Softening Effects in Biological Tissues and NiTi Knitwear during Cyclic Loading

Samples of skin, tendons, muscles, and knitwear composed of NiTi wire are studied by uniaxial cyclic tension and stretching to rupture. The metal knitted mesh behaves similar to a superelastic material when stretched, similar to soft biological tissues. The superelasticity effect was found in NiTi wire, but not in the mesh composed of it. A softening effect similar to biological tissues is observed during the cyclic stretching of the mesh. The mechanical behavior of the NiTi mesh is similar to the biomechanical behavior of biological tissues. The discovered superelastic effects allow developing criteria for the selection and evaluation of mesh materials composed of titanium nickelide for soft tissue reconstructive surgery.

Development of a multiscale testing method for the reduction of soft biological tissues waste during mechanical characterization

Two biaxial mechanical test methods were devised to compare their suitability for the mechanical characterization of soft biological tissues with the least possible tissue waste. Nanoindentation was used to explore the microscopic properties of the tissue and to overcome the macroscopic test limitations.

Predictive constitutive modelling of arteries by deep learning

The constitutive modelling of soft biological tissues has rapidly gained attention over the last 20 years. Current constitutive models can describe the mechanical properties of arterial tissue. Predicting these properties from microstructural information, however, remains an elusive goal. To address this challenge, we are introducing a novel hybrid modelling framework that combines advanced theoretical concepts with deep learning. It uses data from mechanical tests, histological analysis and images from second-harmonic generation. In this first proof of concept study, our hybrid modelling framework is trained with data from 27 tissue samples only. Even such a small amount of data is sufficient to be able to predict the stress–stretch curves of tissue samples with a median coefficient of determination of R 2 = 0.97 from microstructural information, as long as one limits the scope to tissue samples whose mechanical properties remain in the range commonly encountered. This finding suggests that deep learning could have a transformative impact on the way we model the constitutive properties of soft biological tissues.

Computational model of damage-induced growth in soft biological tissues considering the mechanobiology of healing

Healing in soft biological tissues is a chain of events on different time and length scales. This work presents a computational framework to capture and couple important mechanical, chemical and biological aspects of healing. A molecular-level damage in collagen, i.e., the interstrand delamination, is addressed as source of plastic deformation in tissues. This mechanism initiates a biochemical response and starts the chain of healing. In particular, damage is considered to be the stimulus for the production of matrix metalloproteinases and growth factors which in turn, respectively, degrade and produce collagen. Due to collagen turnover, the volume of the tissue changes, which can result either in normal or pathological healing. To capture the mechanisms on continuum scale, the deformation gradient is multiplicatively decomposed in inelastic and elastic deformation gradients. A recently proposed elasto-plastic formulation is, through a biochemical model, coupled with a growth and remodeling description based on homogenized constrained mixtures. After the discussion of the biological species response to the damage stimulus, the framework is implemented in a mixed nonlinear finite element formulation and a biaxial tension and an indentation tests are conducted on a prestretched flat tissue sample. The results illustrate that the model is able to describe the evolutions of growth factors and matrix metalloproteinases following damage and the subsequent growth and remodeling in the respect of equilibrium. The interplay between mechanical and chemo-biological events occurring during healing is captured, proving that the framework is a suitable basis for more detailed simulations of damage-induced tissue response.