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Introduction: Despite the expanding role and need for endoscopic ultrasound, training opportunities for established endoscopists in the USA are limited. ASGE launched a novel competency-based program to address this training need in 2019. It includes an online learning modules, live webinars, a hands-on weekend course, a summative knowledge exam, followed by a customizable preceptorship with an EUS expert. Aim(s): To describe the training experience of a sample from the first cohort of the ASGE Diagnostic EUS training program. Method(s): A total of 26 applicants were chosen for the first cohort of the training program in 2019. We describe the experience of 3 endoscopists (ST at the Swedish Digestive Health Institute, Seattle, WA;BM at the Borland Groover Clinic, Jacksonville, FL and JH at Guthrie, Sayre, PA) who completed their hands-on training. Their case volumes were 160 (4 mentors), 185 (2 mentors) and 185 (3 mentors) respectively over a total of 12 weeks each. While 1 trainee (JH) was able to get trained at the same institute where he was employed, the other 2 (ST, JH) had to seek training in another state due to lack of preceptorship sites within their states of employment. One center tracked TEESAT scores (The EUS and ERCP Skills Assessment Tool) for every 5 procedures for their trainee (ST), and he was noted achieved a global score of 4 by the 150 th procedure. Result(s): All 3 trainees have been credentialed for EUS privileges at their respective institutes, and are performing EUS independently. Conclusion(s): The ASGE EUS diagnostic training program was able to fulfil the training needs of motivated established clinicians in full time practice. The main challenges encountered were identifying willing institutes and expert EUS preceptors, and institutional administrative barriers. COVID restrictions were a unique hurdle to the timely completion of preceptorship. This program's success in the future depends on buy in from EUS experts in the community and their respective institutions.
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COVID 19 is constantly changing properties because of its contagious as an urgent global challenge, and there are no vaccines or effective drugs. Smart model used to measure and prevent the spread of COVID 19 continues to provide health care services is an urgent need. Previous methods to identify severe symptoms of coronavirus in the early stages, but they have failed to predict the symptoms of coronavirus in an accurate way and also take more time. To overcome these issues the effective severe coronavirus symptoms techniques are proposed. Initially, Gradient Conventional Recursive Neural Classifier based classification and Linear Discriminant Genetic Algorithm used feature selection, mutation, and cross-analysis of features of coronary symptoms. These methods are used to select optimized features and selected features, and then classified by neural network. This Gradient Conventional Recursive Neural Classifier selects features based on the correlation between features that reduce irrelevant features involved in the identification process of coronary symptoms. Gradient Conventional Recursive Neural Classifier based on each function, helping to maximize the correlation between the prediction accuracy of coronavirus symptoms. For this reason, it has always been recommended in an effort to increase the accuracy and reliability of diagnostics to use machine learning to design different classification models. © 2023 IEEE.
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Blockchain and artificial intelligence (AI) have shown promise in combating the Covid epidemic. Blockchain in particular may aid in early detection to fight pandemics. The methods established for infection prevention include the use of face masks, public isolation within a 6 metre radius, regular check-ups, and two doses of vaccinations.This system has features for detecting masks, people, temperature, information tracking, in-person interactions, and a person's medical history. Diseases might be monitored and their spread contained with the advancement of technology and the rise in smartphone use. Because additional economic sectors are opening up and because Covid is still spreading widely, adhering to the guidelines is more important than ever for avoiding infection. © 2023 IEEE.
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This chapter focusses on the two-way relationship between migration and development in the context of Asia, addressing the differences and similarities across different sub-regions. As Asia is home to the largest number of migrants globally, we examine changes in migration trends by looking at both intra-regional and inter-regional movements through what Khadria modelled as the ‘Hubs' and ‘Hinterlands' of migration. We address different categories of migrants, such as professionals and workers, students, women;high-and low-skilled, temporary and permanent, voluntary and forced, asylum-seekers, refugees, and trafficked migrants. Diasporas and the role of homeland associations in development, particularly in times of crises like the global pandemic of COVID-19 or natural disasters are addressed within a perspective of the dynamic conflicts of interest between home and host countries in ‘normal' and ‘new normal' times. The frontier skills in migration are categorised and a matrix suggested to interrelate various types of migrants and their differentiated development roles in segregated time horizons of long, short, and immediate term. The role of the state, civil society, and international organisations in migration governance through the changing perspectives of multilateralism, bilateralism, and even unilateralism in the twenty-first-century Asia is also explored. © 2023 selection and editorial matter, Anna Triandafyllidou;individual chapters, the contributors.
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Recent years have witnessed increased pressure across the global healthcare system during the COVID-19 pandemic. The COVID-19 pandemic shattered existing healthcare operations and taught us the importance of a resilient and sustainable healthcare system. Digitisation, specifically adoption of Artificial Intelligence (AI) has positively contributed to developing a resilient healthcare system in recent past. To understand how AI contributes to building a resilient and sustainable healthcare system, this study based on systematic literature review of 89 articles extracted from Scopus and Web of Science databases is conducted. The study is organised around several key themes such as applications, benefits, and challenges of using AI technology in healthcare sector. It is observed that AI has wide applications in radiology, surgery, medical, research, and development of healthcare sector. Based on the analysis, a research framework is proposed using an extended Antecedents, Practices, and Outcomes (APO) framework. This framework comprises AI applications' antecedents, practices, and outcomes for building a resilient and sustainable healthcare system. Consequently, three propositions are drawn in this study. Furthermore, our study has adopted the theory, context and methodology (TCM) framework to provide future research directions, which can be used as a reference point for future studies. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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The education sector in India was among the most affected sectors during the COVID-19 pandemic. While considerable attention has been paid to informal workers' return or reverse migration to their home communities, not much has been reported about the challenges faced by migrant students. Using a mixed-method approach, the current study presents an overview of internal student migration in India prior to the COVID-19 pandemic using data from the 2001 and 2011 Census of India and the 2007–2008 National Sample Survey Organization, and discusses challenges faced by selected migrant learners during the COVID-19 pandemic based on primary research. Based on the census data, nearly 3.3 million migrants in India move for study reasons with 2.9 million migrating within the state (with the duration of residence less than five years) from their last residence within India. The pattern of female student migration suggests an increasingly localized interdistrict migration. Findings from the qualitative data indicate that during the pandemic, students had compromised learning and placement experience, inadequate digital resources and pressure to repay loans. Student migrants experienced varying degrees of the impact of the COVID-19 pandemic based on their destination and migration stream. © Scalabrini Migration Center 2023.
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The healthcare system had been on high vigilant due to the sudden outbreak of the Covid-19 pandemic. This stimulated the need for a smart healthcare system for early diagnosis, prevention from spreading, treatment, and simplified living. Remote monitoring, telemedicine, and body sensors are certain areas where the adaptation of IoMT has proved to be a boon. The advancement in the technology for healthcare devices and their security has been challenging too. The sensor implanted, employed in remote healthcare are vulnerable to attacks, as well as, is resource constraint when used in an IoT environment. Privacy is the biggest issue with IoT, as all linked devices transmit data in real time. This paper proposes, the framework for utilizing blockchain model-based parameters that will be distributed in nature and prevent malicious attacks that may threaten healthcare applications. We also discuss the various vulnerabilities on IoMT devices and methods that are employed for authentication and secure access to medical records for IoMT healthcare management. © 2022 IEEE.
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The effectiveness and cost implications are always top factors for policy makers while deciding upon the appropriate air pollution abatement measures. The present study aimed to understand the actual particulate matter (PM2.5 and PM10) patterns during different phases of COVID-19 lockdown periods and depict their spatial distributions covering the 36 major areas in Delhi, India. Drastic visible reduction in both the pollutants was found during lockdown phase 1 and 2. Average PM2.5 reductions of 41.97%, 39.24%, 56.04%, and 56.77% were recorded comparing lockdown and/or study period with the years 2018, 2019, 2021, and 2022, respectively. Similar average reduction of PM10 to the magnitude of 51.72%, 48.95%, 48.24%, and 49.00% was found for the referred years. However, the reduction during the before-lockdown period of 2018 and 2019 and the year 2020 did not follow such radical reduction returning the values for PM2.5 as 7.66-14.88% and that for PM10 as 12.86-20.67%. The geospatial maps generated for Delhi city followed the similar findings at macro level depicting huge reduction in PM distribution classes for the study period. For instance, the percent surface area under "moderately high"polluted due to PM2.5 came down to 0.61 during lockdown phase 2 from 13.96 during January 2020. Further, about 15 of the 36 locations reported compliance to the National Ambient Air Quality Standards (NAAQS) for either of the pollutants during the study period. Nevertheless, such reductions are short-lived because the levels went up again in the years 2021 and 2022 (except similar lockdowns) as the situation got back to normal daily life activities postlockdown. Although, lockdown may be imposed in case of severe ambient air quality in a densely populated megacity like Delhi, it remains a temporary or quick-fix solution, to be looked as a last line of defense. © 2023 Mary Ann Liebert, Inc., publishers.
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Objectives: To find the impact of COVID-19 on the sports industry and how the industry will adapt to the changes. Methodology: Secondary data was collected from several websites related to various sports' revenues, broadcasting, and sponsorships. Primary data was collected in the form of public opinion to analyze the change in the coming years. A qualitative method was used for the analysis. Findings: After analyzing the data following results are indicated concerning the changes in the sports industry: a) Sports industry is set to take a massive loss in terms of revenue, b) The public would not attend any sports event until early next year or until a vaccine is developed, whichever is the earliest. Limitations: The limitations of the research could be the revenue data which is likely to change with the resumption of sporting activities. Public opinion might vary as the situation improves, and the idea will differ from one country to another. Application: The study will be helpful for all sports enthusiasts as they would get to know the overall industry scenario and for students who would want to explore the sports industry from the outside.
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Background: Cryptococcus lives in the environment all over the globe. Although it spreads via inhalation route still most of the exposed individuals never get sick as the majority of cases are seen in immunocompromised. Objective of this clinical case report is to highlight the rare fungal etiology associated with iliac bone abscess to avoid incorrect diagnosis and prompt management of case. Case Presentation: A 70-year-old elderly female presented with hip pain for a month duration, not relieved with analgesics in September, 2021. In MRI a well-defined irregularly marginated hyperintense focal lesion was found in left iliac bone with joint effusion suggestive of infective etiology, tubercular, or less likely metastasis. CT-guided biopsy reported occasional hyphae-like fragments giving an impression of acute on chronic osteomyelitis with suspicion of fungalinfection.Culture reported Cryptococcus ne oformans.Fungal markers and Beta-dglucan were indeterminate and Galactomannan was found negative for the sample. Extrapulmonary TB was ruled out by AFB staining, MGIT Culture, and GeneXpert MTB. Bone scan, tumor markers, and PET scan ruled out osteolytic lesion secondary to metastasis. Though PET Scan and HRCT thorax confirm pulmonary involvement giving a picture of bilateral interstitial lung disease along with multiple enlarged lymph nodes. Patient serum was found negative for HIV, HBV, and HCV. Liver and renal function tests were within normal range and in hematology, ESR was raised (50;normal range:0-20). Patient is hypertensive with HbA1c of 5.3. There was no history of travel, avian exposure, weight loss, and COVID-19 infection. Patient was started on voriconazole and considering generalized lymphadenopathy, a therapeutic trial of anti-tubercular therapy was started which was stopped within a week on patient non-compliance. Abscess resolved with voriconazole and patient was discharged. In February 2022, Patient presented with similar complaints. CT scan of this fluctuant nodule depicted hypoechoic lesion which was ultrasound-guided drained.Sections show many rounds of oval fungal organism which were found PASpositive with mucicarmine and alcian blue positive capsule.Budding yeast cells were seen on KOH mount and India ink preparation demon-strated capsule which was confirmed by Cryptococcal Antigen test giving an overall impression in favor of Cryptococcosis. Patient was started on oral fluconazole and Injection liposomal amphotericin B 250 mg for 14 days. Discussion and Conclusion(s): This is the first case of skeletal Cryptococcosis at our institution which was managed by antifungals without surgical debridement resulting in resolution of abscess. Isolated focal iliac bone cryptococcosis is unusual but may occur in immunocompetent with everyday exposure to the organism. Herein, Patient had bilateral lung involvement along with multiple lymphadenopathies with no evidence of TB bacilli which inferences that the isolate most likely originated from environmental bird droppings and has disseminated from pulmonary lesion to the iliac bone. The radiological findings of iliac cryptococcosis abscess were nonspecific.A definitive diagnosis was made on histopathological and fungal examinations of ultrasound-guided drained abscess. Patient will be followed in the near future for relapse or any other medical issues related to the case.
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Firms are using blockchain technology to prevent fraudulent activities through consensus mechanisms and help identify data tampering through its underlying characteristics, such as immutability, encryption transparency, and operational resilience. However, a holistic review of blockchain application in building a cyber-secure supply chain remains elusive, which can impede the advancement of the field when competing arguments are made, and pure replications are initiated. Therefore, the present study carries a systematic literature review to assess the state of literature review on the significance of blockchain in developing a cyber-secure and resilient supply chain. A systematic review of 122 peer-reviewed papers published between 2012 and 2022 has been conducted using a structured methodological approach. Further, the Theory, Context and Methodology (TCM) framework has been applied to suggest avenues for future research. Our analysis reveals six dominant themes related to blockchain and its adoption, blockchain application for resilience, blockchain application for cyber-security, blockchain and its role in intermediation, disintermediation and reintermediation, challenges of blockchain adoption and benefits of blockchain applications in the supply chain. Although the role of blockchain application in the cyber secure and resilient supply chain has been recognised, very little attention has been given to contextual factors affecting its adoption and consequences on key performance parameters. The present study contributes to the literature by synthesising the prime findings of reviewed papers and the managerial impli-cations, thereby giving a holistic view of the existing research and presenting an array of future opportunities for firms to adopt blockchain technology in the supply chain.
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A novel coronavirus disease, which is transmitted from human to human has quickly become the cause of the current worldwide health crisis. This virus is, also known as SARS coronavirus, belongs to the Coronaviridae family of viruses. The recent outbreak of acute respiratory disorders starting in Wuhan, China is found to be caused by this virus. The condition caused by it, known as COVID-19 has spread very rapidly all over the world, causing so many death. This led WHO on Mar 11, 2020, to designate it as a global pandemic. An update on the history, etiology, epidemiology, pathophysiology and preventive methods for COVID-19 such as masking, quarantine, and social distancing are discussed in this paper. Repurposed drugs, antibodies, corticosteroids, vaccination and plasma transfusion, are among the treatments explained in the study. Finally, the study discusses India’s COVID vaccination programme. The major aspects of this entire review are to describe COVID-19 infection, its prevention and treatment approach.
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Aim and background: Delirium is the disturbance of consciousness characterised by acute onset, rapid fluctuations in mental status, and impaired cognitive functioning. The patient's ability to receive, process, store, and recall information is impaired in delirium. Objective: To study the incidence of delirium in patients in COVID and non-COVID ICU. To also study various risk factors associated with delirium. Materials and methods: After ethical committee approval and written informed consent, this study was carried out over a period of 1 year (August 2020 to July 2021). Each patient meeting the inclusion criteria was evaluated on the RAAS score within 24 hours of admission, then screened for delirium according to CAM-ICU worksheet every 6th hourly after admission in MICU. 50 patients were studied each in COVID and non-COVID ICU. Patients found to have delirium after the first assessment were classified as new cases. Various risk factors were evaluated prospectively. Results: Incidence of delirium in non-COVID ICU was 29%, while in COVID ICU was 37%. Delirium is present in a patient who has risk factors including smoking, higher severity of illness, oversedation, and mechanical ventilation. Antipsychotics can be used for patients who develop delirium. Conclusion: Delirium is a preventable issue in ICU patients that can be managed by preventing the risk factors that will decrease overall length of stay in ICU.
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Background Patients with coronavirus disease 2019 (COVID-19) presenting for neurosurgery are not rare. Considering the lack of literature informing the outcomes in this subset, present study was conducted to compare perioperative management and postoperative outcomes between COVID-19 and non-COVID-19 neurosurgical patients. Methods After ethics committee approval, data of all patients with COVID-19 along with an equal number of age and diagnosis matched non-COVID-19 patients undergoing neurosurgery between April 2020 and January 2021 was analyzed retrospectively. Predictors of poor outcome were identified using multivariate logistic regression analysis. Results During the study period, 50 COVID-19 patients (28 laboratory confirmed (group-C) and 22 clinicoradiological diagnosed [group-CR]) underwent neurosurgery and were compared with 50 matched non-COVID-19 patients. Preoperatively, clinicoradiological diagnosed COVID-19 patients had higher American Society of Anesthesiologists (ASA) grade ( p = 0.01), lower Glasgow Coma Scale (GCS) score ( p < 0.001), and more pulmonary involvement ( p = 0.004). The duration of intensive care unit stay was significantly longer in laboratory confirmed patients ( p = 0.03). Poor clinical outcome (in-hospital mortality or discharge motor-GCS <= 5) did not differ significantly between the groups ( p = 0.28). On univariate analysis, younger age, higher ASA grade, lower preoperative GCS, and motor-GCS, higher intraoperative blood and fluid administration and traumatic brain injury diagnosis were associated with poor outcome. On multivariable logistic regression. only lower preoperative motor-GCS remained the predictor of poor outcome. Conclusions The concomitant presence of COVID-19 infection did not translate into poor outcome in patients undergoing neurosurgery. Preoperative motor-GCS predicted neurological outcome in both COVID-19 and non-COVID-19 neurosurgical patients.
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Latest technological advancements in the modern world led to innovations which if tackled properly yields value-added outcomes or may result in disruptions if mishandled. One such technology is NoSQL databases that evolved in hundreds of numbers. Though these support a wide number of features such as consistency, availability, fault-tolerance, scalability, and security, there is no single store that bundles all together. Particularly, due to drastic data rise that accounted up to the big data, it has become essential to compromise with security while focusing on consistency and vice versa in similar directions. Another aspect to be considered lies in the ability to handle streaming data which required a special kind of storage to process data on fly. This gains wide support if integrated with various learning platforms such as deep and machine learning to yield an added outcome. However, the application of pre-processing techniques and the identification of training versus test data split irrespective of the dataset is an essential activity to be carried out to infer better results. Also, the selection of an appropriate algorithm to identify the outliers in voluminous data is essential to quantify the results. Toward this end, an efficient hybrid machine learning algorithm polynomial-Bayesian-support vector machine (PBS) is developed to overcome aforementioned big data analysis-based difficulties and results are evaluated to make an inference about the effectiveness of the proposed work. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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With the outbreak of COVID-19 all over the world, it has become increasingly important to understand and effectively manage air quality in enclosed environments. The effect of HVAC (Heating, Ventilation, and Air Conditioning) systems in airborne disease transmission is crucial especially in confined public places such as hospitals, buses, and railway coaches. Computational Fluid Dynamics has become an important tool for studying and understanding the behaviors of fluid flow especially aerosol transport in such enclosed spaces. The current paper focuses on simulating the behavior of air in the indoor environment of a railway coach under four different conditions. These are where the rail coach has no occupants and with three occupants with the effect of three and six different HVAC supply air inlet conditions explored. The results show that without occupants, the air from the inlet spreads into the coach with relatively low velocity and maintains its speed throughout the coach until at the outlet where it speeds up. With occupants in the coach, the incoming air was observed to spread wide and exits the coach with much higher velocity. This is due to the reduced available flow area occasioned by the occupants, and this information will be useful in further studies with aerosol transport within the coach.
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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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The core idea of the paper is to create a multi-purpose drone/unmanned aerial vehicle which are used to counter challenges faced by COVID-19. Our work is structured to give insights into our theory method with a constructive form of qualitative research. This work's main contribution lies in the comparative study of many research articles based on the use of drone in different fields specially in sanitization of a large area in a stipulated period, Thermal screening of people and storing their temperature recorded data in cloud, AI implementation in order to detect abnormal pattern of coughing, heart rate etc and delivery of medicines and necessary essential to quarantined people and migrants this will cause a large-scale implementation of drone delivery may drastically change the business, substituting hundreds of workers, fossil-fuel-powered delivery vehicles, traffic congestion, and centralised distribution centres of drone which performs point A to point B parcel delivery. The challenges faced by drone and its operators during active mission are studied. This study will help others who are new to drones in the covid and medical field. © The Electrochemical Society