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During this COVID-19 outbreak, diagnostic tests were crucial in controlling its spread. This pandemic, which was caused by the SARS-CoV-2 virus, has created an urgent need for rapid diagnostic testing to enable efficient treatment and control of COVID-19. Many institutes and companies are trying to develop effective methods for detecting COVID-19. The essential indicative instrument presently utilized is reverse transcription-polymerase chain response (RT-PCR), which can have great affectability. Tragically, execution costs, time is taken, and false-negative results have limited the use of RT-PCR. Lateral flow assays (LFAs) are a type of diagnostic test that is clinically sensitive enough, might bridge various inadequacies in the present RT-PCR system, especially in low-and middle-income countries. In basic hospitals and laboratories, AuNP-LFA is a viable technique for diagnosing COVID-19, especially in emergency settings where a large number of samples must be examined quickly. This review summarizes the different diagnostic approaches for detecting the SARS-COV-2. This review thus provides guidelines for the scientists and technicians engaged in detecting SARS-COV-2. An accurate and sensitive detection system would enormously benefit in controlling this pandemic. © 2022 by the authors.
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The Internet of things (IoT) is a system of interconnected devices and linked electronic devices which can collect, transmit, and manage data without human or computer intervention. The IoT is also potential technology that transforms everyday objects into dependable and effective intelligent things. It has impacted every element of life;however, its influence on healthcare has become particularly profound because of its slicing facet shift. It is now feasible to link innovative items over the internet and provide more digitally connected world ways for special applications. In published research on IoT in health care and the COVID-19 epidemic, the potential of this platform is examined. This literature-based research might also help specialists imagine related problems and combat the COVID-19 outbreak. This study report examines the human healthcare IoT (H2IoT) state, including existing services, architecture, topology, tools, services, and healthcare applications. In modern hospitals, "IoT” technology and internet-connected digital gadgets are commonplace. Clinicians have now abandoned paper and have become entirely or partly reliant on devices linked to wireless connections and tablets. © 2023 selection and editorial matter, Durgesh Srivastava, Neha Sharma, Deepak Sinwar, Jabar H. Yousif, and Hari Prabhat Gupta;individual chapters, the contributors.
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Purpose: Underpinned by sensation-seeking theory (SST) and regulatory focus theory (RFT), this paper highlights the crucial role of adventurousness in self-protective behavior and future travel avoidance. Furthermore, this paper investigates safety-seeking tendency as a moderator and travel anxiety post-COVID-19 as a mediator. Design/methodology/approach: Data were gathered from 574 potential visitors to St. Catherine post-COVID-19 and analyzed using Smart-PLS approach. Findings: Adventurousness negatively and significantly affected travel anxiety, while the latter negatively influenced self-protective behavior and positively influenced future travel avoidance. Besides, the findings proved that travel anxiety partially mediated the adventurousness linkage with self-protective behavior and future travel avoidance. Moreover, safety-seeking tendencies dampened travel anxiety's connection with self-protective behavior and future travel avoidance. Practical implications: This paper provides valuable insights into travel research in theory and practice to revive tourist attractions post-COVID-19 in developing countries via an adventure tourism pattern. The study helps figure out how to deal with the pandemic and restore the monument of heavenly religions, St. Catherine—sacred mountain peaks, mosques, churches and many monasteries—in addition to its charming and picturesque nature. Originality/value: The current paper examines a traveler's adventurous nature and post-COVID-19 behavior when visiting St. Catherine and their behaviors related to future avoidance and self-protection. This paper adds the first investigation of travel anxiety and safety-seeking through the lens of SST and RFT theories in the Egyptian tourism context. © 2023, Emerald Publishing Limited.
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To stop the spread of COVID-19 in this outbreak, diagnostic testing is essential. Quick diagnostic tests must be employed in this pandemic, which is brought on by the severe acute respiratory syndrome (SARS)-CoV-2 virus, to successfully treat and manage COVID-19. There are several problems with the present RT-PCR system that the lateral flow assay (LFA), a kind of clinically sensitive diagnostic test, may be able to fix, especially in low-and middle-income nations. Gold nanoparticle-(AuNP-LFA) is a practical method for detecting COVID-19 in basic hospitals and laboratories, particularly in emergency situations where many samples must be quickly examined. Safe, accurate, and non-toxic diagnostic tests must be employed during the pandemic, to successfully treat and manage COVID-19. Recombinant SARS-CoV-2 nucleocapsid monoclonal antibody was employed to detect COVID-19 antigens in the presence of patients to establish a fast LFA for COVID-19. Synthesis of colloidal gold particles and antibody colloidal gold conjugates was evaluated by using UV/Vis spectroscopy. A capture line made of anti-SARS-CoV-2 antibody was coated on nitrocellulose membrane. To create the control line, goat anti-mouse IgG monoclonal antibody was coated. On a polystyrene backing board, the immunochromatographic strip was constructed in the ideal order. Using ELISA as the standard procedure, the strips' sensitivity and specificity were assessed. The results' stability and repeatability were evaluated over a 9-month period. Colloidal gold nanoparticle-based LFAs created in this study can be employed for quicker and more accurate detection of SARS-CoV-2. © 2023 Singh, et al.
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Introduction: Left ventricular (LV) thrombus is a known complication of myocardial infarction (MI) and it usually occurs in areas of poorly contracting LV muscle as a result of endocardial injury with associated inflammation. There is a high risk of embolization within 3 months among patients with MI complicated by mural thrombus and this risk is maximum during the first 1-2 weeks. We report a case series of five patients who presented with acute coronary syndrome with LV apical thrombus and treated with triple anti-thrombotic therapy of rivaroxaban, aspirin, and clopidogrel. Case Series: Our series involves 5 cases who developed LV apical thrombus after acute coronary syndrome. Four patients had anterior wall ST-elevation MI (STEMI) whereas 1 patient had inferior wall STEMI. One of the patients with anterior STEMI also had COVID pneumonitis. All of these patients received triple anti-thrombotic therapy consisting of tab Aspirin 75 mg OD, tab clopidogrel 75 mg OD, and tab rivaroxaban 20 mg OD for 3 months duration. Repeat ECHO after 3 months showed complete resolution of LV thrombus in all of our cases. Discussion: LV thrombus reported in STEMI patients is from 1.6% up to 39% in various studies. The incidence of LV thrombus is on decreasing trend as a result of modern revascularization strategies. The role of novel oral anticoagulants (NOACs) in treating LV thrombus is scant as compared to oral Vitamin K antagonists (VKAs) like warfarin. The current recommendation for anticoagulation in the presence of a LV thrombus after acute coronary syndrome is with VKAs for up to 6 months. Conclusion: Although there is uncertainty in decision-making regarding antithrombotic therapy, our case series demonstrate that triple antithrombotic therapy with NOACs results in resolution of LV thrombus without any additional bleeding events in patients presenting with acute coronary syndrome. NOACs have an advantage of not requiring PT/INR monitoring and have less bleeding complications. Further large-scale research or randomized controlled trials are needed to find the optimal therapies in such cases. © 2022 by the Author(s).
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The coronavirus epidemic began in Wuhan and has already spread to practically every country on the planet. Conravirus has a big population in India, and people are becoming infected at an alarming rate. Machine learning algorithms have been utilized to find trends in the number of active cases owing to COIVD in India and the state of Odisha in this study. The data was gathered from the WHO and studied to see if there was a link between the number of current cases, those who died, and those who recovered. The model was entirely based on multiple regression, support vector machine, and random forest which fits as an effective tool for prediction and error reduction. Based on the dataset taken from March 16, 2020, to August 20, 2020, from the ICMR website, the mean absolute error (MAE) of SVM is less for Odisha and multiple linear regression is less for India. The multiple learner regression model is able to predict number of active cases properly as its R2 score value are 1 and 0.999 for Odisha and India, respectively. Machine leaning model helps us to find trends of effected cases accurately. The model is able to predict what extent the COVID cases will grow or fall in the next 30 days which enables us to be prepared in advance and take some preventive measures to fight against this deadly COVID virus. It is observed that features are positively correlated with each other. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Introduction: It is crucial to determine possible factors associated with exacerbation of the disease due to the alarming global spread, morbidity and mortality associated with Coronavirus Disease-2019 (COVID-19). It is important to determine the co-morbidities associated with this disease which will help in better treatment of patients in time and to make amendments to management policy. Aim: To compare the clinical features, and predisposing factors (socio-demographic factors and co-morbidities) influencing the outcome in COVID-19 infected patients admitted in a tertiary care centre in the first and second wave of COVID-19 pandemic. Materials and Methods: The retrospective study was conducted at the Department of Microbiology, Dr. Shankarrao Chavan Government Medical College, Nanded, Maharashtra, India. The data was collected from the electronic resource which was maintained by the institute Integrated Disease Surveillance Program (IDSP) health record reporting database for the duration of June 2020 to August 2021. This data included patient’s demographic details (age, sex, address, contact number), other details (history of close contacts, international travel) clinical history, different types of symptoms (ICMR patient category), co-morbidities, number of patients requiring ICU admission, type of sample, the outcome in terms of death and discharge, cause of death. The analysis was done for the complete data and then for two separate durations of the first and second wave which were compared later with Chi-square test (Bivariate analysis). Results: A total of 8841 patients were involved and the majority of patients in the study were between the age group of 30-75 years, there was a predominance of males in first and second waves with 6514 (73.7%) and 5795 (58.6%) respectively. The paediatric patients had a mortality rate of 100% (n=7) found in the second wave. Fever (39%) and dyspnea (22%) were found as the commonest presentation in both waves. Gastrointestinal manifestations were observed relatively more in the second wave. The serious patients on ventilator were found to have (>91%) the highest mortality. It appeared that the highest attributable risk to severity and mortality (eight to ten times increased) was due to hypertension, diabetes and other co-morbidities. Pregnancy did not predisposed to be as a risk factor. Conclusion: Prompt management and preventive care are needed for patients with co-morbidities to avoid the exacerbation of COVID-19 as well as drug cross interactions.
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Background • Coronavirus disease 2019 (COVID-19) outbreak and lockdown has caused negative impact on mental health status of health care workers including the physiotherapists as physiotherapists are also involved in the care of patients with COVID-19 infection. • Taking care of COVID-19 infected patients can be exhaustive both physically and mentally due to involved risk of contracting the disease which may prove fatal for some. • Due to ongoing lockdown many physiotherapists are not able to work either fully or partially as per their potential. So, physiotherapists like other health care workers are not immune to mental health impacts of COVID-19 lockdown. Aim • To assess relationship between psychological impact of COVID-19 in the form of anxiety and depression, and the resilience among physiotherapists. Methods • Data were collected from total 378 physiotherapists. • Beck's depression inventory (BDI), generalized anxiety disorder 7 item (GAD-7) scale, and brief resilience scale (BRS) were used to assess depression, anxiety, and resilience respectively among the study participants. • Data were analyzed by using SPSS 15 and, Chi-sqaure, Fisher exact, and Person's correlation tests. Results • Prevalence of anxiety and depression were 37.8% and 31% respectively. • As regards the resilience, 2.9% had high, 74.3% had normal, and 22.8% had low resilience levels. • Physiotherapists who had high level of resilience had lower rates of anxiety on GAD-7 and lower rates of depression on BDI. Conclusion • Strengthening the resilience of physiotherapists can promote their mental health in longer run in face of stressful event like COVID-19 lockdown. • So, resilience strengthening programs must be the priority.
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COVID-19 spread has now nearly come to a halt, despite of daily increase in positive cases in India. It has deeply affected daily lives, public health, and the economy of the whole world. An important step in controlling COVID-19 spread is to identify the infected patients as soon as possible and treating them. There is a need for supplementary diagnostic tools apart from RT-PCR, which is easy to use and less contagious. Significant findings have proven that chest X-rays (CXR) in combination with deep learning algorithms for images, like pretrained CNNs are vital in finding features that are related to COVID-19. Using pretrained networks, so-called transfer learning can extract features from CXR images which can help detect COVID presence. In this work, CXR images were analyzed using one of the advanced CNN architectures, DenseNet201 using MATLAB. This architecture is 201 layers deep, capable to classify into 1000 classes. The last layers have been modified so that DenseNet201 can be used to properly predict COVID+VE and COVID-VE CXR images. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Oral Lichen Planus (OLP) is a chronic autoimmune inflammatory disease of the oral mucosa. The erosive variety of OLP is multifocal, ulcerative, and painful in nature. Patients with OLP have a higher risk of developing Oral Squamous Cell Carcinomas (OSCCs). A 49-year-old female patient presented to the Department of oral medicine and radiology with the chief complaint of burning sensation in the inner aspect of right cheek, since one year. A diffuse whitish area surrounded by erythematous area was present in the right buccal mucosa. Lesion was diagnosed primarily as erosive Lichen Planus (LP). She discontinued the medication and did not come for follow-up visits because of the Coronavirus Disease 2019 (COVID-19) pandemic. After 10 months she came back with pain and a diffuse swelling with indurated margin, in the same region involving right buccal vestibule and retromolar area. After incisional biopsy it was found that the lesion has progressed to squamous cell carcinoma, and the patient was referred to the Department of Oral Surgery where segmental mandibulectomy was done with placement of free fibula graft. The OLP is considered as a potentially malignant disorder and patient should be advised to avoid additional risk factors such as tobacco and alcohol and should be followed at minimum a year interval as prognosis and patient survival is directly related to stage and grade of cancer at its initial diagnosis.
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Work from home (or remote working) has become the new normal ever since the pandemic hit the world. This new normal, which represents the unison of social and technical assemblage, has been used as a backdrop in the study to explore the nature of employee job behaviour and its impact on job satisfaction. The sample consisted of employees who are working remotely from their homes in the information technology sector. The Cronbach alpha of the questionnaire was found to be .862. The results indicate that dimensions of employee job behaviour like enhanced work association, need for interaction for information exchange, and increased work responsibility were found to be highly correlated in the backdrop of work from home. Further, employee job behaviour was found to be significantly impacting job satisfaction of employees in the backdrop of work from home. Copyright © 2022, IGI Global.
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Background: Blood services have been challenged to maintain their inventory during COVID-19. An expert group issued a 'Call to Action' to all stakeholders to implement practical, multimodal principles of Patient Blood Management (PBM). Intraoperative cell salvage (IOCS) is central to PBM also for lower segment cesarean section (LSCS). Prior to April 2020, we initiated IOCS during LSCS based on risk assessment for hemorrhage and patient factors. As the pandemic broadened, we mandated IOCS to reduce blood product usage. We examined the impact of routine IOCS on the incidence and degree of post-partum anemia, transfusion, and other maternal outcomes. Methods: We conducted a single-centre before-and-after study of obstetric patients undergoing LSCS in the 2 months prior to change in practice ('usual care', n=203) and the 2 months following ('mandated IOCS', n=228). Recovered blood was processed when a minimal autologous reinfusion volume of 100 ml was expected. Data were analysed using t-tests for normally distributed continuous data (else Mann Whitney U-tests) and Chi Squared test for frequency data. Post-operative iron infusion and length of stay (log-transformed) were modelled using logistic and linear regression, with inverse probability weighting to account for potential confounders. Results: Maternal demographics were similar between groups. (Table 1). More emergency LSCSs occurred in the usual care group. Compared to the Usual Care group, post-operative Hb was higher, and anemia cases, frequency of IV iron and length of stay were all lower in the Mandated IOCS group (Table 2). After statistical modelling, rates of post-partum iron infusion were significantly lower in the Mandated IOCS group but there was no difference in length of stay. Conclusion: Routine cell salvage provision during LSCS resulted in increased post-partum Hb and reduced anemia prevalence. This translated into a reduction in post-partum iron infusions. IOCS for LCSC may contribute to an avoidance of limited blood service products. (Table Presented) .
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Introduction: Lockdown and stay home order has been imposed on people in many countries including India to prevent the community transmission of COVID-19 pandemic. However this social restriction led to disturbed daily routine and lifestyle behaviour that is needed to be attended for proper therapeutic management of overall health during such crisis. The impact of lockdown on the most apparent behavioral changes viz. sleep-wake behaviour, major meal timings, and digital screen duration of Indians were investigated. In addition the effects of gender and age were explored. Methods: After seeking permission from Ethical Institution, an online questionnaire based survey was circulated within India in the first week of May, 2020 for which total 1511 male and female (age ≥18 years) subjects participated. The sleep-wake behavior observed were sleep-wake timings, sleep duration, mid sleep time (MST) as function of lockdown, and social (lockdown) jetlag (SJL = MST before lockdown-MST during lockdown). Results: The sleep onset-wakeup and meal times were significantly delayed during lockdown, which was more pronounced in younger age group. The sleep duration increased, specifically in young individuals during lockdown. Females showed more delayed sleep onsetwaking times and first meal timing with longer sleep duration during lockdown. Increased digital media duration was observed in all age groups, primarily in males. The younger age group and specifically female reported higher SJL and delayed MST. A positive association was obtained between sleep duration & first meal time, and SJL & major meal timings/screen duration, and a significant negative relationship of sleep duration and SJL with age. Conclusion: The study shows delayed sleep-wake schedule, meal timings and increased digital media duration among Indians during COVID-19 lockdown compared to before lockdown. Also, gender and age emerged as important mediating factors for this alteration. The pandemic has given opportunity to sleep more and compensate for the sleep. In spite of that, the higher social jetlag in young age group and female showed the compromised sleep and maladaption with societal timing. These findings have applied implications in sleep health during longer social isolation conditions and for proper therapeutic management.
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With the forced reboot of our lives due to the COVID-19 pandemic, our interpersonal relationships are nowhere yet everywhere. However, opportunities for initiating or maintaining friendships and romance in the physical world have dwindled. Within the context of India where multiple realities exist, the question arises - what is the future of these relationships? In this paper, we present the outcomes of a workshop looking at the future of relationships using design fiction. Participants worked in small teams to create scenarios that critically consider the future of love, friendships, and romance within the Indian context. Through the lenses of criticality, empowerment, and value creation, we examine the design scenarios and the design process including criticality of the designs, empowering experiences of the participants, and the perceived value gained from participating in such a workshop. Our findings indicate the potential of design fiction to allow participants to step out of their comfort zone into a critical stance in discussing love and intimacy. Based upon our findings, we discuss implications for design research, practice, and education. © 2021 Owner/Author.
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We propose a novel approach that integrates machine learning into compartmental disease modeling (e.g., SEIR) to predict the progression of COVID-19. Our model is explainable by design as it explicitly shows how different compartments evolve and it uses interpretable encoders to incorporate covariates and improve performance. Explainability is valuable to ensure that the model’s forecasts are credible to epidemiologists and to instill confidence in end-users such as policy makers and healthcare institutions. Our model can be applied at different geographic resolutions, and we demonstrate it for states and counties in the United States. We show that our model provides more accurate forecasts compared to the alternatives, and that it provides qualitatively meaningful explanatory insights. © 2020 Neural information processing systems foundation. All rights reserved.
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Introduction: Novel coronavirus disease 2019 (COVID-19) has caused a rapidly evolving confused state. Like other front line health care workers (HCWs), physiotherapists are also providing their valuable services to COVID-19 patients. Caring COVID-19 patients is exhaustive both physically and mentally due to associated risks. So physiotherapists are prone to development of psychological problems like depression and anxiety during COVID-19 pandemic and lockdown. Main objective of study was to assess relationship between psychological impact of COVID-19 lockdown in the form of depression and anxiety, and the resilience among physiotherapists across India. Methods: Present cross-sectional online survey was conducted in the rural tertiary health-care center from Maharashtra, India, with sample size was 378. Data were recorded with consecutive sampling method from study participants on socio-demographic details, Beck's Depression Inventory (BDI), Generalized Anxiety Disorder 7 item (GAD - 7) scale and Brief Resilience Scale (BRS). After the collection, data were analyzed using version 15.0 of SPSS software, Chi-square test, Fisher's exact test and Pearson's correlation test. Results: Prevalence of depression and anxiety among study participants were 31% and 37.8% respectively. As regards depression, 56 (14.8%) had mild, 23 (6.1%) had borderline, 29 (7.7%) had moderate, 8 (2.1%) had severe and 1 (0.3%) had extreme depression. As regards anxiety, 96 (25.4%) had mild, 32 (8.5%) had moderate and 15 (3.9%) had severe anxiety. As regards resilience level, 86 (22.8%) had low, 281 (74.3%) had normal and 11 (2.9%) had high resilience. Those respondents who had high resilience had lower rates of depression on BDI and lower rates of anxiety on GAD-7. Conclusion: Programs to strengthen resilience should be priority. In longer run, increasing resilience of physiotherapists can have mental health promoting value during the stressful event of COVID-19 lockdown.