Browsing by Subject "COVID-19"

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Open Access
Active Death Cases and Recovery Rates of the COVID-19 Pandemic: A Comparative Study of Developed, Developing, and Least Developed Countries of the World
(NB Publications, 2022) Rahaman, Saidur; Saha, Snehasish; Mandal, Tapash; Chakrabarty, Kunal; Mitra, Nita; Pal, Sujit
At present, people worldwide are fighting against an unseen enemy. Outbreaks of COVID-19 are on the rise in more than 200 countries worldwide. In the world economy, human life has been dramatically affected. The developed developing, and least developed countries of the world have been affected by the extremities of COVID-19. However, the rate of COVID-19 infection is not the same in every country of the world due to some of their rules and facilities such as treatment, infrastructure, lifestyle, and awareness. The study has been done based on secondary data. Daily data on the number of recovered, active, and deaths cases were collected up to the study endpoint via the Johns Hopkins University data source (https://github.com/CSSEGISandData/COVID-19) on 24 December 2020 at 21.58 GTM and daily situation reports of World Health Organization (WHO). The data used relates to the descriptive statistics and Normalized Z-score and found relations among 15. In Developed countries, on an average, the affected COVID-19 cases were 28.73 people, say 29per thousand; median COVID-19 cases are 27.62 people, say 28 per thousand people. The average recovery rate is 65.95 % in Developed countries. Nevertheless, high confirmed cases have been found in the U.S (1,78,44,690) ...
Open Access
An approach of computer aided drug design tools for in silico pharmaceutical drug design and development
(University of North Bengal, 2024) Sarkar, Kaushik; Das, Rajesh Kumar
The work in this thesis utilizing various in silico techniques to explore potential leads against aurora kinase, hepatitis C virus, COVID-19, monkeypox virus, adenovirus, HKMT, and GOAT. CADD accelerates lead discovery and optimization, leveraging both high speed and low cost, thus enhancing drug development success rates. In this study, both structure-based (molecular docking) and ligand-based (QSAR) techniques were employed, providing a robust tool for ligand investigation. Drug repurposing emerges as a transformative strategy, offering innovative therapeutic avenues for approved drugs. Additionally, QSAR aids in lead optimization, minimizing time, cost, and animal use. Key steps in QSAR model development include dataset collection, descriptor calculation, model construction, and validation. This thesis proficiently employs QSAR to understand structural activity relationships, improve selectivity, and design molecules with enhanced efficacy, predicting the activity of newly designed compounds. CHAPTER I This chapter contains the in details about CADD which has been extensively explored for facilitating lead discovery and optimization with advantages in terms of both high speed and low cost, increases the probability of success in the drug development process. A variety of in silico methods have evolved in CADD that have two major application areas, i.e., LBDD and SBDD. The first part of the chapter deals with the object of CADD with aurora kinase and hepatitis C. The second part of the chapter deals about drug repurposing with COVID-19, monkeypox, and adenovirus. The last part deals about QSAR study with HKMT, and GOAT. CHAPTER II The major in silico techniques that are usually popular among researchers are molecular docking, molecular dynamics (MD) simulation, density functional theory (DFT), molecular mechanics Poisson Boltzmann surface area (MM-PBSA), quantitative structure activity relationship (QSAR), artificial neural network (ANN) and absorption, distribution, metabolism, excretion, toxicity (ADMET) prediction. Collective use of all of the mentioned computer aided techniques is necessary to predict potential inhibitors. It includes methodologies of all of the above mentioned techniques in detail. CHAPTER IIIA Aurora kinase (AURK) belongs to the serine/threonine kinase family and play a crucial role in regulating the cell cycle. Therefore, AURKs are the hopeful target for anticancer therapies and these findings have encouraged researchers to rigorously hunt small molecule aurora kinase inhibitors, not only for research articles but also for use as therapeutic agents. This study helped us to identify and screen the best phytochemicals as potent inhibitors against AURK. These potent inhibitors came from the various substitution of rosmarinic acid (RA). Here, we selected different tested derivatives for designing anticancer drugs by substituting various functional groups of standard drug RA. In silico studies were carried out to Abstract appreciate better drug candidature of some of these derivatives. This study was performed on 56 derived compounds of the standard RA. Out of the 56 derivatives, 11 have passed all the rules of drug candidature, to serve as best AURK inhibitor, in a theoretical manner. This study should be supported by a new proposal to explore future studies with these 11 compounds against cancer. CHAPTER IIIB The NS3/4A protease is a common target for HCV infection. Telaprevir and danoprevir have promising activity in combating these virus-associated infections and are used as HCV protease inhibitors. In this study, we have found different tested derivative compounds for developing various HCV NS3/4A protease inhibitors by designing the chemical structures of telaprevir and danoprevir. In silico studies were carried out to find better drug candidature from these derivative compounds. The docking studies were performed on HCV NS3/4A protease receptors (PDB: 3SV6 & 5EQR). DFT, global reactivity, ADME (Absorption, distribution, metabolism & excretion), and toxicity analysis were also performed for these designed compounds. The stability of the protein-ligand complexes was quantified by MD simulation and MM-PBSA studies. 16 derivatives (four as telaprevir and twelve as danoprevir) have satisfied higher binding affinity of interaction with NS3/4A protease, compared to telaprevir and danoprevir. These compounds have also passed all rules of drug candidature to serve as the best HCV inhibitors. These 16 ligands can be used as effective inhibitors against HCV NS3/4A protease. These ligands could be considered to follow the drug candidate behaviour by in vitro and in vivo analysis to inhibit HCV infection. CHAPTER IVA Novel coronavirus disease, COVID-19 caused the outbreak situation of global public health. In that pandemic situation, all the people lives of 212 Countries and Territories were affected due to partial or complete lockdown and also as a result of mandatory isolations or quarantines. This was due to the non-availability of any secure vaccine. This study helped us to identify and screen the best phytochemicals as potent inhibitors against COVID-19. In this study, we have selected two standard drugs namely hamamelitannin and rosmarinic acid as a probable inhibitor of pandemic COVID-19 receptor, compared to antimalarial drugs hydroxychloroquine, anti-viral drug remdesivir, and also baricitinib. This study was done by taking into consideration of molecular docking study. This work has provided an insightful understanding of protein-ligand interaction of hamamelitannin and rosmarinic acid showing comparable binding energies than that of clinically applying probable COVID-19 inhibitors hydroxychloroquine (an anti-malarial drug) and remdesivir (an anti-viral drug). We would expect that if its anti-SARS-CoV-2 activity is validated in human clinical trials, these two drugs may be developed as effective antiviral therapeutics for infected patients with COVID-19. CHAPTER IVB In view of the non-availability of any secure vaccine for COVID-19 caused by SARS-CoV-2, scientists around the world have been running to develop potential inhibitors against SARS-CoV-2. This study helped us to identify and screen best phytochemicals (chemical drugs or plant based compounds) as potent inhibitors against COVID-19. Here, we have measured the virtual interactions of COVID-19 main protease (PDB: 6LU7) with lung cancer, bronchitis and blood thinner drugs as well as some natural plant based compounds. Best docking results have been considered on the basis of disulfiram, tideglusib and shikonin. ADME and toxicity were also predicted for these compounds. From this study, we would expect these drugs to undergo validation in human clinical trials to be used as promising candidates for antiviral treatment with high potential to fight against COVID-19. CHAPTER IVC Monkeypox virus (MPXV) is considered as zoonotic disease with characteristics comparable to smallpox virus. The disease was also a global epidemic concern. Tecovirimat was approved by US Food and Drug Administration (FDA) for MPXV treatment. The aim of this in silico study was to repurpose approved pharmaceutical drugs as potential inhibitors of MPXV target. In this study, molecular docking was performed on 406 pharmaceutical drugs, and results were compared with reference tecovirimat. Results showed that 7 compounds, bictegravir, glimepiride, glyburide, lasmiditan, olaparib, rimegepant, and ubrogepant, have shown higher binding energies compared to the reference. After that, these best hits were further assessed by 100 ns molecular dynamics simulation and the best results were observed for bictegravir, glimepiride, glyburide, olaparib, and ubrogepant. The docking analysis was further validated by MM-PBSA binding free energy calculations. In addition, pharmacokinetics and density functional theory (DFT) studies were also discussed for these best hits. In conclusion, three compounds, bictegravir, glimepiride, and glyburide, have satisfied all the criteria for better leads against MPXV. CHAPTER IVD Human adenovirus (HADV) infection can pose a serious threat to children, leading to a variety of respiratory illnesses and other complications. Particularly, children with weak immune systems are vulnerable to severe adenovirus infections with high mortality. The main focus of this study was to propose new antiviral agents as lead HADV inhibitors for children. So, several antiviral agents used in children were subjected to finding new HADV inhibitors using important computational methods of molecular docking, molecular dynamics (MD) simulation, MM-PBSA binding free energy calculations, DFT, and pharmacokinetic analysis. Molecular docking of standard cidofovir along with other ligands, suggested that sofosbuvir has the highest binding energy (-10.8 kcal/mol), followed by baloxavir marboxil (-10.36 kcal/mol). Further, the analysis of molecular interactions using MD simulation (100 ns) and MM-PBSA indicated that baloxavir marboxil has formed the most stable protein-ligand complex with HADV, followed by sofosbuvir. The binding free energies of baloxavir marboxil and sofosbuvir were found to be -61.724 kJ/mol and -48.123 kJ/mol, respectively. The DFT and drug-likeness properties of these compounds were also investigated. Overall, two antiviral agents, such as baloxavir marboxil, and sofosbuvir, were suggested as lead repurposed candidates against HADV. CHAPTER VA Initiation and progression of several diseases by post-translational histone modifications are considered a worldwide problem. Enhancer of Zeste Homologue 2 (EZH2), which belongs to HKMT family, has been emphasised as a promising target for cancer therapy. It is a major challenge for the scientific community to find novel approaches to treating this disease. In this study, a series of 51 derivatives of the benzofuran and indole families, previously experimentally evaluated against HKMT, was used to develop the best QSAR model with promising anticancer activity. The multiple linear regression (MLR) method was used with a genetic algorithm (GA) for variable selection. The model with two descriptors (minHBint4 and Wlambdal.unity) was found to be the best and its parameters fit well, and its validation was well established. The applicability domain was also validated for this model. Furthermore, its robustness (R2 = 0.9328), stability (Q2LOO = 0.9212, Q2LMO = 0.9187), and good predictive power (R2ext = 0.929) were also verified. Hence, this model was assumed to have predictive HKMT anticancer activity for designing active compounds. Molecular docking was also performed to identify binding interactions, and new molecules with better predicted biological activity (pIC50) were designed. The binding energy of the three designed compounds demonstrated higher binding activity at the target receptor, followed by complex stability, determined by a 100 ns molecular dynamics simulation and binding free energy calculation. DFT and pharmacokinetic analyses also confirmed their drug-like properties. Finally, it could be declared that the proposed tools allow rapid and economical identification of potential anti-HKMT drugs (anticancer drugs) for further development. CHAPTER VB Diabesity is a major global health concern, and GOAT acts as an important target for the development of new inhibitors of this disease. This work highlighted a detailed QSAR study, which provides an excellent model equation using descriptors. Here, the best model equation developed has two variables, namely MLFER_E and XlogP, with statistical parameters R2 = 0.8433, LOF = 0.0793, CCCtr = 0.915, Q2LOO = 0.8303, Q2LMO = 0.8275, CCCcv = 0.9081, R2ext = 0.7712, and CCCext = 0.8668. A higher correlation of the key structural fragments with activity was validated by the developed QSAR model. Furthermore, molecular docking helped us to identify the binding interactions. Thirty four new molecules with better predicted biological activity (pIC50) were designed. The binding energy of four compounds have shown higher binding activity into the membrane protein. Molecular dynamics simulation has established the stability of the protein-ligand complex over 100 ns. DFT and ADME-toxicity analyses also confirmed their drug-like properties. Based on our findings, we would expect these new oxadiazolo pyridine derivatives to undergo further development.
Open Access
COVID-19 and Women Warriors in Health Sector in West Bengal
(University of North Bengal, 31-03-2021) Saha, Priya Ranjan
The COVID-19 outbreak is impacting societies around the world in an unprecedented manner. With an intention to break the chain of coronavirus spread, India went for complete nationwide lockdown from 24 March 2020. While the comparatively rich and privileged classes could sustain their normal life during the longest period of lock down, it was primarily the poor and the marginalized sections that had to bear the cost. In this pandemic the weaknesses of our health system have been thoroughly exposed but the frontline health workers put up a brave face while attending the COVID-infected patients taking life risk. In this paper, I have tried to capture how our front-line women warriors of the health sector are fighting the disease and the consequences they have to face while carrying out their duties. As the pandemic has given rise to certain fear and anxiety in the public mind, the front-line women health workers have to face additional vulnerability for no fault of their own. Ironically, as compared to the male health workers, the female workers suffer more. For writing this paper, I have relied on secondary data published in newspapers and journals and supplemented those with my own ethnographic findings.
Open Access
Jurisprudential Study on Individual Liberty v. Public Interest: A Case of COVID-19
(University of North Bengal, 2023-03) Rajasekar, G
Human community witnessed many outbreaks of infectious disease from very ancient period. Indian society is also not spared by the nature in this regard. These diseases posed great threats not only to the public health security of the nations but also significantly disrupted the economic and commercial activities of the State. The power exercised by the state in protecting public health during health emergency is limited by the individual right to liberty, right to food, right to privacy, right against discrimination etc. Therefore, a fine balance may be drawn between the individual liberty and the power state to maintain public health.
Open Access
On the Margins: A Tale of the Pandemic and the Funeral Workers in Benares
(University of North Bengal, 2022-03) Kumari, Sarita
Caste is one of the core markers of Hindu society. Many castes still continue with their hereditary traditional occupations across India; burning of the funeral pyre is such an unrecognized occupation performed by the Doms. Outbreak of any infectious disease often adds to the burden of a work, which is already challenging. The pandemic COVID-19 unfolded a series of events in the lives of funeral workers in Benares, as they had to negotiate the transition from normal to pathological conditions while carrying on with their occupation. The stigma attached to their work of dealing with death and their caste identity played a pivotal part in undermining their efforts both by the State and the caste-based society at large.
Open Access
Swachh Bharat Paradox: Issues and Challenges of Manual Scavengers with Special Reference to the COVID-19 Crisis
(University of North Bengal, 2023-09) Chawla, Garima
Despite the enactment of successive legislations in 1993 and 2013, the dehumanising occupational practice of manual scavenging still persists in India. As members of the Dalit community, manual scavengers continue to confront issues such as marginalisation and gross violation of their dignity. This paper critically examines the socio-legal status of manual scavengers in India by assessing key determinants including the legislative and regulatory measures, the role of the judiciary and civil society, as well as the widely celebrated Swachh Bharat Abhiyan. Additionally, the paper provides an empirical analysis of the struggles faced by manual scavengers as frontline workers during the unprecedented humanitarian crisis of COVID-19.
Open Access
Text classification and sentiment analysis using deep learning techniques with special reference to COVID-19
(University of North Bengal, 2024) Dutta, Rakesh; Mukherjee, Mukta
The COVID-19 pandemic has pushed the world into a highly dangerous situation, killing thousands of lives and putting the global health infrastructure at significant risk. The virus has spread worldwide, and every nation has worked together to combat against this virus. To prevent the spread of the Coronavirus. fight with them and for public awareness. Natural Language Processing approaches such as automated systems that can classify mediical documents/research articles, measure public sentiment, and detect fake news/ruimors play vital roles. This research makes significant contributions to text classification, sentiment analysis, and rumor/fake news identification on COVID-19 by applying Deep Learning (DL) and Natural Language Processing (NLP) techniques. The first contribution is a DL technique for classifying and segregating medical docunnents and research papers on COVID-19 to decrease the searching effort of the researchers for relevant content or information. The second contribution is a rumor/fake news classification model for detecting false and misleading narratives on COVID-19: from social media and online news blog to stop the spread of rumours. The third contribution is a sentiment analysis task, which has grown in popularity as an emerging area in NLP. An Aspect-Based Sentiment Analysis (ABSA) system has been propos,ed by applying Best Worst Method (BWM) of Multi-Criteria Decision Making (MCIDM) technique and Deep Neural Network to predict the polarity of public sentime:nt underlies several aspects from 1witter throughout lockdown and unlock stages iin India. The fourth and final contribution of the research is also an opining mining; system using Analytic Hierarchy Process (AHP) of MCDM and DL to identify the public sentiment on different aspects of online teaching-learning from tweets during the pandemic. Several case studies have been conducted on the techniques proposed in this Dissertation based on different datasets to highlight the significance of this research.