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COVID-19 onslaught has led to widespread morbidity and mortality globally. Another major concern, especially in developing countries like India, has been the development of fungal superinfection and colonization of other pathogens in hospitalized COVID-19 patients. Even though an armamentarium of repurposed, antiviral, anticytokine, and antifungal drugs is available to manage the disease progression, no single drug and/or therapy has provided positive clinical outcomes with efficacy and affordability. Therefore, it is imperative to explore innovative approaches for standalone treatment and/or adjunct therapeutic regimes based on our current understanding of disease prognosis. Low-income and emerging economies have less resources to protect themselves against the COVID-19-induced health and economic crisis. With the continuously evolving nature of coronavirus, a cost-effective strain independent mechanism that could be delivered easily even in a nonhealthcare setting is an urgent need of the hour. Methylene blue appears an apt candidate as it is an FDA-approved safe drug that is economically viable and easily available. Since MB has a long-standing history of being used in clinical setup for diverse medical applications and possesses intrinsic anti-inflammatory, anticytokine, and antifungal properties, this study analyzes prospects of its use in the management of COVID-19. Paradox and prospects of MB applications for the management of COVID-19, with or without fungal superinfections, are also discussed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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Intro: Invasive aspergillosis of CNS is a severe form of aspergillosis & is associated with high mortality. Most of these cases are suspected & diagnosed in neutropenic patients. We hereby describe a series of 15 patients with CNS aspergillosis in non-neutropenic patients from a tertiary care hospital in India. Method(s): All patients with clinical & radiological features suggestive of CNS aspergillosis were screened for microbiological evidence of invasive aspergillosis, either by demonstration of hyphae by microscopy or histology, culture or galactomannan assay. Patients demographic details, clinical features, risk factors, diagnosis, management & outcome details were documented. Finding(s): A total of 15 patients were found to have CNS aspergillosis, 5 isolated CNS infections & 10 showing concomitant CNS & pulmonary aspergillosis in one between January 2021 to July 2022. The average age was 41.46+/-14.6y, with majority being male. Among the risk factors, most common ones were fungal sinusitis (46.6%), steroid use (40%), COVID-19 (33.3%). One patient had history of endoscopic sinus repair, another had h/o lung abscess. Most common symptoms of CNS aspergillosis were headache (73.3%), fever (60%), altered sensorium (53.3%) & seizures (47.6%). Radiologically, the common findings included ring enhancing lesion, s/o cerebral abscesses were observed in four patients. Direct microscopy s/o fungal hyphae were reported in 5 patients, with 4 culture positives. Average serum galactomannan was 1, while CSF galactomannan showed better sensitivity with mean CSF galactomannan being 2.53. Almost all patients were treated with Voriconazole based on weight, but showed high mortality of 60% even after initiation of therapy. Complete resolution were seen in only two patients, while 4 patients remaining static in improvement during 6 months follow up. Conclusion(s): Invasive CNS aspergillosis must be suspected even with nonneutropenic patients with newer emerging risk factors like steroid use, COVID-19 & h/o fungal sinusitis presenting with clinical & radiological manifestations.Copyright © 2023
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This book explores the challenges and precarity of higher education post-pandemic, explicitly focusing on higher education in emerging countries. Looking beyond the pandemic, the editors and contributors provide a holistic view of the residual legacies of global health crises like COVID-19 in developing countries. The book calls for the need to reimagine, reevaluate and reposition the higher education system: exploring the challenges experienced by students, staff, administrators and other stakeholders. Bringing forth insights from researchers, practitioners and senior leadership, the book shares theoretical and practical insights on dealing with the aftermath of a pandemic and what can be learned for the future. It will be of interest and value to researchers, practitioners and leaders who wish to understand a develop new approaches for their teaching and management post-pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.
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Coronavirus disease is a contagious respiratory ailment that has spread significantly around the world. Most cases of COVID-19 are spread from person to person by coming into contact with respiratory droplets that are released when an infected person coughs or sneezes. In this manuscript, we have highlighted the possible transmission of COVID-19 through food, water, air and paper. In the case of food, we have extensively covered the transmission of COVID-19 through meat, frozen foods, food packaging and food market along with the incidences worldwide. In the nextsection, we have highlighted the different components of air which are responsible for the transmission and also covered its relation with PM 2.5 incidence. The SARS-CoV-2 was isolated from sewage water/wastewater of various countries namely the United States, India, Australia, Netherlands and France signifying that wastewater can be a mode of virus transmission. The paper circulation by the infected COVID-19 patients can also be a virus conveyance route. It can be concluded that SARS-CoV-2 can therefore be transmitted indirectly through food via the workers involved in food packing or food marts.By following general safety precautions (wearing masks, using hand sanitisers, cleaning and disinfecting contact surfaces, and avoiding close contact), heating and using chemicals like ethanol (67-71%), sodium hypochlorite (0.1%) and hydrogen peroxide (0.5%) on environmental surfaces, along with vaccination, it is possible to reduce the spread of the SARS-CoV-2 virus.Copyright © 2023 The International Journal of Pharmaceutical Research and Allied Sciences (IJPRAS).
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In view of diminishing the transmission of the coronavirus pandemic (COVID-19) in the community, an essential intervention strategy has been the consideration of public health measures. However, at the present scenario, these measures can be considered as the only available tools for mitigation of this virus impact. An attempt was made in this study with the use of grey technique for order of preference by similarity to ideal solution (Grey-TOPSIS) method for prioritizing the precautionary measures for the public health in order to enable taking appropriate steps by the general public of India to protect them from virus transmission. © 2022 IGI Global. All rights reserved.
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In this work, a Twitter data-set was utilized to do sentiment analysis of people's thoughts on the corona-virus (COVID-19) period, which is a major concern throughout the world these days, impacting a number of nations. To better understand people's feelings about the epidemic, machine learning approaches (mla) and sentiment methodology such as Bert Model (BMO), Naive_Bayes_Bernoulli (nBB), Multi Nominal Naive_Bayes (mnNB), Support_ Vector_Machine (svM), Logistic_Regression (IR), Gradient_Boosting_ Classifier (gbR), Decision Tree Classifiers (dtC), K N eighbors(knN) and Random Forest Classifier (rfC) have been presented in this work. Also, we have classified that which Classifiers provides highest accuracy. Additionally, in this paper, we also analysis from the data set, the most that has been tweeted (hashtag), positive, negative as well as neutral with data visualization in the Covid-19 epidemic time. © 2022 IEEE.
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As a major virus outbreak in the twenty-first century, the Coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented risks to global mental health. Because of the severity of the virus, people were forced to isolate themselves and confine themselves to their homes. This was linked to people's inability to work, seek help from loved ones, and participate in their communities. Stressors that contribute to anxiety and depression include loneliness, fear of infection, suffering and death for oneself and loved ones, bereavement grief, and financial worries. As a result, covid 19 is a source of psychological distress. This paper investigates the impact of these stressors on all age groups in society, including today's youth, the elderly, and even health workers. Because Buddhism has a longstanding experience with medicine and preaches calmness and acceptance of fear, it is not surprising that Buddhist ideas come to the aid of those in need during times of crisis. However, it receives little attention. Thus, this paper focuses on theories such as mindfulness meditations, engaged Buddhism, and cultivating compassion, all of which can aid in increasing positive emotions and thus reducing stressors. Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.
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Background. Invasive aspergillosis(IA) is known to occur in immunocompromised patients including neutropenic patients. But there has been a trend of increasing cases in non-neutropenic host with the emergence of newer risk factors like DM, cirrhosis etc. The aim of this study was to evaluate the clinical features & risk factors of IA in non-neutropenic patients & to look at the clinical utility of galactomannan in diagnosis of IA. Methods. This was a prospective observational study which included the suspected cases of IA, based on the clinical & radiological criteria. Patients with haematological & solid organ malignancy were excluded. In patients with suspected Invasive pulmonary aspergillosis (IPA), serum & BAL, while in patients with suspected CNS IA CSF & serum samples were sent for galactomannan analysis (Platelia ELISA). The clinical features, risk factors, outcomes were analysed. Results. We screened 243 patients with suspected IA, of which 49 nonneutropenic patients with IA (16 Proven & 33 Probable cases) were included. The mean age was 47.8 years. Of all IA cases 69.5% (n=34) were IPA, 20.4% (n=10) were CNS aspergillosis & 10.2% (n=5) showed disseminated form of IA. The common symptoms included Fever (71.4%), cough (71.3%), expectoration (44.7%) & dyspnoea (59.1%) in IPA, while in CNS aspergillosis, presented with fever (73.3%), altered sensorium (53%).The predominant risk factor included previous TB, DM, COVID-19. The radiological manifestations in IPA included the typical cavity (40.4%, n=17), Centrilobular nodules with tree in bud appearance in 56.5% (n=23). The CNS aspergillosis was associated with ring enhancing lesion (41.6%, n=5) with leptomeningeal enhancement (50%, n=6), while cerebral abscess was seen in 16.6% (n=2) patients. The positivity of galactomannan were 24.4%, 91.3% & 87.5% in serum, BALF & CSF respectively. Culture positivity & Direct smear positivity was 18.3% & 28.5% respectively. The overall mortality was 20.4%. Complete response in 3 months follow-up period was seen in 69.3% patients. Conclusion. The clinical manifestations of IA in non-neutropenic are diverse & nonspecific. Also, culture & direct microscopy lack sensitivity, hence diagnostic markers like Galactomannan can be used for early diagnosis of IA in patients with newer emerging risk factors.
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In today's era, the healthcare domain is highly influenced by the widespread applications of big data analytics and artificial intelligence (AI). These technologies are being used for high-level molecular research, drug development and predictive analysis of various diseases. Currently, big data and AI are being used in several aspects against corona virus disease 2019 (COVID-19) pandemic. Literature as well as the genomic studies available for various strains of severe acute respiratory syndrome coronavirus 2, suggest how the concepts of big data and AI are being implemented to understand COVID-19 better which can assist with its antiviral drug development as well as vaccine production. These concepts of big data and AI have effectively helped with the contact tracing, epidemiology, molecular studies, medical diagnosis and treatment of COVID-19 that can help against future pandemics. Similarly, with this computational approach, various disease patterns are being recognized in different types of cancer. AI plays a major role in biomarker identification for disease progression to recognize novel hallmarks of cancer as well as in the predictive analysis of the disease. This allows an early-stage diagnosis of cancer which leads to a better prognosis. With an interdisciplinary collaboration of big data and predictive analytics, patient risk can be analyzed and estimated concerning the new treatment options available for cancer. Such high-throughput technologies have improvised the diagnosis and treatment options available for various diseases. With the advancements in science and technology and the introduction of computational models, public health and medicine have been completely revolutionized for the betterment of society. © 2023 Walter de Gruyter GmbH, Berlin/Boston.
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Credit Card Fraud is one of the major threads in the financial industry. Due to the covid-19 pandemic and the advance in technologies, the number of users is increasing, with the increased use of credit cards. Due to more use of credit cards, Fraud cases also increase day by day. The research community striving hard to explore myriad credit card fraud detection techniques, but changes in technology and the varying nature of credit card fraud make it difficult to develop an effective technique for the detection of credit card fraud. This research work used a real-world credit card dataset. To detect the fraud transaction within this dataset three machine learning algorithms are used (i.e. Random Forest, Logistic regression, and AdaBoost) and compared the machine learning algorithms based on their Accuracy and Mathews Correlation Coefficient (MCC) Score. In these three algorithms, the Random Forest Algorithm achieved the best Accuracy and MCC score. The Streamlit framework is used to create the machine learning web application. © 2022 IEEE.
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The COVID-19 pandemic has intensely impacted humanities globally. This scenario has laid down many protocols and procedures to mitigate the risk and thus ensure the safety of individuals. The pandemic has augmented the demand of contactless mechanism especially in the public places.This paper presents novel machine learning and internet of things based solution for contactless entry to premises following mandatory checks at the entry as per COVID-19 protocols. The proposed model senses the body temperature and detects the face mask of an individual prior to entry. The entrance is allowed through contactless opening of the gate only if the body temperature is within prescribed limits and the face mask is properly put on.The current work uses a machine learning model for detection of the face mask which uses the real time image during screening;the algorithm is trained using the data sets with and without mask. The temperature screening is carried out with temperature sensor connected to the Arduino processor.A prototype model using Arduino is prepared based on the inputs received from the temperature sensor and the Machine learning Algorithm. The gate shall open for entry if a person has normal body temperature and wearing a proper face mask, else, will be restricted through a custom alert. Also a track of the number of persons entering is monitored and sent to a web portal on real time basis to account for overcrowding.The model ensures a contactless check at the entry, thus, controlling the outbreak of the Covid situation. © 2022 IEEE.
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Mucormycosis is a fungal infection primarily affecting immunocompromised individuals. We have observed sudden rise of mucormycosis cases in post COVID 19 patients. Here we have reported 600 cases of mucormycosis associated with COVID 19.Liposomal amphotericin B was compared with conventional amphotericin B for antifungal therapy in mucormycosis, double-blind, multicentre trial.The two drugs were equivalent in overall efficacy. However, the liposomal amphotericin B treatment group had fewer proven fungal infections, fewer infusion-related side effects and less nephrotoxicity. Patient data from that study were analysed to compare the pharmacoeconomics of liposomal versus conventional amphotericin B therapy.Data from 600 patients were collected and analysed. Hospital costs from first dose were significantly higher for all patients who received liposomal amphotericin B. The mean duration of therapy was 10.8 days for liposomal amphotericin B (300 patients) and 10.3 days for conventional amphotericin B (300 patients). The composite rates of successful treatment were similar (50 percent for liposomal amphotericin B and 49 percent for conventional amphotericin B. The outcomes were similar with liposomal amphotericin B and conventional amphotericin B with respect to survival (93 percent and 90 percent, respectively. With the liposomal preparation significantly, fewer patients had infusion-related fever (17 percent vs. 46 percent), chills or rigors (18 percent vs. 55 percent), and other reactions, including hypotension, hypertension, and hypoxia. Nephrotoxic effects (defined by a serum creatinine level two times the upper limit of normal) were significantly less frequent among patients treated with liposomal amphotericin B (18 percent) than among those treated with conventional amphotericin B (37 percent, p<0.001). Copyright © 2022 Ubiquity Press. All rights reserved.
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Healthcare workers are the backbone of society, serving and aiding people with biological illnesses. They are the most vulnerable part of society, as there is no work-from-home option available to the health sector workers during the pandemic. The study aims to investigate the issues faced by women working in the health care sector as nurses, doctors, etc., during the pandemic COVID19. The pandemic increased the workload of women as a whole and particularly women working in the health sector. The paper focuses on issues and challenges faced by women working in the healthcare sector during the pandemic, revealing the number of hours they had to work, how they got quarantined, the issues they faced while working, the challenge of stealing time to be spent with family and the nightmare of coping the truth that the virus might be transmitted to their children and other family members, the fear of working in coronavirus ward, the issues faced in wearing the double mask and personal protective equipment. It would also highlight the issues and struggle they had to go through while working in the healthcare sector as a woman. © 2022 Elsevier Inc. All rights reserved.
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COVID-19 has brought lives to a standstill. Business and educational institutions all over the world have adopted the “remote learning” strategy so that there are no interruptions in business or education. There are a good majority of female teachers in India and majorly everyone is facing problems while teaching online. During this pandemic, when the workload of almost all other sectors decreased significantly, the education sector is the one that has been working full time. With immense pressure from the institute and an increase in working hours, teachers are juggling between their professional and personal life resulting in the problem of a time crunch in their personal lives. This paper discusses the issues being faced by teachers during the pandemic time with special reference to teachers having children of different ages. © 2022 Elsevier Inc. All rights reserved.
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The recent developments in the field of deep learning have enabled the efficient diagnosis of medical imaging for determining a broad set of diseases. To reduce the spread and impact of the pandemic (COVID virus), machine learning techniques can be used to diagnose and predict the disease using chest X-ray images. In this research, we present an approach using Siamese Convolutional Neural Network (SCNN) to classify chest x-ray images into four classes, namely pandemic, Severe-COVID, Pneumonia and Normal. We present a comparative study between the performance of our Siamese network and other pre-trained CNN architectures i.e. VGG-16 and ResNet50 in this research. The model performance is tested by merging two publicly available datasets: COVID-Chest-Xray dataset and Chest X-Ray Images (Pneumonia). We achieved an accuracy of 98% on Siamese ResNet50 which gives the best performance in contrast to 95% on VGG-16, 93% on ResNet50 and 96% on Siamese VGG-16. © 2022, International Organization on 'Technical and Physical Problems of Engineering'. All rights reserved.
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Today, digital content is dominated by interactive and immersive technologies (Mckinesy 2019). Brands are rapidly adopting technologies such as Augmented Reality (AR) for virtual product experiences (Pantano 2015). AR refers to the superimposing of the digital content on consumers' real-world contexts, thereby bridging the gap between physical and virtual purchase situations (Mealy 2018). Two popular AR-based applications are Ikea place (furniture) and Youcam Makeup (cosmetics). At present, AR is on a growth trajectory, as immersive technology growth (pre-COVID-19) was pegged to exceed $55 billion by the year 2021(Research and Markets 2019). Existing research on AR focuses on user experiences, customer engagement, brand attitudes, and purchase intentions (Hilken et al. 2017;Kim and Hall 2019;Park and Yoo 2020;Scholz and Smith 2016). As technology increasingly intertwines with consumers' lives and decisions, it is essential to continue expanding the research into the lived experience of AR-mediated shopping (Chylinski et al. 2020). Hence this paper focuses on the self-augmentation experiences through the virtual product try-on apps. The virtual try-on apps let consumers try out virtual replicas of actual products (cosmetics, apparel, jewelry, etc.) on their real-time images, captured via tablet or smartphone-based selfie cameras. The context of the study is AR virtual try-on apps in the cosmetics industry, owing to their popularity and adoption by consumers. Based on the concepts of extended self, consumption experiences, self-referencing theory, and mental imagery (Belk 1988;Holbrook and Hirschman 1982;Lutz and Lutz 1978;Rogers et al. 1977), this study follows an exploratory approach using netnography and in-depth interviews. The initial findings suggest that using AR try-on tools helps consumers choose the products that enable them to craft unique self-expression rather than conforming to societal stereotypes. Further, AR facilitates experimentation with new consumption patterns, irrespective of age and gender, giving opportunities for consumers to embrace their unique interests. Consumers save their self-augmented images to share among connections via social media, indicating digital expression before actual purchase and use. Thus AR provides an opportunity for brands to be a part of consumers' virtual selves before actual consumption through digital expression. The study's findings open up new avenues for brands to be a meaningful part of consumer extended selves and the role of AR in enabling consumers to express their “true selves” which may be seen as more of an aspirational self given enhancements of face shape and eye size commonly employed in these apps. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The whole world is struggling to live with COVID-19 and even a single step of technology revolution can help in dealing with this pandemic. Artificial Intelligence and Machine Learning approaches are being used by the researchers around the globe to completely understand and address this situation. In this Corona crisis, companies are trying to implement this AI and ML techniques in various fields ranging from manufacturing, resource management, remote monitoring etc. On the other hand, ML approach is being used by the researchers for supporting healthcare related issues arisen due to COVID-19. © 2021 Scrivener Publishing LLC.
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It is a well-known fact that consumers may gain significant benefits from the effective use of IoT in pandemic and post-pandemic settings. Security vulnerabilities can be seen in the ever-increasing Internet of Things (IoT) ecosystem from cloud to edge, which is crucial to note in this particular circumstance. Most merchants, even luxury stores, have failed to implement robust IoT cyber security procedures. Therefore, the researchers sought to put forth secondary research methodologies to bring forward efficient scrutiny regarding this particular issue to properly comprehend the influence of IoT in various devices, including a smartwatch, power displaying metre, brilliant weight showing gadgets and many more. The secondary research approach allowed the researchers to collect a large quantity of data quickly, acquiring a wide range of possible solutions for security and privacy issues in Consumer IoT (CIoT) devices. Secondary research also will enable scholars to compare and contrast several papers' philosophies and research findings to get a quick conclusion. To gather information, the researchers used publications and the internet efficiently. In this situation, it helped to save a significant amount of time. Findings suggested that vulnerabilities occur in smart IoT gadgets, including the intelligent power consumption metre and brilliant weight displaying widget, due to their low-standard and conventional security system. Thus, this paper has suggested possible solutions to protect IoT devices against phishing and theft attacks. © 2022 by Dr Avinash Rajkumar, Pankhuri Agarwal, Dr Mohit Rastogi, Dr Vipin Jain, Dr Chanchal Chawla and Dr Manoj Agarwal.
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BACKGROUND: COVID19 outbreak has become a pandemic worldwide. There has been a fairly high rate of clinical recovery among Covid patients but complete resolution or sequelae in terms of radiological findings need to be studied. AIM OF THE STUDY: 1. To understand the common pulmonary sequalae, time taken for complete resolution and factors affecting the resolution process in covid-19 patients who have been discharged after recovery, with Chest HRCT follow up. MATERIAL AND METHODS: This is an observational study which included a total of 100 discharged patients diagnosed with covid-19 by RTPCR at Index Medical College, Hospital & Research Centre, Indore-MP-India, from March 15 to June 30-2021.All the patients underwent an initial chest CT scan done 3-5 days after the onset of symptoms,followed by serial CT scans done at discharge and at 1st, 2nd and 3rd weeks after discharge. The radiological characteristics and patterns on CT chest were studied and a CT severity scoring was done for all the scans. RESULTS: GGO were the most common pattern seen (88%) on chest CT at discharge followed by fibrotic bands (61%) with the right lower lung (85%) most commonly involved.61% of patients showed complete resolution at the end of 3rd week after discharge indicating that COVID 19 induced pulmonary damage is reversible in majority of cases with no long term sequalae. However 39 patients demonstrated residual abnormalities. Older patients are at high risk for residual pulmonary lesions and there is no gender predilection. Patients having comorbidities like hypertension, diabetes or bronchial asthma were not at a higher risk of developing pulmonary sequalae. CONCLUSION: The resolution of most lesions by 3 weeks after discharge implies gradual resolution of inflammation with re-expansion of alveoli and perhaps the reversible nature of the lesions of Covid-19.