effectively using medical images. However, it takes a long time to analyze each image by radiologists who suffer from high pressures, especially due to the high similarity of symptoms between this virus and other respiratory diseases, which can lead to the confusion of cases and, consequently, the inability to identify them quickly, which could be a problem in a pandemic situation. In this paper, a methodology is proposed for the rapid and automatic diagnosis of this virus from chest radiographic images through the use of Artificial Intelligence (AI) techniques. There are two stages of the proposed model. The first step is data augmentation and preprocessing;the second step is the detection of COVID-19 with a transfer learning technique using a pre-trained deep convolutional network (CNN) architecture to extract features, Then, the obtained feature vectors are classified into three classes: COVID-19, Normal, and pneumonia, from two open medical repositories. In the experimentation phase of our model, we evaluate a set of common metrics to measure the performance of the architecture. Experimental conclusions show an accuracy of 96.52% for all classes, then a comparison with existing models in literature demonstrates that our proposed model achieves better classification accuracy © 2022. IAENG International Journal of Computer Science.All Rights Reserved.

20.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-337991

ABSTRACT

One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified novel therapeutics for cancer, lifestyle-related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will bring about a paradigm shift in translational medicine, providing a bridge from bench side to bedside.

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