ABSTRACT
Mucormycosis is an angio-invasive rapidly progressing fungal infection, usually reported in immunocompromised individuals. We present a case of COVID-associated mucormycosis in a patient with a presenting symptom of toothache in the maxilla with a possible mild case of COVID-19. Abstract: Coronavirus-associated mucormycosis (CAM) had reached epidemic status, especially during the second wave of COVID-19. It was especially prevalent in India with a large mortality rate. Mucormycosis, particularly the rhinocerebral type is seen to be greatly associated with COVID-19, especially in patients with altered immunity. Uncontrolled diabetes, chronic kidney disease, immunocompromised patients, malignant hematological diseases, etc. are the major risk factors of CAM, precipitated by the injudicious use of corticosteroids for the treatment of COVID-19. CAM may often present in the maxillofacial region which warrants that dental clinicians be aware of the clinical presentation, diagnostic guidelines, and appropriate management measures for the disease. This report is one such case of CAM involving the posterior maxilla in a middle-aged individual with mild COVID-19 symptoms.
ABSTRACT
The coronavirus disease has spread throughout the world and its fear has made people to be more cautious in public places. Since precautionary measures are the only reliable protocol to defend ourselves, social distancing is the only best approach to defend against the pandemic situation. The reproduction number i.e. R0 factor of COVID-19, can be slowed down only through the physical distancing norms. This research proposes a deep learning approach for maintaining the social distance by tracking and detecting the people present indoor and outdoor scenarios. Surveillance video is taken as the input and applied into you only look once (YOLO) V3 algorithm. The persons in the video are identified based on the segmentation algorithm present within the framework and then using Euclidean distance the image is evaluated. The bounding box algorithm helps to segregate the humans based on the minimum distance threshold. The proposed method is evaluated for images with peoples in the market, availing essential commodities and students entry inside a campus. Our proposed region-based convolutional neural network (RCNN) algorithm gives a better accuracy over the traditional models and hence the service can be implemented in general for places where social distancing is mandatory. © 2023 Institute of Advanced Engineering and Science. All rights reserved.
ABSTRACT
Social distancing has been imposed to prevent substantial transmission of the COVID-19 outbreak, which is presently a global public health issue. Medical healthcare providers rely on telemedicine to monitor their patients, particularly those with chronic conditions. However, telemedicine faces many implementation-related risks, including data breaches, access restrictions within the medical community, inaccurate diagnosis, fraud, etc. The authors propose a transparent, tamper-proof, distributed, decentralized smart healthcare system (DSHS) that uses blockchain-based smart contracts. The authors use an immutable modified Merkel tree structure to hold the transaction for viewing contracts on a public blockchain, updating patient health records (PHR), and exchanging PHR to all entities. It is verified by a performance evaluation based on the Ethereum platform. The simulation results show that the proposed system outperforms existing approaches by enhancing transparency, boosting efficiency, and reducing average latency in the system. The proposed system improves the functionality of the SHS environment.
ABSTRACT
BACKGROUND: COVID-19 is an infectious disease declared as a global pandemic caused by SARS-CoV-2 virus. Genomic changes in the receptor binding domain (RBD) region of SARS-CoV-2 led to an increased, infectivity in humans through interaction with the angiotensin-converting enzyme2 (ACE2) receptor. Simultaneously, the genetic variants in ACE2 provide an opportunity for SARS-CoV-2 infection and severity. We demonstrate the binding efficiencies of RBDs of SARS-CoV-2 strain with ACE2 variants of the human host. METHODOLOGY: A Total of 615 SARS-CoV-2 genomes were retrieved from repository. Eighteen variations were identified contributing to structural changes in RBD that are distributed in 615 isolates. An analyses of 285 single nucleotide variances at the coding region of the ACE2 receptor showed 34 to be pathogenic. Homology models of 34 ACE2 and 18 RBD structures were constructed with 34 and 18 structural variants, respectively. Protein docking of 612 (34 *18) ACE2-RBD complexes showed variable affinities compared to wildtype Wuhan's and other SARS-CoV-2 RBDs, including Omicron B.1.1.529. Finally, molecular dynamic simulation was performed to determine the stability of the complexes. RESULTS: Among 612, the top 3 complexes showing least binding energy were selected. The ACE2 with rs961360700 variant showed the least binding energy (-895.2 Kcal/mol) on binding with the RBD of Phe160Ser variant compared to Wuhan's RBD complex. Interestingly, the binding energy of RBD of Omicron B.1.1.529 with ACE2 (rs961360700) structure showed least binding energy of -1010 Kcal/mol. Additionally, molecular dynamics showed structure stability for all the analysed complexes with the RMSD (0.22-0.26 nm), RMSF (0.11-0.13 nm), and Rg (2.53-2.56 nm). CONCLUSION: In conclusion, our investigation highlights the clinical variants contributing to structural variants in ACE2 receptors that lead to efficient binding of SARS-CoV-2. Therefore, screening of these ACE2 polymorphisms will help detect COVID-19 risk population so as to provide additional care and for safe management.