ABSTRACT
Introduction: The Coronavirus disease-19 (COVID-19) pandemic mediated by Severe Acute Respiratory Syndrome-CoV2 (SARS-CoV2), made the use of face masks mandatory to check the spread of the disease. With the increased use of face masks, more people started presenting to the ophthalmologist with symptoms of dry eye. The proposed mechanism of dry eye was attributed to air blowing upwards from behind the mask into the eyes, especially in loose fitting masks. This air leads to rapid evaporation of tears and disturbance of homeostasis of the tear film.Aim: To measure self reported symptoms of dry eye and to establish mask use as a risk factor for the development of Dry Eye Disease (DED) in healthcare workers in a Tertiary Care Hospital.Materials and Methods: This cross-sectional, observational study was conducted at Nilratan Sircar (NRS) Medical College and Hospital for a duration of three months from December 2021 to February 2022. The study was conducted on 146 participants. An online survey was conducted using Google Forms, sent via email to hospital employees working in different departments of the hospital. All healthcare workers employed at NRS Medical College and Hospital who wore a face mask during duty hours and were willing to participate in the study were included. The Ocular Surface Disease Index (OSDI) questionnaire was used and modified by adding "while wearing a facemask" to the end of each question. To establish face mask use as a causative agent for development of DED, a few other questions related to face mask usage were included in the survey. The data was tabulated in Microsoft Excel and analysed with Statistical Package for Social Sciences (SPSS) version 24. Results: The mean age of the study population was 27.4+/-8.28 years. The mean hours of wearing a mask was 6.38+/-3.04 hours. N95 face mask was the most common type of mask used. The study population included 100 doctors, 14 nursing staff, 18 optometrists, eight group D staff (sweepers and ward attendants), and six dieticians. The mean OSDI score was 14.24. Increased usage of face masks, in particular surgical, more hours of reading significantly correlated with higher incidence of DED. Conclusion: This study showed that increased hours of face mask use in particular surgical was associated with development of DED. To encourage more people to wear face masks, all possible problems arising from face mask use should be promptly identified and dealt with.
ABSTRACT
As India has successfully developed a vaccine to fight against the COVID-19 pandemic, the government has started its immunization program to vaccinate the population. Initially, with the limited availability in vaccines, a prioritized roadmap was required to suggest public health strategies and target priority groups on the basis of population demographics, health survey information, city/ region density, cold storage facilities, vaccine availability, and epidemiologic settings. In this paper, a machine learning-based predictive model is presented to help the government make informed decisions/insights around epidemiological and vaccine supply circumstances by predicting India's more critical segments that need to be catered to with vaccine deliveries as quickly as possible. Public data were scraped to create the dataset;exploratory data analysis was performed on the dataset to extract important features on which clustering and ranking algorithms were performed to figure out the importance and urgency of vaccine deliveries in each region. Copyright © 2022 IGI Global.
ABSTRACT
Recent advances in deep learning have given rise to high performance in image analysis operations in healthcare. Lung diseases are of particular interest, as most can be identified using non-invasive image modalities. Deep learning techniques such as convolutional neural networks, convolution autoencoders, and graph convolutional networks have been implemented in several pulmonary disease identification applications, e.g., lung nodule classification, Covid-19, and pneumonia detection. Various sources of medical images such as X-rays, computed tomography scans, magnetic resonance imaging, and positron emission tomography scans make deep learning techniques favorable to identify lung diseases with great accuracy. This paper discusses state-of-the-art methods that use deep learning on various medical imaging modalities to detect and classify diseases in the lungs. A description of a few publicly available databases is included in this study, along with some distinct deep learning techniques developed in recent times. Furthermore, several challenges and open research areas for pulmonary disease diagnosis using deep learning are discussed. The objective of this work is to direct researchers in the field of diagnosis of lung diseases.
ABSTRACT
The spreading and Development of COVID-19 have analyzed which was first officially reported in Wuhan City, in December 2019. Firstly the data have explored in terms of information and quality and after that, the data have cleaned and gone through with feature engineering. Analyzed different types of machine learning-based prediction methods, namely Linear Regression, ARIMA, and SARIMA on the spread of COVID-19 in different regions all over the world. In the end, It has been concluded with the best machine learning model among them for COVID-19 spread forecasting based on theoretical and results in analysis. And also we have discussed that how deep learning can be considered with data limit problem in order to improve the result more dynamically with combination and comparisons of state-of-art approaches for time series problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.