Numerical simulation of mechanical tests on a living skin using anisotropic hyperelastic law

Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining

The surge in 'Big data' has significantly influenced biomaterials research and development, with vast data volumes emerging from clinical trials, scientific literature, electronic health records, and other sources. Biocompatibility is essential in developing safe medical devices and biomaterials to perform as intended without provoking adverse reactions. Therefore, establishing an artificial intelligence (AI)-driven biocompatibility definition has become decisive for automating data extraction and profiling safety effectiveness. This definition should both reflect the attributes related to biocompatibility and be compatible with computational data-mining methods. Here, we discuss the need for a comprehensive and contemporary definition of biocompatibility and the challenges in developing one. We also identify the key elements that comprise biocompatibility, and propose an integrated biocompatibility definition that enables data-mining approaches.

Shape or size matters? Towards standard reporting of tensile testing parameters for human soft tissues: systematic review and finite element analysis

Material properties of soft-tissue samples are often derived through uniaxial tensile testing. For engineering materials, testing parameters (e.g., sample geometries and clamping conditions) are described by international standards; for biological tissues, such standards do not exist. To investigate what testing parameters have been reported for tensile testing of human soft-tissue samples, a systematic review of the literature was performed using PRISMA guidelines. Soft tissues are described as anisotropic and/or hyperelastic. Thus, we explored how the retrieved parameters compared against standards for engineering materials of similar characteristics. All research articles published in English, with an Abstract, and before 1 January 2023 were retrieved from databases of PubMed, Web of Science, and BASE. After screening of articles based on search terms and exclusion criteria, a total 1,096 articles were assessed for eligibility, from which 361 studies were retrieved and included in this review. We found that a non-tapered shape is most common (209 of 361), followed by a tapered sample shape (92 of 361). However, clamping conditions varied and were underreported (156 of 361). As a preliminary attempt to explore how the retrieved parameters might influence the stress distribution under tensile loading, a pilot study was performed using finite element analysis (FEA) and constitutive modeling for a clamped sample of little or no fiber dispersion. The preliminary FE simulation results might suggest the hypothesis that different sample geometries could have a profound influence on the stress-distribution under tensile loading. However, no conclusions can be drawn from these simulations, and future studies should involve exploring different sample geometries under different computational models and sample parameters (such as fiber dispersion and clamping effects). Taken together, reporting and choice of testing parameters remain as challenges, and as such, recommendations towards standard reporting of uniaxial tensile testing parameters for human soft tissues are proposed.

Need for transverse strain data for fitting constitutive models of arterial tissue to uniaxial tests

The study deals with the process of estimation of material parameters from uniaxial test data of arterial tissue and focuses on the role of transverse strains. Two fitting strategies are analyzed and their impact on the predictive and descriptive capabilities of the resulting model is evaluated. The standard fitting procedure (strategy A) based on longitudinal stress-strain curves is compared with the enhanced approach (strategy B) taking also the transverse strain test data into account. The study is performed on a large set of material data adopted from literature and for a variety of constitutive models developed for fibrous soft tissues. The standard procedure (A) ignoring the transverse strain test data is found rather hazardous, leading often to unrealistic predictions of the model exhibiting auxetic behaviour. In contrast, the alternative fitting method (B) ensures a realistic strain response of the model and is proved to be superior since it does not require any significant demands of computational effort or additional testing. The results presented in this paper show that even the artificial transverse strain data (i.e., not measured during testing but generated ex post based on assumed Poisson's ratio) are much less hazardous than total disregard of the transverse strain response.