Therapeutic Repurposing Approach: New Opportunity for Developing Drugs Against COVID-19

The coronavirus disease 2019 (COVID-19) pandemic initiated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has encouraged the repurposing of various drugs to treat the morbidity, mortality, and extent of the disease. Nowadays, the COVID-19 pandemic is a major health concern as it has already affected the whole world in all aspects. Drug repurposing is considered a new potential strategy as it is a cost-effective and less time-consuming process to establish a new indication for existing drugs. The present chapter has focused on the pathophysiology of COVID-19 and the reuse of the drugs based on pharmacological mechanisms. In the literature, various drugs like favipiravir, lopinavir, ritonavir, arbidol, chloroquine, hydroxychloroquine, interferons, etc. have been reported for repurposing purposes against COVID-19. Most of them are effective in in vitro and clinical studies. Drugs act mainly on viral entry, viral replication, angiotensin-converting enzyme-2 (ACE2), inflammatory mechanisms, etc. Based on viral pathogenesis and the mechanism of drugs using in silico, in vitro, and clinical studies, repurposing medicines might be considered an excellent opportunity to cure COVID-19. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Topical timolol for treatment of pyogenic granulomas in children: A case series

Background: Pyogenic granuloma (PG) is benign vascular tumour presenting as small, friable, reddish papule or nodule. Bleeding tendency of PGs warrants their removal.Various available treatment options require local anesthesia, cause pain, local side effects, scarring and become undesirable for treating children. Hence, topical beta blocker (timolol 0.5%) becomes a valuable option for treating PG in children. Method(s): We enrolled 3 healthy children aged less than 14 years with clinical diagnosis of PG for the case series. Patients with any cardiopulmonary abnormalities were not enrolled. All the patients were prescribed 0.5% ophthalmic solution, topically twice a day for 6 weeks. Due to the prevailing COVID-19 pandemic, patients were called for follow up visits every three weeks. At the end of 6 weeks, patients were evaluated for the improvement and followed up for one year after treatment to look for any recurrences or any long-term side effects of timolol. Result(s): All three patients enrolled in the study showed partial improvement at 6weeks and complete improvement in two patients after one year. No recurrence or any long-term side effects with timolol were noted in any patient. Conclusion(s): Topical beta blocker (Timolol) is easy to administer, safe and noninvasive. It appears to have a promising role in children with no recurrences or any associated long-term side effects. Copyright © 2022 Ubiquity Press. All rights reserved.

Modeling of cyber threat analysis and vulnerability in IoT-based healthcare systems during COVID

The fast advancement of IoT technology has transformed human existence by introducing the facilitation of home automation, traffic systems, e-healthcare, oil and gas stations, and smart vehicles. The cyber threat and vulnerabilities injection have increased to a great extent during the COVID-19 period, when the world went into virtual mode for technology usage. The security of IoT devices has been a major apprehension in recent years, particularly in the healthcare industry, where recent threats by cyber criminals and terrorists have shown severe IoT security flaws. The IoT-Attributes and Threat Analyzer for Tracing Malicious Traffic in Smart Sensor Environment, abbreviated as IoT-ATATMT, is presented in the proposed work to address the aforementioned challenge and is based on the IoT-Flock, an open-source sensor data generating tool. The IoT-Flock tool creates a use-case for both regular and suspicious smart devices to analyze traffic. In addition, the work also includes an open-source application for transforming IoT-recorded Flock’s traffic into an IoT dataset. We first created an IoT healthcare dataset using the suggested architecture in this study, which includes both regular and IoT attack traffic. After that, we used several machine learning algorithms for detecting cyber threats to safeguard the e-healthcare system toward using them via a created dataset. © 2022 Elsevier Inc. All rights reserved.

Impact of Covid-19 on Indian Economy & the Challenges Ahead

As we reach the mid of year 2020 and virus COVID-19 takes most of the world under its influence, it's high time we should start looking at a post COVID-19 world. The changes its going to bring upon us, how vastly world is going to get impacted both economically as well as emotionally, as damage has left many in dire situation. It's the moment of global lockdown where everyone is predicting a worst economic downturn since the days of Great Depression. The heart-wrenching images of migrants are there for everyone to see, a moment of reverse migration, happening across India and other parts of world are showing the anxiety and desperation of many due to loss of livelihood and social security. As more and more companies announcing layoffs, rising unemployment data in world's major economies and the possibilities of discovering a vaccination in near term looking grim, we are certainly in a situation never seen before in recent times. Wearing a mask has become a new fashion statement, work from home has become a new norm in corporate world as more and more companies adopting this new change to bring cost effectiveness as well as keeping it safe for everyone, sanitization, social distancing have become new trending words for the masses. World is looking away from China and everyone wants to grab the opportunity, virtual classes to virtual meeting rooms, world is changing quite rapidly and we are witnessing an anxious nation with rural distress among many of its worries. This paper provides us the opportunity to study the impact of COVID-19 on global economy with special focus on Indian Economy. The aim is to study the possible challenges we going to face as nation on many fronts and how different the world is going to be post novel corona virus outbreak.

A study on work from home during covid-19 pandemic: A sem of employee performance

The covid-19 virus changed everything suddenly, the lockdown was imposed, and social distancing became the new normal. This pandemic has affected the routine operations of every business organisation overall industries globally in the year 2020. With the lockdown in place, billions of people, excluding the essential workers, have been forced to work from home. Corporate authorities around the world have no other alternative than providing flexible worksetting including work from home. This research was an attempt to explore the relationships/impact of work from home on employee performance under descriptive research design.The research was aimed to test the relationship between the variables affecting employee performance while working from home during the lockdown, based on the hypothecated model of the theory of planned behaviour. Hypothetical relations among the variables were tested using parametric statistical analysis with the help of AMOS model testing. It was found that employee’s performanceduring their tenure of work from home is dependent on their attitude towards work from home and home atmosphere. © 2021, Ashwin Anokha Publications and Distributors. All rights reserved.

AI based impact of COVID 19 on food industry and technological approach to mitigate

The food industry or the restaurant business has always been one of the most profitable and growing businesses. As technology is evolving day by day and to sustain itself in the food industry the restaurant needs to come up with new and innovative services which they can provide to the customers. Pandemic has made a major impact on the business of the restaurant industry in 2020. This paper will help to understand how technology can help in food ordering, while taking care of the COVID-19 pandemic guidelines. The purpose of this paper is to analyze the impact of COVID-19 in the food industry and suggest methods that would help restaurants to adapt to challenges that originate from COVID-19. One of the ways would be to automate the food ordering process. Our Project 'Contactless food ordering system' is a mobile application operated by a voice assistant. That the client (The Restaurant Customer) can use to scan through the restaurant menu and view various food categories and items like 'Starter' 'Drinks' etc. and place their order through the Application. They can modify and confirm their order, as well as make their payment. The aim of the study is to reduce the point of contact during COVID-19 and automate the ordering and billing process, simplifying the work. Thus, providing a totally computerized, automated, and scalable food ordering system that will assist the business by reducing their workload, provide better management and smooth operation. The restaurant can also use various other means to increase their presence as a business. © 2021 IEEE.

COVID-19 detection using convolutional neural network architectures based upon chest X-rays images

The novel Coronavirus has really been the unexpected Catastrophe, which no one could have even thought of. It has emerged as a pandemic and has raised questions on the health infrastructure and facilities available as it is required to get a large number of people tested. RT-PCR is the standard diagnostic test being used. But there are several issues with RT-PCR testing like relying only on one approach even when it is getting difficult for most countries to procure the required amount of testing kits, also there have been some cases of false positive results. All these factors definitely call for a search of alternative testing methodologies, so it not reqired to rely on just one approach. Apart from RT-PCR testing, Chest X-rays can be a great tool to know about the status and severity of COVID-19. Once obtaining the chest X-rays of a person a call has to be made on the COVID-19 status and that needs very high accuracy and expertise and again there will be a shortage of the expertise. So, a solution has been proposed to this problem by developing a COVID-19 detection system, which can assist the medical experts regarding the report and then the experts can make a final call. As observed, DeepLearning techniques, especially Convolutional Neural Network (CNN) have proven to be outstanding in medical image classification and analysis, and as our task is similar to medical image classification, CNN is a good choice for our use case. So, we analyzed four different CNN architectures on Chest X-ray for Covid-19 Diagnosis. The models used are pretrained on ImageNet datasets. Transfer Learning has been used to generate results. A comparative study of the results obtained with different architecture reveals that structures based on CNN have great potential for Covid-19 diagnosis and detection. © 2021 IEEE.