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Yara Sarkis,1 Nasir Saleem,2 Raj Vuppalanchi,2 Mark Gromski2 1Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; 2Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USACorrespondence: Mark Gromski, Gastroenterology and Hepatology Department, Indiana University Hospital, 550 N. University Blvd, Suite 1634, Indianapolis, IN, 46202, USA, Tel +317-944-0925, Fax +317-968-1265, Email mgromski@iu.eduAbstract: The complete impact of COVID-19 infection continues to develop since the onset of the COVID-19 pandemic. COVID-19 cholangiopathy has been recently described in a subset of patients who recovered from severe COVID-19 infection. The most common phenotype of patients suffering from COVID-19 cholangiopathy had severe infection requiring a stay in the intensive care unit, mechanical ventilation and vasopressor medications. Patients with COVID-cholangiopathy present with severe and prolonged cholestatic liver injury. In cases where biliary cast formation is identified, we defined the entity as “COVID-19 cast-forming cholangiopathy”. This subset of COVID-19 cholangiopathy is not well understood and there are no standardized diagnosis or management to this date. The reported clinical outcomes are variable, from resolution of symptoms and liver test abnormalities to liver transplant and death. In this commentary, we discuss the proposed pathophysiology, diagnosis, management, and prognosis of this disease.Keywords: COVID-19 infection, cast-forming cholangiopathy, COVID-19 cholangiopathy, long COVID syndrome

Abstract Artificial Neural Networks are incredibly efficient at handling complicated and nonlinear mathematical problems, making them very useful for tackling these challenges. Artificial neural networks offer a special computational architecture that is extremely valuable in disciplines like biotechnology, biological computing, and computational fluid dynamics. The present work investigates the applicability of back-propagation artificial neural networks in conjunction with the Levenberg-Marquardt algorithm for evaluating heat transmission in hybrid nanofluids. This work focuses on the computational analysis of a MgO + GO/EG hybrid nanofluid’s steady mixed convection flow over an exponentially stretched sheet, considering multiple slip boundary conditions, thermal conductivity, heat generation, and thermal radiation. A nonlinear system of ordinary differential equations is produced from the basic associated partial differential system by performing the proper exponential similarities modifications. For generating benchmark datasets, the resulting ordinary differential equations are processed employing the bvp4c method. Considering benchmark datasets set aside for training (70%), testing (15%), and validation (15%), the Levenberg-Marquardt algorithm, which employs back-propagation in artificial neural networks, is implemented. The accuracy of the suggested strategy for handling nonlinear problems is verified utilizing mean squared error, error histograms, and regression analysis, which are all used to evaluate the methodology. Outstanding agreement is seen when ANN outputs are compared to numerical results. The flow properties, including temperature, velocity, and concentration profiles, are shown graphically and numerically. For practical purposes, it is therefore essential to analyze the flow and heat transfer in hybrid nanofluids over exponentially extending and shrinking surfaces under mixed convection and heat source scenarios. Hybrid nanofluid problems have a wide range of practical and industrial applications, such as medication delivery, manufacturing, microelectronics, nuclear plant cooling, and marine engineering.