Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional calibration methods are computationally intensive, requiring iterative, derivative-free optimization algorithms that often take days to converge. This study addresses these challenges by introducing a novel, efficient, and effective calibration method demonstrated on a human L4-L5 IVD FE model as a case study using a neural network (NN) surrogate.
View Article and Find Full Text PDFScope: Hepatitis E virus (HEV) is a significant global health issue, impacting both low- and middle-income countries and industrialized nations. HEV genotypes 1 and 2, primarily transmitted through contaminated water, are endemic in low- and middle-income countries, whereas genotypes 3 and 4 are zoonotically transmitted in industrialized regions. Acute HEV infection poses severe risks, particularly to pregnant women and immunocompromised individuals, whereas chronic HEV infection leads to serious complications in those with pre-existing liver disease and transplant recipients.
View Article and Find Full Text PDFClin Biomech (Bristol)
December 2024
Numerical modeling of the intervertebral disc (IVD) is challenging due to its complex and heterogeneous structure, requiring careful selection of constitutive models and material properties. A critical aspect of such modeling is the representation of annulus fibers, which significantly impact IVD biomechanics. This study presents a comparative analysis of different methods for fiber reinforcement in the annulus fibrosus of a finite element (FE) model of the human IVD.
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