RESUMO
The corona virus disease-19 (COVID-19) produced by SARS-CoV-2 has resulted in a wide spectrum of illness ranging from mild to life-threatening conditions thus enhancing the incidence of opportunistic infections among individuals harbouring comorbidities. Mucormycosis is a dreadful angio-invasive opportunistic infection produced by zygomycetes fungus in an immunocompromised host. The clinical manifestations of mucormycosis include rhino-orbital-cerebral (ROC), pulmonary, cutaneous, gastrointestinal, with ROC accounting for around 40% of cases. Diabetes, neutropenia, iron overload, cancer, and organ transplant are all major culprits. Both Aspergillus and Candida have been identified as the primary fungal pathogens causing co-infection in COVID-19 preyed individuals. The most predominant variety, Rhizopus Oryzae, is responsible for roughly 60% of mucormycosis infections in humans, as well as 90% of the Rhino-orbital-cerebral (ROCM) variant. Mucormycosis is the most widespread ailment in India. Until lately, India was declared to be the world's diabetes capital, with the second-largest number of people suffering from diabetes mellitus (DM). Diabetes has been recognised as the most common predictive marker for mucormycosis which explains the dramatic rise in Mucor cases in India lately particularly during second wave of COVID-19. The inflammatory onslaught caused by COVID 19 has debilitated patients' immune systems, making individuals vulnerable to mucormycosis outbreaks. The possible explanation that Mucorales spores appear to be expediting germination in people with COVID-19 is due to the perfect scenario of oxygen deprivation (hypoxia), hyperglycemia (steroid-induced), acidic form of media (metabolic acidosis, diabetic ketoacidosis), increased iron levels (elevated ferritin), and significantly reduced phagocytic activity of white blood cells (WBC) due to immunosuppression (SARS-CoV-2 or steroid mediated or associated comorbidities). Copyright © 2022 International Medical Sciences Academy. All rights reserved.
RESUMO
The COVID-19 pandemic has rendered social distancing and use of face masks as an absolute necessity today. Coming out of the epidemic, we're going to see this as the new normal and therefore most workplaces will require an identification system to permit employees based on the compliance of protocols. To ensure minimal contact and security, automatic entrance systems need to be employed in workplaces and institutions. For the implementation of such systems, we have investigated the performance of three object detection algorithms, namely SSD MobileNet V2, YOLO v3 and YOLO v4 in the context of real-time face mask detection. We conducted training and testing of these algorithms on our dataset focusing on various type of masks in the Indian community. We have exhibited in this paper that YOLOv4 transcends both YOLO v3 and SSD MobileNet V2 in sensitivity and precision and thus has a major use case in building AI identification systems. © 2021, Springer Nature Singapore Pte Ltd.
RESUMO
In the light of recent events, an epidemic - COVID-19 which took the world by surprise and continues to grow day by day. This paper describes an idea to control the spread of disease by monitoring Social Distancing. As of now from where we stand, the only way to avoid further spreading is to maintain proper social distance. Combining the advanced detection algorithms such as SSD, YOLO v4, and Faster-RCNN along with pedestrian datasets we reached the desired conclusion of calculating the distance between two detected persons in a video and identifying whether the social distancing norm is followed or not. This method can be implemented in CCTV’s, UAV’s, and on any other surveillance system. The rapid advancements in technologies led to more precise and accurate values. © 2021, Springer Nature Singapore Pte Ltd.