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A Mathematical Approach to Liver Fibrosis Diagnosis

Hepatitis C virus (HCV), the infectious agent causing hepatitis C, affects 170 million people worldwide, leading to more than 350,000 deaths per year.  Serial liver biopsies, which are typically used for the diagnosis of liver fibrosis progression, can be invasive, expensive, risky, and inconvenient to patients.  Fortunately for hepatitis C sufferers, a preferred diagnosis process may be on the way.  In a study appearing in a 2022 issue of Sensors, TSYS School computer scientists Anastasia Angelopoulou and Rania Hodhod, and their colleague from the CSU Department of Mathematics, Nehal Shukla, present the development, validation, and evaluation of a prediction mathematical model for non-invasive diagnosis of liver fibrosis in chronic hepatitis C virus.  Their model uses a set of nonlinear ordinary differential equations as its core and divides the population into six groups: susceptible, treatment, responder, non-responder, cured, and fibrosis.  According to Hodhod, “The numerical analysis and simulation results discussed in the study indicate that the earlier the hepatitis C virus treatment is implemented, the smaller the group of people who will develop complications such as fibrosis.”  The simulations also indicated that after about two years, the number of individuals in the treatment, responder and non-responder groups gradually diminishes, while the number who are cured increases over a four- to five-year period.
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