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1.
Journal of Education and Health Promotion ; 11(1):218, 2022.
Article in English | Scopus | ID: covidwho-2024741

ABSTRACT

BACKGROUND: Vaccine hesitancy leads to an increase in morbidity, mortality, and health-care burden. Reasons for vaccine hesitancy include anti-vax group statements, misinformation about vaccine side effects, speed of vaccine development, and general disbelief in the existence of viruses like COVID-19. Medical students are future physicians and are key influencers in the uptake of vaccines. Hence, investigating vaccine hesitancy in this population can help to overcome any barrier in vaccine acceptance. METHODS: In this paper, we review five articles on COVID-19 vaccine hesitancy in medical students and consider potential future research. All published papers relevant to the topic were obtained through extensive search using major databases. Inclusion criteria included studies that specifically investigated COVID-19 vaccine hesitancy in medical students published between 2020 and 2021. Exclusion criteria included studies that investigated vaccine hesitancy in health-care professionals, allied health, and viruses apart from COVID-19. A total of 10 studies were found from our search. RESULTS: Based on our exclusion criteria, only five studies were included in our review. The sample size ranged from 168 to 2133 medical students. The percentage of vaccine hesitancy in medical students ranged from 10.6 to 65.1%. Reasons for vaccine hesitancy included concern about serious side effects, vaccine efficacy, misinformation and insufficient information, disbelief in public health experts, financial costs, and belief that they had acquired immunity. CONCLUSION: These results suggest that vaccine hesitancy is an important cause of the incidence and prevalence of COVID-19 cases. Identifying the barriers of vaccine hesitancy in prospective physicians can help increase vaccination uptake in the general public. Further research is necessary to identify the root cause of these barriers. © Shahad A. Hafez et al., 2022;Published by Mary Ann Liebert, Inc. 2022.

2.
American Journal of Respiratory and Critical Care Medicine ; 205(1), 2022.
Article in English | EMBASE | ID: covidwho-1927860

ABSTRACT

RATIONALE: Around 4.6 million people in the United Kingdom (UK) have asthma, with an estimated 5.7% treated for severe asthma. Benralizumab is indicated for the treatment of severe eosinophilic asthma (SEA) in adults inadequately controlled despite appropriate maintenance therapy. The Connect 360 Patient Support Programme (PSP) for patients on benralizumab includes options for home-based drug administration, education and adherence support by trained nurses - of particular relevance during the COVID-19 pandemic. Limited evidence exists on the benefit of PSPs for asthma patients or those administering biological therapies at home. This study aims to describe patient characteristics, key outcomes and experience with the PSP using UK data from Connect 360. METHODS: A non-interventional, retrospective cohort study of patients, enrolled in the PSP (Oct-2019 onwards) and consenting to the use of personal data for research purposes (“study cohort”). Patients opting for additional support services with at least one nurse interaction within described study timeframes formed the clinical cohort. Patients were observed up to 48 weeks post-PSP enrolment (interim data taken on 31-Mar-2021;data collection ongoing) with study endpoints assessed at baseline (0-4 weeks), 24 (±4) weeks and 48 (±8) weeks post-PSP enrolment. Characteristics at enrolment are described for the study cohort. Patient-reported clinical outcomes (hospitalisations, maintenance oral corticosteroid [mOCS] use, Asthma Control Questionnaire [ACQ-6] scores) and service satisfaction (1-5 point scale, 5 being most satisfied) were analysed where available from routine PSP nurse calls/visits. Analysis was descriptive;Kaplan-Meier estimators were used to estimate PSP discontinuation rates. RESULTS: The study cohort was 611 patients (mean enrolment age: 54.1 years, 63.2% female [N=323]). Most (98.9%) were benralizumab users on maintenance dosing (8-weekly) at enrolment. The clinical cohort consisted of 149 (baseline), 175 (24 weeks) and 195 (48 weeks) patients. PSP discontinuation rates were 4.4% and 11.6% at 24 and 48 weeks. Proportion of patients reporting mOCS use was 49.7%, 44.0% and 32.8% at each timepoint and hospitalizations were 10.9% and 4.1% at 24 and 48 weeks. Mean ACQ-6 scores decreased over time. Mean (SD) satisfaction scores were 4.6 (0.7) and 4.8 (0.5) at 24 and 48 weeks, respectively. (Table 1). CONCLUSIONS: Overall patients' experience with the PSP was positive, evidenced by high satisfaction with and persistence to the PSP. Where data were available, proportion of patients reporting mOCS and hospitalizations at 48 weeks were numerically lower than previous timepoints and mean ACQ-6 scores improved, suggesting a positive impact of benralizumab treatment within the PSP.

3.
4th RSRI Conference on Recent trends in Science and Engineering, RSRI CRSE 2021 ; 2393, 2022.
Article in English | Scopus | ID: covidwho-1890379

ABSTRACT

Background: Globally Covid 19 has leased almost all domains of human life in and around;business, education, tourism and so on are not an exception. Medical professionals are group which actively got involved in this disaster management arena. Training of medical and nursing professionals has also got into stake or it got narrowed into online mode of education. Methods: A phenomenological qualitative study was conducted to identify perceived benefits and challenges confronted by nursing students of a selected nursing college in Northern India. Data was collected from thirty nursing students with the help of a validated focus group discussion guide. Five FGD were conducted to collect data until availing data saturation. Data was transcribed, coded and developed themes, sub-themes based on codes keeping participant verbatim in support. Results: The thematic content analysis of Data revealed four core themes 1) academic usefulness, 2) Time effectiveness, 3) Challenges faced, 4) attitude and perception of students and couple of sub themes along with it. Overall impression of the study shows nursing students were less favorable to online mode of learning. Conclusion:This study portrays out all aspects pertaining to online education mode considering ill effects and benefits as perceived by nursing students from their nearly one year of experience. © 2022 Author(s).

4.
Journal of the American College of Cardiology ; 79(9):2322-2322, 2022.
Article in English | Web of Science | ID: covidwho-1849100
5.
Egyptian Journal of Radiology and Nuclear Medicine ; 53(1), 2022.
Article in English | EMBASE | ID: covidwho-1799083

ABSTRACT

Background: The occurrence of invasive fungal infections in COVID-19 patients is on surge in countries like India. Several reports related to rhino-nasal-sinus mucormycosis in COVID patients have been published in recent times;however, very less has been reported about invasive pulmonary fungal infections caused mainly by mucor, aspergillus or invasive candida species. We aimed to present 6 sputum culture proved cases of invasive pulmonary fungal infection (four mucormycosis and two invasive candidiasis) in COVID patients, the clues for the diagnosis of fungal invasion as well as difficulties in diagnosing it due to superimposed COVID imaging features. Case presentation: The HRCT imaging features of the all 6 patients showed signs of fungal invasion in the form of cavities formation in the pre-existing reverse halo lesions or development of new irregular margined soft tissue attenuating growth within the pre-existing or in newly formed cavities. Five out of six patients were diabetics. Cavities in cases 1, 2, 3 and 4 of mucormycosis were aggressive and relatively larger and showed relatively faster progression into cavities in comparison with cases 5 and 6 of invasive candidiasis. Conclusion: In poorly managed diabetics or with other immunosuppressed conditions, invasive fungal infection (mucormycosis, invasive aspergillosis and invasive candidiasis) should be considered in the differential diagnosis of cavitary lung lesions.

6.
International Journal of Information and Learning Technology ; : 31, 2022.
Article in English | Web of Science | ID: covidwho-1779042

ABSTRACT

Purpose The purpose of this study is two-fold. First, to identify and encapsulate the enablers that can facilitate technology integration in higher education and second, to understand and analyze the interplay between technology agility enablers. Design/methodology/approach The study used the Total Interpretive Structural Modeling (TISM) approach to construct a theoretical model of the technology agility enablers in higher education and MICMAC analysis for ranking and segregating the enablers based on their dependence power into four categories: Autonomous, Dependent, Linkage and Independent. Findings The study helped identify eight technology agility enablers, with the Covid-19 pandemic as the most significant enabler. The Covid-19 pandemic has catalyzed the diffusion of technology across the education sector in India, including tertiary higher education. The study revealed government initiatives and institutional commitment as other enablers that can promote technology agility in higher education. Practical implications The results of this study would assist the policymakers and management of universities and colleges in understanding the important enablers that can facilitate technology integration in higher education. Originality/value Research in the past on technology adoption in higher education has looked into each enabler in isolation. This research provides a comprehensive view of the enablers and has attempted to establish a multidirectional interplay between the enablers.

7.
9th International Conference on Frontiers in Intelligent Computing: Theory and Applications, FICTA 2021 ; 266:271-281, 2022.
Article in English | Scopus | ID: covidwho-1750604

ABSTRACT

The demographic dividend is an essential measure of the growth and development of a country. It refers to the economy’s growth due to a shift in the age structure in the country’s population. In India, around 90% of the population is under the age of 60, which is a stark contrast compared to the world, where more than 20% of the population lies above 60. Such a young population ensures that the working-age group will be vibrant in the coming years, adding to the country’s overall productivity. Today, COVID has caused much damage to an already vibrant economy, because of which millions of people have lost their jobs and have had to migrate back to their hometowns. To recover from this severe damage and take stock of the existing and incoming workforce, it is necessary to identify and analyze the current population that lies in suitable age ranges and understand how to use them optimally. Therefore, analyzing the demographic dividend to identify the workforce of the country becomes an essential task. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

8.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 4387-4395, 2021.
Article in English | Scopus | ID: covidwho-1730874

ABSTRACT

COVID-19 is an air-borne viral infection, which infects the respiratory system in the human body, and it became a global pandemic in early March 2020. The damage caused by the COVID-19 disease in a human lung region can be identified using Computed Tomography (CT) scans. We present a novel approach in classifying COVID-19 infection and normal patients using a Random Forest (RF) model to train on a combination of Deep Learning (DL) features and Radiomic texture features extracted from CT scans of patient's lungs. We developed and trained DL models using CNN architectures for extracting DL features. The Radiomic texture features are calculated using CT scans and its associated infection masks. In this work, we claim that the RFs classification using the DL features in conjunction with Radiomic texture features enhances prediction performance. The experiment results show that our proposed models achieve a higher True Positive rate with the average Area Under the Receiver Curve (AUC) of 0.9768, 95% Confidence Interval (CI) [0.9757, 0.9780]. © 2021 IEEE.

9.
Research Journal of Pharmacy and Technology ; 14(12):6725-6731, 2021.
Article in English | Scopus | ID: covidwho-1644183

ABSTRACT

Introduction: The Coronavirus Virus Disease 2019 (COVID-19) pandemic has affected many social organisations with the workplace being amongst those most affected. Organisation faced the challenge of continued productivity during a global health crisis. Employees have shown signs of fatigue caused by living with the persistent fear and anxiety of falling sick and this which can consequently resulted in a reduction in productivity and frequently reduced income. Measures that were introduced to keep people safe such as social distancing, lockdowns, new working styles together with necessary lifestyle changes such as social isolation have reinforced feelings of uncertainty and fear amongst the workforce. These factors have influenced the mental health of workers and will continue to do so as society reorganise and make the changes necessary to accommodate new systems. Purpose: The purpose of this literature review is to conceptualize the psychological aspects linked to workplace factors following the rise of COVID-19 to epidemic proportions and in order to address upcoming psychological critical issues in the workplaces. Method: This literature study proceeded a search engine using Google Scholar, PubMed, and Scopus, using keywords SARS-CoV-2, COVID-19 pandemic, occupational health and safety, mental health, psychological disorders, COVID 19 and working people, workplace organization and selected 30 articles out of thirty 20 articles were analyzed corroding to researchers need. The literature information is further narrated and draws on insights of the researcher. Conclusion: The review study conceptualizes the pandemic spread of COVID-19 and reviews its effect in the workplace as companies reorganize and establish new patterns of operations in response the COVID-19 virus and to ensure precautionary measure against further spread of the disease. The adaptations necessary in the wake of this disease have novel reorganization in both structural and functional areas. The workforce at the workplace underwent loss of its psychological homeostasis. The mitigation which ensured has led to multiple organizational and work-related interventions intended to be instrumental in defending health-related safety in the workplaces. © RJPT All right reserved.

10.
CHEST ; 161(1):A247-A247, 2022.
Article in English | Academic Search Complete | ID: covidwho-1625114
11.
2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020 ; : 858-862, 2020.
Article in English | Scopus | ID: covidwho-1393668

ABSTRACT

Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes. We believe conditional cGAN to be a tractable approach to generate 3D CT volumes, even though the problem of generating full resolution deep fakes is presently impractical due to GPU memory limitations. We present results for autoencoder, denoising, and depixelating tasks which are trained and tested on two novel COVID19 CT datasets. Our evaluation metrics, Peak Signal to Noise ratio (PSNR) range from 12.53 - 46.46 dB, and range from 0.89 to 1. © 2020 IEEE.

12.
Journal of Research of the National Institute of Standards and Technology ; 126, 2021.
Article in English | Scopus | ID: covidwho-1380070

ABSTRACT

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, a plethora of ultraviolet-C (UV-C) disinfection products have come to market, especially in emerging economies. UV-C-based disinfection products for mobile phones, food packaging, face masks and personal protective equipment (PPE), and other everyday objects are available in popular electronic-commerce platforms as consumer products. Product designers from multinational to startup companies began to design UV-C disinfection products but had no prior-art reference, user feedback, or validation of product efficacy, which are important stages in product design. A UV-C disinfection product cannot be assessed by most consumers for its viricidal efficacy. Many firms entered the domain of UV-C products and were unaware of the necessary validation requirements. Lack of availability and access to virology laboratories, due to lockdowns in countries, and lack of standards and certification for UV-C disinfection products limited product designers and firms in benchmarking their UV-C-based devices before market release. This work evaluates two UV-C disinfection devices for viricidal efficacy on PPE fabric and National Institute for Occupational Safety and Health (NIOSH)-certified N95 respirators through controlled experiments using the H1N1 virus, which is enveloped and is transmitted via the respiratory route similar to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19. The experiment also evaluated the effectiveness of chemical disinfectants along with and versus UV-C disinfection. Experiments for material selection, UV dose calculation, and UV endurance of PPE samples to be disinfected are also discussed. The outcome of this work establishes a systematic method to validate the efficacy of UV-C disinfection products. The design guidelines would benefit product designers in designing UV-C-based disinfection products. © 2021 National Institute of Standards and Technology. All rights reserved.

13.
2020 Ieee International Conference on Big Data ; : 1216-1225, 2020.
Article in English | Web of Science | ID: covidwho-1324897

ABSTRACT

COVID-19 is a novel infectious disease responsible for over 1.2 million deaths worldwide as of November 2020. The need for rapid testing is a high priority and alternative testing strategies including x-ray image classification are a promising area of research. However, at present, public datasets for COVID-19 x-ray images have low data volumes, making it challenging to develop accurate image classifiers. Several recent papers have made use of Generative Adversarial Networks (GANs) in order to increase the training data volumes. But realistic synthetic COVID-19 x-rays remain challenging to generate. We present a novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID-19 chest x-ray images of high quality. In order to create a more accurate GAN, we employ transfer learning from the Kaggle pneumonia x-ray dataset, a highly relevant data source orders of magnitude larger than public COVID-19 datasets. Furthermore, we employ the Mean Teacher algorithm as a constraint to improve stability of training. Our qualitative analysis shows that the MTT-GAN generates x-ray images that are greatly superior to a baseline GAN and visually comparable to real x-rays. Although board-certified radiologists can distinguish MTT-GAN fakes from real COVID-19 x-rays, quantitative analysis shows that MTT-GAN greatly improves the accuracy of both a binary COVID-19 classifier as well as a multi-class pneumonia classifier as compared to a baseline GAN. Our classification accuracy is favorable as compared to recently reported results in the literature for similar binary and multi-class COVID-19 screening tasks.

14.
Bjog-an International Journal of Obstetrics and Gynaecology ; 128:194-195, 2021.
Article in English | Web of Science | ID: covidwho-1268860
15.
American Journal of Obstetrics and Gynecology ; 224(6):S756-S756, 2021.
Article in English | Web of Science | ID: covidwho-1267137
16.
18.
American Journal of Obstetrics and Gynecology ; 224(6, Supplement):S762-S763, 2021.
Article in English | ScienceDirect | ID: covidwho-1242848
19.
American Journal of Obstetrics and Gynecology ; 224(6, Supplement):S756, 2021.
Article in English | ScienceDirect | ID: covidwho-1242847
20.
Research on Biomedical Engineering ; 2020.
Article in English | Scopus | ID: covidwho-932689

ABSTRACT

Introduction: From public health perspectives of COVID-19 pandemic, accurate estimates of infection severity of individuals are extremely valuable for the informed decision-making and targeted response to an emerging pandemic. This paper presents machine learning based prognostic model for providing early warning to the individuals for COVID-19 infection using the healthcare dataset. In the present work, a prognostic model using random forest classifier and support vector regression is developed for predicting the infection susceptibility probability (ISP) score of COVID-19, and it is applied on an open healthcare dataset containing 27 field values. The typical fields of the healthcare dataset include basic personal details such as age, gender, number of children in the household, and marital status along with medical data like coma score, pulmonary score, blood glucose level, HDL cholesterol, etc. An effective preprocessing method is carried out for handling the numerical and categorical values (non-numerical) and missing data in the healthcare dataset. The correlation between the variables in the healthcare data is analyzed using the correlation coefficient, and heat map with a color code is used to identify the influencing factors on the infection susceptibility probability (ISP) score of COVID-19. Based on the accuracy, precision, sensitivity, and F-scores, it is noted that the random forest classifier provides an improved classification performance as compared to support vector regression for the given healthcare dataset. Android-based mobile application software platform is developed using the proposed prognostic approach for enabling the healthy individuals to predict the susceptibility infection score of COVID-19 to take the precautionary measures. Based on the results of the proposed method, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. Methods: In the present work, random forest classifier and support vector regression techniques are applied to a medical healthcare dataset containing 27 variables for predicting the susceptibility score of an individual towards COVID-19 infection, and the accuracy of prediction is compared. An effective preprocessing is carried for handling the missing data in the healthcare dataset. Correlation analysis using heat map is carried on the healthcare data for analyzing the influencing factors of infection susceptibility probability (ISP) score of COVID-19. A confusion matrix is calculated for understanding the performance of classification based on the number of true-positives, true-negatives, false-positives, and false-negatives. These values further used to calculate the accuracy, precision, sensitivity, and F-scores. Results: From the classification results, it is noted that the random forest classifier provides a classification accuracy of 99.7%, precision of 99.8%, sensitivity of 98.8%, and F-score of 99.29% for the given medical dataset. Conclusion: Proposed machine learning approach can help the individuals to take additional precautions for protecting people from the COVID-19 infection, and clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. © 2020, Sociedade Brasileira de Engenharia Biomedica.

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