Long term effects of soluble endoglin and mild hypercholesterolemia in mice hearts.

Soluble endoglin (sEng) released into the circulation was suggested to be related to cardiovascular based pathologies. It was demonstrated that a combination of high sEng levels and long-term exposure (six months) to high fat diet (HFD) resulted in aggravation of endothelial dysfunction in the aorta. Thus, in this study, we hypothesized that a similar experimental design would affect the heart morphology, TGFβ signaling, inflammation, fibrosis, oxidative stress and eNOS signaling in myocardium in transgenic mice overexpressing human sEng. Three-month-old female transgenic mice overexpressing human sEng in plasma (Sol-Eng+ high) and their age-matched littermates with low levels of human sEng (Sol-Eng+ low) were fed a high-fat diet containing 1.25% of cholesterol and 40% of fat for six months. A blood analysis was performed, and the heart samples were analyzed by qRT-PCR and Western blot. The results of this study showed no effects of sEng and HFD on myocardial morphology/hypertrophy/fibrosis. However, the expression of pSmad2/3 and p-eNOS was reduced in Sol-Eng+ high mice. On the other hand, sEng and HFD did not significantly affect the expression of selected members of TGFβ signaling (membrane endoglin, TGFβRII, ALK-5, ALK-1, Id-1, PAI-1), inflammation (VCAM-1, ICAM-1), oxidative stress (NQO1, HO-1) and heart remodeling (PDGFβ, COL1A1, β-MHC). In conclusion, the results of this study confirmed that sEng, even combined with a high-fat diet inducing hypercholesterolemia administered for six months, does not affect the structure of the heart with respect to hypertrophy, fibrosis, inflammation and oxidative stress. Interestingly, pSmad2/3/p-eNOS signaling was reduced in both the heart in this study and the aorta in the previous study, suggesting a possible alteration of NO metabolism caused by six months exposure to high sEng levels and HFD. Thus, we might conclude that sEng combined with a high-fat diet might be related to the alteration of NO production due to altered pSmad2/3/p-eNOS signaling in the heart and aorta

High Soluble Endoglin Levels Affect Aortic Vascular Function during Mice Aging

Endoglin is a 180 kDa transmembrane glycoprotein that was demonstrated to be present in two different endoglin forms, namely membrane endoglin (Eng) and soluble endoglin (sEng). Increased sEng levels in the circulation have been detected in atherosclerosis, arterial hypertension, and type II diabetes mellitus. Moreover, sEng was shown to aggravate endothelial dysfunction when combined with a high-fat diet, suggesting it might be a risk factor for the development of endothelial dysfunction in combination with other risk factors. Therefore, this study hypothesized that high sEng levels exposure for 12 months combined with aging (an essential risk factor of atherosclerosis development) would aggravate vascular function in mouse aorta. Male transgenic mice with high levels of human sEng in plasma (Sol-Eng+) and their age-matched male transgenic littermates that do not develop high soluble endoglin (Control) on a chow diet were used. The aging process was initiated to contribute to endothelial dysfunction/atherosclerosis development, and it lasted 12 months. Wire myograph analysis showed impairment contractility in the Sol-Eng+ group when compared to the control group after KCl and PGF2α administration. Endothelium-dependent responsiveness to Ach was not significantly different between these groups. Western blot analysis revealed significantly decreased protein expression of Eng, p-eNOS, and ID1 expression in the Sol-Eng+ group compared to the control group suggesting reduced Eng signaling. In conclusion, we demonstrated for the first time that long-term exposure to high levels of sEng during aging results in alteration of vasoconstriction properties of the aorta, reduced eNOS phosphorylation, decreased Eng expression, and altered Eng signaling. These findings suggest that sEng can be considered a risk factor for the development of vascular dysfunction during aging and a potential therapeutical target for pharmacological intervention

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools