Angiogenesis contributes to the development of nonalcoholic steatohepatitis (NASH) and promotes inflammation, fibrosis, and progression to hepatocellular carcinoma (HCC). Angiopoietin‐2 (Ang‐2) is a key regulator of angiogenesis. We aimed to investigate the role of Ang‐2 and its potential as a therapeutic target in NASH using human samples, in vivo mouse models, and in vitro assays. Serum Ang‐2 levels were determined in 104 obese patients undergoing bariatric surgery and concomitant liver biopsy. The effect of the Ang‐2/Tie2 receptor inhibiting peptibody L1‐10 was evaluated in the methionine‐choline deficient (MCD) and streptozotocin‐western diet nonalcoholic fatty liver disease mouse models, and in vitro on endothelial cells and bone marrow–derived macrophages. The hepatic vasculature was visualized with µCT scans and scanning electron microscopy of vascular casts. Serum Ang‐2 levels were increased in patients with histological NASH compared with patients with simple steatosis and correlated with hepatic CD34 immunoreactivity as a marker of hepatic angiogenesis. Serum and hepatic Ang‐2 levels were similarly increased in mice with steatohepatitis. Both preventive and therapeutic L1‐10 treatment reduced hepatocyte ballooning and fibrosis in MCD diet‐fed mice and was associated with reduced hepatic angiogenesis and normalization of the vascular micro‐architecture. Liver‐isolated endothelial cells and monocytes from MCD‐fed L1‐10–treated mice showed reduced expression of leukocyte adhesion and inflammatory markers, respectively, compared with cells from untreated MCD diet‐fed mice. In the streptozotocin‐western diet model, therapeutic Ang‐2 inhibition was able to reverse NASH and attenuate HCC progression. In vitro, L1‐10 treatment mitigated increased cytokine production in lipopolysaccharide‐stimulated endothelial cells but not in macrophages. Conclusion: Our findings provide evidence for Ang‐2 inhibition as a therapeutic strategy to target pathological angiogenesis in NASH.
BackgroundThe increasing incidence of hepatocellular carcinoma in Western countries has led to an expanding interest of scientific research in this field. Therefore, a vast need of experimental models that mimic the natural pathogenesis of hepatocellular carcinoma (HCC) in a short time period is present. The goal of our study was (1) to develop an efficient mouse model for HCC research, in which tumours develop in a natural background of fibrosis and (2) to assess the time-dependent angiogenic changes in the pathogenesis of HCC.MethodsWeekly intraperitoneal injections with the hepatocarcinogenic compound N-nitrosodiethylamine was applied as induction method and samples were taken at several time points to assess the angiogenic changes during the progression of HCC.ResultsThe N-nitrosodiethylamine-induced mouse model provides well vascularised orthotopic tumours after 25 weeks. It is a representative model for human HCC and can serve as an excellent platform for the development of new therapeutic targets.
In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization
In computerized tomography, it is important to reduce the image noise without increasing the acquisition dose. Extensive research has been done into total variation minimization for image denoising and sparse-view reconstruction. However, TV minimization methods show superior denoising performance for simple images (with little texture), but result in texture information loss when applied to more complex images. Since in medical imaging, we are often confronted with textured images, it might not be beneficial to use TV. Our objective is to find a regularization term outperforming TV for sparse-view reconstruction and image denoising in general. A recent efficient solver was developed for convex problems, based on a split-Bregman approach, able to incorporate regularization terms different from TV. In this work, a proof-of-concept study demonstrates the usage of the discrete shearlet transform as a sparsifying transform within this solver for CT reconstructions. In particular, the regularization term is the 1-norm of the shearlet coefficients. We compared our newly developed shearlet approach to traditional TV on both sparse-view and on low-count simulated and measured preclinical data. Shearlet-based regularization does not outperform TV-based regularization for all datasets. Reconstructed images exhibit small aliasing artifacts in sparse-view reconstruction problems, but show no staircasing effect. This results in a slightly higher resolution than with TV-based regularization.
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