Alterations in Sleep Cycle among Nonhospitalized COVID-19-Affected Population during the Pandemic in Karnataka State-A Web-Based Study

Objectives The psychological and physiological distress and social isolation could adversely affect sleep. As sleep disturbances may persist and deteriorate health, it is important to assess prevalence and related factors of sleep disturbances among the residents of Karnataka state during the coronavirus disease 2019 (COVID-19) infection.Methods In this study, 250 responders across the state of Karnataka accessed the survey voluntarily during September 2021 and January 2022. Pittsburgh Sleep Quality Index, Insomnia Severity Index scales, features that are related to sleep quality, and some hypothesized risk factors of sleep disturbances in context of COVID-19 infection are employed to format a self-reported questionnaire.Results In this study, females 27(19.1%) experienced more sleep disturbances than males 9 (8.3%). The study revealed the participants who received both the doses of vaccination (126;54.3%) had better sleep. The participants who reported themselves to be stressed (182;72.8%) had more disturbances in sleep. A total number of 45 participants (18.4%) experienced difficulty in breathing and out of all the responders 11 (47.8%) were actually worried about their current sleep problem.Conclusion COVID-19 infection and changes imposed during the pandemic have led to a surge in individuals reporting sleep problems across the globe. The findings raise the need to screen for worsening sleep patterns to create more awareness about the secondary consequences of COVID-19 infection.

Machine Learning-Based Social Distance Detection: An Approach Using OpenCV and YOLO Framework

In the fight against coronavirus, social distance has proven to be a very effective tool. To minimize the risk of the virus spreading through physical contact or proximity, the public is being advised to limit their contact with one another. It has previously been demonstrated that deep learning can solve a variety of issues. In our proposed system, we utilize Python, image analysis, and other learning techniques to monitor social distance in public areas and offices to corroborate the social distancing protocol. By analysing live video feeds from cameras, this tool will track if people remain within a safe distance from each other. With this tool, it is possible to predict people at malls, company offices, and stores to see if they are at an appropriate distance from one another. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Blockchain with Corona Virus: Moving Together to Prevent Future Pandemics

Coronavirus 2 (SARS-CoV-2), which is a severe acute respiratory syndrome, is spreading rapidly all over the world. This disease has been declared a pandemic by the World Health Organization (WHO). The death toll has been increasing due to COVID-19. Global effort is needed to stop the spread of the virus and decrease the death rate. Technically, the death toll is because of improper data management and information sharing. The traditional database system has limitations on tampering with data and collective control systems, especially when data is shared with others. To overcome this problem, “blockchain” technology can be used. It is a distributed ledger technology that acts as a shared database by keeping all the copies verified and synced. This article focuses on blockchain technology and how future pandemics can be prevented using this concept. Blockchain technology can help in preventing pandemics and tracking drug trials, early detection of epidemics, and management of outbreaks and treatment. The containment of outbreaks becomes manageable with easy access to data. It will also be helpful to health authorities. In this chapter, a model is proposed that uses blockchain where each block contains information regarding Covid patients so it becomes easier to track and get the count of infected patients, and data will be secure and can be used for future analysis. © 2022 selection and editorial matter, Vineet Kansal, Raju Ranjan, Sapna Sinha, Rajdev Tiwari, and Nilmini Wickramasinghe;individual chapters, the contributors.

Integrated treatment strategies and prosthetic rehabilitation for COVID-19-associated mucormycosis

Mucormycosis is a life-threatening opportunistic infection caused by the fungi belonging to the order Mucorales. Mucormycosis commonly develops in patients with diabetes mellitus having a background of immunosuppression. An integrative approach in the management of COVID-19-associated mucormycosis can be more effective and beneficial to the patients, as the delay in diagnosis and treatment is commonly associated with COVID-19. The rehabilitation of the resected facial structures in these patients would be very crucial in sustaining the quality of life. Thus, our review aims in providing insights into management strategies across different fields of medicine and the various rehabilitation methods. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

CT severity score: an imaging biomarker to estimate the severity of COVID-19 pneumonia in vaccinated and non-vaccinated population

Background: In India, two vaccines received emergent use authorization, namely Covishield (a brand of the Oxford—Astra Zeneca vaccine manufactured by the Serum institute of India) and Covaxin (developed by Bharat Biotech) against COVID-19 disease. Chest CT is an objective way to assess the extent of pulmonary parenchymal involvement. This study aims to estimate the disease severity and outcome due to COVID-19 among vaccinated and non-vaccinated symptomatic patients and compare the same in Covishield versus Covaxin recipients using CT severity score. Results: A total of 306 patients were retrospectively evaluated. The mean age was 62.56 ± 8.9 years, and males [n-208 (67.97%)] were commonly affected. Of 306 patients, 143 were non-vaccinated (47%), 124 were partially vaccinated (40%), and 39 were completely vaccinated (13%). CT severity scores were reduced in both Covishield and Covaxin recipients in comparison with the non-vaccinated group [χ2 (2) = 16.32, p < 0.001]. There is a reduction in LOS among the vaccinated group, predominantly among the Covishield recipients. Conclusion: Vaccination confers protection from severe SARS-CoV2 infection and is associated with an overall reduction in mortality.

COVID-19 Death: Autopsy and Pathological Findings – A Case Report

Corona viruses are a large family of viruses that may cause respiratory illness in animals or humans. The most recently discovered corona virus causes Corona Virus Disease-19 (COVID-19), also known as SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2), which became a global pandemic and is persisting. The data on the spectrum of gross and microscopic changes in COVID-19 infections is insufficient to accurately outline the disease process due to its ever-changing viral nature. The current knowledge on this aspect is not comprehensive since limited numbers of autopsies are conducted on such dead bodies because of higher infectivity and increased risk of transmission. We report a case of a 33-year-old male who was admitted to our institute with an alleged history of road traffic accident, later tested positive for COVID-19 and developed Covid-19 Pneumonia and succumbed. The detailed autopsy and histopathological findings are discussed. Performing autopsies on cases of COVID-19 deaths are helpful in understanding the pathology, disease process and variability in clinical presentation of COVID-19. © 2021, Punjab Academy of Forensic Medicine and Toxicology. All rights reserved.

PRIVACY CONCERNS AND PROTECTION BEHAVIOR DURING THE COVID-19 PANDEMIC

This paper aims to analyze the protection behavior of employees while working remotely during the Covid-19 pandemic using online video chat software. This pandemic changed the way organizations work, managers meet with employees, and employees communicate. An e-mail-based survey among computer users who use video chat software for remote working is employed in this study. Using 306 responses, structural equation modeling explores the relationship between privacy concerns, protection behavior, and antecedents. The technological changes induced due to Covid-19 influence privacy concerns and protection behavior. Privacy efficacy increases privacy concerns and protection behavior. Perceived vulnerability increases privacy concerns. Perceived effectiveness of organization software affects privacy concerns but does not affect protection behavior. There is a positive relationship between privacy concerns and protection behavior;however, this positive relation is negatively moderated by a propensity to trust. A finding of threat severity measure using Covid-19 factors concludes that both privacy concerns and protection behavior increased for online video chat software users. The theoretical model explicates 75% of variances in privacy concerns and 57% of variances in protection behavior. Every one-unit increase in Covid-19 induced changes regarding the work environment increases the privacy concern by 35%, and every one-unit increase in perceived effectiveness of organization software increases privacy concern by 22%. Every one-unit increase in the privacy concern increases the protection behavior by 48%, and every one-unit increase in privacy efficacy increases protection behavior by 59%. © 2022 The author(s).

Respirators and Surgical Masks Artefacts on Phantom using Magnetic Resonance Imaging during COVID-19: A Cross-sectional Study

Introduction: It is suitable for a patient to wear a respirator or face mask during any radiological investigation during Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) pandemic. Some face masks may have nanoparticles, or antimicrobial coating, which may comprise metal to help shape the mask according to user face shape. This kind of ferromagnetic substances can cause artefacts in the image. Aim: To detect and compare the artefacts while using different types of respirators and surgical masks in the Magnetic Resonance Imaging (MRI) phantom images. Materials and Methods: This was a prospective cross-sectional study which was conducted from July 2021-September 2021. Two Not resistant to oil-based aerosols with 95% efficiency to airborne particles (N95) respirators and two types of three-ply surgical disposable masks with a metal and plastic nose holder were used. The N95 respirators were of Halo N95 Filtering Facepiece 2 Particulate Matter (FFP2) PM 2.5 and Suchi N95 S-7400, while the surgical masks were from Venus 3 ply V-1010 with a metal nose holder and the Thea Tex Filtra 3 ply with plastic nose holder. A polymethyl methacrylate plastic phantom was used with 1.5 Tesla (Siemens Magnetom Avanto) MRI scanner for imaging. Results: When exposed to the metal detector both N95 respirators and one of the surgical masks with a metal nose clip showed strong ferromagnetic attraction. Both respirators and a surgical mask with a metal nasal holder showed magnetic susceptibility artefacts. The signal loss is caused by dephasing of spins from metal strip on the image. Conclusion: All the patients must have a recognised MR safe masks prior to an MRI investigation. When this is not possible to follow, metallic components from the face mask should be removed before the patient's arrival at the MR room. After removing the metal strip from the mask, the paper tape may be applied across the nasal bridge region for adequate transmission control and to maintain the intended function of the mask. The mask with a plastic nasal holder was ideal to use in an MR environment since it doesn't have any distortion in the image.

The effect of Brahmi (Bacopa monnieri (L.) Pennell) on depression, anxiety and stress during Covid-19

Introduction: Despite overall impact on health during COVID-19, mental health was least explored to assess psychological wellbeing. Hence, this study to screen the symptoms of depression, anxiety, and stress among adolescents and adults and evaluate the effectiveness of Ayurveda herbal intervention becomes relevant. This study examined the effectiveness of Brahmi (Bacopa monnieri (L.) Pennell) in reducing depression, anxiety, and stress among populations aged between 12 to 60 years in COVID-19 negative patients Hassan district of Karnataka State, India after screening them for psychological distress. Methods: 198 eligible participants (140 female and 58 male) selected by Depression Anxiety and Stress Scale-21 (DASS-21) was used to evaluate depression, anxiety, and stress from 1657 screened participants at baseline. Two 500 mg capsule of Brahmi (Bacopa monnieri (L.) Pennell) was administered twice daily after food in morning and night for 30 days. Paired t-test, and Wilcoxon signed rank test was applied to see the change in each DASS-21 indicator after intervention. Results: At the DASS-21, 4.09% of the responders presented pathological levels of depression, 10.85% of anxiety, and 0.72% of stress among general population. Significant decrease in means were found for scales of depression, anxiety, stress and total DASS-21 after intervention (p<0.001) with Brahmi for a period of 1 month Conclusion: The population during COVID-19 experienced mild to moderate levels of anxiety, depression and stress. This study result highlights the effectiveness of Brahmi in improving the psychological health during COVID-19. These results have important implications in clinical practice in improving psychological health in the context of COVID-19 pandemic. Trial Registration: CTRI/2020/07/026952. Available at: http://www.ctri.nic.in. Keywords: COVID-19, DASS-21, psychological health, depression, anxiety, stress, Brahmi, Bacopa monnieri

Social Distancing and Face Mask Detection using Deep Learning Models: A Survey

Over the recent past, the raging pandemic due to COVID-19 is making the headlines, bringing about a global crisis with an inevitable spread. The use of a face mask and maintaining physical distancing is a precautionary measure as suggested by the WHO. The individuals infected with COVID-19 suffer respiratory problems accompanied by shortness of breath. The surroundings of the concerned individuals can be contaminated by their droplets carrying the virus. It is mandatory to wear a mask and follow physical distancing, yet many citizens violate the regulations. In such scenarios, frequent checks for face masks in public places and imposing fines are common. As object detection has unfolded to be an approachable biometric process, it has been widely applied in surveillance, security, autonomous driving, etc. With the rapid development of deep learning models, object detectors are highly suitable to develop social distancing and face mask detectors to administer the crowd via CCTV and surveillance cameras. The paper surveys various deep learning networks to develop such detectors. In this survey, the existing object detection models used for surveillance and people detection are analyzed. The one-stage and two-stage detectors along with their applications and performance are outlined in a comprehensive manner. Deep Learning models such as AdaBoost, Voila-Jones, variants of CNN including ResNet, VGG-16, single-shot detectors MobileNet, and versions of YOLO are discussed and compared. © 2021 IEEE.

Over 60 years of age as an independent prognostic factor of in-hospital mortality among COVID-19 patients: a cohort study in an Iranian high-incidence area.

OBJECTIVES: COVID-19 continues to cause devastation throughout the world. Various factors influence the perioperative course and prognosis of COVID-19. This study aims to collate the independent prognostic factors among hospitalised COVID-19 patients in east Iran. STUDY DESIGN: In this cohort study, all patients with a confirmed diagnosis of COVID-19 between 19 February 2020 and 1 August 2020 who were admitted to nine public hospitals of South Khorasan province, Iran, were enrolled. METHODS: Univariate analysis (chi-square [χ2], and Mann-Whitney U test) and multiple logistic regression were performed. RESULTS: This study included 1290 participants; 676 patients (52.4%) were male. A total of 1189 (92.2%) recovered, and 101 (7.8%) died. The results show that in-hospital mortality increases with advanced age (the optimal cut-off point = 62 years). The following three variables were shown to have the most significant role in in-hospital mortality: age >60 years (odds ratio [OR] = 8.01, 95% confidence interval [CI] 4.8-13.35), shortness of breath (OR = 2.65, 95% CI: 1.4-69.17) and atypical radiological manifestations in a chest X-ray on admission (OR = 2.16, 95% CI: 1.3-28.64). In the univariate analysis, associated comorbidities, such as cardiovascular diseases, influenced the in-hospital mortality rate, while the same could not be replicated in the multiple variable analysis. CONCLUSIONS: This study revealed the potential predictors of COVID-19 and highlighted the need to be cautious with advanced age and heightened clinical symptoms at the time of admission.

Cybersecurity During COVID-19

The world today has become fully digitalized. The increase of digitalization has provided a huge platform for the cybercriminals to launch their attacks. With the outbreak of coronavirus (COVID-19) followed by lockdowns, digitalization has been the only way to stay connected virtually. With the growing technology every day, the attacks become more and more sophisticated as the attackers quickly learn and invent new ways to find their prey. Every sector whether public or private has been affected due to breaches in cybersecurity especially during the COVID-19. In this paper, we have tried to analyze various types of cyberattacks carried out during the pandemic with its impact and preventive measures to be taken for protection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Use of Deep Learning Based Frameworks on Pixel Scaled Images of Chest CT Scans for Detection of COVID-19

As on 15th September 2020, the total cases of SARS-CoV-2 infected patients in the world has crossed 29 million, with more than 930,000 deaths occurring due to the virus. Real time RT-PCR (Reverse Transcription Polymerase Chain Reaction), which is the standard detection method for COVID-19, is likely to have low detection rate in early stages of the infection possibly due to less viral load in the patients. On the other hand, in comparison to RT-PCR, patterns obtained from radiography on chest CT scans show higher sensitivity and specificity. However, due to the sensitive nature and difficulties in publicly acquiring medical data, only 2 open-sourced COVID CT datasets with images containing clinical findings of COVID-19 could be found. Applying existing deep learning models to the limited CT scans can distinguish COVID-19 and non-COVID-19 CT images but with lesser accuracy. The paper proposes to use existing deep learning-based frameworks on an augmented dataset consisting of pixel scaled images (of the original CT images) and the original CT images to diagnose COVID-19 infection. Since this is a binary classification problem the paper proposes to use Convolutional Neural Networks (CNN) to classify CT images into infected and not infected categories. The implementation is done with Keras and Tensorflow using an 80/20 ratio for training and validation. The proposed methodology (using pixel scaled images) achieved a validation accuracy of over 90% in detecting COVID-19 with an F1-score of 0.96 compared to the best F1-score of 0.86 on the original dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Awareness, Knowledge and Risk Perception of COVID-19 among Dental Health Care Professionals and its Impact on Clinical Dental Practice and Oral Healthcare Delivery in the Western Region of Saudi Arabia

Introduction: The outbreak of novel coronavirus disease (COVID-19) has influenced every aspect of life. Healthcare professionals, especially dentists, are exposed to a higher risk of getting infected due to close contact with infected patients primarily through aerosol generated procedures. This study aimed to assess the awareness and risks of getting infected among dentists in the western region of Saudi Arabia, while working during the current novel coronavirus disease (COVID-19) pandemic. In addition, dentists' knowledge about various practice modifications to combat COVID-19 cross infection and spread were evaluated. Materials and Methods: A total of 329 participants including 123 males and 196 females responded to an online questionnaire consisting of 30 questions, which was circulated among all kinds of dental healthcare professionals through social media and e-mails after obtaining approval from the institutional ethical committee. Results: Chi square tests revealed significant differences when comparing the responses of the participants based on qualification with regard to key responses related to awareness and practice perceptions such as HEPA filters, pulse oximeter, wearing an N-95 mask, using a face shield, a High volume suction and rubber dam.(p<0.05) Conclusion: Although, there were variations in responses among the various dental professionals, it can be concluded that it is important to ensure increased knowledge and awareness of covid -19 risks and transmission which is absolutely essential in dental practice to ensure optimal oral care during the ongoing pandemic.

Hyper acute dialysis access pathway (HADAP)-undertaking creation of arteriovenous fistulae in the renal ward

Introduction: The impetus to apply the lean method to creation of arteriovenous fistulae came with the pandemic restricting access to theatres. Here we present HADAP implemented in a dedicated procedure room in the Renal ward. Method: Between April and September 2020, 44 patients underwent ward-based arteriovenous fistula operations with no more than 3 people in the room - surgeon, runner and patient. The parameters assessed were duration of operation, complications, patency, patient and surgeon feedback and financial implications. Results: The service was predominantly registrar-led who created 91% of the fistulae. 95% operations were completed successfully with a 2- week primary patency of 73% and no surgical site infections. 40% of the procedures were radiocephalic fistulae. 14% procedures required surgical assistance by the runner. The average operating time reduced from 90 minutes for the first 10 cases to 50 minutes for the last 9. A structured questionnaire showed positive feedback from both patients and surgeons (4-5 out of 5). This service has cleared potentially 9 main theatre lists for more complex procedures. Conclusions: Our study demonstrates the successful implementation of a novel operating environment allowing expedited care for renal failure patients. It has enhanced our ability to deliver a dialysis access program despite the challenges of COVID.

Connect - Blockchain and Self-Sovereign Identity Empowered Contact Tracing Platform

The COVID-19 pandemic in 2020 has resulted in increased fatality rates across the world and has stretched the resources in healthcare facilities. There have been several proposed efforts to contain the spread of the virus among humans. Some of these efforts involve appropriate social distancing in public places, monitoring and tracking temperature at the point of access, etc. In order for us to get back to the “new normal", there is a need for automated and efficient human contact tracing that would be non-intrusive and effective in containing the spread of the virus. In this paper, we have developed “Connect", a Blockchain and Self-Sovereign Identity (SSI) based digital contact tracing platform. “Connect" will provide an automated mechanism to notify people in their immediate proximity of an occurrence of a positive case and would reduce the rate at which the infection could spread. The platform’s self-sovereign identity capability will ensure no attribution to a user and the user will be empowered to share information. The ability to notify in a privacy-preserving fashion would provide businesses to put in place dynamic and localized data-driven mitigation response. “Connect’s" SSI based identity wallet platform encodes user’s digital identities and activity trace data on a permissioned blockchain platform and verified using SSI proofs. The user activities will provide information, such as places travelled, travel and dispatch updates from the airport etc. The activity trace records can be leveraged to identify suspected patients and notify the local community in real-time. Simulation results demonstrate transaction scalability and demonstrate the effectiveness of “Connect" in realizing data immutability and traceability. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.