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Mucormycosis is an uncommon illness caused by the fungus Mucorales. India was concerned about mucormycosis and COVID-19 in 2020. To minimize morbidity and occurrence, prevent, and treat mucormycosis, analysis is required. Combining systems biology and bioinformatics-based mucormycosis research, this study simulates the Genome-scale metabolic model (GSSM) of a Rhizopus oryzae strain for the comprehension of the organism's metabolic mechanism. Several key metabolic pathways for a mucormycosis-causing fungus strain were identified in research publications and targeted for inclusion in a model of a metabolic network. Based on the Flux Balance Analysis (FBA) approach, an integrated model of these pathways at the scale of the genome's metabolism was developed and appropriate constraints were applied to the numerous reactions involved in Rhizopus oryzae's metabolism using the COBRA package in MATLAB. Hence, unique evidence of pharmacological targets and biomarkers that may function as diagnostic, early analytic, and therapeutic agents in mucormycosis was discovered. Our study investigates the role of key metabolites in the model by applying constraints and altering fluxes, which provides valuable candidates for drug development. . © 2023 IEEE.
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The article discusses the importance of digital HR transformation in organizations in the post-pandemic world. The COVID-19 pandemic has made it necessary for HR departments to prioritize restructuring the culture of organizations by adopting a hybrid working model that provides flexibility to employees. Digitalization has become an enabler that changes behaviors, communication, and interactions with customers and employees, and HR's role as a change agent is crucial in this regard. HR departments are being asked to lead the digital transformation sweeping organizations worldwide, and they need to support organizations in redesigning job roles with outcome-based performances. HR digital transformation is the process of changing operational HR processes to become automated and data-driven. The article explains that HR digitalization processes incorporate the use of digital channels and the use of social media platforms for recruiting, and it provides examples of HR functions that have successfully been innovated. The article concludes by emphasizing the need for HR to develop competencies as a strategic business partner to lead the organization's overall growth strategy based on digitalization tools.
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Introduction/Aim: COVID-19 has fast-tracked changes to healthcare delivery, including the introduction of a broad range of telehealth services. Work is needed to assess the ongoing suitability of telemedicine for the post-pandemic era. We sought to explore perceptions of telemedicine amongst patients and providers (clinicians and health administrators) who had appointments in cardiology, respiratory, neurology, rheumatology and gastroenterology services in hospital specialist outpatient clinics in Sydney Local Health District. Method(s): Semi-structured interviews explored perceptions of consultations undertaken virtually compared to in-person, seeking perspectives on the benefits, limitations and risks of outpatient telemedicine consultations. The 37 participants comprised 16 patients, 14 specialists, 3 Clinical Nurse Consultants and 4 administrators. Result(s): Patients indicated satisfaction with telemedicine consultations, especially during the pandemic. They valued saving on travel time and costs, and being able to access care from their homes, thereby minimising risk of COVID exposure. The chief disadvantage perceived by patients was inability to receive a physical examination. They noted greater challenges for people with hearing and visual impairments, limited digital skills and/or computer access. Providers' perceptions were more ambivalent. Although telehealth was understood to help meet demands during the pandemic, mis- and under-diagnoses were considered major concerns arising from the inability to perform physical examination. Most regarded telemedicine as an effective tool for review appointments but mentioned an associated increased workload. All participants mentioned the need for relevant education and training, better integration of telemedicine platforms into existing infrastructure and the need for a hybrid model of care. Conclusion(s): Telemedicine played a unique role in meeting patients' needs during the pandemic and its convenience was valued by patients. Clinicians expressed concerns about missed diagnoses, uncertain clinical outcomes, lack of administrative and technological infrastructure. The ultimate test of telemedicine will be its impact on clinical outcomes versus longstanding models of in person care.
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Background: Several therapeutic possibilities have been explored against Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), such as convalescent plasma (CP), intravenous immunoglobulin (IVIG) and monoclonal antibodies. Compounds such as hydroxychloroquine have also been found to have fatal drawbacks. Repurposing of existing antiviral drugs can be an effective strategy, which could fasten up the process of drug discovery. Objective(s): The present study is designed to predict the computational efficacy of pre-existing antiviral drugs as inhibitors for the Nsp10-Nsp16 complex protein of SARS-CoV-2. Method(s): Twenty-six known antiviral drugs along with their similar structures based on Tanimoto simi-larity, were screened towards the Nsp10-Nsp16 complex's active site. Result(s): Our study reports competitive binding of 1-[3-[2-(2-Ethoxyphenoxy) ethylamino]-2-hydroxypropyl]-9H-carbazol-4-ol against AdoMet binding site in Nsp10-Nsp16 complex. Formation of the stable ligand-receptor complex with 1-[3-[2-(2-Ethoxyphenoxy) ethylamino]-2-hydroxypropyl]-9H-carbazol-4-ol could functionally inhibit the Nsp10-Nsp16 complex, thereby making the SARS-CoV-2 vulnerable to host immuno-surveillance mechanisms. Conclusion(s): We conclude that these computational hits can display positive results in in-vitro trials against SARS-CoV-2.Copyright © 2021 Bentham Science Publishers.
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Since the beginning of the 21st century the world had to face several outbreaks ofviral diseases like Chikungunya, Ebola, Nipah, H7H9 Bird flu, H1N1, SARS, MERS and above all the Coronavirus or SARS-CoV-2 which has brought the whole world to a standstill, as of now it as infected around 200 million people and has caused over 4 million deaths. The countries of USA, India, Brazil and Russia have been the most seriously affected countries of all. outbreak of Covid-19 a 'Public Health Emergency of International Concern' on 30th January 2020 and a 'pandemic' on 11th march 2020. Although the SARS-CoV-2 (Severe acute respiratory syndrome Coronavirus 2) or the Coronavirus has a very low fatality rate i.e. 2.9%, but the most problematic aspect of the Coronavirus is its unusually high infectivity rate. Such unique attributes of the virus called for more stringent and advanced methods of investigation, research and analysis and this is where Artificial Intelligence (AI) comes in;the scientific community strongly believe that AI and data science can be used to fight against the Coronavirus and can fill in the blanks still left by science. In current study of approach, we have emphasis the role of AI during the pandemic and implemented the study. Further, a trend analysis is conducted from year 2020 till 2021 to analyze the pattern for future forecasting. © 2022 IEEE.
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The novel coronavirus (COVID-19) outbreak has been announced as a “Public Health Emergency of International Concern” and further identified as a pandemic by the World Health Organization. Considering the unprecedented spread of the disease across the world, the development of noncontact and unobtrusive methods for quick screening of potential carriers is the need of the hour. The chapter describes a multimodal approach for rapid screening of potential COVID-19 carriers based on symptomatic sensing using a combination of sensors, followed by generation of a metric to indicate the severity of symptoms. The proposed system is likely to be a significant addition to the present body of systems to scan COVID-19 carriers. © 2022, Springer Nature Switzerland AG.
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In the era of digital world, due to constantly evolving medical technology, Artificial Intelligence (AI) propelled out of research labs into our everyday lives. Emerging developments in AI have an advantage in healthcare system as they will reduce the manual process performed by humans. Hence, AI is a game-changer in health care because when AI is combined with three-dimensional (3D) printing technologies it could increase the performance by reducing the risk of error and facilitating automated production. Nowadays, 3D printing has become an essential and potentially transformative approach to revolutionize health care rapidly. One of the objectives of this chapter is to present an overview of basic concepts of AI including machine learning, internet of things, cloud computing, and deep learning. The further dimensions will also describe types of 3D printing. A snapshot of stereolithography (SLA), which is the oldest and reliable approach in 3D printing is also described. The present chapter will also highlight the key healthcare trends impacting the 3D printing industry such as use of 3D printing to fight the pandemic outbreak COVID-19;regenerative medicine;eliminating the prerequisite for animal experimentation to confirm their efficacy and safety before human testing begins and personalized medicines. The last section encompasses key applications, challenges, and future of 3D printing, which will help the readers to understand that this emerging technology is becoming society’s most helpful tool and further may effectively use the information for their research endeavors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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During this Covid - 19 pandemic situation, encryption of medical images takes a major role in medical information systems as well as in telemedicine. However according to government rule, it is essential to hide the information of the patients. Recent development in computer network enhances a lot of facilities in communication area. Unfortunately, hackers are misusing this facility and always try to attack on the transmitted information in insecure network. For secure transmission of medical images, it is essential to encrypt the information before transmitting. In this communication, we are going to present a novel approach of medical image encryption using cyclic coding. We have proved that it is quite difficult to decrypt the original information from encoded data in one common mode of attacking- chosen ciphertext attack. Moreover, we have proved the effectiveness of the encryption using correlation coefficients. Our proposed scheme is suitable for efficient encoding of multiple medical images. © 2021 Institute of Physics Publishing. All rights reserved.