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People's way of consuming media changed tremendously with rapid technological improvements and increased internet penetration levels across India due to emergence of over-the-top media services (OTT) platforms. COVID-19 outbreak has tremendously increased the demand for OTT streaming channels like Netflix, Amazon prime, Zee 5, Alt Balaji and Disney Hotstar which transformed the world of entertainment and media by contributing mind blowing services during the lockdown period.This research paper is an attempt to study the shift in media consumption patterns from old ways of entertainment like cinema, television to new ways of entertainment like OTT platforms, study and analyze the consumer preference towards choice of OTT platforms, watching habits of online over the top (OTT) applications among Indian viewers. © 2023 IEEE.
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BackgroundLupus is a heterogenous diseases which results in significant premature mortality. Most studies have evaluated risk factors for lupus mortality using regression models which considers the phenotype in isolation. Identifying clusters of patients on the other hand may help overcome the limitations of such analyses.ObjectivesThe objectives of this study were to describe the causes of mortality and to analyze survival across clusters based on clinical phenotype and autoantibodies in patients of the Indian SLE Inception cohort for Research (INSPIRE)MethodsOut of all patients, enrolled in the INSPIRE database till March 3st 2022, those who had <10% missing variables in the clustering variables were included in the study. The cause of mortality and duration between the recruitment into the cohort and mortality was calculated. Agglomerative unsupervised hierarchical cluster analysis was performed using 25 variables that define SLE phenotype in clinical practice. The number of clusters were fixed using the elbow and silhouette methods. Survival rates were examined using Cox proportional hazards models: unadjusted, adjusted for age at disease onset, socio-economic status, steroid pulse, CYC, MMF usage and cluster of the patients.ResultsIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting.Out of 2211 patients in the cohort, 2072 were included into the analysis. The median (IQR) age of the patients was 26 (20-33) years and 91.7% were females. There were 288 (13.1%) patients with juvenile onset lupus. The median (range) duration of follow up of the patients was 37 (6-42) months. There were 170 deaths, with only 77 deaths occurring in a health care setting. Death within 6 months of enrollment occured in in 80 (47.1%) patients. Majority (n=87) succumbed to disease activity, 23 to infections, 24 to coexisting disease activity and infection and 21 to other causes. Pneumonia was the leading cause of death (n=24). Pneumococcal infection led to death in 11 patients and SARS-COV2 infection in 7 patients. The hierarchical clustering resulted in 4 clusters and the characteristics of these clusters are represented in a heatmap (Figure-1A,B). The mean (95% confidence interval [95% CI] survival was 39.17 (38.45-39.90), 39.52 (38.71-40.34), 37.73 (36.77-38.70) and 35.80 (34.10-37.49) months (p<0.001) in clusters 1, 2, 3 and 4, respectively with an HR (95% CI) of 2.34 (1.56, 3.49) for cluster 4 with cluster 1 as reference(Figure 1C). The adjusted model showed an HR (95%CI) for cluster 4 of 2.22 (1.48, 3.22) with an HR(95%CI) of 1.78 (1.29, 2.45) for low socioeconomic status as opposed to a high socioeconomic status (Table 1).ConclusionIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting. Disease activity as determined by the traditional activity measures may not be sufficient to understand the true magnitude of organ involvement resulting in mortality. Clinically relevant clusters can help clinicians identify those at high risk for mortality with greater accuracy.Table 1.Univariate and multivariate Cox regression models predicting mortalityUnivariateMultivariateVariablesHazard ratio (95% Confidence interval)P valueHazard ratio (95% Confidence interval)P valueCluster1Reference-Reference-20.87 (0.57, 1.34)0.5320.89 (0.57, 1.38)0.59831.22 (0.81, 1.84)0.3371.15 (0.76, 1.73)0.51342.34 (1.56, 3.49)<0.0012.22(1.48, 3.22)<0.001Socioeconomic statusLower1.78 (1.29, 2.45)<0.001Pulse steroidYes1.6 (0.99, 2.58)0.051MMFYes0.71 (0.48, 1.05)0.083CYCYes1.42 (0.99, 2.02)0.052Proliferative LNYes0.99 (0.62, 1.56)0.952Date of birth age0.99 (0.98, 1.01)0.657CYC- cyclophosphamide, MMF- Mycophenolate mofetilFigure 1.A. Agglomerative clustering dendrogram depicting the formation of four clusters. B.Heatmap depicting distribution of variables used in clustering C. Kaplan-Meier curve showing the survival function across the 4 clusters[Figure omitted. See PDF]REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone eclared.
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The Blood Bank mobile application is an effort of easing the process of receiving and donating blood. This application helps the users to seamlessly donate and receive the required blood and also gives the availability of oxygen and ambulance in nearby hospitals. It gives the user information related to the availability of blood types in different hospitals and blood banks. Taking in mind the COVID-19 pandemic situation, in which the requirement for blood and oxygens were reached an unmanageable level. Blood and Oxygen is an essential part of the healthcare system. Day by day, the requirement for blood and oxygen is increasing, but still, there is unavailability and shortage. This project aims to give people a single platform to resolve these issues. © 2023 IEEE.
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On 11 March 2020, the World Health Organization (WHO) declared the novel coronavirus (COVID-19) outbreak a global pandemic. Knowledge about COVID-19 is an important determinant towards successful disease control. This study was designed to elucidate the knowledge, attitudes and practices (KAP) and mental health status regarding COVID-19 amongst trainees of two institutes in the state of Uttar Pradesh, India. The cross-sectional study was conducted from January to March 2021. A bilingual survey recorded the responses of 400 participants from the institutes. Variables, such as sex, age, marital status, occupation, level of education, number of family members and living place, were collected in a structured proforma. Unstandardised regression coefficients (95%) were used for evaluating the association among variables with KAP and mental health during COVID-19. The statistical significance level was determined at p<0.05.The mean correct answer score regarding knowledge about COVID-19 was 23.5 (SD=2.1), indicating an overall 87% correct rate. The mean correct answer score regarding attitude towards COVID-19was 18.4 (SD=2.3), indicating an overall 87.2% correct rate. The mean correct answer score regarding practice towards COVID-19 was 35 (SD=3.4), indicating an overall 89.7% correct rate. The mean correct answer score regarding mental health status during COVID-19 was 24 (SD=4.7), indicating an overall 80% correct rate. Thus, the results showed that being female;belonging to a higher age group;having a healthcare related occupation;being married;and having a higher level of education were significantly associated with higher KAP test scores. Furthermore, belonging to a higher age group;having a healthcare related occupation;being married;and having a higher level of education were significantly associated with poor mental health.
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This case study outlines the struggle that Accredited Social Health Activists (ASHAs) encountered at the grassroots level in India while combating the spread of the corona virus. Due to misinformation and a lack of specific knowledge, rural communities began to mistrust government workers attempting to track the spread of the pandemic, resulting in instances of physical and verbal abuse. The impact on many ASHAs, who provided a vital frontline health service, was very negative. A communication strategy had to be adopted to avoid a worsening situation. Seema Barma (Block ASHA coordinator) had the job of providing a sustainable solution for managing the uncertainty in an effective manner and to rebuild public trust in authorities, and health workers including ASHAs. Students will learn how to formulate an effective communication strategy for dealing with such a pandemic crisis situation in a developing nation with specific reference to India. © 2022 NeilsonJournals Publishing.
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Background: COVID-19 vaccine booster uptake remains low and preventable COVID-19 deaths continue to occur, making access to oral antivirals for those most at risk of severe COVID-19 outcomes essential. Method(s): We estimated age and gender adjusted prevalence ratios of oral nirmatrelvir-ritonavir (NMV/r) uptake by sociodemographics, clinical characteristics, and prescription eligibility (based on age, underlying medical conditions, body mass index, physical inactivity, pregnancy, or smokers), among participants in a large U.S. national prospective cohort who were infected with SARS-CoV-2 between December 2021 and October 2022. Among participants who reported NMV/r uptake, we also described the proportion who reported (1) taking NMV/r as directed and (2) NMV/r was helpful for reducing COVID-19 symptoms. Result(s): Among 1,594 participants with a SARS-CoV-2 infection as of October 2022, 1,356 were eligible for NMV/r prescription;of whom 209 (15.4% [95%CI:13.5-17.3]) reported receiving NMV/r. NMV/r uptake increased from 2.2% (95%CI:1.0-3.4) between December 2021 and March 2022 to 16.5% (95% CI:13.0-20.0) between April and July 2022 and 28.6% (95%CI:24.4-32.8) between August and October 2022, respectively. Participants >=65 years of age reported the highest uptake of NMV/r (30.2% [95%CI:22.2-38.2]). Black non-Hispanic participants (7.2% [95%CI:2.4-12.0]) and those in the lowest income group (10.6% [95%CI:7.3-13.8]) had lower uptake than white non-Hispanic (15.8% [95%CI:13.6-18.0]) and high-income individuals (18.4% [95%CI:15.2-21.7]), respectively. Participants with type 2 diabetes had greater uptake (28.8% [95%CI:20.4-37.3]), compared to those without it (12.4% [95%CI:4.8-20.0]). Among a subset of 278 participants who had a prior SARSCoV-2 infection, those who had a history of long COVID reported greater uptake (22.0% [95%CI:13.9-30.1]) for a subsequent SARS-CoV-2 infection than those without a history of long COVID (7.9% [95%CI:3.9-11.8]). Among all participants who were prescribed NMV/r (N=216), 89% (95%CI:85-93) reported that they took NMV/r as directed and 63% (95%CI:57-70) stated NMV/r was helpful for reducing COVID-19 symptoms. Conclusion(s): Uptake of NMV/r increased over time coinciding with national efforts to increase awareness and access. However, most individuals who were eligible for NMV/r did not receive it. Lower NMV/r uptake among racial/ethnic minorities and individuals with lower household income suggests a need to improve awareness and address barriers to uptake in these populations.
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In the last two years, billions of individuals worldwide have been safely vaccinated against SARS-CoV-2, the virus that causes COVID-19. However, a substantial number of people experience mild to moderate side effects, which may hamper vaccine and booster uptake;understanding the processes underlying differential responses to these vaccines can help to improve global vaccination efforts. Variation in HLA has been linked to disease outcome in COVID-19, and HLA-A*03:01 has previously been reported to increase risk for side effects following vaccination. Here, we expand on those findings, examining HLA variation for association with vaccine side effects in 6470 patients of European ancestry from the United States. In our cohort, ~30% of individuals experienced systemic side effects (e.g., fever, chills, headache) after their initial vaccination series, while that proportion climbed to >60% in individuals receiving booster doses. We confirm the association of HLA-A*03:01 with systemic side effects to COVID-19 vaccines, particularly the Pfizer-BioNTech vaccine (OR = 1.52 [95% CI 1.23-1.97], p = 0.002). We observed similar effect size of this allele in individuals reporting side effects from the initial series or boosters (OR = 1.25 [95% CI 1.15-1.53];p>0.0001), but comparatively higher effect size in individuals who subsequently experienced breakthrough infections (OR = 2.11 [95% CI 1.12-4.31];p = 0.04). Our results confirm prior reports regarding HLA association with vaccine side effects, and suggest that the immunopathology underlying the HLAA* 03:01 association with side effects may increase those individuals' propensity for breakthrough infections after vaccination. Our results highlight the need to explore the functional mechanisms underlying this association to improve vaccine design and implementation strategies against emergent SARS-CoV-2 variants.
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Covid – 19 is a sudden unexpected jolt on the functioning of every sector's day-to-day work and dragged everybody to find the best alternative for the automation of processes to enable more work from home. Robotic Process Automation (RPA) is one of such promising technologies which would help in the automation of mundane everyday tasks. With this technology, software robots would be deployed to perform the repetitive tasks done by humans in various sectors. This technology is also sometimes termed software robotics. In this paper, we have studied and discussed about the different tools used for developing software robots, traditional methods of completing the task without RPA being used and also how the task is automated after the use of software robots in various fields, viz., banking, e-commerce, educational institutions, human resources, health care and office. We have also discussed the pros and cons of automating the task using robotic process automation. In short, this paper will give an overview of the emerging technology – RPA. © 2023 Seventh Sense Research Group®
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Infectious disease forecasting for ongoing epidemics has been traditionally performed, communicated, and evaluated as numerical targets - 1, 2, 3, and 4 week ahead cases, deaths, and hospitalizations. While there is great value in predicting these numerical targets to assess the burden of the disease, we argue that there is also value in communicating the future trend (description of the shape) of the epidemic - for instance, if the cases will remain flat o r a s urge i s expected. To ensure what is being communicated is useful we need to be able to evaluate how well the predicted shape matches with the ground truth shape. Instead of treating this as a classification problem ( one out of n shapes), we define a transformation of the numerical forecasts into a "shapelet"-space representation. In this representation, each dimension corresponds to the similarity of the shape with one of the shapes of interest (a shapelet). We prove that this representation satisfies the property that two shapes that one would consider similar are mapped close to each other, and vice versa. We demonstrate that our representation is able to reasonably capture the trends in COVID-19 cases and deaths time-series. With this representation, we define an evaluation measure and a measure of agreement among multiple models. We also define the shapelet-space ensemble of multiple models as the mean of their shapelet-space representations. We show that this ensemble is able to accurately predict the shape of the future trend for COVID-19 cases and trends. We also show that the agreement between models can provide a good indicator of the reliability of the forecast. © 2022 IEEE.
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Lung diseases include a wide spectrum of illnesses, such as asthma, COPD (chronic obstructive pulmonary disease), pneumonia, tuberculosis, lung cancers, and the recent COVID-19 pandemic, and have been a huge threat to human health and life. However, the treatment and diagnosis of various lung diseases are challenging. Among the several treatment strategies and diagnostic techniques, the adverse effect to chemotherapy in cancers, multidrug resistance in tuberculosis, side effects, toxicity, poor drug delivery, and metabolism require the development of novel and promising alternative treatments. Nanotechnology provides a promising tool for the development of innovative treatment overcoming many drug challenges. Nanotechnology being widely studied in medicinal field has given rise to the interdisciplinary nanomedicine field allowing fundamental changes in the diagnosis, treatment, and prevention of disease. Lungs provide a good target organ for drug delivery via an aerosol inhalation mode. Lungs provide a large surface area for local drug action and systemic drug absorption and hence nanomedicines have been a boon in treating many of the lung diseases without leading to any side effects or toxicity. The present chapter aims to review nanoparticles-based drug delivery systems studied over the last decade as therapeutic agents in lung diseases. © 2023 Elsevier Inc. All rights reserved.
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Humanity has suffered as a result of the COVID-19 pandemic for more than two years. Testing kits were not widely accessible during the pandemic, which caused alarm. Any technical development that enables a quicker and more accurate identification of COVID-19 infection can be very beneficial for the medical field. X-rays can be used to examine a patient's lungs since COVID-19 targets the epithelial cells that line the respiratory system. It is challenging to determine COVID-19 from other Viral Pneumonia cases, though. The purpose of this paper is to examine the effectiveness of deep learning models in the quick and precise detection of COVID-19 in chest X-ray scans. © 2022 IEEE.
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Web Information Processing (WIP) has enormously impacted modern society since a huge percentage of the population relies on the internet to acquire information. Social Media platforms provide a channel for disseminating information and a breeding ground for spreading misinformation, creating confusion and fear among the population. One of the techniques for the detection of misinformation is machine learning-based models. However, due to the availability of multiple social media platforms, developing and training AI-based models has become a tedious job. Despite multiple efforts to develop machine learning-based methods for identifying misinformation, there has been very limited work on developing an explainable generalized detector capable of robust detection and generating explanations beyond black-box outcomes. Knowing the reasoning behind the outcomes is essential to make the detector trustworthy. Hence employing explainable AI techniques is of utmost importance. In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms. DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify and explain its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN model. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets and compared results with and without DANN implementation. It is observed that using DANN significantly improves the F1 score of classification and increases the accuracy by 5% and AUC by 11%. The results show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation. This can enable trustworthy information processing and extraction to combat misinformation effectively. Author
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Bamboo, the fastest-growing plant, has several unique characteristics that make it appropriate for diverse applications. It is low-cost, high-tensile, lightweight, flexible, durable, and capable of proliferating even in ineffectual areas (e.g., incline). This review discusses the unique properties of bamboo for making charcoal and biochar for diverse applications. To produce bamboo charcoal and biochar, this study reports on the pyrolysis process for the thermal degradation of organic materials in an oxygen-depleted atmosphere under a specific temperature. This is an alternative method for turning waste biomass into products with additional value, such as biochar. Due to various advantages, bamboo charcoal is preferred over regular charcoal as it has four times the absorption rate and ten times more surface area reported. According to the reports, the charcoal yield ranges from 24.60 to 74.27%. Bamboo chopsticks were the most useful source for producing charcoal, with a high yield of 74.27% at 300 °C in nitrogen, but the thorny bamboo species have a tremendous amount of minimal charcoal, i.e., 24.60%. The reported biochar from bamboo yield ranges from 32 to 80%. The most extensive biochar production is produced by the bamboo D. giganteus, which yields 80% biochar at 300 °C. Dry bamboo stalks at 400 °C produced 32% biochar. One of the sections highlights biochar as a sustainable solution for plastic trash management produced during the COVID-19 pandemic. Another section is dedicated to the knowledge enhancement about the broad application spectrum of the charcoal and biochar. The last section highlights the conclusions, future perspectives, and recommendations on the charcoal and biochar derived from bamboo.
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At present supply chains are dynamic and interactive in nature which integrates suppliers, manufacturers, distributors, and consumers. An important objective of supply chain management is to ensure that each supply chain partner is in the coordination with others so that supply chain potential and enhanced surplus can be realized in sales. In general, this coordination breaks due to distrust, misinformation, poor logistics and transportation infrastructure;however, in specific cases like Covid-19, it arises due to uncertainties caused by various types of risks such as delays and disruptions. During pandemic Covid-19 global supply chains have been distorted badly due to multiple lockdowns and country specific decisions to contain the spread of coronavirus. For dealing with such pandemic situation in future, we have learned and proposed some of the strategies from literature and practice that a supply chain manager can think of to minimize supply chain disruptions during a pandemic. These supply chain strategies include Resilience, Outsourcing/Offshoring, Agility, and Digitalization. For helping in decision making to the practitioners, we have applied Best Worst Method (BWM) to evaluate these strategies during pandemic times and found that Digitalization strategy (0.574) has been most differentiating among the proposed four strategies in a pandemic scenario;whereas, Outsourcing/Offshoring strategy is most hampered/ineffective during such times. © 2022 Kharazmi University. All rights reserved.
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Artificial intelligence (AI), deep learning (DL), and neural networks (NN), though these words sound flashy and may leave you perplexed, represent powerful technologies that have the capabilities to transform the world. It is just now emerging how valuable these machine learning-based techniques are and how they can solve many real-world problems ranging from fraud detection, resource management to driver-less cars.One such field where the application of AI systems is progressively growing is in medical diagnosis. A lot of research is going on to enhance computer-Aided diagnosis and detection of diseases. Recent world events have tested the healthcare systems all around the world. Suppose we have sophisticated deep learning systems (DLS) that could help in faster and efficient disease detection and diagnosis;how beneficial it would be to assist both medical professionals and patients.This study explores how AI and machine learning techniques could be used for disease detection, giving COVID-19 and Diabetic Retinopathy detection examples. We present two deep learning (DL) models, one to detect COVID-19 from chest x-ray image scans and the other to detect Diabetic Retinopathy at various stages of the disease from retinal fundus images. With reasonably high accuracy, >95% for the COVID-19 detection model and >80% for the Diabetic Retinopathy detection model, these results highlight AI and deep learning potential to assist general practitioners. © 2022 IEEE.
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Introduction: Coronavirus Disease-2019 (COVID-19) vaccine provides strong protection against transmission, serious illness, hospitalisation, and death. As India carried out robust vaccination drive covering more than two third of its population, the study was aimed to highlight the effects of vaccination status of patient on the outcome of COVID-19 infection. Aim: To describe the relation of vaccination with disease severity and its outcome during the third wave of COVID-19. Materials and Methods: It was a single centre retrospective, cross-sectional study conducted in a dedicated COVID-19 hospital (Guru Tegh Bahadur Hospital) in Delhi, India. A total of 257 patients were admitted between January 10th 2022 to 9th February 2022, and 246 were included in the study. For each individual, demographic, and clinical data was collected. Vaccination data was extracted via the CoWin platform which included vaccine type as well as date of administration. The profile of patients was established based on clinical examination, laboratory data, nursing record and radiological record during the course of hospitalisation. The clinical outcome was described as discharge, length of hospital stays, and in-hospital death in relation to the vaccination status. Statistical Analysis was done using Statistical Package for the Social Sciences (SPSS) v22. Results: Total of 246 patients were divided into three groups -97 were fully vaccinated, 46 were partially vaccinated and 103 were unvaccinated. Both vaccinated and unvaccinated groups had similar percentage of co-morbidities i.e. 61.3% vs 63.5%. Those who were fully vaccinated were more likely to maintain saturation at room air 30.9% vs 26.1% vs 3.9%, had lesser requirements of mechanical ventilation (6.2% vs 15.2% vs 21.4%), shorter duration of hospital stay (4.2 vs 5.3 vs 7.2 days), and lesser mortality (9.3% vs 21.7% vs 33%) as compared to the partially vaccinated and unvaccinated patients respectively. Conclusion: The beneficial effect of the vaccination was observed in severity, mortality, morbidity, and lesser number of hospitalisations. Hence, vaccination coverage was critical in reducing the severity in reducing the and the hospitalisation in third wave of COVID-19.
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A deadly novel coronavirus disease or severe acute respiratory syndrome (COVID-19 or SARS-CoV-2) has taken the entire globe in its grip and claimed over more than 0.1 million lives across the globe in barely four months of time. This has attracted researchers, medical practitioners, scientists, biologist's fraternity, etc. all over the world to join hands in fighting the pandemic. Therefore, a detailed study in the field of coronavirus, especially related to the research status and gaps under a common umbrella, will further help in understanding and improving the current scenario. In the present paper, the scientometric analysis technique was utilized for understanding the recent research activities, scientific trends, and global involvement in the research on coronavirus. Herein, Web of Science database was used for searching the documents. The "articles” in the "English” language were considered in the study. The VoSviewer software was used for carrying out the scientometric analysis. The scanning of the research publication status on a year-on-year basis suggested an increase in the field of research on coronavirus in the recent past. From 2000 till 2020, a total of 9257 number of research articles were published. Among all other countries, USA has the most number of documents published. Analysis of the journals, authors, organizations, funding agencies of the countries and their co-operation network were also analyzed based on citations. Further, co-occurrence analysis of the different keywords suggested that coronavirus related diseases are known to precipitate severe acute respiratory syndrome in the patients. This is also true in the case of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Personal protective equipment designated as PPE is considered to be the most important protective equipment designed for safeguarding both public and personal health against infectious microbial agents other toxic materials. This chapter gives an overview about the importance and use of the personal protective equipment (PPE) as coronavirus shielding material. The various contents of the chapter focused on different types of PPE, its formation, and preparation. The importance and values of personal protective equipment (PPE) are also included in the chapter. The details of disposing of contaminated PPE are included along with the discussion about annual procurement (demand) for PPE via UNICEF. The Indian scenarios as well the world scenario about the availability of PPE are also investigated. Also, the future perspective of the PPE market is also discussed. The polymers which are used in the manufacturing of PPE kits take part in a vital character from the prevention of the disease. The global overview is also presented which focuses on the production rate of PPE along with the materialistic points of the polymers which have been used. Globally, the demand for PPE kits is at a high inclined position since the coronavirus pandemic is widely affected. PPE is considered as the last line of defence which protects the workers against the hazards. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may impact neurological function acutely or chronically, even in the absence of severe respiratory illness. Developing clinically relevant laboratory models to understand the neuropathogenesis of SARS-CoV-2 infection is an important step towards unravelling this neurologic consequence. We hypothesize that mouse adapted SARS-CoV-2 viral infection will induce neuroinflammation in immuno-competent C57BL/6J (10 weeks old male) as well as immunodeficient RAG2-/- (10 weeks old male) and BALB/c (1 year old female) mice. Method(s): All three mouse strains were inoculated intranasally with a dose of 1x103 PFU/mouse (50 microL) of either mock or the mouse-adapted (MA)10 strain of SARS-CoV-2 (BEI resource, NR-55329). Mice were euthanized on day 2, 3, 7, 15 or 30 post infection and brain samples processed for qRT-PCR, immunofluorescence, and H&E analysis. Result(s): SARS-CoV-2 MA10 resulted in a significantly higher (p < 0.05) mRNA expression for chemokine ligand 2 (CCL2) and lower (p < 0.05) mRNA expression for the blood-brain barrier component Claudin-5 in RAG2-/- and WT mice when compared to mock infection. Also, SARS-CoV-2 MA10 infection increased microglial expression in 1 year old female BALB/c mice after 2 days of infection, compared to mock infected group. At 30 days post infection, MA10 infected BALB/c mice had a higher perivascular lymphocyte cuffing in the brain. Conclusion(s): This study demonstrates that the mouse-adapted MA10 strain of SARS-CoV-2 can induce a neuroinflammatory state in the brain and more so in immunodeficient and aging mouse models. These mouse models will enable the investigation of the long-term neurological effects of SARS-CoV-2 infection. Copyright © 2022 the Alzheimer's Association.