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Aim: To study the various presentations and manifestations of complicated rhinosinusitis in COVID era- ranging from bacterial rhino sinusitis to invasive fungal rhino sinusitis. Methods: Design-A retrospective observational study was carried out from March 2020 to May 2021. Setting-Tertiary care hospital subjects—all COVID-positive patients who had paranasal sinus involvement. Methods-Patients were evaluated based on their symptomatology profile. Fungal stains and culture were carried out for all. They underwent Magnetic resonance Imaging and Computed Tomography scan on case-to-case basis, apart from routine nasal endoscopy. All were managed both medically and surgically depending upon their diagnosis. The natural course including outcomes, was studied, documented and analyzed. Results: Out of 496 patients presenting with sinonasal disease, 126 were COVID-positive, 16 patients had complicated rhino sinusitis, of which 4 patients had complicated rhinosinusitis with intraorbital, intracranial or combined complications. All patients were managed successfully with combined medical and surgical approach. Twelve patients had invasive mucormycosis with overall mortality rate of 37%. Conclusion: Complicated sinusitis was encountered in COVID-positive patients either when they were being actively treated for COVID-19 or as part of post-COVID sequalae. Though rhino-orbito-cerebral mucormycosis constituted the major disease burden in such patients but the possibility of bacterial rhino sinusitis with or without complications must also be kept in mind while evaluating such patients. We must remember every complicated rhinosinusitis in COVID-positive patient may not be mucor and manage appropriately. © 2023, The Author(s).
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The challenges faced by the Indian Aviation market are complex and perplexing for the decision-makers in this sector. This industry has faced debilitating losses during COVID-19 pandemic times. The travel restrictions have been gradually lifted worldwide, and therefore the airlines can expect to witness a growth in domestic and international air passenger traffic during 2021-22. The losses incurred during the pandemic times need to be recovered. Thus, there is an urgent need to strategically plan and improve the operations so that the airlines can perform efficiently and earn revenues. Airlines in India work under heterogeneous environments depending upon their segment of operations. Some of the airlines operate for only domestic passengers and others for both domestic and international passengers. Therefore these airlines must be assessed as per their homogeneous peer group to obtain substantial results. The proposed study has discussed an approach to study airlines' operational performance using hierarchical categorical data envelopment analysis (DEA). The airlines have been categorized based upon their segment of operations. The efficiency of the airlines has been evaluated for the period 2014-19 as per their categorical input value. Window analysis has also been performed for a meaningful analysis of the results. The outcomes of the study will be helpful for policymakers to improvise the present working model of the industry and alleviate their performance.
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The chapter provides for developing an understanding of the various aspects of employee engagement, namely the concept, levels, and techniques for measuring employee engagement during and after COVID-19. It explores employee engagement practices and its implications in some selected organizations during and after the pandemic. It highlights the most affected dimensions of employee engagement. Additionally, this chapter maps the practices followed by various industry organizations and presents these practices as a way forward to overcome the hurdle of keeping employees engaged in the new normal. The findings indicate that the organisations appeared to have made considerable changes in their employee engagement strategies and have integrated technology at a large scale to suit the remote work environment and emotional well-being as well. © 2022, IGI Global.
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Background: Coronavirus disease 2019 (COVID-19), a global pandemic which undoubtedly hit the whole world so hard. There have been multiple waves across the globe of varying time, duration, and intensity, India has also witnessed two waves sweeping the entire nation. The second wave had startling intensity with massively increased oxygen requirement, intensive care unit (ICU) admissions. The effect was even more pronounced in the pregnant women as there was increased maternal morbidity and mortality. However, there are limited reports on the impact of COVID-19 during pregnancy. Objective: This study is aimed at highlighting the variance in clinical profile of pregnant patients in first and second wave of COVID-19 in India. Materials and methods: A retrospective observational comparative hospital-based study was conducted in a tertiary care hospital in Delhi during the two waves of COVID-19. The first wave in India lasted from May 2020 to October 2020, and the second wave lasted from April 2021 to June 2021.We obtained the medical records and compiled clinical and outcome data for all pregnant patients, who were admitted in the Department of Obstetrics and Gynaecology of our Hospital during the first and second wave of pandemic with a laboratory-confirmed report of SARS-CoV-2. Results and conclusion: The second wave definitely saw more number of symptomatic patients, an increase in symptom of shortness of breath, increase in oxygen requirement, ICU admissions, marginally increase lower segment cesarean section (LSCS) rates and associated comorbidity such as hypertensive disease of pregnancy.
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According to the World Health Organization, the coronavirus outbreak poses a daily threat to the global health system. Almost all countries' health resources are insufficient or unequally distributed. There are several issues, such as a lack of health care workers, beds, and intensive care units, to name a few. The key to the country's health systems overcoming this epidemic is to use limited resources at optimal levels. Disease detection is critical to averting an epidemic. The greater the success, the more tightly the covid viral spread may be managed. PCR (Polymerase chain reaction) testing is commonly used to determine whether or not a person has a virus. Deep learning approaches can be used to classify chest X-RAY images in addition to the PCR method. By analyzing multi-layered pictures in one go and establishing manually entered parameters in machine learning, deep learning approaches have become prominent in academic research. This popularity has a favorable impact on the available health datasets. The goal of this study was to detect disease in persons who had x-rays done for suspected COVID-19 (Coronavirus Disease-2019). A bi-nary categorization has been used in most COVID-19 investigations. Chest x-rays of COVID-19 patients, viral pneumonia patients, and healthy patients were obtained from IEEE [17] (Institute of Electrical and Electronics Engineers) and Kaggle [18]. Before the classification procedure, the data set was subjected to a data augmentation approach. These three groups have been classified through multiclassclassification deep learning models. We are also debating a taxonomy of recent contributions on the eXplainability of Artificial Intelligence (XAI). © 2022 IEEE.
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The COVID-19 pandemic has birthed a wealth of information through many publicly accessible sources, such as news outlets and social media. However, gathering and understanding the content can be difficult due to inaccuracies or inconsistencies between the different sources. To alleviate this challenge in Australia, a team of 48 student volunteers developed an opensource COVID-19 information dashboard to provide accurate, reliable, and real-time COVID-19 information for Australians. The students developed this software while working under legislative restrictions that required social isolation. The goal of this study is to characterize the experiences of the students throughout the project. We conducted an online survey completed by 39 of the volunteering students contributing to the COVID-19 dashboard project. Our results indicate that playing a positive role in the COVID-19 crisis and learning new skills and technologies were the most cited motivating factors for the students to participate in the project. While working on the project, some students struggled to maintain a work-life balance due to working from home. However, the students generally did not express strong sentiment towards general project challenges. The students expressed more strongly that data collection was a significant challenge as it was difficult to collect reliable, accurate, and upto-date data from various government sources. The students have been able to mitigate these challenges by establishing a systematic data collection process in the team, leveraging frequent and clear communication through text, and appreciating and encouraging each other's efforts. By participating in the project, the students boosted their technical (e.g., front-end development) and nontechnical (e.g., task prioritization) skills. Our study discusses several implications for students, educators, and policymakers.
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The Latest emergency issues are impacting the world as a result of the new 2019 corona virus rise and pandemic. It has spread to the worldwide and afflicted people to Covid-19. The virus transmitting has regarded as being transmitted by individuals who find it a convenient disease explosion. While coughing and sneezing the infection spreads from the infectious droplets. Although in the air, these droplets will still survive and transfer the virus on to humans. In worldwide, robots have been used to alleviate the proliferation of new corona virus infections, COVID-19 with food preservation, food supplies, sanitation tasks, spraying disinfectant, temperature monitoring, hand sanitizers distributing, work on sensitizing, etc. For fast strategizing, that are considered hazardous for human beings. This paper discuss the difficulties and opportunities associated with using humanoid robots to minimize the risk of spread of COVID-19 in.public healthcare. The primary application of humanoid robots is the minimization of individual interaction in public places, and the provision of containment to hygiene, disinfectants and helping. The following discussion aims to underline the value of humanoid robot's purposes in specific and to link their use as the COVID-19 perspectives. Throughout the testing, review and diagnosis of a vulnerability and for subset of events, artificial intelligence plays a crucial role. It may be used during potential for the forecast of events but also to record the number of alternative cases, restored instances and deaths. Technology based on artificial intelligence is being used to provide outstanding services such as the detection and substitution of drugs for the care of employees by robotics for the provision of prescriptions and nutrition in clinics. It also disinfects the substances in response to the spread of Covid-19. © 2022 Author(s).
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Background: Long-COVID syndrome is now a real and pressing public health concern. We cannot reliably predict who will recover quickly or suffer with mild debilitating long COVID-19 symptoms or battle life-threatening complications. In order to address some of these questions, we studied the presence of (post covid) symptoms and various correlates in COVID-19 patients who were discharged from hospital, 3 months and up to 12 months after acute COVID-19 illness. Methods: This is an observational follow-up study of RT-PCR confirmed COVID-19 patients admitted at 3 hospitals in north India between April – August 2020. Patients were interviewed telephonically using a questionnaire regarding the post-COVID symptoms. The first tele-calling was done in the month of September 2020, which corresponded to 4- 16 weeks after disease onset. All those who reported presence of long COVID symptoms, were followed-up with a second call, in the month of March 2021, corresponding to around 9-12 months after the onset of disease. Results: Of 990 patients who responded to the first call, 615 (62.2%) had mild illness, 227 (22.9%) had moderate and 148 (15.0%) had severe COVID-19 illness at the time of admission. Nearly 40% (399) of these 990 patients reported at least one symptom at that time. Of these 399 long-COVID patients, 311 (almost 78%) responded to the second follow-up. Nearly 8% reported ongoing symptomatic COVID, lasting 1-3 months and 32% patients having post-COVID phase with symptoms lasting 3-12 months. Nearly 11% patients continued to have at least one symptom even at the time of the second interview (9-12 months after the disease onset). Overall, we observed Long-COVID in almost 40% of our study group. Incidence of the symptoms in both the follow-ups remained almost same across age-groups, gender, severity of illness at admission and presence of comorbidity, with no significant association with any of them. Most common symptoms experienced in long COVID phase in our cohort were fatigue, myalgia, neuro-psychiatric symptoms like depression, anxiety, “brain fog” and sleep disorder, and breathlessness. Fatigue was found to be significantly more often reported in the elderly population and in those patients who had a severe COVID-19 illness at the time of admission. Persistence of breathlessness was also reported significantly more often in those who had severe disease at the onset. The overall median duration of long COVID symptoms was 16.9 weeks with inter-quartile range of 12.4 to 35.6 weeks. The duration of symptom resolution was not associated with age, gender or comorbidity but was significantly associated with severity of illness at the time of admission (P=0.006). Conclusions: Long-COVID was seen in almost 40% of our study group with no correlation to age, gender, comorbidities or to the disease severity. The duration of symptom resolution was significantly associated with severity of illness at the time of admission (P = 0.006). In our study, all patients reported minor symptoms such as fatigue, myalgia, neuro-psychiatric symptoms like depression, anxiety, “brain fog” and sleep disorder and persistence of breathlessness.
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Background: 70-90 % of the adult population carries latent cytomegalovirus (CMV), which may be reactivated by inflammation and immune suppression. CMV reactivation has been seen in up to one-third of critically ill patients, and is associated with worse clinical outcomes. Here, the authors present two challenging cases, wherein the management of severe COVID-19 disease was complicated by CMV pneumonia. Case Reports: Our patients presented with severe COVID-19 pneumonia with acute respiratory distress syndrome and were admitted in the intensive care unit (ICU). The patients received immunosuppressive therapy, either tocilizumab or methylprednisolone pulse therapy. Both the patients had a prolonged hospital stay, and showed an initial improvement followed by clinical deterioration, with recurrence of fever, worsening respiratory failure, and development of consolidations on CT thorax. A thorough work up for opportunistic infections revealed CMV infection. Both patients were treated with intravenous Ganciclovir and showed marked improvement. Discussion: The use of steroids and other immunomodulatory therapies in the treatment of severe COVID-19 disease, along with immune suppression caused by severe COVID-19 itself, predisposes patients to reactivation of CMV. Furthermore, CMV reactivation is associated with a longer ICU length of stay, prolonged mechanical ventilation, increased risk of secondary infections, and mortality. Conclusion: These cases highlight the importance of considering CMV disease as a differential diagnosis in critically ill patients with COVID-19 with unexplained worsening, especially in the setting of immunomodulatory therapies, as early treatment may prevent adverse clinical outcomes and mortality.
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Vein detection forms a crucial part in medical diagnostics as it personalizes the health care given to an individual. But the problems related to it cause discomfort to patients. This paper proposes a IR imaging-based vein detection and projection device that is both portable, low-cost and contactless, thus ensuring complete functionality and widespread use even during the COVID-19 Pandemic. The image obtained using an IR camera is pre-processed by enhancing the contrast using Contrast Limited Adaptive Histogram Equalization (CLAHE) and then denoised using Median and Gaussian filtering. Two different pipelines have been tested for image segmentation. The first method employs a combination of local thresholding and Otsu binarization while the other employs a combination of Niblack and Otsu binarization with morphological transformations. It is observed that the first pipeline gave better results in terms of eliminating false positives and negatives in the final image. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Online training has been present for over a decade, and its importance is increasing every day. Today, it has become very important due to ongoing COVID-19 pandemic. As it has been accepted widely by the educational systems;nowadays, challenges like hardware resources, network resources, software resource, and security have become more demanding. Security threats among all challenges require more researches to develop rigid systems where data of all stakeholders remain secured. ML has a proven track record to solve such problems. In terms of security, ML continuously learns by analyzing data to find patterns so unauthorized access to encrypted traffic is detected better and find insider threats to keep information safe. Here, a new system is being developed using an improved algorithm, described in proposed work. Using this new algorithm, machines are trained to identify unauthorized access attempts and stop them from stealing data even if, they are authenticated. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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To control the spread of the coronavirus (COVID-19) pandemic, the Government of India imposed various phases of lockdown starting from the third week of March 2020. Improvement in city air quality has emerged as a benefit of this lockdown in India. The objective of this paper is to quantify the health benefits due to this lockdown. PM2.5 concentrations in nonattainment cities (NACs) in Uttar Pradesh and the Delhi-National Capital Region (NCR) in North India were studied. Data from prelockdown and the various lockdown phases were compared, with 2019 as a benchmark. Compared with those in 2019, the PM2.5 concentrations during lockdown Phase 1 were approximately 44.6% lower for cities in Uttar Pradesh and approximately 58.5% lower for the Delhi-NCR. The health impacts of particle inhalation were quantified using the multiple-path particle dosimetry and AirQ+ models, which revealed that the most considerable improvement was during lockdown Phase 1. Among the prelockdown and lockdown phases, Phase 1 exhibited the minimum PM2.5 concentration and thus the greatest health benefits. For the selected cities, the concentration of particle deposition in the tracheobronchial region of human lungs showed its maximum reduction during lockdown Phase 1(30.14%). Furthermore, the results highlighted a decrease of 29.85 deaths per 100,000 persons during lockdown Phase 1, primarily due to the reduction in PM2.5 concentrations. This quantification of the health benefits due to a decrease in PM2.5 may help policymakers implement suitable control measures, especially for NACs, where the respirable particulate matter concentrations remain very high. © 2021, AAGR Aerosol and Air Quality Research. All rights reserved.
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COVID-19 has caused a global pandemic since December 2019. It has impacted not only the wellbeing of human society but also has been damaging to the global economy. This has imposed severe threats and challenges on businesses. The British government has launched aid schemes to combat the new scenarios developed as a result of the pandemic. This paper aims to assess the impact of COVID-19 on foundries in the UK. Recorded responses from a detailed survey of the British foundries were analysed and short- and long-term action plans for the foundries are suggested. The current status, challenges, and future direction of the UK foundries are discussed. An opinion for the use of additive technologies with business model innovation for the de-centralised foundries is presented. © 2021, The Minerals, Metals & Materials Society.