Dementia and COVID-19

While both dementia and coronavirus disease 2019 (COVID-19) have differing etiology, there is a complex interplay between the two, especially when looking into their effects on certain sub-populations. Hispanic Americans face a higher burden of dementia and COVID-19 due to both modifiable and unmodifiable risk factors, age-related chronic diseases, and environmental factors. The major unmodifiable risk factors include increasing age and predisposing genetics, while the major modifiable risk factors include income/socioeconomic status, educational attainment, exercise, diet, and smoking/tobacco use. Furthermore, specific age-related chronic diseases such as diabetes, kidney disease, hypercholesterolemia, cardiovascular disease, and chronic lung diseases place Hispanic Americans at high risk for dementia and COVID-19. Lastly, Hispanic Americans face the additional disadvantage of environmental factors, such as social inequalities and lack of access to adequate healthcare resources. Given that Hispanic Americans are the largest racial/ethnic minority group within the United States, this chapter will focus upon the research associated with dementia and COVID-19 within the Hispanic American population of the United States. Furthermore, this chapter will explore the four major risk factor categories (unmodifiable risk factors, modifiable risk factors, age-related chronic diseases, and environmental factors), which contribute to the development of dementia and COVID-19 within the Hispanic American population of the United States. © 2023 Elsevier Inc. All rights reserved.

Women and Alzheimer's disease risk: a focus on gender

A previous chapter highlighted the biological mechanisms by which female sex contributes to Alzheimer's disease (AD) risk and outcomes. However, discussion of AD in women is incomplete without considering the impact of female gender on AD risk, as gender encompasses psychosocial and cultural differences between women and men that also modulate risk for cognitive decline. The current chapter discusses several main social determinants of health and explains how women, as a historically oppressed population, may be particularly vulnerable to the effect of each on cognition. This chapter also considers the disproportionate female burden of dementia caregiving, how associated stresses augment risk for later cognitive decline among caregivers themselves, and how the COVID-19 pandemic may add to this risk. Understanding the gender-specific factors that affect AD risk and disease progression is essential for developing targeted preventative interventions and treatments. Future research is necessary to better characterize how social determinants of health uniquely impact female cognition compared to males. Moreover, future studies focused on gender identities outside of the male–female binary are critical to developing a holistic understanding of how gender may impact late-life cognition. © 2023 Elsevier Inc. All rights reserved.

Women and Alzheimer's disease

Approximately, two-thirds of individuals with Alzheimer's disease (AD) are women. Though previously attributed to differences in lifespan, accumulating evidence suggests that the reasons for the higher prevalence of AD in women are multifactorial and related to differences in risk factors, biomarkers, and neuropathology. Sex also contributes to significant disease heterogeneity, which has important implications for prevention and treatment. This chapter discusses the evidence for sex differences in AD, with an emphasis on disease presentation, biomarkers, pathophysiology, progression, and risk. Women tend to present later in the disease course and with different clinical features, progress faster, and are disproportionately affected by the APOE-ϵ4 risk allele and AD neuropathologic changes. Lifetime estrogen exposure, pregnancy, and menopause also affect a woman's risk for cognitive decline later in life. Despite such differences, women are dramatically underrepresented in pharmacologic randomized control trials, leading to significant gaps in knowledge regarding the most effect AD treatment strategies for women. Both researchers and providers need to be aware of sex differences in AD risk, presentation, and outcomes to develop sex-specific prevention and treatment strategies, as well as provide optimum healthcare to women as they age. © 2023 Elsevier Inc. All rights reserved.

Covid 19 prediction system using CNN

Today, the whole world is fighting the war against Coronavirus. The spread of the virus has been observed in almost all the parts of the world. Covid-19 also known as SARS-Cov-2 was initially observed in China which rapidly multiplied all over the world. The disease is said to spread by cough, normal cold, sneezing or when a person is in close contact with someone who is already infected. Therefore, the spread of the virus can occur when there is direct contact with an infected person or with the objects touched by the infected person. Hence, it is important to detect the contiguous spread of the virus and control it by taking appropriate measures. Several deep learning models have been used in detecting many diseases like Malaria disease, Lung infection, Parkinson's disease etc. Likewise, CNN model along with other transfer techniques is best proven to detect whether a person is infected with covid positive or not. The dataset consists of 1000 images of covid positive and normal x-rays. The proposed model has been trained and tested on the image dataset with the help of transfer learning models in order to improve the performance of the model. The models VGG-16, ResNet-50, Inception v3 and Xception have achieved an overall accuracy of 93%,82%,96% and 92% respectively. The performance of all the 4 architectures are analyzed, understood and hence presented in this paper. It is hence important to classify and detect covid positive infection and contribute towards making the world Covid-free. © 2023 Author(s).

COVID-19 in Alzheimer's Disease and Dementia

COVID-19 in Alzheimer's Disease and Dementia crucially summarizes the current status of the coronavirus in patients suffering from these conditions, describing why they are a common cause of morbidity among those with COVID-19. The first section includes chapters that provide a general description of COVID-19, including SARS-CoV-2 structure, function, and biology, and its impact on the elderly with chronic conditions include hypertension, diabetes, obesity, kidney disease, respiratory illnesses, and infectious diseases. Also discussed are effects of the virus on the immune system. The second section shifts to the impact of COVID-19 on those with dementia or Alzheimer's disease, with special emphasis on age, gender, ethnic background, and lifestyle. Bringing this focus on neurodegenerative disease in one comprehensive resource, this volume is an essential reference for neuroscientists, clinicians, biomedical scientists, and all others working or interested in the field. © 2023 Elsevier Inc. All rights reserved.

The effects of lifestyle in Alzheimer's disease during the COVID-19 pandemic

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease affected by multiple elements such as exercise, food, and social stimulation. Research has demonstrated the positive effects of exercise such as community-based programs and aerobic activities in reducing rates of decline in cognition. Another protective measure is avoiding red meat and alcohol and instead incorporating a Mediterranean diet to reduce inflammation and inhibit free radicals. Finally, social stimulation can serve to reduce the progression of the disease by increasing a sense of connection and meaningful purpose. COVID-19 has made it difficult for AD patients, especially those living in nursing homes or advanced facilities, to participate in exercise classes due to restrictions, to eat a fresh diet due to resource shortages, and to see friends and family due to social distancing. This chapter delves into the effects of COVID-19 on elements such as physical activity, diet, and social interaction on the disease progression of AD. © 2023 Elsevier Inc. All rights reserved.

COVID-19 and immunity

Coronavirus disease-19 (COVID-19), caused by a β-coronavirus and its genomic variants, is associated with substantial morbidities and mortalities globally. The COVID-19 virus enters host cells upon binding to the angiotensin converting enzyme two receptors. Patients afflicted with COVID-19 may be asymptomatic or present with critical symptoms possibly due to diverse lifestyles, immune responses, aging, and underlying medical conditions. Geriatric populations, especially men in comparison to women, with immunocompromized conditions, are the most vulnerable to severe COVID-19-associated infections, complications, and mortalities. Notably, whereas immunomodulation, involving nutritional consumption, is essential to protecting an individual from COVID-19, immunosuppression is detrimental to the host with this hostile disease. As such, immune health is inversely correlated to COVID-19 severity and resulting consequences. Advances in genomic and proteomic technologies have helped us to understand the molecular events underlying symptomatology, transmission, and pathogenesis of COVID-19 and its genomic variants. Accordingly, there has been development of a variety of therapeutic interventions, ranging from mask wearing to vaccination to medication. Regardless of various measures, a strengthened immune system can be considered as a high priority of preventive medicine for combating this highly contagious disease. This chapter provides an overview of pathogenesis, effects of comorbidities on COVID-19 and their correlation to immunity, and prospective therapeutic strategies for the prevention and treatment of COVID-19. © 2023 Elsevier Inc. All rights reserved.

Immune enhancers for COVID-19

SARS-CoV-2, also known as COVID-19, is a novel coronavirus that began sweeping the globe at the end of 2019, causing mild illness in some patients while leading to devastating shock, immune dysregulation, multiorgan failure, and even death in others. Immune dysregulation may lead to increased susceptibility to severe disease from COVID-19. Immune enhancers could aid in immune regulation and protect against severe COVID-19 infection. Herbal supplements, spices, and lifestyle modifications have been shown to enhance immune responses to a number of pathogens, which may include COVID-19. These immune enhancers could be used adjunctively with vaccines, social distancing, and pharmacologic treatments to prevent life-threatening infection in susceptible patients. © 2023 Elsevier Inc. All rights reserved.

Surgical and nonsurgical interventions for Alzheimer's disease

Over the years, many surgical and nonsurgical interventions have been adapted to manage Alzheimer's disease (AD). While many of these tools were developed to primarily treat other neurological conditions, increased understanding of AD pathology has opened up new opportunities to apply established techniques in novel fashions. This chapter discusses neurosurgical interventions for AD especially in the context of the coronavirus pandemic. © 2023 Elsevier Inc. All rights reserved.

MicroRNAs and COVID-19

Currently, there are no reliable biomarkers for identifying COVID-19 patients and no definite therapeutics to control this deadly disease. MicroRNAs (miRNA) have been explored in several human diseases for their potential role as biomarkers and their therapeutic potential. However, there is very little information available about the roles of miRNAs in COVID-19 infection. This chapter outlines the recent updates and developments of miRNAs in COVID-19 such as miRNAs as potential biomarkers for COVID-19, the molecular basis of miRNAs in COVID-19 infection, and the use of miRNAs as therapeutics targets for COVID-19. While a few potential miRNAs have been researched for the aforementioned reasons, more research is needed to determine the roles of individual miRNAs in COVID-19 infection. © 2023 Elsevier Inc. All rights reserved.

Impact of COVID-19 on ischemic stroke condition

Coronavirus disease 2019 (COVID-19) results from the infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first reported in Wuhan, China, when patients were found to be suffering from severe pneumonia and acute respiratory distress syndrome. It has now grown to be the first global pandemic since 1920. Patients infected with SARS-CoV-2 develop a multitude of ailments, including arterial thrombosis, which leads to acute conditions like stroke. Stroke in COVID-19 cannot be explained by a single mechanism but instead is defined by the interplay of many mechanisms, including the development of cytokine storms resulting in activation of the innate immune system, thrombotic microangiopathy, endothelial disruption, and the multifactorial activation of the coagulation cascade. Thromboprophylaxis in low–molecular-weight heparin has been shown to affect severely ill patients infected with COVID-19 beneficially. However, patients who develop stroke because of COVID-19 have poorer outcomes despite maximal medical, endovascular, and microsurgical treatment compared with non-COVID-19-infected patients. A significant challenge in managing stroke during the pandemic is maintaining high-quality care for stroke patients while protecting healthcare team members and staff. © 2023 Elsevier Inc. All rights reserved.

Emotion Detection of People Wearing Face Mask

Though many facial emotion recognition models exist, after the Covid-19 pandemic, majority of such algorithms are rendered obsolete as everybody is compelled to wear a facemask to protect themselves against the deadly virus. Face masks can hinder emotion recognition systems, as crucial facial features are not visible in the image. This is because facemasks cover essential parts of the face such as the mouth, nose, and cheeks which play an important role in differentiating between various emotions. This study intends to recognize the emotional states of anger-disgust, neutral, surprise-fear, joy, sadness, of the person in the image with a face mask. In the proposed method, a CNN model is trained using images of people wearing masks. To achieve higher accuracy, the classes in the dataset are combined. Different combinations of clubbing are performed, and results are recorded. Images are taken from FER2013 dataset which consists of a huge number of manually annotated facial images of people. © 2023 IEEE.

Performance evaluation of in-house developed Covid-19 IgG/IgM antibody rapid diagnostic kit (preprint)

In the interest of preventing the Coronavirus Disease 2019 (COVID-19) pandemic from spreading, it is crucial to promptly identify and confine afflicted patients. Serological antibody testing is a significant diagnostic technique that is increasingly employed in clinics, however its clinical use is still being investigated. A meta-analysis was carried out to scrutinize how well Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) antibody testing using in-house developed rapid antibody assay worked for diagnosing COVID-19 patients against the chemiluminescence (CLIA) assay. IgG- positive but IgM-negative (IgG-, IgM+); IgG-positive but IgM-negative (IgG+, IgM-); IgM+ IgG+; both IgM-positive and IgG-positive (IgM+IgG+); and either IgM-positive or IgG-positive (IgM+ or IgG+) have been evaluated. A total of 300 samples with diverse age and sexual identity data were included. The combined sensitivities for IgG+IgM+, IgM+IgG-, IgG+IgM- and negative were evaluated. More accurate diagnostic results may be obtained using molecular diagnostic tools. The Antibody Rapid Diagnostic kit's (in-house developed) performance was satisfactory for determining the presence of Covid-19 infection with IgG and IgM positivity. The IgG and IgM positivity helped evaluate the immune response in the individual for the COVID-19 infection. These results lend support to the additional utilisation of serological antibody tests in the COVID-19 diagnosis.

AI Enabled Chat Bot for COVID'19

The coronavirus outbreak has far-reaching ramifications for civilizations all around the world. People are worried and have a lot of requests. A research department from Covid19 Awareness was our recommendation. We supplemented it with AI-based chatbot models to aid hospitals, patients, medical facilities, and congested areas such as airports. We propose to develop this chatbot to support current scenarios and enable hospitals or governments to achieve more to solve the objective, given the two primary factors that inexpensive and fast production is now necessary. It is an immediate necessity in this epidemic circumstance. We built this bot from the ground up to be open source, so that anybody or any institution can use it to fight Corona, and commercialization is strictly prohibited. This bot isn't for sale;instead, we'd like to devote it to the country to help with current pandemic situations. The design of advanced artificial intelligence is presented in this paper (AI). If patients are exposed to COVID-19, the chatbot assesses the severity of the illness and consults with registered clinicians if the symptoms are severe, evaluating the diagnosis and recommending prompt action. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Ensemble deep honey architecture for COVID-19 prediction using CT scan and chest X-ray images.

Recently, the infectious disease COVID-19 remains to have a catastrophic effect on the lives of human beings all over the world. To combat this deadliest disease, it is essential to screen the affected people quickly and least inexpensively. Radiological examination is considered the most feasible step toward attaining this objective; however, chest X-ray (CXR) and computed tomography (CT) are the most easily accessible and inexpensive options. This paper proposes a novel ensemble deep learning-based solution to predict the COVID-19-positive patients using CXR and CT images. The main aim of the proposed model is to provide an effective COVID-19 prediction model with a robust diagnosis and increase the prediction performance. Initially, pre-processing, like image resizing and noise removal, is employed using image scaling and median filtering techniques to enhance the input data for further processing. Various data augmentation styles, such as flipping and rotation, are applied to capable the model to learn the variations during training and attain better results on a small dataset. Finally, a new ensemble deep honey architecture (EDHA) model is introduced to effectively classify the COVID-19-positive and -negative cases. EDHA combines three pre-trained architectures like ShuffleNet, SqueezeNet, and DenseNet-201, to detect the class value. Moreover, a new optimization algorithm, the honey badger algorithm (HBA), is adapted in EDHA to determine the best values for the hyper-parameters of the proposed model. The proposed EDHA is implemented in the Python platform and evaluates the performance in terms of accuracy, sensitivity, specificity, precision, f1-score, AUC, and MCC. The proposed model has utilized the publicly available CXR and CT datasets to test the solution's efficiency. As a result, the simulated outcomes showed that the proposed EDHA had achieved better performance than the existing techniques in terms of Accuracy, Sensitivity, Specificity, Precision, F1-Score, MCC, AUC, and Computation time are 99.1%, 99%, 98.6%, 99.6%, 98.9%, 99.2%, 0.98, and 820 s using the CXR dataset.

Structural and physiological changes of the kidney with age and its impact on chronic conditions and COVID-19.

The kidney is an essential organ that removes waste products, balances the body's fluids, releases hormones that regulate blood pressure, produces an active form of vitamin D, promotes healthy bones, and controls the production of red blood cells. Structural and functional abnormalities occur in kidney with age. Alterations in kidney structure are based on physiological functions and environmental pressures. Variations in its structure across vertebrates are primarily due to the nature of alterations in number, complexity, arrangement, and location of the kidney tubules. Globally, individuals aged 65 and older are part of the fastest expanding population demographic, and as a result, a greater number of older patients are receiving a diagnosis of impaired renal function. The purpose of our mini-review is to summarize recent findings of the structural and functional differences between the normal and aging kidney, examine the evolutionary biology of the kidney across species, and demonstrate the role of aging in conditions such as diabetes, chronic kidney disease, and hypertension, along with their impact on SARS-CoV-2. Additional aims include discussing the potential therapeutic strategies to treat aged individuals with kidney health issues and how the impact of a healthy lifestyle, diet, and exercise can improve health conditions with aged kidneys.

The Efficacy and Safety of Imusil® Tablets in the Treatment of Adult Patients With Mild COVID-19: A Prospective, Randomized, Multicenter, Open-Label Study.

Introduction Coronavirus disease 2019 (COVID-19) is a serious concern of the new era. Along with antiviral synthetic medications, there is a need to discover efficacious herbal antiviral medicines with minimum side effects in patients against COVID-19. This study aimed to assess the efficacy and safety of Imusil® among patients with mild COVID-19. Methods A prospective, randomized, multicenter, open-label, interventional study was conducted in patients with mild COVID-19 infection. Patients received either Imusil one tablet four times a day (seven days) along with the standard of care (SoC) or only SoC. The study endpoints were reverse transcription-polymerase chain reaction (RT-PCR) negativity, changes in cycle threshold (CT), clinical improvement, change in blood inflammatory indexes, and safety assessment. Results A total of 100 patients were enrolled, and 98 received at least one dose of treatment. The median age of patients was 36.0 years, and 58 were males. By day 4, 85.4% of patients in the Imusil+SoC group tested negative for RT-PCR compared to 64% of patients exhibiting the same outcome in the SoC group (P=0.0156). After eight days, clinical improvement was observed in all patients from the Imusil+SoC group, while in the SoC group, clinical improvement was observed in 94.0% of patients (P=0.4947). During follow-up visits, the average C-reactive protein (CRP) levels decreased from baseline in both treatment groups. The decrease in the levels of CRP (-7.3 mg/dL versus -5.5 mg/dL), D-dimer (-231.0 ng/mL versus -151.6 ng/mL), and interleukin 6 (IL-6) (-2.3 pg/mL versus -2.0 pg/mL) at eight days was comparatively higher in the Imusil+SoC group versus the SoC group. There were no serious treatment-emergent adverse events in the drug arm. Conclusion Imusil provides effective antiviral activity and safety in mild COVID-19 patients. Imusil ensures faster RT-PCR negativity and clinical improvement and ensures effective reduction of inflammatory markers such as CRP, D-dimer and interleukin 6.

Clinico-epidemiological profile of Mucormycosis patients admitted in VIMS hospital, Ballari: Case series

Background: Mucormycosis is caused by the fungi belonging to the order Mucorales. Humans acquire the infection predominantly by inhalation of sporangiospores, occasionally by ingestion of contaminated food or traumatic inoculation. In the backdrop of COVID-19 expression, there has been notable increase in the incidence of invasive fungal infection (IFI), namely Mucormycosis and aspergillosis. In the present study we aim to know the Clinico-epidemiological profile of Mucormycosis patients admitted in Vijayanagar institute of medical sciences (VIMS), Ballari, Karnataka. Methodology: A descriptive study was carried out at VIMS Hospital, Ballari, Karnataka after obtaining ethical clearance. The data was collected using structured questionnaires through interview and case records on risk factors, clinical profile and management of patients who were suspected of Mucormycosis. Frequencies and Proportion were used to describe the variables. Study period was from April 2021-June 2021. Result(s): Out of 52 patients, 45(86.5%) were male and 7(13.5%) were female. Age group between 41-50 years (40.4%) were most commonly affected followed by 31-40 years (28.8%) and 50% were positive for COVID 19, 26.9% were post COVID and 23.1% were NON COVID. Twenty two patients were on steroids, 21 (95.5%) of them due to COVID 19 and 1(4.5%) due to asthma. Comorbid conditions like diabetes mellitus 38(73.1%) and hypertension 12(23.1%) were most commonly present. 12(31.6%) out of 38 patients had uncontrolled diabetes mellitus. Mucormycosis was confirmed by KOH and histopathological results and were positive in 21(43.7%) and 27(77.1%) patients respectively. Management of Mucormycosis included both medical and surgical intervention. Conclusion(s): Mucormycosis is a life threatening fungal infection. The present study emphasizes the need for further understanding of the disease and to take aggressive measures for early diagnosis and management.Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.

Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based Approaches

Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks. © 2023 by the authors.

Perception of E-learning among Medical Students of VIMS, Ballari, Karnataka: A Cross Sectional Study

With COVID-19 pandemic disrupting the educational system, the professional medical teaching has been shifted to online mode soon after the Government's decision to impose nation-wide lock-down. A cross sectional study was carried out among medical undergraduate in VIMS to know their perception on e-learning. The questionnaire was administered through google forms and the results were analysed using descriptive statistics. Among 340 study subjects, the mean age was found to be 20.47 and the majority of responders were first year undergraduates. Most students depended on smartphones for attending classes. The maximum satisfaction index (55.47%) observed with more time spend on homework and the minimum (39.85%) with greater ability to concentrate in online class. On a Likert scale of perception assessment 32.9% of students disagreed with conducting online classes followed by 31.2% remained neutral. Overall experience recorded bad with 57.6%. Online learning has been the need of the hour but it should be backed up with traditional learning for effective results.Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.