| 1. |
- Jahangir, Sana, et al.
(författare)
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Sensitivity of simulated knee joint mechanics to selected human and bovine fibril-reinforced poroelastic material properties
- 2023
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Ingår i: Journal of Biomechanics. - 0021-9290. ; 160
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Tidskriftsartikel (refereegranskat)abstract
- Fibril-reinforced poroviscoelastic material models are considered state-of-the-art in modeling articular cartilage biomechanics. Yet, cartilage material parameters are often based on bovine tissue properties in computational knee joint models, although bovine properties are distinctly different from those of humans. Thus, we aimed to investigate how cartilage mechanical responses are affected in the knee joint model during walking when fibril-reinforced poroviscoelastic properties of cartilage are based on human data instead of bovine. We constructed a finite element knee joint model in which tibial and femoral cartilages were modeled as fibril-reinforced poroviscoelastic material using either human or bovine data. Joint loading was based on subject-specific gait data. The resulting mechanical responses of knee cartilage were compared between the knee joint models with human or bovine fibril-reinforced poroviscoelastic cartilage properties. Furthermore, we conducted a sensitivity analysis to determine which fibril-reinforced poroviscoelastic material parameters have the greatest impact on cartilage mechanical responses in the knee joint during walking. In general, bovine cartilage properties yielded greater maximum principal stresses and fluid pressures (both up to 30%) when compared to the human cartilage properties during the loading response in both femoral and tibial cartilage sites. Cartilage mechanical responses were very sensitive to the collagen fibril-related material parameter variations during walking while they were unresponsive to proteoglycan matrix or fluid flow-related material parameter variations. Taken together, human cartilage material properties should be accounted for when the goal is to compare absolute mechanical responses of knee joint cartilage as bovine material parameters lead to substantially different cartilage mechanical responses.
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| 2. |
- Korhonen, Rami K., et al.
(författare)
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Multiscale In Silico Modeling of Cartilage Injuries
- 2023
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Ingår i: Advances in Experimental Medicine and Biology. - 2214-8019 .- 0065-2598. ; 1402, s. 45-56
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Bokkapitel (refereegranskat)abstract
- Injurious loading of the joint can be accompanied by articular cartilage damage and trigger inflammation. However, it is not well-known which mechanism controls further cartilage degradation, ultimately leading to post-traumatic osteoarthritis. For personalized prognostics, there should also be a method that can predict tissue alterations following joint and cartilage injury. This chapter gives an overview of experimental and computational methods to characterize and predict cartilage degradation following joint injury. Two mechanisms for cartilage degradation are proposed. In (1) biomechanically driven cartilage degradation, it is assumed that excessive levels of strain or stress of the fibrillar or non-fibrillar matrix lead to proteoglycan loss or collagen damage and degradation. In (2) biochemically driven cartilage degradation, it is assumed that diffusion of inflammatory cytokines leads to degradation of the extracellular matrix. When implementing these two mechanisms in a computational in silico modeling workflow, supplemented by in vitro and in vivo experiments, it is shown that biomechanically driven cartilage degradation is concentrated on the damage environment, while inflammation via synovial fluid affects all free cartilage surfaces. It is also proposed how the presented in silico modeling methodology may be used in the future for personalized prognostics and treatment planning of patients with a joint injury.
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| 3. |
- Orozco, Gustavo A., et al.
(författare)
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Effect of patient specificity on predicting knee cartilage degeneration in obese adults : Musculoskeletal finite-element modeling of data from the CAROT trial
- 2024
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Ingår i: Journal of Orthopaedic Research. - 0736-0266.
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Tidskriftsartikel (refereegranskat)abstract
- Obesity is a known risk factor for development of osteoarthritis (OA). Numerical tools like finite-element (FE) models combined with degenerative algorithms have been developed to understand the interplay between OA and obesity. In this study, we aimed to predict knee cartilage degeneration in a cohort of obese adults to investigate the importance of patient-specific information on degeneration predictions. We used a validated FE modeling approach and three different age-dependent functions (step-wise, exponential, and linear) to simulate cartilage degradation under overloading in the knee joint. Gait motion analysis and magnetic resonance imaging data from 115 obese individuals with knee OA were used for musculoskeletal and FE modeling. Cartilage degeneration predictions were contrasted with Kellgren–Lawrence (KL) and Boston–Leeds Osteoarthritis Knee Score (BLOKS) grades. The findings show that overall, the similarities between numerical predictions and clinical measures were better for the medial (average area under the curve (AUC) = 0.62) compared to the lateral compartment (average AUC = 0.52) of the knee. Classification results for KL grades, full patient-specific models and patient-specific geometry with generic gait data showed higher AUC values (AUC = 0.71 and AUC = 0.68, respectively) compared to generic geometry and patient-specific gait (AUC = 0.48). For BLOKS grades, AUC values for both full patient-specific models and for patient-specific geometry with generic gait locomotion were higher (AUC = 0.66 and AUC = 0.64, respectively) compared to when the generic geometry and patient-specific gait were used (AUC = 0.53). In summary, our study highlights the importance of considering individual information in knee OA prediction. Nevertheless, our findings suggest that personalized gait play a smaller role in the OA prediction and classification capacity than personalized joint geometry.
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