This study investigates methodological variability across various expert laboratories worldwide, with regards to characterizing the mechanical properties of biological tissues. Two testing rounds were conducted on the specific use case of uniaxial tensile testing of porcine aorta. In the first round, 24 labs were invited to apply their established methods to assess inter-laboratory variability. This revealed significant methodological diversity and associated variability in the stress-stretch results, underscoring the necessity for a standardized approach. In the second round, a consensus protocol was collaboratively developed and adopted by 19 labs in an attempt to minimize variability. This involved standardized sample preparation and uniformity in testing protocol, including the use of a common cutting and thickness measurement tool. Despite protocol harmonization, significant variability persisted across labs, which could not be solely attributed to inherent biological differences in tissue samples. These results illustrate the challenges in unifying testing methods across different research settings, underlining the necessity for further refinement of testing practices. Enhancing consistency in biomechanical experiments is pivotal when comparing results across studies, as well as when using the resulting material properties for in silico simulations in medical research.

Aims: Abdominal aortic aneurysm (AAA) treatment is upon a diameter threshold. Attempts for medical growth abrogation have failed thus far. This study aims to elucidate the heterogeneity of AAA histomorphology in correlation with individual patient and aneurysm metrics.

Methods and results: Samples from the left anterior aneurysm wall underwent histologic analysis including angiogenesis, calcification, fibrosis, type, and grade of inflammation in adventitia and media. Clinical information and state of aneurysm (intact, symptomatic, ruptured, and inflammatory) were retrieved. Semi-automated geometric analysis (Endosize©, Therenva, Rennes, France) and finite element methods (A4Clinics© Research Edition, Vascops GmbH, Graz, Austria) were included. A total of 364 patients’ samples (85.4% male, median age 69 years) were scored for acute or chronic inflammation, both not associated with rupture (52×), symptomatic disease (37×), or diameter [57 (52–69) mm; P = 0.87]. The degree of fibrosis and the presence of angiogenesis were significantly higher (both P < 0.001) with increasing inflammation, which in turn significantly decreased with patient age (est = −0.015/year, P = 0.017). No significant differences were seen for acute (vs. elective), male (vs. female), or diabetic patients. Aneurysm geometry (n = 252) or annual growth rate (n = 142) were not associated with histologic characteristics. Yet, local luminal thrombus formation was significantly higher with increasing inflammation (P = 0.04).

Conclusion: Type and degree of inflammation are the most distinguishable histologic characteristics in the AAA wall between individual patients, yet are not associated with diameter or rupture. Local luminal thrombus formation is associated with inflammatory features and suggests a vivid bio-physical compartment with intra-individual age-dependent differences.

Atherosclerosis remains the leading cause of cardiovascular morbidity worldwide. However, its onset and progression are still difficult to predict, and it remains unclear why certain arterial segments develop lesions while others remain unaffected. Recent findings highlight a prominent role of the vasa vasorum (VV)-the small blood vessels embedded within the walls of larger arteries-in driving disease development through an outside-in mechanism. In this view, perfusion deficits caused by vascular dysfunction may trigger chronic inflammation and promote plaque formation. To investigate this mechanism, we propose a novel multi-field model that combines tissue turnover, inflammation, and kinematics-based tissue growth. Perfusion is governed by a diffusion-reaction equation and accounts for VV dysfunction in supplying the outer arterial layers. Inflammation is captured through a phase-field representation that tracks the evolving interface between non-inflamed and inflamed tissue. A multiplicative decomposition of the deformation gradient then combines the inflammation-driven swelling and the homeostasis-driven tissue turnover, which itself is regulated by mechanical stress. The numerical implementation is realized using the standard finite element method. We assess model performance and plausibility through well-designed numerical case studies. The acquired simulation results highlight the coupled interaction among transport of blood-borne factors, inflammation, and mechanics, ultimately emphasizing how compromised VV can initiate a vicious cycle of ischemia, inflammation, and plaque growth in an outside-in fashion. In addition, we show how a moderate increase in blood pressure may result in a progressive increase in peak stress within atherosclerotic plaque tissue. Although our atherosclerosis model yields plausible predictions and allows deep insights into the interaction of mechanics, inflammation and tissue turnover, it is based on multiple modeling approximations, assumptions that would need sound validation in the future.

Objective: Despite elective repair of a large portion of stable abdominal aortic aneurysms (AAAs), the diameter criterion cannot prevent all small AAA ruptures. Since rupture depends on many factors, this study explored whether machine learning (ML) models (logistic regression [LogR], linear and non-linear support vector machine [SVM-Lin and SVM-Nlin], and Gaussian Naïve Bayes [GNB]) might improve the diameter based risk assessment by comparing already ruptured (diameter 52.8 – 174.5 mm) with asymptomatic (diameter 40.4 – 95.5 mm) aortas. Methods: A retrospective case-control observational study included ruptured AAAs from two centres (2010 – 2012) with computed tomography angiography images for finite element analysis. Clinical patient data and geometric and biomechanical AAA properties were fed into ML models, whose output was compared with the results from intact cases. Classifications were explored for all cases and those having diameters below 70 mm. All data trained and validated the ML models, with a five-fold cross-validation. SHapley Additive exPlanations (SHAP) analysis ranked the factors for rupture identification. Results: One hundred and seven ruptured (20.6% female, mean age 77 years, mean diameter 86.3 mm) and 200 non-ruptured aneurysmal infrarenal aortas (21.5% female, mean age 74 years, mean diameter 57 mm) were investigated through cross-validation methods. Given the entire dataset, the diameter threshold of 55 mm in males and 50 mm in females provided a 58.0% accurate rupture classification. It was 99.1% sensitive (AAA rupture identified correctly) and 36.0% specific (intact AAAs identified correctly). ML models improved accuracy (LogR 90.2%, SVM-Lin 89.5%, SVM-Nlin 88.7%, and GNB 86.4%); accuracy decreased when trained on the ≤ 70 mm group (55/50 mm diameter threshold 44.2%, LogR 82.5%, SVM-Lin 83.6%, SVM-Nlin 65.9%, and GNB 84.7%). SHAP ranked biomechanical parameters other than the diameter as the most relevant. Conclusion: A multiparameter estimate enhanced the purely diameter-based approach. The proposed predictability method should be further tested in longitudinal studies.

Objective: We aimed to predict patient-specific rupture risks and growth behaviors in abdominal aortic aneurysm (AAA) patients using biomechanical evaluation with finite element analysis to establish an additional AAA repair threshold besides diameter and sex. Methods: A total of 1219 patients treated between 2005 and 2024 (conservative and repaired AAAs) were screened for a pseudo-prospective single-center study. A total of 15 ruptured (rAAA) vs. 15 non-ruptured AAAs (control group) were matched for pre-rupture imaging (first rAAA) and the initial post-rupture imaging (second rAAA) with two images in the asymptomatic control group (first and second control). The matching criteria were as follows: aneurysm diameter, sex, and time period between imagings. The biomechanical properties were analyzed with the finite element method (A4clinicsRE, Vascops GmbH, Graz, Austria). Results: Both groups had the same median aortic diameter of 5.5 cm in the first imaging but had significantly different aneurysm progressions with 6.9 cm (5.5-9.4 cm) in the second rAAA vs. 6.0 cm (5.1-7.3 cm) in the second control group (p = 0.006). The first rAAA, compared to the first control, showed significantly a higher peak wall stress (PWS) (211.8 kPa vs. 180.5 kPa, p = 0.029) and luminal diameter (43.5 mm vs. 35.3 mm; p = 0.016). The second rAAA, compared to the matched second control, showed a significantly higher PWS (281.9 kPa vs. 187.4 kPa, p = 0.002), luminal diameter (58.3 mm vs. 39.7 mm; p = 0.007), PWRR (0.78 vs. 0.49, p = 0.014) and RRED (79.8 vs. 56.5, p = 0.014). The rAAA group showed over-proportional averages, over the observation time, and an increase in PWS (nearly 10x faster in rAAA) and luminal diameter (nearly 4x faster in rAAA) per month. Conclusions: The finite element analysis of biomechanical properties could be used for the early prediction of an increased rupture risk in AAA patients. This was confirmed by matched imaging analyses before and after AAA rupture. Further multicenter data are needed to support these findings.

Aneurysm rupture is a life-threatening event, yet its underlying mechanisms remain largely unclear. This study investigated the fracture properties of the thoracic aneurysmatic aorta (TAA) using the symmetry-constraint Compact Tension (symconCT) test and compared results to native and enzymatic-treated porcine aortas’ tests. With age, the aortic stiffness increased, and tissues ruptured at lower fracture energy. Patients with bicuspid aortic valves were more sensitive to age, had stronger aortas and required more than tricuspid valves individuals (peak load: axial loading 4.42 1.56 N vs 2.51 1.60 N; circumferential loading 5.76 2.43 N vs 4.82 1.49 N. Fracture energy: axial loading 1.92 0.60 kJ m-2 vs 0.74 0.50 kJ m-2; circumferential loading 2.12 2.39 kJ m-2 vs 1.47 0.91 kJ m-2). Collagen content partly explained the variability in, especially in bicuspid cases. Besides the primary crack, TAAs and enzymatic-treated porcine aortas displayed diffuse and shear-dominated dissection and tearing. As human tissue tests resembled enzymatic-treated porcine aortas, microstructural degeneration, including elastin loss and collagen degeneration, seems to be the main cause of TAA wall weakening. Additionally, a tortuous crack developing during the symconCT test reflected intact fracture toughening mechanisms and might characterize a healthier aorta.

This work introduces a mathematical model and its numerical implementation within a finite element (FE) framework to investigate the progression of atherosclerosis, a prevalent vascular disease characterized by abnormal thickening of the arterial wall. The model follows the outside-in paradigm, which attributes the disease's origin to the dysfunction of the vasa vasorum (VVs) the microvascular network responsible for nourishing the artery wall. Vasa vasorum malfunction triggers an inflammatory response, leading to excessive tissue growth and wall thickening, ultimately causing stenosis and narrowing of the lumen. Additionally, this inflammatory process induces abnormal mechanical stresses within the arterial wall and activates homeostatic growth mechanisms. The interplay between inflammation and stress-driven growth governs the disease's progression. The numerical implementation is facilitated by AceGen, a symbolic and automatic differentiation tool, enabling the generation of a FORTRAN subroutine that interfaces with the FEM solver ANSYS.

Abdominal aortic aneurysms (AAA) are common, especially in men, and associated with a high mortality when ruptured. Clinical guidance for surgical repair is based on the maximum aortic diameter (>5.5 cm in men or 5 cm in women) but this is a poor indicator of clinical risk. Here, we examined micromechanical and biochemical properties of tissue excised from 21 patients undergoing repair for degenerative AAA using nanoindentation and biochemical assays (collagen elastin and glycosaminoglycans (GAGs)), along with 6 control aortic samples. AAA tissue properties were compared with peak wall stress (PWS), peak wall rupture risk (PWRR) localised rupture risk index (RRI) and wall stress (WS) determined from patient-specific finite element (FE) models, and with abdominal aortic calcification (AAC) scoring obtained from CT scans. The AAA samples had a lower median elastic modulus (72.4 kPa) and a higher IQR (86.8 kPa) relative to the controls (median 91.2 kPa, IQR 53.8 kPa). A heteroscedastic relationship was found in the AAA samples; patients with the highest median stiffness exhibited the largest IQR. Relative to controls, collagen was higher in the AAAs, whilst GAG and elastin were lower. Microcalcification was higher in the inner and middle layers of the vessel wall, matching the trend observed with stiffness. Correlative analysis showed that E was related to RRI but a complex, interplay of tissue properties contributed to overall PWRR. AAC was found to be inversely correlated with PWRR. Random forest modelling demonstrated that RRI is most influenced by E measured in the belly of the aneurysm, GAG, and collagen. In conclusion, micromechanical properties and calcification may be useful for patient-specific rupture risk prediction. Statement of significance: Abdominal aortic aneurysms (AAA) are more prevalent with age, and rupture is associated with a high mortality rate. Maximum aortic diameter, the main clinical criteria for surgical repair is a poor indicator of rupture risk (RR). We used micromechanical and biochemical characterisation, and computational modelling to understand RR in degenerative AAAs. The tissue elastic modulus was found to be an indicator of RR as was the in vivo abdominal aortic calcification (AAC) score with the latter having an inverse relationship with RR. Collagen and glycosaminoglycans levels were also key to RR. We demonstrate that RR is better indicated by AAC and tissue elastic properties than conventional clinical markers such as diameter alone. These findings can be exploited for patient-specific RR determination.

The biomechanical behaviour of vascular tissues is influenced by the presence of residual stresses, yet their role in vascular adaptation to pathological conditions remains largely unexplored. These residual stresses may arise within the vessel wall as a result of growth and remodelling (G&R) processes governed by the principles of tensional homeostasis. This study extends our previous work by refining a computational workflow that integrates homeostasis-driven G&R into patient-specific carotid geometries. Key advancements include adopting a total Lagrangian framework to handle complex geometries, introducing novel post-processing metrics for improved comparisons and conducting statistical analyses to assess G&R's impact on biomechanical evaluations of atherosclerotic vessels. These improvements enabled the analysis of a cohort of 18 cases, incorporating patient-specific geometries and pathological tissue distributions reconstructed from clinical imaging data. Results suggest that G&R generally reduces peak stress, though its effectiveness depends on plaque morphology and tissue composition. High calcification leads to localized stress concentrations, limiting remodelling, whereas matrix-rich regions promote stress homogenization. At the cohort level, findings underscore the need for patient-specific analyses in plaque risk evaluation, reinforcing the importance of personalized biomechanical modelling in assessing atherosclerotic disease and guiding clinical decision-making.

AAA disease, the local enlargement of the infrarenal aorta, is a serious condition that causes many deaths, especially in men exceeding 65years of age. Over the past quarter of a century, computational biomechanical models have been developed toward the assessment of AAA risk of rupture, technology that is now on the verge of being integrated within the clinical decision-making process. The modeling of AAA requires a holistic understanding of the clinical problem to set appropriate modeling assumptions and to draw sound conclusions from the simulation results. In this chapter, we summarize and critically discuss the state-of-the-art in AAA modeling, including the level of model validation. While most aspects concerning computational mechanics have already been settled, the exploration of the failure properties of the AAA wall and the acquisition of robust input data for simulations have the greatest potential for advancing this technology.