Inflammation and mechanical loading in osteoarthritis- an integrative multi-scale approach
Inflammation and mechanical loading in osteoarthritis- an integrative multi-scale approach
批准号:
2747654
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --
中文摘要
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英文摘要
Osteoarthritis (OA) is a chronic disease affecting the joints, with the knee being one of the joints most commonly affected. Although excessive mechanical loading, in particular in obese people, contributes greatly to its onset, OA is now seen as a multifactorial disorder in which low-grade, chronic inflammation has a central role. This project will create a novel computational platform for the study of OA by combining for the first time (1) weight and physical activity as mechanical loading factors and (2) obesity and nutritional factors with the release of cytokines and adipocytes from the surrounding adipose tissue of the joint. The main aim is to develop a spatial and temporal multiscale computational approach that describes the interactions between the molecular pathways related to joint inflammation and the daily physical loading that contributes to cartilage and bone damage within the joint. This approach is a new departure towards the modelling of systemic changes resulting from several inflammation-related factors.State-of-the-art biomechanical finite element (FE) models will be coupled with non-linear ordinary differential equations (ODE) describing the pro- and anti-inflammatory activities within the knee joint. A set of ODEs will describe the concentration over time of pro- and anti-inflammatory molecules available from the surrounding adipose tissue. The simulation of cartilage and subchondral bone degradation over time through the production of enzymes released by macrophages and overloading of the cartilage and subchondral bone will be modelled. The complex time interaction between the FE and the ODE model makes this task particularly challenging. The overall modelling platform will be adapted to enable testing the predictive capability of OA onset and development under clinical setting.
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