Artificial intelligence against COVID-19 Pandemic: A Comprehensive Insight

: COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infective disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives with emergency use authorisation vaccines are being done across many countries, however, their long term efficacy and side-effects study are yet to be done. The research community is analysing the situation by collecting the datasets from various sources. Healthcare professionals must thoroughly analyse the situation, devise preventive measures for this pandemic, and even develop possible drug combinations. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses on the application of state-of-the-art methods in this combat against COVID-19. The application of Artificial intelligence (AI), and AI-driven tools are emerging as effective tools, especially with X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions etc. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e. >95%, as reported in various studies. AI-driven tools are being used in COVID diagnostic, therapeutics, trend prediction, drug design and prevention to help fight against this pandemic. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in this battle against the COVID-19 pandemic. The extensive literature is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 Prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing.

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The Discerning Ear: Cardiac Auscultation in the Era of Artificial Intelligence and Telemedicine

European Heart Journal - Digital Health ◽

10.1093/ehjdh/ztab059 ◽

2021 ◽

Author(s):

Vivek Jani ◽

David A Danford ◽

W Reid Thompson ◽

Andreas Schuster ◽

Cedric Manlhiot ◽

...

Keyword(s):

Artificial Intelligence ◽

Computer Assisted ◽

Cardiac Auscultation ◽

Cardiac Murmur ◽

New Era ◽

History Of ◽

The Impact ◽

Paradigm Shifting ◽

Insight Into

Abstract Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of pediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, artificial intelligence assisted auscultation, and the implications of these technologies on telemedicine in pediatric cardiology. It is our hope that this article provides an updated perspective on the impact of artificial intelligence on cardiac auscultation for the modern pediatric cardiologist.

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Drug Repurposing for Prevention and Treatment of COVID-19: A Clinical Landscape

Discoveries ◽

10.15190/d.2020.18 ◽

2020 ◽

Vol 8 (4) ◽

pp. e121

Author(s):

Md. Shahadat Hossain ◽

◽

Ithmam Hami ◽

Md. Sad Salabi Sawrav ◽

Md. Fazley Rabbi ◽

...

Keyword(s):

Clinical Trials ◽

Treatment Plan ◽

Drug Repurposing ◽

The Novel ◽

Long Term Effects ◽

Novel Therapy ◽

Worldwide Distribution ◽

The World ◽

Novel Coronavirus

SARS-CoV-2, the novel coronavirus strain responsible for the current pandemic of COVID-19, has rendered the entire humanity suffering. Several months have passed since the pandemic has struck. However, the world is still looking for an effective treatment plan to battle the viral infection. The first vaccine just received emergency approval in December 2020 for use in USA and UK. These are excellent news, however, the worldwide distribution of such vaccine, the possibility of virus mutation and the lack of data regarding the long-term effects of such vaccines are a significant concern. In addition, although remdesivir was recently approved by the FDA to be used as a clinical drug against COVID-19, it hasn’t stood out yet as a proven form of therapeutics. Such inability to produce a novel therapy has caused enough inconveniences for the affected people worldwide. Repurposing the already available drugs to fight against the virus seems to be a reasonable option amidst such uncertainty. Given the vast collection of potential treatment candidates to be explored against COVID-19, there is a decent chance that a success in this regard will serve the intermediary purpose of clinically treating the infection until a COVID-19 vaccine is widely distributed worldwide and will be able to treat COVID-19 patients that do not adequately respond to vaccines. Such treatments may prove very useful in future coronavirus outbreaks too. Proper research into these repurposing treatments may yield a certain insight into the field of novel treatment production as well. This review study accumulates a relevant set of information about drugs and vaccines against COVID-19, in terms of their repurposing properties and the specific phases of clinical trials they are undergoing across the world. A potential timeline is also suggested to estimate when an effective result can be expected from the ongoing clinical trials for a better anticipation of the drug landscape. This study will hopefully help accelerate investment of resources into development and discovery of drugs and vaccines against the infection.

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COV-DrugX Pipeline for Covid-19 Drug Repurposing

10.31219/osf.io/whka3 ◽

2021 ◽

Author(s):

Robin Sinha ◽

Preeti P

Keyword(s):

Artificial Intelligence ◽

Drug Discovery ◽

De Novo ◽

Drug Repurposing ◽

Therapeutic Agents ◽

The Novel ◽

Novel Coronavirus ◽

The Cost

The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has killed over 5 million people to date. Despite the introduction of population-wide vaccination drives, countries such as Austria and Germany are witnessing the re-emergence of infections and deaths. Scientists, administrators and clinicians are scrambling to find solutions that include vaccines, and active therapeutic agents. So, there is an urgent requirement for new and effective medications that can treat the disease caused by SARS-CoV-2. Artificial intelligence (AI) enabled drug repurposing, has the potential to shorten the time and reduce the cost compared to de novo drug discovery.

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Turkish Muslims in a German city: Entrepreneurial and residential self-determination

MIGRATION LETTERS ◽

10.33182/ml.v12i1.252 ◽

2015 ◽

Vol 12 (1) ◽

pp. 1-12

Author(s):

Sarah Hackett

Keyword(s):

Residential Patterns ◽

Academic Literature ◽

Self Employment ◽

German City ◽

Post War ◽

Oral History Interviews ◽

The City ◽

Insight Into ◽

Term Objective

Drawing upon a collection of oral history interviews, this paper offers an insight into entrepreneurial and residential patterns and behaviour amongst Turkish Muslims in the German city of Bremen. The academic literature has traditionally argued that Turkish migrants in Germany have been pushed into self-employment, low-quality housing and segregated neighbourhoods as a result of discrimination, and poor employment and housing opportunities. Yet the interviews reveal the extent to which Bremen’s Turkish Muslims’ performances and experiences have overwhelmingly been the consequences of personal choices and ambitions. For many of the city’s Turkish Muslim entrepreneurs, self-employment had been a long-term objective, and they have succeeded in establishing and running their businesses in the manner they choose with regards to location and clientele, for example. Similarly, interviewees stressed the way in which they were able to shape their housing experiences by opting which districts of the city to live in and by purchasing property. On the whole, they perceive their entrepreneurial and residential practices as both consequences and mediums of success, integration and a loyalty to the city of Bremen. The findings are contextualised within the wider debate regarding the long-term legacy of Germany’s post-war guest-worker system and its position as a “country of immigration”.

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Drug Repurposing Studies Targeting SARS-nCoV2: An Ensemble Docking Approach on Drug Target 3C-like Protease (3CLpro)

10.26434/chemrxiv.12228831 ◽

2020 ◽

Author(s):

Shruti Koulgi ◽

Vinod Jani ◽

Mallikarjunachari Uppuladinne ◽

Uddhavesh Sonavane ◽

Asheet Kumar Nath ◽

...

Keyword(s):

Drug Repurposing ◽

Drug Binding ◽

Ensemble Docking ◽

Drug Molecules ◽

Drug Binding Site ◽

Main Protease ◽

Docking Approach ◽

Novel Coronavirus ◽

Markov State Modeling ◽

Viral Polyprotein

The COVID-19 pandemic has been responsible for several deaths worldwide. The causative agent behind this disease is the Severe Acute Respiratory Syndrome – novel Coronavirus 2 (SARS-nCoV2). SARS-nCoV2 belongs to the category of RNA viruses. The main protease, responsible for the cleavage of the viral polyprotein is considered as one of the hot targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for targeting the main protease of SARS-CoV, which caused SARS in the year 2002-03. Hence, drug repurposing approach may prove to be useful in targeting the main protease of SARS-nCoV2. The high-resolution crystal structure of 3CLpro (main protease) of SARS-nCoV2 (PDB ID: 6LU7) was used as the target. The Food and Drug Administration (FDA) approved and SWEETLEAD database of drug molecules were screened. The apo form of the main protease was simulated for a cumulative of 150 ns and 10 μs open source simulation data was used, to obtain conformations for ensemble docking. The representative structures for docking were selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble docking approach for main protease helped in exploring the conformational variation in the drug binding site of the main protease leading to efficient binding of more relevant drug molecules. The drugs obtained as best hits from the ensemble docking possessed anti-bacterial and anti-viral properties. Small molecules with these properties may prove to be useful to treat symptoms exhibited in COVID-19. This in-silico ensemble docking approach would support identification of potential candidates for repurposing against COVID-19.

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The Antiviral and Antimalarial Drug Repurposing in Quest of Chemotherapeutics to Combat COVID-19 Utilizing Structure-Based Molecular Docking

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323999200824115536 ◽

2020 ◽

Vol 23 ◽

Author(s):

Sisir Nandi ◽

Mohit Kumar ◽

Mridula Saxena ◽

Anil Kumar Saxena

Keyword(s):

Molecular Docking ◽

In Silico ◽

Drug Repurposing ◽

Clinical Settings ◽

The Novel ◽

In Silico Docking ◽

Biochemical Mechanisms ◽

Novel Coronavirus ◽

In Silico Molecular Docking

Background: The novel coronavirus disease (COVID-19) is caused by a new strain (SARS-CoV-2) erupted in 2019. Nowadays, it is a great threat that claims uncountable lives worldwide. There is no specific chemotherapeutics developed yet to combat COVID-19. Therefore, scientists have been devoted in the quest of the medicine that can cure COVID- 19. Objective: Existing antivirals such as ASC09/ritonavir, lopinavir/ritonavir with or without umifenovir in combination with antimalarial chloroquine or hydroxychloroquine have been repurposed to fight the current coronavirus epidemic. But exact biochemical mechanisms of these drugs towards COVID-19 have not been discovered to date. Method: In-silico molecular docking can predict the mode of binding to sort out the existing chemotherapeutics having a potential affinity towards inhibition of the COVID-19 target. An attempt has been made in the present work to carry out docking analyses of 34 drugs including antivirals and antimalarials to explain explicitly the mode of interactions of these ligands towards the COVID-19protease target. Results: 13 compounds having good binding affinity have been predicted towards protease binding inhibition of COVID-19. Conclusion: Our in silico docking results have been confirmed by current reports from clinical settings through the citation of suitable experimental in vitro data available in the published literature.

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Critical Insight into the Attributes of Emerging Novel Coronavirus (COVID-19) in India and Across the World

Coronaviruses ◽

10.2174/2666796701999200623172631 ◽

2020 ◽

Vol 1 (1) ◽

pp. 49-56

Author(s):

Gaurav M. Doshi ◽

Hemen S. Ved ◽

Ami P. Thakkar

Keyword(s):

Review Article ◽

World Health ◽

Effective Vaccine ◽

Human Contact ◽

The World ◽

Critical Insight ◽

Novel Coronavirus ◽

Health Organization ◽

Insight Into ◽

Old Drugs

The World Health Organization (WHO) has recently announced the spread of novel coronavirus (nCoV) globally and has declared it a pandemic. The probable source of transmission of the virus, which is from animal to human and human to human contact, has been established. As per the statistics reported by the WHO on 11th April 2020, data has shown that more than sixteen lakh confirmed cases have been identified globally. The reported cases related to nCoV in India have been rising substantially. The review article discusses the characteristics of nCoV in detail with the probability of potentially effective old drugs that may inhibit the virus. The research may further emphasize and draw the attention of the world towards the development of an effective vaccine as well as alternative therapies. Moreover, the article will help to bridge the gap between the new researchers since it’s the current thrust area of research.

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AI Narratives

10.1093/oso/9780198846666.001.0001 ◽

2020 ◽

Cited By ~ 3

Keyword(s):

Artificial Intelligence ◽

New Technologies ◽

Ancient Greece ◽

Historical Overview ◽

Intelligent Machines ◽

Narrative History ◽

History Of ◽

Imaginative Thinking ◽

The Relationship ◽

Insight Into

This book is the first to examine the history of imaginative thinking about intelligent machines. As real artificial intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed, and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing prehistory of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first centuries in which a greater density of narratives emerged alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI’s social, ethical, and philosophical consequences, including questions of dehumanization, automation, anthropomorphization, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.

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Moving Songs

The Oxford Handbook of Musical Repatriation ◽

10.1093/oxfordhb/9780190659806.013.42 ◽

2019 ◽

pp. 590-606

Author(s):

Sally Treloyn ◽

Matthew Dembal Martin ◽

Rona Goonginda Charles

Keyword(s):

Cultural Heritage ◽

Knowledge Transmission ◽

Intercultural Relationships ◽

Cultural Negotiation ◽

Intercultural Collaboration ◽

Present Collection ◽

Intergenerational Knowledge ◽

Early Phases ◽

Insight Into

Repatriation has become almost ubiquitous in ethnomusicological research on Australian Indigenous song. This article provides insights into processes of a repatriation-centered song revitalization project in the Kimberley, northwest Australia. Authored by an ethnomusicologist and two members of the Ngarinyin cultural heritage community, the article provides firsthand accounts of the early phases of a long-term repatriation-centered project referred to locally as the Junba Project. The authors provide a sample of narratives and dialogues that deliver insight into experiences of the work of identifying recordings “in the archive” and cultural negotiation and use of recordings “on Country.” The entanglement of local epistemological frameworks with past and present collection, archival research, repatriation, and dissemination for intergenerational knowledge transmission between spirits, Country, and the living, is explored, showing how recordings move song knowledge from community to archive to community and from generation to generation, and move people in present-day communities. The chapter considers how these “moving songs” allow an interrogation of the fraught endeavor of intercultural collaboration in the pursuit of revitalizing Indigenous song traditions. It positions repatriation as a method that can support intergenerational knowledge transmission and as a method to consider past and present intercultural relationships within research projects and between cultural heritage communities and collecting institutions.

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Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

Scientific Reports ◽

10.1038/s41598-020-78758-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Brandon Malone ◽

Boris Simovski ◽

Clément Moliné ◽

Jun Cheng ◽

Marius Gheorghe ◽

...

Keyword(s):

Artificial Intelligence ◽

Monte Carlo ◽

Cell Surface ◽

Antigen Presentation ◽

Human Population ◽

Host Immune System ◽

T Cell Epitopes ◽

Digital Twin ◽

Novel Coronavirus ◽

Global Population

AbstractThe global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

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