ABSTRACT
Hypercoagulability and the need for prioritizing coagulation markers for prognostic abilities have been highlighted in COVID-19. We aimed to quantify the associations of Ddimer with disease progression in patients with COVID-19. This systematic review and meta-analysis was registered with PRO SPERO, CRD42020186661. We included 113 studies in our systematic review, of which 100 records (n = 38,31 0) with D-dimer data) were considered for meta-analysis. Across 68 unadjusted (n = 26,960) and 39 adjusted studies (n = 15,653) reporting initial D-dimer, a significant association was found in patients with higher D-dimer for the risk of overall disease progression (unadjusted odds ratio (uOR) 3.15;adjusted odds ratio (aOR) 1.64). The time-to-event outcomes were pooled across 19 unadjusted (n = 9743) and 21 adjusted studies (n = 13,287);a strong association was found in patients with higher D-dimers for the risk of overall disease progression (unadjusted hazard ratio (uHR) 1.41;adjusted hazard ratio ( aHR) 1.1 0). The prognostic use of higher D-dimer was found to be promising for predicting overall disease progression (studies 68, area under curve 0.75) in COVID-19. Our study showed that higher D-dimer levels provide prognostic information useful for clinicians to early assess COVID-19 patients at risk for disease progression and mortality outcomes. This study, recommends rapid assessment of Ddimer for predicting adverse outcomes in COVID-19.
ABSTRACT
Bitcoin is a type of Cryptocurrency that relies on Blockchain technology and its growing popularity is leading to its acceptance as an alternative investment. However, the future value of Bitcoin is difficult to predict due to its significant volatility and speculative behavior. Considering this, the key objective of this research is to assess Bitcoins' explosive behavior during 2013-2022 including the most volatile COVID-19 pandemic and Russia-Ukraine war period and to forecast its price by comparing the predictive abilities offive different econometric, machine learning and artificial Intelligence methods namely, ARIMA, Decision Tree, Random Forest, SVM, and Artificial Intelligence Long Short-Term Memory Network (AI-LSTM). The precision of such methodologies has been assessed using root mean square error (RMSE) and mean average per cent error (MAPE) values. The findings confirmed that the AI-LSTM model performs better than other forecast models in predicting Bitcoins' opening price on the following working day. Therefore, Bitcoin traders, policymakers, and financial institutions can use the model effectively to better forecast the next day's opening price. © 2023 World Scientific Publishing Company.
ABSTRACT
Along with most countries, India is committed to following the United Nations Sustainable Development Agenda 2030. It is widely accepted that North–South development cooperation models alone will not be sufficient for developing countries to achieve the bold ambitions of the associated Sustainable Development Goals (SDGs). The Covid-19 pandemic further highlights the importance of South–South Cooperation (SSC) to foster the development of complementary capacities of countries facing similar kinds of challenges. SSC will contribute to, and strengthen collective self-reliance among developing countries through the exchange of experiences, sharing of the best practices, and technical expertise. As a part of its foreign policy initiatives like "Neighbors First”, "Act East”, and "India-Africa Partnership”, India strives to keep good relationships with neighborhood, and wishes to play an active role in SSC by providing technical and financial assistance to the other countries according to their needs and priorities. As a result, India's engagement in SSC has increased manifold in the last decade. South–South cooperation has become vital to achieving the SDGs, but also to foster regional peace and stability. There is, however, a need to develop effective mechanisms for the control monitoring of the effectiveness of SSC. © 2023, The Centre for Science and Technology of the Non-Aligned and Other Developing Countries.
ABSTRACT
The existing COVID-19 global epidemic has altered our lifestyles in different manners. It has created the need to always understand our position, which we have been seen in interaction with, as well as other specifics such as our body temp and to prevent the spread of such a viral infection. The above knowledge is especially important for students, as their yield to college tends to increase their occupational exposure. To solve this issue, several control systems must always be incorporated. Humans presently use thermoelectric detectors to search a person's temperature changes, card machines in bus routes to verify educators’ existence, and web platforms in which educators can register the attendance of the student for every class. Humans can use blockchain technology to connect and interact and develop an efficient, translucent COVID-19 tracking system. Throughout this paper, we start debating a fresh distributed ledgers traceability system that guarantees accountability among both students, schools, and the authorities to avoid virus spread and aid in interaction tracing. Utilizing blockchain technology and classroom Identity card, we support the implementation of the ancient nearby. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The number of the elderly experiencing cognitive disorders or dementia including Alzheimer's has been increasing worldwide. During COVID-19 pandemic, they are vulnerable not only to the coronavirus, but also to the social isolation with no family visits. The elderly are increasingly depressed, resisting care, refusing medications, and showing signs of agitation. These behavioral disturbances are leading to an increased use of psychotropic medications and/or increased hospitalizations to the already overloaded healthcare systems. To address this, the authors focus on a mobile intervention, named AlzhaTV, as a suite of mobile apps. The intervention, not requiring any technical interaction from the elderly, is available on both Android and iOS platforms. The mobile intervention has the potential to improve the quality of life and reduction in medications for the elderly. The results show that multiple personalized and non-personalized videos can be transmitted over existing wireless networks. During the current and future pandemics and disasters, AlzhaTV can actively support the elderly.
ABSTRACT
India reported highest incidence of Mucormycosis in the past and during the second wave of COVID-19. Google Trends is a potential tool for infoveillance of infectious disease like Mucormycosis. Relative Search Volume (RSV) for term Mucormycosis (+ Black fungus), diabetes, steroid, oxygen, D-dimer (+ferritin) had been extracted from Google Trends for three months (March to May 2021) and state-wise prevalence for Mucormycosis released by Ministry of Health and Family Welfare was used for the analysis. India reported highest RSV for Mucormycosis among the top five countries with COVID-19 cases. RSV peak for oxygen preceded the Mucormycosis spike. Correlation between state-wise prevalence of Mucormycosis and their corresponding RSV was not significant. While a positive correlation was observed between Mucormycosis, diabetes and steroid, it was not statistically significant. Infoveillance using Google trends can serve as a proxy marker for the public health needs and priorities much before actual field surveys. © 2021, Indian Association of Preventive and Social Medicine. All rights reserved.
ABSTRACT
Global health diplomacy has given birth to vaccine diplomacy and later it got linked with vaccine science diplomacy which itself an amalgamation of science diplomacy. Since India is leader in vaccine manufacturing, it contributes approximately 60% of vaccines to the global vaccine supply. According to the present scenario, India is harnessing the power of soft skill by offering COVID?19 vaccines to its immediate neighbors to leverage diplomacy. Vaccine diplomacy could serve humanity and suffering countries if significant collaborations and efforts by global entities are to be done on the multilateral level.
ABSTRACT
Over the length of time, there has been an exponential increase in the number of netizens throughout the world. The surge in internet users has been especially prominent in times of the Covid-19 pandemic. Consequently, there has been a rise in social data collection from social media sites and apps. In addition to it, there is machine data generated by the sensors and other industrial and medical equipment and finally, transactional data generated from transactions performed online as well as offline. The big data collected from these principal sources provides invaluable insights to various small as well as leading organizations. Hence, these organizations and researchers direct considerable attention and efforts towards the high volume, velocity and variety (referred to as the '3V') challenges. However, due to the sheer volume of the big data, high computing power and substantial storage are needed. This feat is achieved by the use of a network of distributed systems. Since multiple systems are involved in this process, the risk of privacy breach is increased manifold. The prevention of such breaches is crucial as it may result in leakage of highly sensitive data and impose a severe threat to the privacy of individuals. The primary objective of this paper is to provide a detailed synopsis of countermeasures that can be adopted against possible data breaches at the several stages of the big data life cycle (i.e., data generation, data storage and data processing) to ensure better security of the exabytes of big data generated each day. © 2020 IEEE.
ABSTRACT
In today's world, there are a lot of outdoor navigation apps for visually challenged people, but there are none that can precisely tell a user's location inside a large structure. Indoor navigation is a complex task for the visually challenged as well as for the general public, especially in large structures like malls, airports, museums, factories, etc. Present solutions and technologies are not cost-effective as well as complex. Hence, we are proposing a low-cost model that uses Augmented Reality to place virtual anchors across a structure, so a person can navigate from one location to another with the help of these anchors. The model doesn't use technologies like GPS. Machine Learning, and Artificial Intelligence but here, the anchors placed are pervasive and persistent across the indoor environment for smooth navigation. Once placed, these virtual anchors remain at their location and can be used at any time by any person registered on our app. This model can be extended for the general public in any indoor space and also can be enhanced by gamification for better user interaction and retention. This model can also be extended to collaborate with the Aarogya Setu app, which can help us identify routes that go through spaces through which covid positive patients have passed which in turn helps us avoid those routes in real-time navigation. © 2020 IEEE.
ABSTRACT
The coronavirus (COVID-19) has already left its footprints over vast geography worldwide and the rest of the globe is also under a big threat to this ongoing pandemic. Its effect can be felt amongst every sector of the economic ecosystem, and one of the most severely affected components is the food sector. The impact of COVID-19 on food supply chains (FSCs), especially agriculture, has created grave concerns for food security globally. This paper presents a perspective on future FSCs and the predicted outcomes which are likely to occur post COVID-19 based on the plans, reviews of critical reports, and information made available to date. The food industry needs unprecedented safety, technology & policy-measures to shine again and to manage the supply chain operations. In this highly-connected world, technological innovations and improvisations will create more resilient and sustainable food supply chains. Further, key production and distribution challenges are also highlighted. © 2020 Kharazmi University. All rights reserved.
ABSTRACT
Background: COVID-19, a disease caused by SARS-CoV-2 has become a public health emergency of worldwide concern. Originated in Wuhan District of China, it has spread globally at a very rapid rate causing millions of deaths worldwide. Aims and Objectives: To summarize findings from all the current literature available from different databases regarding the epidemiological, clinical characteristics, laboratory diagnosis, treatment, prevention and control of the SARS-CoV-2. This will help out the reader to have a fine gist of all the data available on SARS-CoV-2 in an effective manner. Methods: All the research literature from Jan-March 2020 and available on the following online databases: bio-Rxiv, PubMed, Google Scholar, Embase as well as CNKI and Wang Fang data were included in the review. The keywords used for data search were “coronavirus,” “nCoV,” “2019-nCoV,” and “COVID-19.” Conclusion: After undergoing extensive literature search, it can be concluded that it mainly effects elderly male population. Mode of transmission is droplet transmission and human to human transmission is present. The main diagnostic modality remains molecular assays though several rapid testing methods are on the way. Due to lack of specific treatment guidelines, Infection control and supportive treatment remain the mainstay. Plethora of experiments are under trails for development of effective vaccine which can be a ray of hope in future. © 2020, Indian Association of Preventive and Social Medicine. All rights reserved.