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Recurrent stock market fall and rise sequel by COVID-19, rising global inflation, increase in Fed interest rates, the unprecedented meltdown of technology stocks, fear of trade wars, tightening of governments' fiscal policies call for a new trend in international investing. It is time for the investors to rethink, rebalance and reset their investment strategies to position and protect their portfolios during and post-pandemic period. This paper attempts to forecast the gold prices for the post-pandemic era and explores whether gold will serve as a decisive hedge during this transition period. The techniques of ARCH, GARCH, E-GARCH, A-PARCH, and GARCH-M is employed in forecasting the conditional volatility of gold spot price from Multi Commodity Exchange (MCX) of India. A total of 3631 observations were collected from the daily spot prices of gold from January 2009 to December 2022. The findings show that the gold prices in India are highly persistent similar to other emerging markets and that gold will remain a safe haven for investors and institutional investors in the post-pandemic period. This paper is the first of its kind to forecast gold prices for the post-pandemic period. The forecast price of 10-gram gold is expected to trade for 65,948 ₹ in the Indian MCX by 2026 if the gold prices behold its previous momentum. This forecast will help the investors to plan their portfolio diversification for the post-pandemic period. © 2023, MDPI. All rights reserved.
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The entire world is suffering from the corona pandemic (COVID-19) since December 2019. Deep convolutional neural networks (deep CNN) can be used to develop a rapid detection system of COVID-19. Among all the existing literature, ResNet50 is showing better performance, but with three main limitations, i.e.: 1) overfitting;2) computation cost;3) loss of feature information. To overcome these problems authors have proposed four different modifications on ResNet50, naming it as LightWeightResNet50. An image dataset containing chest X-ray images of coronavirus patients and normal persons is used for evaluation. Five-fold cross-validation is applied with transfer learning. Ten different performance measures (true positive, false negative, false positive, true negative, accuracy, recall, specificity, precision, F1-score and area under curve) are used for evaluation along with fold-wise performance measures comparison. The four proposed methods have an accuracy improvement of 4%, 13%, 14% and 7% respectively when compared with ResNet50.
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On a global scale, most of the nations are looking forward toward implementing the Industry 4.0 solutions across all the sectors. It is imperative to understand how the fourth industrial revolution also known as the digital revolution is going affect the Human Resources of an organization. Industry 4.0 components assist the humans in their day-to-day tasks and reduce the physical strain arising out of repetitive jobs and help them in seamless decision-making. This manuscript aims at highlighting the effect Industry 4.0 shall have on the future of work especially in the post pandemic world. It also proposes a framework which represents the future of work when integrated with Industry 4.0 solutions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Machine learning (ML) uses statistical theory to create models from data samples. Using the predictive and statistical models, computers can clean and curate the data, interpret and predict the outcomes of certainties (or uncertainties) with precise accuracy. Of course, the interpretation of the produced results and algorithmic solution designed for each problem needs to be fine-tuned and proficient for the target problem. Biomedical images relevant to different diseases are recorded from a body and are generally employed to diagnose precise physiological or pathological conditions. The objective of biomedical image analysis is exact modeling by using pattern recognition and computer vision to diagnose diseases by employing ML techniques. This chapter explains how artificial intelligence (AI) and ML techniques are utilized in disease diagnosis. An automated COVID-19 diagnosis approach based on deep feature extraction is also presented. After extracting features using deep transfer learning (DTL), the X-ray images are fed into the shallow ML model to diagnose COVID-19 from X-ray images. With chest X-ray, a patient can be identified as a potential COVID-19 patient and can be quarantined. X-ray equipment are already accessible in most hospitals, and already digitized. Since X-ray images are high dimensional data, a Convolutional Neural Network based feature extraction via transfer learning models are appropriate for the diagnosis of COVID-19. It may help an inpatient environment where the existing programs find it difficult to determine whether to keep the patient inward with other patients or separate them. This technique will also help classify patients with high COVID-19 risk who need to repeat testing with a false negative RT-PCR. © 2022 Elsevier Inc. All rights reserved.
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In this manuscript, we study a fractional order time delay SEIR model of COVID-19 disease. Some conditions on stability and Hopf bifurcation have been derived for the model by using Laplace transformation. Further numerical simulation has been carried out for the purpose of better understanding of our results. © CSP - Cambridge, UK, I&S - Florida, USA, 2022
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Background: SARS-CoV-2 infection can compromise respiratory function and cause thrombotic events. SARS-CoV-2 binds to and mediates downregulation of angiotensin converting enzyme 2 (ACE2) on infected cells. Diminished enzymatic activity of ACE2 could result in increased concentrations of the pro-inflammatory molecules angiotensin II and bradykinin, contributing to SARS-CoV-2 pathology. Methods: Immunofluorescence microscopy and digital image data quantification, Computer assisted molecular docking analyses, Western blot. Results: Using immunofluorescence microscopy of lung tissues from uninfected and SARS-CoV-2 infected individuals, we find evidence that ACE2 is highly expressed in the pulmonary alveolar epithelium and is significantly reduced along the alveolar lining of SARS-CoV-2 infected lungs. Ex vivo analyses indicate that ACE2 is readily detected on primary human pulmonary alveolar epithelial and primary human aortic endothelial cells (HAoECs). Exposure of these cells to recombinant SARS-CoV-2 spike protein was sufficient to reduce surface ACE2 expression. Moreover, exposure of HAoECs to spike protein induced endothelial dysfunction (increased expression of von Willebrand Factor and decreased expression of Krüppel-like Factor 2), caspase activation, and apoptosis. Exposure of HAoECs to bradykinin (BK, 10μ M) induced calcium signaling and endothelial dysfunction but did not adversely affect viability. Computer assisted analyses of molecules with potential to bind bradykinin receptor B2 (BKRB2) suggested a potential role for aspirin as a bradykinin antagonist. When tested in our in vitro model, we found that aspirin (1μM) could significantly blunt cell signaling, and endothelial dysfunction caused by bradykinin in these cells. Conclusion: SARS-CoV-2 causes complex effects on microvascular homeostasis that potentially contribute to organ dysfunction and coagulopathies. Reduced ACE2 enzymatic activity could contribute to inflammation and pathology in the lung. Our studies add to this understanding by providing evidence that spike protein alone can mediate adverse effects on vascular cells. Understanding these mechanisms of pathogenesis may provide rationale for interventions, such as interference with the interactions of spike protein or bradykinin with endothelial cells, that could limit microvascular events associated with SARS-CoV-2 infection and stabilize microvascular homeostasis in COVID-19 disease.
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The emotional dimensions of health workers are emerging as a serious issue in academic discourse in the current decade. The World Health Organization (WHO) report underscores the significance of Emotional Stability among health workers as a factor affecting the performance of the health sector organization. Purpose. This study investigates the role of Emotional Intelligence on Performance through Psychological resilience in this pandemic situation of COVID19 in the Health sector Organization in Odisha, India. Method. Taking samples from public and private hospitals in Odisha 103 nurses’ were administered Schutte’s Self Report Emotional Intelligence Scale. The questionnaire has dimensions as Emotional Perception, Facilitating Cognition, Emotional Understanding, and Emotional Management. Findings. Regression analysis showed that the Emotional intelligence of health workers can be a predictive factor of Organization performance in the health industry. The nurses of different hospitals are proficient in taking on new challenges, they are also competent in adapting to any situation, they can recognize their strengths and weaknesses, and also they can treasure good humor during bad times. Principal component factor analysis showed that emotional perception and understanding as well as facilitating cognition used in regulating the overall performance of the hospital. Value of results. The amount of EI a nurse of a hospital has developed significantly impacts organization performance as a major part of the hospital nurses act as the central pillar of hospitals so they have a sense of understanding and can critically analyze patient behaviors and minimizes issues within the territory. This study going to help the hospital managers for managing the emotions of nurses who work under him / them. © 2021 Organizatsionnaya Psikhologiya. All rights reserved.
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Background: The COVID-19 outbreak has been declared as a global pandemic by WHO. It has placed an unprecedented burden on health systems and authorities.In this complex context, their knowledge and attitude towards the pandemic can influence their psychological state and information about their behavioral insights can be valuable to ascertain effective strategies to reduce overall burden and tackle the crisis better 3. Aims: To assess knowledge, risk perceptions, preventive behaviors and psychological variables regarding COVID-19 in Resident doctors. Methodology: A list of all resident doctors working in New Civil Hospital were informed about the study and after obtaining an informed consent, they were asked to fill the WHO tool for Behavioral insight. After collecting data, statistical analysis of the data was done using appropriate Statistical test like z test, chi-square test. Results: A total of 110 doctors participated in the study.Depression was very low (14.7%) to extremely low (38.2%) in majority of doctors. In our study, 9% of the doctors attributed their psychological burden of worries and fears to losing someone close to them in the pandemic. Conclusion:Since most participants use almost all sources for gathering information about covid-19, regular updates should be provided by media and relevant authorities, regular group meetings, trainings to disseminate knowledge about guidelines and recent treatment protocols should be organized by the concerned tertiary care hospitals.
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Background: Knowledge, attitude, and practice of the community can determine progress from localized to community transmission of COVID-19 in India. Aims and Objectives: To assess the knowledge, attitude and practice levels of the general population during the early phase of COVID-19 pandemic. Materials and Methods: A rapid cross-sectional survey was conducted among hospital visitors of a lead tertiary care hospital. Realtime data collection from 223 respondents was done and analyzed using Stata 12.1 SE. Results: Mean age was 38.2 ±13.5 years, 68.2% were males, mean education was 12.1 ± 3.7 years and 45.3% were gainfully employed. Almost all had heard about CORONA (221, 99.1%). Adequate knowledge was present on: a) mode of spread (48.0%);b) preventive measures (70.9%). Television (77.1%), social media (60.5%) and WhatsApp (44.0%) were sources of information. Positive attitude was seen for: a) wearing a mask (83.9%);b) health-seeking (85%) on presenting with symptoms;c) Self-isolation or home-quarantine (20%). Adequate knowledge towards the mode of spread, symptoms and control measures or positive attitudes for health-seeking were found to positively translate into correct practices (p≤0.001). Conclusion: Attitudes and practices of social distancing, self-isolation or home-quarantine were not adequate to prevent the epidemic progress in India. © 2021, Indian Association of Preventive and Social Medicine. All rights reserved.
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Epidemiological models are a system of partial differential equations that model the spread of any epidemics in a closed population. These models are crucial tools for public health policy makers and medical practitioners. Reliable model descriptions often demand optimal parameter estimations. The model parameters are often estimated using numerical methods and traditional optimization algorithms. The inherent stochasticity in real-world outbreaks demand powerful optimizers for parameter estimation. Such ill-defined problems have been potential candidates for meta-heuristic optimization algorithms. The objectives of the proposed study include formulating parameter estimation as an optimization problem and finding optimal/near-optimal parameters for existing COVID models and to analyze the COVID epidemiological models (with optimal model parameters) based on their prediction efficacy. Using the parameters, forecasts for upcoming days can be produced. This paper compares epidemiological models with different machine learning models based on evaluation techniques. The top-five heavily affected states of India having the highest number of cases are considered for the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The present paper is based on a survey method while attempts have been made to assess the knowledge of the Anganwadi workers who are working as frontline workers during the widespread COVID-19 epidemic at Gosani block of Paralakhemundi municipality, Gajapati district. For the study, structured questionnaire was distributed among the total workers i.e. 350 covering 163 Anganwadi worker centres of 16 wards of Paralakhemundi Municipality. The survey was conducted from March to April 2021 at the beginning stage of the second outbreak of COVID-19. The main purpose of the study is to understand the knowledge of Anganwadi workers with regards to the COVID-19 guidelines. In this regard, focus has been made to analyse knowledge on the four general aspects of COVID-19 including stress level of Anganwadi workers because stress management during this pandemic is essential. Thus, analysis has been made on the use of information sources, knowledge on preventive practices, knowledge on maintaining social distance, knowledge on the use of personal protective equipments (PPE) and causes of stress to manage the situation. The study finds that social media like Whatsapp groups, mobile messages are the principal sources of information for them. The workers have clear knowledge on preventive measures such as hand sanitisation, maintenance of social distance and use of personal protective equipments (PPE). The job is stressful for the workers due to the less educated rural people, their carelessness towards the disease, hiding of the symptoms by the individuals and work overload © 2021, Library Philosophy and Practice. All Rights Reserved.
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Introduction: The COVID-19 pandemic has caused unprecedented crisis in the care of non-Covid patients all across the globe and care for lung cancer patients is no exception. Lung Cancer patients are at increased risks of COVID due to increased mortality from dual aggressive pathology in the lungs. Our aim was to examine the impact of COVID-19 on lung cancer therapy and factors responsible delay in optimal care during the pandemic. Methods: This study included all patients with a diagnosis of lung cancer being treated at our Institute, a tertiary level referral hospital in eastern part of India during on going pandemic. Seven developed COVID-19, confirmed by RT-PCR method (Table 1). Results: The mean age of the patients was 49 years. All the patients had metastatic lung cancer. All males had history of smoking. Hypertension was present in only one patient. Four patients (57%) died from concomitant COVID-19 at our institution.Three patients who recovered from COVID were stable. One of them (positive for ROS1) was switched over to Crizotinib and other patient resumed chemotherapy only after he had recovered from COVID-19. Most of the patients acquired COVID-19 infection during the process of evaluatio/chemotherapy and that further delayed the treatment. [Formula presented] Conclusion: The results were disappointing because more than half of lung cancer patients died due to COVID-19. The disease course of COVID-19 has been more severe in patients with lung cancers with Chemotherapy. There have been delay in resuming treatment among survivors by few weeks. Fear of aggressiveness of disease was the key factor for interruption or delay in chemotherapy. Other variable factors like stage of cancer, palliative intent of chemotherapy, ECOG status were key determinants for interruption of treatment. The decision to temporarily suspend chemotherapy while waiting for recovery from SARS-CoV-2 and then restart, was not easy due to the risk of cancer progression. The findings amplify the importance of optimizing of lung cancer care in the context of the COVID-19 prevalence. The decisions need to be taken on the basis of individual cases rather than rely on a generalized approach. In resource limited country like India diverting the existing resources to an emergency leads to compromise of routine outpatient care, especially patients with chronic illness. We need to be flexible in restarting of services guided by local COVID prevalence, while awaiting a definite management plan for COVID-19. Keywords: lung cancer, India, COVID-19, SARS-CoV-2
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Introduction: COVID-19 pandemic has led to reconsider the traditional management approach in lung cancer. Oral tyrosine kinase inhibitors (TKIs) are being preferred in oncogene addicted advanced non-Small Cell Lung Cancer. Methods: A young never-smoker woman presented with left hilar mass with left pleural effusion in February 2020. There was an incidental detection of thrombus in right main and descending pulmonary artery. She was diagnosed to have ROS1 rearranged advanced NSCLC with pulmonary embolism. She was initiated on conventional chemotherapy with carboplatin and pemetrexed along with anticoagulation. After fourth cycle of chemotherapy, although there was clinical improvement attributed to reduction of amount of effusion and disappearance thrombus, RECIST 1.1 criteria suggested the progression of disease in view of appearance of adrenal metastasis. Meanwhile, she had a history of high risk exposure with a patient of COVID- 19 in July 2020. Although she was asymptomatic, RT- PCR for SARS-COV2 came out to be positive. Results: She remained asymptomatic for next 10 days during hospitalization. Then she was started on Crizotinib and was discharged with advice on further follow up. Conclusion: Although patients on cancer chemotherapy are at high risk of severe COVID-19 diseases, asymptomatic cases can happen. The patients preferably should be shifted to or started on oral TKIs as per available expert recommendations. Keywords: ROS1, COVID-19, TKI