Dyslipidemia complicates renal function leading to disturbances of major homeostatic organs in the body. Here we examined the effect of chronic renal dysfunction induced by uninephrectomy on fat redistribution and lipid peroxidation in rats treated with an angiotensin-converting enzyme (ACE) inhibitor (lisinopril) for up to 10 months. Uninephrectomized rats developed fat redistribution and hypercholesterolemia typical of chronic renal failure when compared with sham-operated rats or lisinopril-treated uninephrectomized rats. The weight of the peri-renal fat was significantly less in the untreated compared to the lisinopril-treated uninephrectomized rats or those rats with a sham operation. We also found that there was a shift of heat-protecting unilocular adipocytes to heat-producing multilocular fat cells in the untreated uninephrectomized rats. Similarly in these rats we found a shift of subcutaneous and visceral fat to ectopic fat with excessive lipid accumulation and lipofuscin pigmentation. Lisinopril treatment prevented fat redistribution or transformation and lipid peroxidation. This study shows that ACE inhibition may prevent the fat anomalies associated with chronic renal dysfunction.

Diabetes and obesity are complex diseases associated with insulin resistance and fatty liver. The latter is characterized by dysregulation of the Akt, AMP-activated protein kinase (AMPK), and IGF-I pathways and expression of microRNAs (miRNAs). In China, multicomponent traditional Chinese medicine (TCM) has been used to treat diabetes for centuries. In this study, we used a three-herb, berberine-containing TCM to treat male Zucker diabetic fatty rats. TCM showed sustained glucose-lowering effects for 1 week after a single-dose treatment. Two-week treatment attenuated insulin resistance and fatty degeneration, with hepatocyte regeneration lasting for 1 month posttreatment. These beneficial effects persisted for 1 year after 1-month treatment. Two-week treatment with TCM was associated with activation of AMPK, Akt, and insulin-like growth factor-binding protein (IGFBP)1 pathways, with downregulation of miR29-b and expression of a gene network implicated in cell cycle, intermediary, and NADPH metabolism with normalization of CYP7a1 and IGFBP1 expression. These concerted changes in mRNA, miRNA, and proteins may explain the sustained effects of TCM in favor of cell survival, increased glucose uptake, and lipid oxidation/catabolism with improved insulin sensitivity and liver regeneration. These novel findings suggest that multicomponent TCM may be a useful tool to unravel genome regulation and expression in complex diseases.

Fine renal artery segmentation on abdominal CT angiography (CTA) image is one of the most important tasks for kidney disease diagnosis and pre-operative planning. It will help clinicians locate each interlobar artery's blood-feeding region via providing the complete 3D renal artery tree masks. However, it is still a task of great challenges due to the large intra-scale changes, large inter-anatomy variation, thin structures, small volume ratio and small labeled dataset of the fine renal artery. In this paper, we propose the first semi-supervised 3D fine renal artery segmentation framework, DPA-DenseBiasNet, which combines deep prior anatomy (DPA), dense biased network (DenseBiasNet) and hard region adaptation loss (HRA): 1) Based on our proposed dense biased connection, the DenseBiasNet fuses multi-receptive field and multi-resolution feature maps for large intra-scale changes. This dense biased connection also obtains a dense

Renal cancer is one of ten most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) becomes a main therapeutic approach in treating renal cancer. Accurate kidney and tumor segmentation in CT images is a prerequisite step in the surgery planning. However, automatic kidney and renal tumor segmentation in CT images is still a challenge work. In this paper, we propose a new method to perform precise segmentation of kidney and renal tumor in CT angiography images. The method mainly relies on a new threedimensional (3D) fully convolutional network (FCN) which combines the pyramid pooling module (PPM) and gradually enhanced feature module (GEFM). The proposed 3D network can utilize the 3D spatial contextual information to improve the segmentation of the kidney as well as the tumor lesion. According to the experimental results in the CT images of 140 patients, our proposed method can segment the kidney and renal tumor with a high accuracy. The average dice coefficients of kidney and renal tumor obtained by the proposed method are 0.923 and 0.826 respectively, which are higher than the other two advanced segmentation methods. Furthermore, our approach shows an excellent performance for renal tumor detection in high sensitivity and specificity.