In this study, the effect of moisture on the elastic and failure properties of elastomeric polyurethane (EPU 40) 3D printed via Vat Photopolymerization was investigated. EPU 40 samples were printed, and uniaxial tensile tests were performed on Dry-fresh, Dry-aged (eight months aged), and after various times of being immersed in water (0–8 months). Elastic response, initial stiffness, failure strength, and failure elongation were analyzed. Besides, gravimetric analysis was performed to determine the increase in weight and thickness after water immersion. The elastic response was fitted by the Arruda-Boyce constitutive model. Results show that initial stiffness decreased after immersion (mean 6.8 MPa dry vs. 6.3 MPa immersed p-value 0.002). Contrary, the initial stiffness increased due to physical aging under a dry state from a mean 6.3 MPa to 6.9 MPa (p = 0.006). The same effect was observed for stiffness parameter G of the constitutive model, while the limit stretch parameter ${\lambda }_L$ was not affected by either aging. The 95% confidence intervals for strength and failure stretch were 5.27–9.48 MPa and 2.18–2.86, respectively, and were not affected either by immersion time or by physical aging. The median diffusion coefficient was $3.8·{10}^{-12} \mathrm{m}^2/\mathrm{s}$. The immersion time has a significant effect only on stiffness, while oxidative aging has an inverse effect on the mechanical properties compared to water immersion. The transition process is completed within 24 h after immersion.

Soft tissue sample thickness measurement is one of the major sources of differences between mechanical responses published by different groups. New method for the estimation of unloaded sample thickness of soft tissues is proposed in this study. Ten 30 × 30 mm and ten 20 × 20 mm samples of porcine anterior thoracic aortas were loaded by gradually increased radial force. Their deformed thickness was then recorded in order to generate a pressure-thickness response. Next, the limit pressure to which the response can be considered linear was estimated. Line was fitted to the linear part of the curve and extrapolated towards zero pressure to estimate unloaded thickness (7 kPa fit). For comparison, data near zero pressure were fitted separately and extrapolated towards zero (Near Zero fit). The limit pressure for the linearity of the response was around 7 kPa. The Unloaded thickness for 30 × 30 mm samples was 2.68 ± 0.31 mm and 2.68 ± 0.3 mm for Near Zero fit and 7 kPa fit, respectively. The Unloaded thickness for 20 × 20 mm samples was 2.60 ± 0.35 mm and 2.59 ± 0.35 mm for Near Zero fit and 7 kPa fit, respectively. The median of thickness difference between smaller and larger samples was not found statistically different. Proposed method can estimate unloaded undeformed sample thickness quickly and reliably.

Background: The acute phase of the COVID-19 pandemic requires a redefinition of healthcare system to increase the number of available intensive care units for COVID-19 patients. This leads to the postponement of elective surgeries including the treatment of abdominal aortic aneurysm (AAA). The probabilistic rupture risk index (PRRI) recently showed its advantage over the diameter criterion in AAA rupture risk assessment. Its major improvement is in increased specificity and yet has the same sensitivity as the maximal diameter criterion. The objective of this study was to test the clinical applicability of the PRRI method in a quasi-prospective patient cohort study. Methods: Nineteen patients (fourteen males, five females) with intact AAA who were postponed due to COVID-19 pandemic were included in this study. The PRRI was calculated at the baseline via finite element method models. If a case was diagnosed as high risk (PRRI > 3%), the patient was offered priority in AAA intervention. Cases were followed until 10 September 2021 and a number of false positive and false negative cases were recorded. Results: Each case was assessed within 3 days. Priority in intervention was offered to two patients with high PRRI. There were four false positive cases and no false negative cases classified by PRRI. In three cases, the follow-up was very short to reach any conclusion. Conclusions: Integrating PRRI into clinical workflow is possible. Longitudinal validation of PRRI did not fail and may significantly decrease the false positive rate in AAA treatment.