In the literature: June 2026 highlights

Carpenedo, Linda and La Barbera, Luigi “An efficient framework for fully deformable finite element musculoskeletal spine modeling: development, sensitivity analysis, and validation” Journal of Biomechanics, 2026

Computational musculoskeletal models are fundamental tools for investigating spinal biomechanics. However, currently available approaches still rely on major simplifications. In particular, fully deformable finite element (FE) models capable of quantifying load-sharing across all spinal structures rarely incorporate the action of muscles. As a consequence, detailed predictions of internal loads deriving from realistic loading conditions still lack. This study integrates an active muscle architecture into a lumbar FE model using a multi-stage dimensionality reduction strategy, combining Plackett–Burman screening, Latin Hypercube, and Farthest Point Sampling, to develop surrogate Generalized Linear Models. These models, built on a reduced set of simulations, efficiently predict spinal kinematics as a function of muscle forces. Using these surrogates, an optimization procedure identified the muscle activations that satisfy kinematic targets while minimizing an energy criterion. The results accurately captured physiological trends: abdominal activations and intradiscal pressure increased with trunk flexion. Compressive forces (CFs) at L4–L5 in standing were 74 % of body weight, within the 58 %–98 % literature range. The model’s full deformability also allowed for a detailed load-sharing assessment. For instance, in standing, the disc consistently carried over 44 % of CFs, followed by facet capsules (FCLs) contributing up to 26 %. During flexion, disc load nearly doubled, while FCLs became markedly tensioned (reaching 49 % at 20°). Also, shear forces (anterior 31 % to posterior 17 % of body weight) matched literature values, with the disc providing the primary contribution. These findings confirm the model’s ability to predict complex loading patterns while reducing the computational cost of musculoskeletal FE simulations.

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Lai, Juntong, and Damien Lacroix. “A novel integrative multi-scale framework of inflammation and mechanical loading in knee osteoarthritis.” Biomechanics and Modeling in Mechanobiology 25.3 (2026): 55.

Obesity is a well-known prominent risk factor for knee osteoarthritis (OA). The onset and development of knee OA are affected by multifactorial interplays, involving the degeneration of articular cartilage. Emerging evidence shows the negative effects of obesity on cartilage degeneration across multi-scales. Specifically, obesity can stimulate inflammation and alter the biomechanical responses of the joint. However, the pathology of obesity-associated knee OA is still poorly understood. The aim of this study was to develop a multi-scale modelling framework to simulate and evaluate the mechanobiological roles of obesity in the degenerative process of cartilage. This framework integrated the inflammatory and biomechanical effects of obesity on cartilage degeneration in knee OA. A validated finite element model of a subject-specific knee joint was coupled with a mathematical model of adipokine-mediated OA inflammation. In the algorithm, excess stress resulted in mechanical damage that activated obesity-related inflammatory responses. The degeneration of cartilage was driven by both mechanical damage and body mass index (BMI). Parameter sensitivity analysis showed a good adaptivity of this framework to simulate cartilage degeneration. In addition, BMI and the stress threshold were sensitive to the degenerative process. Results indicate that a higher BMI level could not only increase the degeneration level but also lead to a larger degenerative volume of cartilage. Due to the elevated baseline of inflammation in the obese joint, the relative contributions of inflammation and mechanical damage might vary as cartilage degeneration progressed. This computational framework combines for the first time obesity-associated inflammation and mechanical loading in knee OA. It could be extended by specifying different degenerative pathways in cartilage degeneration. With further calibration, the framework has the potential to empower the identification of different phenotypes and endotypes of OA.

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Killen, Bryce A., et al. “A predictive simulation framework for personalised in silico gait retraining in knee osteoarthritis.” Journal of Biomechanics (2026): 113409.