Development of a mouse iron overload-induced liver injury model and evaluation of the beneficial effects of placenta extract on iron metabolism

Hepatic iron deposition is seen in cases of chronic hepatitis and cirrhosis, and is a hallmark of a poorer prognosis. Iron deposition is also found in non-alcoholic steatohepatitis (NASH) patients. We have now developed a mouse model of NASH with hepatic iron deposition by combining a methione- and choline-deficient (MCD) diet with an iron-overload diet. Using this model, we evaluated the effects of human placenta extract (HPE), which has been shown to ameliorate the pathology of NASH. Four-week-old male C57BL/6 mice were fed the MCD diet with 2% iron for 12 weeks. In liver sections, iron deposition was first detected around the portal vein after 1 week. From there it spread throughout the parenchyma. Biliary iron concentrations were continuously elevated throughout the entire 12-week diet. As a compensatory response, the diet caused elevation of serum hepcidin, which accelerates excretion of iron from the body. Accumulation of F4/80-positive macrophages was detected within the sinusoids from the first week onward, and real-time PCR analysis revealed elevated hepatic expression of genes related inflammation and oxidative stress. In the model mice, HPE treatment led to a marked reduction of hepatic iron deposition with a corresponding increase in biliary iron excretion. Macrophage accumulation was much reduced by HPE treatment, as was the serum oxidation-reduction potential, an index of oxidative stress. These data indicate that by suppressing inflammation, oxidative stress and iron deposition, and enhancing iron excretion, HPE effectively ameliorates iron overload-induced liver injury. HPE administration may thus be an effective strategy for treating NASH.ArticleHeliyon 5(5) : e01637-(2019)journal articl

Placental extract suppresses cardiac hypertrophy and fibrosis in an angiotensin II-induced cachexia model in mice

Cachexia is an intractable metabolic disorder that causes extreme weight loss. It is a symptom of many chronic diseases, including cancer, liver failure, congestive heart failure and chronic kidney disease, and there is as yet no effective treatment. While the mechanisms underlying cachexia are complex, it is often accompanied by elevated angiotensin II (Ang II). Human placental extract (HPE) is a source of numerous biologically active molecules and has been used clinically to treat chronic hepatitis, liver cirrhosis and other chronic diseases. Here, we investigated the effects of HPE in an Ang II-induced cachexia model in mice. HPE treatment preserved both fat mass and lean body mass and suppressed weight loss in the cachexia model, though food intake was unaffected. Ang II infusion also caused cardiac hypertrophy and fibrosis. HPE suppressed these effects as well as Ang II-induced cardiac expression of genes related to heart failure and cardiac remodeling. HPE also reversed Ang II-induced downregulation of mitochondria-related molecules and suppressed cardiac inflammation and oxidative stress. HPE administration may thus be an effective approach to the treatment of cachexia, cardiac hypertrophy and fibrosis.ArticleHeliyon 5(10) : e02655-(2019)journal articl

A blind source separation cascading separation and linearization for low-order nonlinear mixtures

A network structure and its learning algorithm have been proposed for blind source separation applied to nonlinear mixtures. Nonlinearity is expressed by low-order polynomials, which are acceptable in many practical applications. A separation block and a linearization block are cascaded. In the separation block, the cross terms are suppressed, and the signal sources are separated in each group, which include its high-order components. The high-order components are further suppressed through the linearization block. A learning algorithm minimizing the mutual information is applied to the separation block. A new learning algorithm is proposed for the linearization block. Simulation results, using 2-channel speech signals, instantaneous mixtures, and 2nd-order post nonlinear functions, show good separation performance

A cascade form blind source separation connecting source separation and linearization for nonlinear mixtures

A network structure and its learning algorithm have been proposed for blind source separation applied to nonlinear mixtures. The network has a cascade form consists of a source separation block and a linearization block in this order. The conventional learning algorithm is employed for the separation block. A new learning algorithm is proposed for the linearization block assuming 2nd-order nonlinearity. After, source separation, the outputs include the nonlinear components for the same signal source. This nonlinearity is suppressed through the linearization block. Parameters in this block are iteratively adjusted based on a process of solving a 2nd-order equation of a single variable. Simulation results, using 2-channel speech signals and an instantaneous nonlinear mixing process, show good separation performance