Department of Computer Science and Information Systems

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Subject: search.filters.subject.COVID-19

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Now showing 1 - 10 of 13
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    Use of stem cell-derived cardiomyocyte and nasal epithelium models to establish a multi-tissue model platform to validate repurposed drugs against sars-cov-2 infection
    (2024-05) Agarwal, Vinti
    The novel coronavirus disease (COVID-19) and any future coronavirus outbreaks will require more affordable, effective and safe treatment options to complement current ones such as Paxlovid. Drug repurposing can be a promising approach if we are able to find a rapid, robust and reliable way to down-select and screen candidates using in silico and in vitro approaches. With repurposed drugs, ex vivo models could offer a rigorous route to human clinical trials with less time invested into nonclinical animal (in vivo) studies. We have previously shown the value of commercially available ex vivo/3D airway and alveolar tissue models, and this paper takes this further by developing and validating human nasal epithelial model and embryonic stem cells derived cardiomyocyte model. Five shortlisted candidates (fluvoxamine, everolimus, pyrimethamine, aprepitant and sirolimus) were successfully compared with three control drugs (remdesivir, molnupiravir, nirmatrelvir) when tested against key variants of the SARS-CoV-2 virus including Delta and Omicron, and we were able to reconfirm our earlier finding that fluvoxamine can induce antiviral efficacy in combination with other drugs. Scalability of this high-throughput screening approach has been demonstrated using a liquid handling robotic platform for future ‘Disease-X’ outbreaks.
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    A Three-Fold Machine Learning Approach for Detection of COVID-19 from Audio Data
    (Springer, 2021-09) Sharma, Yashvardhan
    Most work on leveraging machine learning techniques has been focused on using chest CT scans or X-ray images. However, this approach requires special machinery, and is not very scalable. Using audio data to perform this task is still relatively nascent and there is much room for exploration. In this paper, we explore using breath and cough audio samples as a means of detecting the presence of COVID-19, in an attempt to reduce the need for close contact required by current techniques. We apply a three-fold approach of using traditional machine learning models using handcrafted features, convolutional neural networks on spectrograms and recurrent neural networks on instantaneous audio features, to perform a binary classification of whether a person is COVID-positive or not. We provide a description of the preprocessing techniques, feature extraction pipeline, model building and a summary of the performance of each of the three approaches. The traditional machine learning model approaches state-of-the-art metrics using fewer features as compared to similar work in this domain.
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    Achieving Ambient Intelligence in Addressing the COVID-19 Pandemic Using Fog Computing-Driven IoT
    (IGI Global, 2022) Gupta, Shashank
    In this chapter, the authors present a comprehensive review on how the fog computing-based IoT can be utilized for the outbreak prevention and its existing control systems. The authors have also explained how numerous edge computing devices (e.g., sensors/actuators, RFID systems, webcams, drones, etc.) are playing a key role in controlling this disease using IoT protocols like 6LoWPAN. In addition, the authors also emphasize IoT security attacks and vulnerabilities which are prevalent in the existing infrastructure setup of smart cities. The key aspects of emerging uses of IoT (such as smart retail store automation, smart transportation, smart waste management, etc.) are described that played a key role in controlling this epidemic in the existing infrastructure of sustainable smart cities. Finally, some future research directions are also discussed that highlight the steps in mitigating the effect of this pandemic using fog-enabled IoT and AI techniques.
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    D-insta: A Decentralized Image Sharing Platform
    (Springer, 2023-03) Bhatia, Ashutosh; Tiwari, Kamlesh
    Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage.
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    A Taxonomy of e-Healthcare Techniques and Solutions: Challenges and Future Directions
    (CRC Press, 2022) Dua, Amit
    Technology has intruded all spheres of our lives, whether it be communication, travel, work, or leisure. Industries have been quick to respond to our growing needs and have explored technological interventions to aid their aid. Healthcare, on the other hand, has been slow in adapting to the evolving technology. With the rapid increase in the world population and people's life expectancy and the uncertainty of global pandemics like COVID-19, there has been a massive shortage of healthcare workers across the world. It is of utmost importance for technology to come to the aid of the healthcare domain. The purpose of e-healthcare is to improve the quality of patient care and ease access to healthcare and prepare for the high demand in the healthcare sector that we are witnessing amidst the COVID-19 outbreak in 2020. The research work done in the e-healthcare domain is majorly focused on one or other specific aspects of e-healthcare. It fails to provide an overall picture. This survey paper is aimed at providing a broader view of the techniques used in the e-healthcare domain. The survey broadly classifies the e-healthcare techniques into four categories based on the analysis done on the existing e-healthcare proposals: Machine learning techniques, cloud computing techniques, privacy techniques, and data analytics techniques. It was observed that big data analytics and 5G technology can play a prominent role in shaping the future of e-healthcare. Big data analytics can be used for drawing useful insights from healthcare data. In contrast, 5G technology can be used for scaling purposes by achieving ultra-low latency, high density, and high bandwidth requirements. Besides, suggestions for improvement and future research directions in the e-healthcare domain have been explored for a better understanding of the readers and to motivate future work.
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    Internet of Things and Web Services for Handling Pandemic Challenges
    (Springer, 2021-10) Rao, Shreyas Suresh
    Within the past few months, the COVID-19 pandemic has disrupted millions of lives and caused unforeseen economic damage, whose impact is both significant and far-reaching. There is an immediate need to utilize emerging technologies across various industries to fight the pandemic in this light. Internet of Things (IoT) and Web Services (Cloud services) are two such technologies that provide promising solutions to combat the virus outbreak. To monitor, track, and control the spread of viruses during the pandemic, IoT and similar sensor-based technologies have been employed. Innovative technologies that enable monitoring of health delivers live observation by using smart devices to monitor the health and can handle remotely with support of cloud and Artificial Intelligence. The HMS establishes a secure remote monitoring system between patients and doctors, facilitating telehealth services to be rendered. For tracking, the HMS uses a combination of personal health data and social data in real-time, enabled through technologies such as Machine Learning, distributed Cloud computing, and AI-based speech recognition. Because of lightweight Application Programming Interfaces (APIs) and edge computing capacity, the IoT-enabled HMS is now accessible through mobile apps and web-based applications. Web services are playing an integral role in Industry’s response to fight the global pandemic. To access the data on the COVID-19 provided by World Health Organization a separate interface is provided over a web service. Some other RESTful APIs to track COVID-19 include: CORD-19, deployed on Vespa Cloud, that enables search and navigation on Open Research Dataset; CoronaTab that provides localized health information; COVID-19 India API sourced from the Ministry of Health and Family Welfare that retrieves case counts, testing statistics and hospital data from the Indian subcontinent. Cloud-based services are employed to support remote work-from-home operations, e-commerce, retail, healthcare, and entertainment segments, to name a few. Enterprises effectively use cloud services to build robust and disaster-averse networks worldwide to respond to a distributed workforce and protect data and business applications’ integrity. Another sector is the energy and utility verticals, which uses IT service management (PaaS and SaaS) and infrastructure (IaaS) for digital transformation during this pandemic. This chapter discusses how IoT and Web services support handling global COVID-19 challenges, especially in Healthcare, retail, and social sectors.
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    A study on psychological implications of COVID-19 on nursing professionals
    (Taylor & Francis, 2021) Rao, Shreyas Suresh
    The World Health Organization declared COVID-19 as a pandemic on 11 March, 2020, followed by an unprecedented global increase of the disease in recent times. Healthcare workers, including Nursing Professionals (NP), are more likely to experience psychological distress during the pandemic. The purpose of the study is to examine the stress, depression, and anxiety experienced by the nursing professionals in India, who provide care to COVID positive patients.
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    Healthcare Delivery through Telemedicine during the COVID-19 Pandemic: Case Study from a Tertiary Care Center in South India
    (Taylor & Francis, 2021) Rao, Shreyas Suresh
    The Coronavirus disease 2019 (COVID-19) pandemic has necessitated medical centers across the world to deliver healthcare through telemedicine. We discuss the adoption, delivery of telemedicine services at a tertiary care center and patient satisfaction involving 456 patients in south India. Most respondents had sought telemedicine care at the department of Medicine (16.23%). The maximum satisfaction was reported by patients in OBG (100%). The responses were generally positive across all the age groups. The paper offers insights on best practices adopted at the center, lessons learnt, and provides recommendations for health care systems offering telemedicine during COVID-19 times.
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    Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
    (ARXIV, 2022-10) Agarwal, Vinti
    Due to high mutation rates, COVID-19 evolved rapidly, and several variants such as Alpha, Gamma, Delta, Beta, and Omicron emerged with altered viral properties like the severity of the disease caused, transmission rates, etc. These variants burdened the medical systems worldwide and created a massive impact on the world economy as each had to be studied and dealt with in its specific ways. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and then compares the results from different dimensionality reduction methods including: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation Projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for a particular variant (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We conclude that the proposed framework can effectively distinguish between the major variants and hence can be used for the identification of emerging variants in the future.
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    Systematic Down-Selection of Repurposed Drug Candidates for COVID-19
    (IJMS, 2022) Agarwal, Vinti
    SARS-CoV-2 is the cause of the COVID-19 pandemic which has claimed more than 6.5 million lives worldwide, devastating the economy and overwhelming healthcare systems globally. The development of new drug molecules and vaccines has played a critical role in managing the pandemic; however, new variants of concern still pose a significant threat as the current vaccines cannot prevent all infections. This situation calls for the collaboration of biomedical scientists and healthcare workers across the world. Repurposing approved drugs is an effective way of fast-tracking new treatments for recently emerged diseases. To this end, we have assembled and curated a database consisting of 7817 compounds from the Compounds Australia Open Drug collection. We developed a set of eight filters based on indicators of efficacy and safety that were applied sequentially to down-select drugs that showed promise for drug repurposing efforts against SARS-CoV-2. Considerable effort was made to evaluate approximately 14,000 assay data points for SARS-CoV-2 FDA/TGA-approved drugs and provide an average activity score for 3539 compounds. The filtering process identified 12 FDA-approved molecules with established safety profiles that have plausible mechanisms for treating COVID-19 disease. The methodology developed in our study provides a template for prioritising drug candidates that can be repurposed for the safe, efficacious, and cost-effective treatment of COVID-19, long COVID, or any other future disease. We present our database in an easy-to-use interactive interface (CoviRx that was also developed to enable the scientific community to access to the data of over 7000 potential drugs and to implement alternative prioritisation and down-selection strategies.