Optimism Bias as Moderator to Deal with Anxiety & Depression: Empirical Evidence from PLS – SEM Based Approach

The research aims at examining the influence of loneliness and stress on anxiety and depression. The role of optimism bias in regulating anxiety and depression dimensions among 726 students in India is investigated. Partial least squares-structural equation modelling (PLS-SEM) approach is used to study the relationships between constructs. According to the data, optimism bias significantly moderates the association among stress and anxiety & between loneliness and anxiety. The research enables understanding of the consequences of Covid-19 upon the psychology of the students while providing an insight into the behavioural implications of loneliness, stress and optimism bias on the anxiety level and depression of students. The study enables the academicians and management in formulating communication and management strategies for students. Limited studies have been found on stress and depression in Indian student's context. It is the first study to employ an optimistic bias scale to investigate the behaviour of students in India and its impact on the students' mental health using anxiety and depression as variables. © The Author(s) 2023.

The conditional impact of market conditions, volatility and liquidity shocks on the arbitrage opportunities during pre-COVID and COVID periods

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.

Progress of medical undergraduates to an era of computer vision syndrome and insomnia as an aftermath of increased digitalization during covid-19 pandemic

Background: WHO declared COVID 19 as a global pandemic in March 2020. Lockdown and travel restrictions were imposed in most countries including India, to reduce the spread of SARS-COV-2 Virus and reduce mortality. Aftermath of this was that technology has become the only tool for people to interact, communicate and even to continue their responsibilities. Educational institutions including Medical colleges were closed globally, pedagogical innovations including technology and simulation based teaching were brought to the forefront during the current pandemic worldwide. This lead to excessive exposure to digital screen for any reason, be it for education or entertainment. Aim & Objective: To estimate the prevalence of computer vision syndrome and to identify whether medical undergraduates suffered from sleep disorder like insomnia during Covid -19 pandemic as well as to assess the relationship between insomnia and computer vision syndrome in these students. Method(s): Descriptive cross-sectional study was carried out on medical undergraduates with Questionnaires based on Google form. The survey instruments were Computer Vision Syndrome Questionnaire (CVS-Q) to assess the frequency of (i) symptoms of computer vision syndrome/ digital eye strain, pattern of computer usage and (ii) Insomnia severity index questionnaire including the demographic details of the participants. Result(s): The study shows that e-learning by medical undergraduates during the COVID 19 pandemic has given rise to various side effects leading to deterioration of their health parameters. Most common effects were both ocular as well as non-ocular symptoms of computer vision syndrome. Even clinical insomnia of moderate severity was reported by 70% of the participants. Conclusion(s): Health issues related to excessive use of digital devices has become alarmingly high during COVID-19 pandemic. Preventive measures to reduce Computer vision syndrome associated symptoms and Insomnia should also be imparted to the students. There is also an urgent need to make an institutional policy involving all stakeholders to formulate effective strategies to prevent young generation from the detrimental health effects of excessive digitalization during the pandemic.Copyright © 2020 Ubiquity Press. All rights reserved.

Covid-19 classification using CNN with radiography image data set

COVID-19 is a novel coronavirus disease that has been reported in Wuhan, China since late December 2019 and has subsequently spread around the world. In severe cases of illness, there may be no option but to die due to substantial alveolar damage and progressive respiratory failure. Testing with RT-PCR, for instance, is the gold standard for clinical diagnosis, but it is possible for the tests to produce false negatives. Further, the lack of resources for conducting RT-PCR testing may deter the next clinical decision and treatment under the pandemic situation. As a result, chest CT imaging has become a valuable tool for diagnostic and prognostic purposes in COVID-19 patients. Detection of COVID-19 early enables the development of prevention plans and a disease control plan. Through this experimentation, the main objective is to utilize transfer learning to leverage pre-trained weights from CNNs. We propose the ResNet50 architecture based on the ImageNet pre-trained weights to detect the Covid-19. The proposed model is evaluated on X-ray images of COVID-19 chests and on images taken with a Computerized Tomography scanner. Using the 746 images of covid and non-covid patient datasets are bifurcated into train and test datasets for training and validate our model and achieved 84.90 % model accuracy. The Accuracy, precision, recall and F1-Scores are presented along with the receiver operating characteristic (ROC) curve, the precision-recall curve, the average prediction, and the confusion matrix of three distinct models. © 2022 IEEE.

Health index scores and health insurance coverage across India: A state level spatiotemporal analysis

Introduction: Since the Government of India has decided to continue with the publicly funded health insurance (PFHI) strategy, it is now pertinent to attempt to determine the factors that drive health insurance coverage in India. The NITI (National Institute for Transforming India) Aayog (i.e. Commission) is the apex public policy think tank of the Government of India. The NITI Aayog assesses the health status of the states through its acclaimed health index consisting of 24 indicators for health outcomes, governance and infrastructure. All states and Union Territories (UTs) are ranked on the index. This study aims to assess associations between NITI Aayog health index scores and health insurance coverage across India through a state-level lens. Method(s): Health insurance coverage data has been extracted from the National Family Health Survey (NFHS) 4 and NFHS-5 data. NFHS-4 was conducted during 2015-16. NFHS-5 was interrupted by the COVID-19 pandemic and conducted in two phases from 2019 to 2021. This change in health insurance coverage is mapped to the NITI Aayog health index scores for the states and UTs. The NITI Aayog has classified states into two categories: Larger states and smaller states. Based on performance in health indices, NITI Aayog also classifies the states and UTs as Aspirants, Achievers and Front runners. Results and discussion: There is a positive linear relationship between the health index scores of front-runners (Pearson's r = 0.6037, p = 0.029) and the total insurance coverage. We observe poor linear relationship between the health index scores of achievers (Pearson's r = 0.2822, p = 0.498) and the total insurance coverage. There is no linear relationship between the NFHS-5 Total Insurance Coverage and the NITI Health Index Scores (Pearson's r = 0.2766, p = 0.125). Also, we observe a moderate positive linear relationship between the health index scores and the total insurance coverage among the Union Territories which is not statistically significant (Pearson's r = 0.4343, p = 0.465). A similar conclusion is made in the context of smaller states (Pearson's r = 0.3692, p = 0.368) and larger states (Pearson's r = 0.2103, p = 0.387). At the same time, we observe a decrease in insurance coverage across NFHS-4 and NFHS-5 in some states and UTs. Further research is needed to identify the determinants of these spatial changes across a span of five years, from a temporal lens. Copyright © 2022

Understanding the Impact of COVID-19 on Economy and Environment in the Asia-Pacific Region

The COVID-19 pandemic has provoked longstanding and competing interests of the economy and environment. In January 2020, countries across the globe began implementing various levels of safety measures to slow the spread of the virus. Safety measures have run the gamut of restrictions: physical distancing guidelines, proper handwashing practices, and the use of face masks are on the lower end of the restriction spectrum, while travel restrictions, business closures, and country-wide lockdowns are instances of more stringent measures. Policy responses have drastically differed among governments across the globe, but the economic strife has plagued countries regardless of their COVID-19 response plan. Lockdowns in the first half of 2020 impeded economic activity, leading to a reduction in industrial activity and hence emissions. During this time period, observations from publicly available satellite sensors have shown that concentrations of various atmospheric pollutants, nitrogen dioxide especially, have decreased. The Asia-Pacific region was no exception, with China, Japan, South Korea, Australia, and New Zealand all experiencing slowdown in growth and large reductions in various economic sectors. Using these five Asia-Pacific countries, we will analyze how government policy, lockdowns, and travel restrictions implemented during the COVID-19 outbreak have slowed economic growth in the transportation, manufacturing, and agriculture sectors, and in turn, impacted air quality and water quality. Conclusions and statistical significance of our analysis comparing coronavirus-related policies and their effect on economic growth and environmental health will help drive future decisions made by policymakers should another pandemic or similar global crisis arise.

Review of Psychometric Data Analysis for Healthcare Based On Emotional Intelligence

The technological advancements in the current world and also due to the Covid-19 pandemic there is high competition among people resulting in locked in syndrome, stress, and various other psychological problems like bipolar disorders, schizophrenia severe depression, So, the need for proper psychometric analyzer is tremendously increasing to analyze the emotions of people, so that a person's emotion can be found and necessary actions can be taken according to their health condition. To overcome these issues of people around us, our loved ones and also the issue in the online consultation with psychologists, here is the proposed system. In this system, a person's emotions are recognized using their video, audio and text in three different modules separately and an analysis of their emotion are provided. Some of the existing systems helps in indentifying the emotion of people in video, audio and text separately. This system aims at combining the three features separately in a single website and provides analysis for emotion using the best model for each. Identifying a person's emotions becomes crucial to know about his/her mental state in the pandemic. The main objectives of the proposed system are, (1) To identify the emotion of a person in video, audio and text using machine learning models. (2) The proposed system makes it easy for the psychologist to identify the patient's emotions in online consultations. (3) While talking in a video call the emotion of a person can be identified using his/her facial expressions. In a audio phone call, the emotion is recognized using the tone of the person. Text chat emotions can also be identified using the words and their context. © The Electrochemical Society

Pets During Covid-19 Pandemic: Significant Role as Stress Buster for their Owners

Background: COVID-19 pandemic has affected everyone’s lives in many different ways since January 2020 globally. COVID-19 pandemic has revealed the vulnerability of humans to disorders related to loneliness due to physical distancing measures and it has also highlighted the positive aspects of the relationship between animals and humans. Apart from physical wellbeing, psychological wellness has also been a matter of concern during lockdown. Animal assisted therapy aims at improving physical, mental and emotional aspects of human life. Aim & Objective: To evaluate the Role of pets in an individual’s life during Covid-19 Pandemic Methodology: Questionnaire on PSS-10-C and a questionnaire on human animal relationship with relevant demographic data was distributed electronically via social media to subjects of age group more than 18 years after taking consent for participation in the study during April-May 2021. Subjects having any psychiatric disorder or taking any medication were excluded from the study. Result: Out of 230 participants 69 (30%) had pets amongst which dog was the most preferred pet. Many pet owners were concerned about their pet during lockdown, reason being restriction to veterinary treatment, etc. Stress of non-pet owners was significantly high as compared to pet owners. Regardless of owning a pet, 82% participants agreed that pets act as stress busters. Conclusion: Human-animal bond has beneficial effect on human’s physical and mental health. Animal assisted therapy utilises this interaction to promote the health of patients. Animals as pet played an important role in reducing stress among their owners during COVID-19 pandemic. Animal assisted therapy therefore can be promoted in certain ailments which will not only cure the patients but improve the well-being of animals too.

Thromboembolism in children with multisystem inflammatory syndrome: a literature review.

Multisystem inflammatory syndrome in children (MIS-C) is a hyperinflammatory response observed in children several weeks to months after acute infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). On review of all published cases of thromboembolism (TE) as a complication of MIS-C, 33 cases of TE were found with incidence ranging from 1.4 to 6.5%. TE occurred mostly in children aged 12 years and above. One-third of the cases were cerebral infarcts and the remaining cases included intracardiac and radial arterial thromboses, upper and lower extremity deep vein thrombosis, pulmonary embolism, and splenic infarcts. Five were asymptomatic cases and 3/33 (9%) patients (all three with cerebral infarcts) died. To conclude, TE appears to be a significant complication of MIS-C caused by SARS-CoV-2 infection, associated with morbidity and/or mortality. Patients ≥12 years are affected more often, and TE occurs despite thromboprophylaxis in some patients. Thromboprophylaxis should be considered in all cases after reviewing the concomitant bleeding risk. Prospective studies are needed to confirm the role of standard-dose thromboprophylaxis and to explore whether higher-dose thromboprophylaxis is required in certain high-risk patients with MIS-C. IMPACT: Compiles all cases of thromboembolism associated with COVID-19-related MIS-C, a report that has not been published to date.

Predictors of Humoral Response to SARS-CoV-2 Vaccination after Hematopoietic Cell Transplantation and CAR T-cell Therapy.

Cellular therapies including allogeneic hematopoietic cell transplant (allo-HCT) and autologous hematopoietic cell transplant (auto-HCT) and chimeric antigen receptor (CAR) T-cell therapy render patients severely immunocompromised for extended periods after therapy, and data on responses to COVID-19 vaccines are limited. We analyzed anti-SARS-CoV-2 spike IgG Ab (spike Ab) titers and neutralizing Ab among 217 recipients of cellular treatments (allo-HCT, n = 149; auto-HCT, n = 61; CAR T-cell therapy, n = 7). At 3 months after vaccination, 188 patients (87%) had positive spike Ab levels and 139 (77%) had positive neutralization activity compared with 100% for both in 54 concurrent healthy controls. Time from cellular therapy to vaccination and immune recovery post-cellular therapy were associated with response. Vaccination against COVID-19 is an important component of post-cellular therapy care, and predictors of quantitative and qualitative response are critical in informing clinical decisions about optimal timing of vaccines and the requirement for booster doses. Significance: Identifying predictors of response to vaccination against SARS-CoV-2 in patients following cellular therapy is critical to managing this highly vulnerable patient population. To date, this is the most comprehensive study evaluating quantitative and qualitative responses to vaccination, providing parameters most predictive of response and potentially informing booster vaccination strategies.See related article by Chung et al., p. 568. This article is highlighted in the In This Issue feature, p. 549.

Disease- and Therapy-Specific Impact on Humoral Immune Responses to COVID-19 Vaccination in Hematologic Malignancies.

Coronavirus disease-19 (COVID-19) vaccine response data for patients with hematologic malignancy, who carry high risk for severe COVID-19 illness, are incomplete. In a study of 551 hematologic malignancy patients with leukemia, lymphoma, and multiple myeloma, anti-SARS-CoV-2 spike IgG titers and neutralizing activity were measured at 1 and 3 months from initial vaccination. Compared with healthy controls, patients with hematologic malignancy had attenuated antibody titers at 1 and 3 months. Furthermore, patients with hematologic malignancy had markedly diminished neutralizing capacity of 26.3% at 1 month and 43.6% at 3 months, despite positive seroconversion rates of 51.5% and 68.9% at the respective time points. Healthy controls had 93.2% and 100% neutralizing capacity at 1 and 3 months, respectively. Patients with leukemia, lymphoma, and multiple myeloma on observation had uniformly blunted responses. Treatment with Bruton tyrosine kinase inhibitors, venetoclax, phosphoinositide 3-kinase inhibitors, anti-CD19/CD20-directed therapies, and anti-CD38/B-cell maturation antigen-directed therapies substantially hindered responses, but single-agent immunomodulatory agents did not. Significance: Patients with hematologic malignancy have compromised COVID-19 vaccine responses at baseline that are further suppressed by active therapy, with many patients having insufficient neutralizing capacity despite positive antibody titers. Refining vaccine response parameters is critical to guiding clinical care, including the indication for booster vaccines, for this vulnerable population.See related article by Tamari et al., p. 577. This article is highlighted in the In This Issue feature, p. 549.

Hematopoietic Cell Transplantation is Feasible in Patients with Prior COVID-19 Infection.

There are limited data on outcomes of patients with prior Coronavirus disease 2019 (COVID-19) who proceeded to autologous or allogeneic hematopoietic cell transplantation (HCT). Whether these patients are more susceptible to poor outcomes and recurrence of COVID-19 is unknown. We report a retrospective analysis of outcomes of 15 consecutive patients with hematologic malignancies who experienced COVID-19 and subsequently underwent autologous (n = 8) or allogeneic (n = 7) HCT between June 17, 2020, and February 17, 2021. The cohort included patients with asymptomatic past infections or symptomatic COVID-19 disease. Data were obtained from chart review. Descriptive statistics were used to summarize patient characteristics. Among eight patients who underwent autologous HCT, four had a diagnosis of multiple myeloma and four had a diagnosis of non-Hodgkin's lymphoma. Four of these eight patients did not test positive for anti-SARS-CoV-2 IgG antibody at any point during the course of treatment. The other four patients had detectable anti-SARS-CoV-2 IgG antibodies before undergoing autologous HCT, but only two of these patients remained anti-SARS-CoV-2 IgG antibody-positive at their last follow-up. One patient died from progression of disease. Seven patients with prior COVID-19 underwent allogeneic HCT for acute lymphoblastic leukemia (n = 3), acute myelogenous leukemia (n = 1), chronic myelogenous leukemia in lymphoid blast crisis (n = 1), myelodysplastic syndrome (n = 1), or myelofibrosis (n = 1). Three of the seven patients tested positive for anti-SARS-CoV-2 IgG antibodies following the initial COVID-19 diagnosis; however, only one of these patients retained anti-SARS-CoV-2 IgG antibody following allogeneic HCT. One patient died of infection (fungal and Pneumocystis jirovecii pneumonia) occurring in the context of ongoing treatment for graft-versus-host disease. None of the 15 patients had recurrent COVID-19 infection. Based on our experience, autologous and allogeneic HCT can be safely performed in selected patients with previous COVID-19 infection.

Face Mask detection with fine rate

The COVID-19 pandemic is causing a worldwide wellbeing emergency so the powerful assurance strategies are wearing a face cover in open territories as per the World Health Organization (WHO). The COVID-19 pandemic constrained governments across the world to force lockdowns to forestall infection transmissions. Reports show that wearing face covers while at work unmistakably decreases the danger of transmission. An effective and financial methodology of utilizing AI to establish a protected climate in an assembling arrangement. A half and half model utilizing profound and old style AI for face cover location will be introduced. A face veil location dataset comprises of with cover and without cover pictures. We will construct a continuous framework to recognize whether the individual on the webcam is wearing a veil or not. After the breakout of the overall pandemic COVID-19, there emerges an extreme need of assurance components, face veil being the essential one. The essential point of the venture is to distinguish the presence of a face veil on human appearances on live web based video just as on pictures. We have utilized profound figuring out how to build up our face identifier model.

The New York State SARS-CoV-2 Testing Consortium: Regional Communication in Response to the COVID-19 Pandemic.

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2, created an unprecedented need for comprehensive laboratory testing of populations, in order to meet the needs of medical practice and to guide the management and functioning of our society. With the greater New York metropolitan area as an epicenter of this pandemic beginning in March 2020, a consortium of laboratory leaders from the assembled New York academic medical institutions was formed to help identify and solve the challenges of deploying testing. This report brings forward the experience of this consortium, based on the real-world challenges which we encountered in testing patients and in supporting the recovery effort to reestablish the health care workplace. In coordination with the Greater New York Hospital Association and with the public health laboratory of New York State, this consortium communicated with state leadership to help inform public decision-making addressing the crisis. Through the length of the pandemic, the consortium has been a critical mechanism for sharing experience and best practices in dealing with issues including the following: instrument platforms, sample sources, test performance, pre- and post-analytical issues, supply chain, institutional testing capacity, pooled testing, biospecimen science, and research. The consortium also has been a mechanism for staying abreast of state and municipal policies and initiatives, and their impact on institutional and laboratory operations. The experience of this consortium may be of value to current and future laboratory professionals and policy-makers alike, in dealing with major events that impact regional laboratory services.

Covid-19 analysis by seir model with multiple controls

A SEIR mathematical model with multiple controls self-preven-tion, treatment and vaccination is formulated. The properties of Pontryagin’s maximum principle were verified and found the optimal levels of controls. Nu-merical simulations were shown to exhibit the flow of variables with or without control strategies. © 2021, Advances in Mathematics: Scientific Journal. All rights reserved.

Non-Invasive Technique-Based Novel Corona(COVID-19) Virus Detection Using CNN

A novel human coronavirus 2 (SARS-CoV-2) is an extremely acute respiratory syndrome which was reported in Wuhan, China in the later half 2019. Most of its primary epidemiological aspects are not appropriately known, which has a direct effect on monitoring, practices and controls. The main objective of this work is to propose a high speed, accurate and highly sensitive CT scan approach for diagnosis of COVID19. The CT scan images display several small patches of shadows and interstitial shifts, particularly in the lung periphery. The proposed method utilizes the ResNet architecture Convolution Neu-ral Network for training the images provided by the CT scan to diagnose the coronavirus-affected patients effectively. By comparing the testing images with the training images, the affected patient is identified accurately. The accuracy and specificity are obtained 95.09% and 81.89%, respectively, on the sample dataset based on CT images without the inclusion of another set of data such as geographical location, population density, etc. Also, the sensitivity is obtained 100% in this method. Based on the results, it is evident that the COVID-19 positive patients can be classified perfectly by using the proposed method.