Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.

Chronic mesenteric ischemia is caused by stenosis or occlusion of one or more visceral arteries. It represents a therapeutic challenge and diagnosis and treatment require close interdisciplinary cooperation between gastroenterologist, vascular surgeon and radiologist. Although endovascular treatment modalities have been developed, the number of restenoses ultimately resulting in treatment failure is high. In patients fit for open surgery, the visceral arteries should be revascularized conventionally. These patients will then experience long term relief from the symptoms, a better quality of life and a better overall survival.

Different approaches to study liver regeneration in murine models have been proposed. We investigated the effect of different liver resection models on liver damage and regeneration parameters in mice. We compared the technical aspect of the 2 most commonly used techniques of 50% and 70% liver resection. Liver damage, as determined by the change in serum alanine aminotransferase and aspartate aminotransferase, as well as the regeneration parameters VEGF and FGF-2 were analyzed at 6 time points. A postoperative vitality score was introduced. Cholestasis was not observed for either technique. Both resection techniques resulted in full weight recovery of the liver after 240 hours, with no significant difference between sham and resection groups. Postoperative animal morbidity and total protein levels did not differ significantly for either method, indicating early and full functional recovery. However, comparing the mitogenic growth factors FGF-2 and VEGF, a significant increase in serum levels and, therefore, increased growth stimulus, was shown in the extended resection group. Extended resection led to a greater response in growth factor expression. This finding is important since it shows that growth factor response differs acdording to the extent of resection. We have demonstrated the need to standardize murine hepatic resection models to adequately compare the resulting liver damage.

Blue light regulates biological function in various cells, such as proliferation, oxidative stress, and cell death. We employed blue light illumination on human umbilical vein endothelial cells utilizing a LED device at 453 nm wavelength and revealed a novel biphasic response on human umbilical vein endothelial cells (HUVECs). The results showed that low fluence blue light irradiation promoted the fundamental cell activities, including cell viability, migration and angiogenesis by activating the angiogenic pathways such as the VEGF signaling pathway. In contrast, high fluence illumination caused the opposite effect on those activities by upregulating pro-apoptotic signaling cascades like ferroptosis, necroptosis and the p53 signaling pathways. Our results provide an underlying insight into photobiomodulation by blue light and may help to implement potential treatment strategies for treating angiogenesis-dependent diseases.