Approximation of extracted features enabling 3D design tuning for reproducing the mechanical behaviour of biological soft tissues.

This article describes a new method, inspired by machine learning, to mimic the mechanical behaviour of target biological soft tissues with 3D printed materials. The principle is to optimise the structure of a 3D printed composite consisting of a geometrically tunable fibre embedded in a soft matrix. Physiological features are extracted from experimental stress-strain curves of several biological soft tissues. Then, using a cubic Bézier curve as the composite inner fibre, we optimised its geometric parameters, amplitude and height, to generate a specimen that exhibits a stress-strain curve in accordance with the extracted features of tensile tests. From this first phase, we created a database of specimen geometries that can be used to reproduce a wide variety of biological soft tissues. We applied this process to two soft tissues with very different behaviours: the mandibular periosteum and the calvarial periosteum. The results show that our method can successfully reproduce the mechanical behaviour of these tissues. This highlights the versatility of this approach and demonstrates that it can be extended to mimic various biological soft tissues.

Definition of thermal indicators for the study of thermoregulation alterations in the foot of people living within diabetic peripheral neuropathy: A proof of concept.

AIMS: This study investigates how diabetic peripheral neuropathy is linked to impairment of thermoregulatory mechanisms using a thermal camera, spectral thermal analysis and a physical test. METHODS: The plantar skin temperature of all participants was measured using a thermal camera following a 6-min walking exercise. The data were subjected to frequency decomposition, resulting in two frequency ranges corresponding to endothelial and neurogenic mechanisms. Then, 40 thermal indicators were evaluated for each participant. ROC curve and statistical tests allowed to identify indicators able to detect the presence or absence of diabetic peripheral neuropathy. RESULTS: The study included 33 participants living with diabetes. The results revealed that a 6-min walk exercise increased plantar foot temperature and highlighted a significant difference between people living with diabetes with and without peripheral neuropathy (p < 0.01). The results also revealed the advantages of using thermal images rather than single point measurements. CONCLUSIONS: Diabetic peripheral neuropathy is linked to impairment of thermoregulatory mechanisms. This link can be highlighted after a dedicated 6-min walk exercise, enabling to activate these mechanisms, and measuring with a thermal camera the temporal plantar skin temperature. Assessment of this link gave best results by filtering the thermal signal in the neurogenic range.

Identification of a visco-hyperelastic model for mandibular periosteum.

In this paper, a visco-hyperelastic model representing the mechanical behavior of the human mandibular periosteum as an anisotropic and homogeneous material is identified. Different models, extracted from the literature, are tested and associated in order to describe the elastic and visco-elastic contributions of the cellular matrix on one hand and the collagen fibers on the other hand. The parameters of these models are determined using five human mandibular periosteum. Each harvested sample is cut and tested, at two different velocities, either longitudinally or transversely to collagen fibers main direction. The hyperelastic and visco-elastic contributions of the cellular matrix are extracted using tensile tests performed transversely. The hyperelastic and visco-elastic contributions of the collagen fibers are extracted using tensile tests performed longitudinally. In a second time, the identified combination of models is validated using twelve samples only tested longitudinally. The selected combination uses the simplified Rivlin's 2nd order law to model the hyper-elasticity of the cellular matrix, the Kulkarni's law to model its visco-elasticity contribution, and the Kulkarni's laws to model the whole contributions of collagen fibers.