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The COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. Thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. The article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. The authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.
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The traditional de novo drug discovery is time consuming, costly and in some instances the drugs will fail to treat the disease which result in a huge loss to the organization. Drug repurposing is an alternative drug discovery process to overcome the limitations of the De novo drug discovery process. Ithelps for the identification of drugs to the rare diseases as well as in the pandemic situationwithin short span of time in a cost-effective way. The underlying principle of drug repurposing is that most of the drugs identified on a primary purpose have shown to treat other diseases also. One such example is Tocilizumab is primarily used for rheumatoid arthritis and it is repurposed to treat cancer and COVID-19. At present, nearly30% of the FDA approved drugs to treat various diseases are repurposed drugs. The drug repurposing is either drug-centric or disease centric and can be studied by using both experimental and in silico studies. The in silico repurpose drug discovery process is more efficient as it screens thousands of compounds from the diverse libraries within few days by various computational methods like Virtual screening, Docking, MD simulations,Machine Learning, Artificial Intelligence, Genome Wide Association Studies (GWAS), etc. with certain limitations.These limitationscan be addressed by effective integration of advanced technologies to identify a novel multi-purpose drug.Copyright © 2023, Association of Biotechnology and Pharmacy. All rights reserved.
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In the era of COVID pandemic, it is highly desirable to improve body's immune responses. Many of the diseases including COVID impact the immune system and make it defenseless. There is a growing interest of herbal medicine around the world owing to effective pharmacological actions attributed by its unique phytocompounds. Majority of the immunomodulatory drugs including both immunosuppressor and immuno-stimulators are synthetic organic compounds having side effects. Therefore, there is a growing interest to use plant-based products to regulate immune responses. The present chapter deals with the recognition of members of the family Solanaceae for their use as potential immunomodulators. There are quite a few reports of active phytocompounds isolated from different solanaceous plants that have a myriad of medicinal properties. Various phytochemicals such as phenolic compounds, flavonoids, alkaloids, terpenes, lactones, and glycosides have been shown to have various therapeutic effects on human body owing to their ability to influence body's immune system. The present chapter will give a comprehensive overview of plant-based therapeutics of solanaceous plants including withanolides isolated from Withania somnifera and Physalis sps, capsaicinoids isolated from Capsicum, and lycopene from tomato. Solanaceae is one of the superfamily having huge repertoire of plants belonging to major food crops utilized throughout the world (tomato, potato, capsicum etc.) as well as in medicine category. Besides several members of the Solanaceae family such as Physalis sps, Atropa sps, Datura stramonium, and Solanum xanthocarpum have potential phytomolecules belonging to alkaloids, terpenoids, and steroid category, which are recognized as medicines. This chapter will also give a critical overview of the possibilities, facts, and prospects of the use of members of Solanaceae family as potential future immunomodulatory drugs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
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Background: Patients with cancer are at a higher risk of severe forms of coronavirus disease 2019 (COVID-19) and mortality. Therefore, widespread COVID-19 vaccination is required to attain herd immunity. Objective(s): We aimed to evaluate the uptake of the COVID-19 vaccine in Indian patients with cancer and to collect information regarding vaccine hesitancy and factors that contributed to vaccine hesitancy. Material(s) and Method(s): This was a questionnaire-based survey conducted between May 7, 2021 and June 10, 2021 in patients aged 45 years and over, with solid tumors. The primary end points of the study were the proportion of Indian patients with cancer aged 45 years and older who had not received the COVID-19 vaccine, and the reasons why these patients had not received the COVID-19 vaccine. Our secondary end points were the proportion of patients with a history of COVID-19 infection, and the proportion of the patients who had vaccine hesitancy. Additionally, we attempted to assess the factors that could impact vaccine hesitancy. Result(s): A total of 435 patients were included in the study. Of these, 348 (80%) patients had not received even a single dose of the COVID-19 vaccine;66 (15.2%) patients had received the first dose, and 21 (4.8%) had received both the doses. Approximately half (47.1%) of the patients reported that they took the COVID-19 vaccine based on the advice from a doctor. The reasons for not taking the COVID-19 vaccine could be considered as vaccine hesitancy in 259 (77%) patients. The two most common reasons were fear in 124 (38%) patients (fear of side-effects and of the impact of the vaccine on the cancer/therapy) and lack of information in 87 (26.7%) patients. On the multivariate analysis, the two factors found to be significantly associated with vaccine hesitancy were a lower educational level (OR, 1.78;95% CI, 1-3.17;P = 0.048) and a lack of prior advice regarding the COVID-19 vaccine (OR, 2.80;95% CI, 1.73-4.53;P < 0.001). Conclusion(s): Vaccine hesitancy is present in over half of our patients, and the most common reasons are a fear of the vaccine impacting the cancer therapy, fear of side-effects, and lack of information. Widespread vaccination can only be attained if systematic programs for education and dissemination of information regarding the safety and efficacy of the COVID-19 vaccine are given as much importance as fortification of the vaccination supply and distribution system.Copyright © 2021 Cancer Research, Statistics, and Treatment Published by Wolters Kluwer - Medknow.
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PurposeHealthcare workers' (HCWs') job-related high exposure to Covid-19 virus arouses fear of Covid-19 among them. Based on the Theory of Mind (ToM), the study predicts that fears will lead to negative psychological (psychological distress) and behavioral (withdrawal intentions) outcomes. ToM is also used to identify social intelligence as a means to counter fear of Covid-19 on heightened psychological distress and increased withdrawal intentions.Design/methodology/approachTo investigate the study design, a sample of 262 HCWs, including doctors, nurses and technicians, were surveyed using standardized questionnaires.FindingsAs predicted, Covid-19 fear led to increased withdrawal intentions with heightened psychological distress partially mediating the relationship. The alleviating role of social intelligence on the effects of Covid-19 was supported as high social intelligence reduced HCWs' turnover intentions, with decreased psychological distress partially mediating the relationship.Originality/valueGiven the universality of the Theory of Mind (ToM), the findings of this study are likely to be generalizable to all pandemics. The study results support the increased application of ToM in organizational settings and have both theoretical and practical implications for health administrators. Based on study results, health administrators are exhorted to develop ToM-based mental models to understand and deal with the fear of contagious diseases. Health administrators can also increase HCWs' social intelligence to deal with the negative perceptual and behavioral outcomes arising from the emotions aroused by the nature of their work.
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As tourism research focuses on experience, memorable tourism experience (MTE) is gaining importance among academia and destination managers. However, only limited studies have examined the antecedents and consequences of MTE. Therefore, this study will investigate human emotions with their underlying factors in influencing MTE to cater to revisit intention in tourists. For this study, CFA-SEM is applied to a sample of 1120 tourists from central India to access the empirical relationships. The empirically validated model confirmed the significant relationships among human emotions, MTE, and revisit intention. Results suggested that human emotions significantly influence MTE, which is reflected as revisit intention in tourists. Findings also confirmed the moderating role of the perceived risk of COVID-19 further, exploring the health-linked effects on tourism. This study will help the researchers and destination managers make more informed decisions and strategies to make tourism destinations more sustainable by incorporating human emotions as a significant factor in influencing the tourism experience. The findings will also help destination managers in making tourism more risk-averse.
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The study analyses the contribution of the tourism, banking, property fund and real estate, and finance and securities industries to the systemic risk of Thailand. Using quantile regression, the study estimates the measures of systemic risk (conditional Value-at-Risk (CoVaR) and delta conditional Value-at-Risk ( increment CoVaR)) and examines the relevance of various industries in increasing the systemic risk of Thailand using Kolmogorov - Smirnov test. The findings support the relevance of the tourism industry in significantly contributing to the systemic risk in Thailand. The study also highlights the systemic relevance of the tourism industry during the COVID-19 period in Thailand, when the industry was severely impacted, which adversely affected its systemic risk.
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The coronavirus disease 2019 (COVID-19) pandemic has drastically impacted life around the globe. As life returns to pre-pandemic routines, COVID-19 testing has become a key component, assuring that travellers and citizens are free from the disease. Conventional tests can be expensive, time-consuming (results can take up to 48h), and require laboratory testing. Rapid antigen testing, in turn, can generate results within 15-30 minutes and can be done at home, but research shows they achieve very poor sensitivity rates. In this paper, we propose an alternative test based on speech signals recorded at home with a portable device. It has been well-documented that the virus affects many of the speech production systems (e.g., lungs, larynx, and articulators). As such, we propose the use of new modulation spectral features and linear prediction analysis to characterize these changes and design a two-stage COVID-19 prediction system by fusing the proposed features. Experiments with three COVID-19 speech datasets (CSS, DiCOVA2, and Cambridge subset) show that the two-stage feature fusion system outperforms the benchmark systems of CSS and Cambridge datasets while maintaining lower complexity compared to DL-based systems. Furthermore, the two-stage system demonstrates higher generalizability to unseen conditions in a cross-dataset testing evaluation scheme. The generalizability and interpretability of our proposed system demonstrate the potential for accessible, low-cost, at-home COVID-19 testing. IEEE
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The recent COVID-19 pandemic has made the world suffer ravaging costs and damage to human lives, perhaps never seen in modern world. Pandemics will keep reviving till such time the humans attain a disease-less world state. Till such realizations are attained, we need to attempt retarding the pandemics by exploiting information systems enabled with new genre IT technologies, and blockchain offers one such way for realization. This paper proposes medical IoT architecture enabled by blockchain and further augmented with distributed storage protocol to retard any such pandemics ahead. The works have been performed on a Multichain permissioned blockchain platform and IPFS protocol. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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In view of increasing importance of emerging market currencies in the global foreign exchange markets and the growing concerns regarding the vulnerability of these currencies to global crises, we assess the connectedness of 16 emerging currencies by employing asymmetric domains of time and frequency spanning March 2011 to January 2022. We first notice bidirectional interconnectedness (both positive and negative) among three clusters of sampled exchange rates. The currency contagions follow divergent directions during crisis periods. During US debt selling crisis, there is a short-run negative contagion pointing to the appreciation of currencies. Following the Chinese financial market crisis, emerging market currencies demonstrated devaluation. There is long-run positive contagion (devaluation) in response to European Debt Crisis, Russian Ruble Crisis, Brazilian economic crisis, and Argentinian monetary crisis. The sampled exchange rates demonstrate negative long-run connectedness (appreciation) after COVID-19. The major transmitters to total connectedness are South Africa, Poland, and Mexico and major receivers include Thailand, the Philippines, Malaysia, India, Indonesia, and Egypt. In the long run, China is emerging as a significant transmitter. Our study draws significant policy and practical implications for regulators, investors, and financial market participants. © 2023 Taylor & Francis Group, LLC.
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The study examines the effects of market conditions, volatility and liquidity shocks on the arbitrage profits during pre-COVID and COVID periods. The study uses a conditional quantile regression and finds no significant difference in the impact of market conditions on the arbitrage profits during pre-COVID and COVID crisis periods. The increase in volatility combined with low liquidity during the COVID period makes arbitrage non-viable. However, the decline in volatility during the COVID period encourages investors to initiate arbitrage. The results are useful to fund managers and market analysts to develop suitable trading strategies and stock market regulators to take necessary steps to improve price discovery mechanisms and market efficiency. © 2023 John Wiley & Sons Ltd.
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This case study examines the Indian Institute of Technology Gandhinagar (IITGN) campus's monthly energy consumption profile in detail to understand how it varies according to academic calendar, seasonal variability, and the recent COVID 19 pandemic. In addition, a detailed assessment of the electricity bill and its sub-component calculations are intended to understand how the energy consumption pattern affects the overall monthly electricity bill. From this study, it is observed that the energy consumption of academic areas, hostel areas, and chiller plants account for 80-90% of total energy consumption. The on-site solar PV energy generation at IITGN campus accounts for 1014% of total monthly energy consumption, which varies greatly by season. The analyses performed in this paper were inferred by three years of historical data of actual energy consumption and monthly electricity bills. Based on the analysis presented in this paper some recommendations towards the energy conservation measures are also given. © 2022 IEEE.
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Aim SARS-CoV-2 virus primarily attacks the upper and lower respiratory tract of the individuals leading to common cold-like symptoms and pneumonia in immunocompromised patients. Also, the virus replicates in the intestinal epithelial cells and causes cytopathic alterations leading to gastric upset and diarrhoea. Dentists, auxiliaries as well as patients undergoing dental procedures are at high risk of cross-infection since most dental procedures require close contact with the patient's oral cavity, saliva, blood, and respiratory tract secretions. Ministry of health and family welfare (MOHFW) released unified guidelines regarding dentistry practice in India on 19rd May 2020. The article presents challenges faced in practising dentistry in India during the pandemic and review on various protocols released worldwide regarding the same. Conclusion Post pandemic dental practices may be accompanied by changes in products, delivery systems, communication and technologies regarding treatment protocols along with healthcare systems but proper behavioural counselling and efficient management of patients pre- and post-treatment can lead to better outcome during this challenging time of the pandemic. © ARIESDUE.
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During the COVID-19 pandemics we have all witnessed the clinical importance of mRNA as current vaccines and future therapeutics. mRNA therapies have a potential to revolutionize cancer treatment. Delivery of mRNA requires lipid nanoparticles (LNP) to protect the cargo from degradation. mRNA has a negative charge and depends on positively charged lipids to be encapsulated in LNP. These lipids can be either ionizable at certain pH or constantly cationic. Even though previous studies had evaluated the formulation properties of ionizable and cationic LNP systems, there is the need to understand their specificity in terms of mRNA delivery and protein expression in breast cancer tumor microenvironment. The objective of this work was to assess the kinetics of LNP cellular uptake and mRNA expression inv breast cancer (BC) cells and fibroblasts, the most frequent cell type in the tumor microenvironment cells, while studying the mechanisms involved in differential behaviors of LNP formulated with cationic and ionizable lipids. To achieve this goal mRNA-LNP containing ionizable lipids (LNP-A) and cationic lipids (LNP-B) were designed and formulated using Nanoassemblr Benchtop microfluidics mixer (Precision NanoSystems). mRNA-LNP were characterized for size, zeta potential using dynamic light scattering (DLS) and mRNA encapsulation efficiency using RiboGreen assay. LNP were tagged with rhodamine lipid to investigate the uptake kinetic and a reporter GFP mRNA to evaluate mRNA expression in murine 4T1 and human MCF7, MDA-231, SUM-159 and T47D breast cancer cells and BJ fibroblasts. Live fluorescence microscopy imaging, IncuCyte S3, was used to determine the LNP uptake and GFP mRNA expression. In vitro biocompatibility was assessed with WST-1 assay. Additionally, expression of mRNA delivered from LNP in tumor microenvironment was evaluated in vivo in a syngeneic 4T1 breast cancer model using mRNA luciferase and IVIS imaging. mRNA-LNPs possessed an average diameter of 77 - 107 nm, narrow size distribution, neutral zeta potential and high mRNA encapsulation efficiency (>94%). Our results demonstrated that mRNA expression was higher in breast cancer cells when delivered from LNP-A formulation and in BJ fibroblasts when delivered from LNP-B. LNP-A, the ionizable LNP, was tested in the breast cancer cells to confirm the efficacy of the delivery. The highest transfection efficacy, from high to low, T-47D, MCF7, SUM-159, 4T1 and MDA-231.We have further investigated the cellular uptake mechanisms of LNP using uptake pathway inhibitors for caveolae endocytosis, clathrin endocytosis, and phagocytosis. Our data confirm that there are differences in mechanisms that govern the uptake of mRNA LNP in breast cancer cells and fibroblasts. Clathrin-mediated endocytosis was active in 4T1 breast cancer cells for ionizable and cationic LNP. Interestingly, despite in vitro differences in uptake and mRNA expression, in vivo results show that both formulations efficiently delivered luciferasemRNA in the tumor microenvironment. Histology results demonstrated similar luciferase expression for both LNP in tumors. Additionally, we were able to confirm the prominent presence of fibroblast and similar distribution in the 4T1 subcutaneous model which could explain the similar efficacy of cationic and ionizable LNP. Understanding uptake and mRNA expression of different LNP formulations in the tumor microenvironment can help in achieving the necessary protein expression for breast cancer therapies. Furthermore, determining the most efficient carrier in early stages may reduce the time required for clinical translation. Acknowledgement: This research was supported in part by CPRIT Core for RNA Therapeutics and Research.
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In the majority of the affected nations, suicidal behavior against COVID-19 leads to various concerns. This study aimed to analyze determinants affecting suicidal behaviour among university students in Uttarakhand. An online cross-sectional survey of 18-year-old university students in Uttarakhand was conducted between April 2 and May 13, 2022. The questionnaire comprised socio-demographic information, the Suicidal Behaviors' Questionnaire-Revised (SBQ-R) scale, and elements related to the physical and psychological health of COVID-19 (CRPPF). The statistical study included demographic information, basic statistics in terms of frequency and percentage, and logistic regression. In comparison to students with fewer than seven family members, students with more than seven family members were less likely to participate in suicide behaviour (AOR = 2.21;95% CI: 1.79 to 2.67) and vice versa (AOR = 0.81;95% CI: 0.56 to 0.97). According to the study, a substantial majority of students (76.35%) claimed that the lockdown implemented to stop the spread of COVID-19 was extremely upsetting for them and that the pandemic had caused them to miss their graduation (73.90%). Adjusted multivariate logistic regression shows that feelings of a burden on family, (AOR= 1.98, 95% CI: 1.09 to 2.82), distancing from family or friends, (AOR =1.66;95% CI: 1.26 to 2.01), having relationship dilemmas, (AOR= 2.31;95% CI: 1.84 to 2.97), and being anxious during the lockdown, (AOR= 1.84;95% CI: 1.08 to 2.27), are significant factors among participants that are linked to higher risk of engaging in suicidal behaviour. The possibility of university students engaging in suicide behaviour was significantly affected by numerous factors. In addition to defending the students' mental health, the concerned authorities should devise and implement strategies to safeguard the students' physical health. © 2023, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.
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With the COVID-19 pandemic serving as a trigger, 2020 saw an unparalleled global expansion of tele-health [23]. Tele-health successfully lowers the need for in-person consultations and, thus, the danger of contracting a virus. While the COVID-19 pandemic sped up the adoption of virtual healthcare delivery in numerous nations, it also accelerated the creation of a wide range of other different technology-enabled systems and procedures for providing virtual healthcare to patients. Rightly so, the COVID-19 has brought many difficulties for patients (https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide ) who need continuing care and monitoring for mental health issues and/or other chronic diseases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Recently, the major environmental change and a pandemic called COVID-19 have heavily impacted the economy, business, and health of each country. Moreover, the climatic changes and COVID-19 are calamities to human life. In other words, these two aspects threaten the existence of humans and the sustenance of the overall development of a country. These two factors particularly influence the tourism sector, so a strategy balancing environmental quality and dealing with the ill effects of COVID-19 is formulated to uplift the economic sectors. Atannasov's intuitionistic fuzzy domain is used to model the environmental quality and COVID-19 due to the involvement of hesitancy and uncertainty. The precise measurement of the imprecision in the information is obtained with the help of entropy measure. The paper analyzes the two aspects using a novel entropy measure based on multiple criteria sorting (MCS). Here, the two MCS problems are solved with the help of two proposed techniques: TOPSIS-GREY-sort and ENTROPY-TOPSIS-GREY-sort. A case study showing the impact of COVID-19 in the Philippines and the environmental quality of Tehran (the capital city of Iran) are considered to validate the functioning of the proposed techniques. We use "A novel sorting method TOPSIS-SORT: an application for Tehran environmental quality evaluation (2016), Ekonomica a management, " and "Current Issues in Tourism 25.2(2022): 168-178, Taylor and Francis " for the comparative analysis.
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Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks. © 2023 CRL Publishing. All rights reserved.
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The global outbreak of SARS-CoV-2 has spurred a reassessment of Municipal Solid Waste management strategies and approaches. A significant need for sanitation and hygiene was accentuated for disease prevention and control with the onset of the pandemic. With an alteration of the status quo in waste management system, an unprecedented amount of face masks, protective equipment, and other biological wastes was generated in the form of Municipal Solid Waste. This upsurge of potentially infected wastes originated a risk of transmission amongst frontline workers. Furthermore, the potential contamination of Municipal Solid Waste was rendered as a legitimate threat due to improper collection practices, disposal and handling of solid waste. Several novel waste disposal techniques and waste management policies were also introduced during this period. However, the sanitation-policy making-occupational safety nexus remains inadequately explored under the prevalent COVID-19 scenario. Through the prism of shifting waste composition, this review offers a global assessment of existing solid waste management systems during the COVID-19 pandemic. The physiological and psychological hazards faced by the frontline workers were explored and instances of best-case and worst-case policies on solid waste handling were recorded. Modern methods of waste disposal and latest trends of policymaking were evaluated. A model study of unsupervised learning via Partition Around Medoids cluster analysis was undertaken to reveal underlying patterns of waste management policies. Although, the clusters were formed devoid of any socio-economic parameters, this study strives to indicate proof of concept and can serve as a precursor to advanced clustering studies. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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Background: Allogeneic hematopoietic stem cell transplantation activity is growing globally as one of the curative treatment options for many hematological diseases. A stem cell transplant registry plays an important role in such treatment. Setting up a functional stem cell donor registry is quite challenging with several issues such as resources, donor recruitment, donor attrition, ethnicity, lack of support, and impact of coronavirus disease 2019 (COVID-19). Aim(s): The aim of the current study was to present the experience of a resource-constrained registry in India as well as the effect of COVID-19 on its operations. Settings and Design: The present study was a descriptive study which was designed to study the functioning of a resource-constrained registry from north India. Material(s) and Method(s): The study data for the period of 2012-2020 pertaining to donor recruitment, number of searches, number of matched donors, number of transplants performed, and donor attrition was collected from the registry software "Prometheus." Statistical Analysis: Descriptive statistics such as frequency and percentage was used. Result(s): During the past 9 years of operation, the registry has faced several issues pertaining to lack of funds, donor recruitment, donor attrition, and COVID-19 has exacerbated their pain points significantly. The registry has recruited a total of 20,093 donors, of which only 7794 have been human leukocyte antigen typed, with the remaining samples awaiting funding. Out of this small number of typed donors, registry has performed 15 matched unrelated donor transplants for Indian and international patients. As a result of COVID-19, donor attrition was on the rise and showed a peak in 2020. During the year 2020, the number of searches, donor recruitment camps, and donors all decreased considerably. Conclusion(s): The establishment and operation of a stem cell transplant registry necessitate extensive planning and resources. The resource-constrained registries face a number of issues pertaining to effective functioning and future developments. The external support and awareness for the cause can help minimize the pain points of these registries. Copyright © 2022 Indian Journal of Transplantation.