A precise and timely graph‐based approach to identify SARS Covid19 infection from medical imaging data using IsoCovNet

More than 100 million individuals have been infected by the COVID19 virus since 2019. Even if the vaccination procedure has already begun, it will take time to attain an adequate supply. There have been several efforts by computer scientists to filter COVID19 from CXR or CT scans due to the disease's extensive prevalence. These patients' CT and CXR scans are utilized to identify COVID19 using IsoCovNet, a Graph‐Isomorphic‐Network, that is, GIN‐based model for detecting COVID19. A GIN‐based design dictates that our suggested model only takes data in the form of graphs. At the outset, the input image undergoes a conversion into an unordered network, that is, a graph that considers only the links between elements rather than the entire image. This approach significantly reduces the model's processing time. We verified the effectiveness of our proposed IsoCovNet network by using four datasets, which consist of both x‐ray and CT‐scan images, from five standard sources that are publicly available on platforms like Kaggle and GitHub. The network achieved an accuracy of 99.51% on binary datasets and a higher accuracy of 99.84% on the multi‐classification task of detecting Covid19. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Rare Case of Granulomatous Interstitial Nephritis in a Patient With COVID-19.

Acute kidney injury (AKI) in patients with coronavirus disease 2019 (COVID-19) is common, especially among severely ill patients. While acute tubular necrosis (ATN) is one of the most common findings in published kidney biopsy series for patients with COVID-19 infections, a number of glomerular pathologies have been described as well. Among glomerular pathologies in COVID-19, COVID-19-Associated Collapsing Glomerulopathy (COVAN) remains the most common pattern of injury. Patients with 2 high-risk APOL1 alleles appear to be at increased risk for COVAN, similar to other forms of collapsing glomerulopathy such as HIV-Associated Nephropathy. Acute interstitial nephritis (AIN) is a less common finding in patients with COVID-19 and reported cases have been mild. Reports of a subtype of AIN, granulomatous interstitial nephritis (GIN), among COVID-19 patients are extremely rare and have not been reported in association with COVAN. Here, we report a case of COVAN associated with severe GIN.