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The rapid rate of mutation of the RNA genome of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for the emergence of viral variants, leading to the enhanced survivability of the virus. Hence, searching for new drugs that can restrict new viral infections by interacting with wild-type and mutated viral proteins is important. However, new drug development's economic and time-constraining nature makes drug repurposing a more viable solution to address the problem. In this work, we conducted a computational study to screen 23 Non-Steroidal Anti-Inflammatory Drugs (NSAID) interactions with 5 major viral proteins of SARS-CoV-2 that are mainly involved in host infection. Our in-silico results establish a database that shows that different NSAID ligands interact with the different viral proteins with good binding affinities. Stabilizing point mutations were introduced within the conserved amino acids involved in ligand-protein interactions. Redocking the NSAID ligands with these mutated viral proteins showed that the NSAID ligands could bind with the mutated and wild-type viral proteins with comparable binding affinities. We conclude that the NSAID ligands could be repurposed as therapeutic drugs against the SARS-CoV-2 virus. Additionally, our work generated a repository that includes binding affinities, possible modes of interaction, and specific interacting residues of the protein (wild-type and mutated) ligand complexes that could be used for future validation studies. Further, our results point to the potential of these drugs to treat other viral infections with similar disease etiology.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.
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Background: Chronic obstructive pulmonary disease (COPD) is a prominent cause of illness and mortality on a global scale. In 2019, it was predicted to rank as the sixth largest cause of mortality. COPD is one of the most prevalent non-communicable illnesses in the field of pulmonology. The DECAF score (Dyspnea, Eosinopenia, Consolidation, Acidemia, and Atrial Fibrillation) is a risk stratification tool for patients with AECOPD that can be used at the bedside to guide treatment, such as hospital at home for low-risk patients. The purpose of this study is to predict the in-hospital mortality in acute exacerbation of COPD patients with modified DECAF scores. Modified DECAF score includes Dyspnea, Eosinopenia, Consolidation, Acidemia and Frequency of Hospitalization. Material(s) and Method(s): A total of 50 patients attending Emergency Medicine Department with Acute Exacerbation of COPD were recruited to this hospital based observational study. This study was conducted at the Department of Emergency medicine & Pulmonary medicine, at APOLLO GLENEAGLES HOSPITALS, Kolkata. Result(s): COPD was more prevalent in the age groups of 41-50 years (28%) and 61-70 years (28%) followed by those having age between 51-60 years (22%). Majority of the COPD patients were males (88%) compared to (12%) females. Majority of the COPD patients were males (88%) compared to (12%) females. Most common co-morbid condition associated with COPD washypertension (16%) followed by IHD (8%), pulmonary hypertension (6%) and diabetes mellitus (4%). Out of 50 patients with COPD, 11 (22%) had previous history of AECOPD, 38 (76%) were regular user of inhaler, 33 (66%) had history of influenza vaccination, 16 (32%) had Pneumococcal Vaccination and 2 (4%) patients had COVID-19 pneumonia. Out of 50 patients, 24 (48%) had Dyspnea (eMRCD) score of 5a whereas 26 (52%) had Dyspnea (eMRCD) score of 5b as well as 7 (14%) had Eosinopenia (<50 cells/mm3) and 20 (40%) had Consolidation. Conclusion(s): We conclude that the Modified DECAF score is both sensitive and specific in predicting in-hospital mortality in AECOPD patients. Modified DECAF is a simple tool that predicts mortality that incorporates routinely available indices. It effectively stratifies COPD patients admitted with acute exacerbations into mortality risk categories. Copyright © 2022 Ubiquity Press. All rights reserved.
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Sustainable development, finance and related global policies and mechanisms have evolved over the decades. Today, regional initiatives for classifying sustainable activities exist, and several decades' research and development of ecosystem services and natural capital have identified and tested alternative economic models. The World Bank has the potential to finance them and sustainability at the landscape scale is achievable. But economic and environmental values can come into conflict. In developing countries, sustainable alternatives exist in business activities such as coastal and marine tourism. Financing small businesses through sound digital infrastructure is critical, as is the use of public fiscal instruments for the sustainable use of natural resources. Despite its developed status, renewable energy policies in the EU are leading to forest destruction. Financial vehicles such as green bonds have a similar potential. To avoid greenwashing, more focus needs to be on meeting the needs of those at the base of the economic pyramid, resourcing them with smart technologies and valuing civic engagement. Climate finance must be ethical and its allocation have integrity;this will foster community resilience. To avoid repeating the mistakes of terrestrial development, the world's oceans need to be protected and new business models adopted in this expanding frontier. Now is the time for all sectors to create a sustainable future for the planet and its inhabitants in the post-COVID, postcarbon era to come. © 2022 Walter de Gruyter GmbH, Berlin/Boston.
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AI (artificial intelligence) and blockchain are promising technologies for all countries, including India. While these two technologies have a landscape of associated risks, there are distinct aspects like decentralisation and distribution, which make these technologies particularly suitable for India. It seems that the promise of technology could not come any sooner, especially as India faced the COVID-19 crisis in 2021. With this in mind, the chapter will do three things. First, the chapter will draw out a framework of effective risk management of AI and blockchain and put forward methods of effective decentralised decision-making. Second, the chapter will comment on the nature and sources of finance in this technology transition in India with empirical evidence. Third, the chapter will trace policy feedback and suggest a way forward with a focus on the COVID-19 crisis. Thus, the chapter will seek to draw out a meaningful narrative about the landscape of AI and blockchain in India particularly in its use to attain effective local governance and sustainable development. © 2022 Walter de Gruyter GmbH, Berlin/Boston.
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This report describes the progress towards WHA milestones and measles elimination objectives during 2000-2020 and updates a previous report. During 2000-2010, estimated MCV first dose (MCV1)coverage increased globally from 72% to 84%, peaked at 86% in 2019, and declined to 84% in 2020 during the COVID-19 pandemic. All countries conducted measlesurveillance, although fewer than one third achieved the sensitivity indicator target of 2 discarded cases per 100,000 population in 2020. From 2000 to 2016, annual reported measles incidence fell 88%, from 145 to 18 cases per 1 million population, before rebounding to 120 in 2019 and falling to 22 in 2020. During 2000-2020, the annual number of estimated measles deaths decreased by 94%, from 1,072,800 to 60,700, averting an estimated 31.7 million measles deaths. To reach regional measles eradication goals, more work needs to be done to make sure all children get two MCV doses, to have strong surveillance, and to find and close immunity gaps.
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To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on the severity of COVID-19 manifestation in the patient. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework, on UK BioBank data we extract novel insights on the complex interplay. The algorithm is also sensitive enough to detect the changing influence of the emergent B.1.1.7 SARS-CoV-2 (alpha) variant on disease severity, and, changing treatment protocols. The genomic component also implicates biological pathways that can help in understanding the disease etiology. Our work demonstrates that it is possible to build a robust and sensitive model despite significant bias, noise and incompleteness in both clinical and genomic data by a careful interleaving of clinical and genomic methodologies.