Echocardiographic integrated backscatter for assessing reduction of aortic valve calcifications by R-568 in a rat model of chronic kidney disease

Inhibition of acetylation of histones 3 and 4 attenuates aortic valve calcification

Aortic valve calcification develops in patients with chronic kidney disease who have calcium and phosphate metabolic disorders and poor prognoses. There is no effective treatment except valve replacement. However, metabolic disorders put patients at high risk for surgery. Increased acetylation of histones 3 and 4 is present in interstitial cells from human calcific aortic valves, but whether it is involved in aortic valve calcification has not been studied. In this study, we found that treating cultured porcine aortic valve interstitial cells with a high-calcium/high-phosphate medium induced calcium deposition, apoptosis, and expression of osteogenic marker genes, producing a phenotype resembling valve calcification in vivo. These phenotypic changes were attenuated by the histone acetyltransferase inhibitor C646. C646 treatment increased the levels of class I histone deacetylase members and decreased the acetylation of histones 3 and 4 induced by the high-calcium/high-phosphate treatment. Conversely, the histone deacetylase inhibitor suberoylanilide hydroxamic acid promoted valve interstitial cell calcification. In a mouse model of aortic valve calcification induced by adenine and vitamin D treatment, the levels of acetylated histones 3 and 4 were increased in the calcified aortic valves. Treatment of the models with C646 attenuated aortic valve calcification by restoring the levels of acetylated histones 3 and 4. These observations suggest that increased acetylation of histones 3 and 4 is part of the pathogenesis of aortic valve calcification associated with calcium and phosphate metabolic disorders. Targeting acetylated histones 3 and 4 may be a potential therapy for inoperable aortic valve calcification in chronic kidney disease patients.

Interaction of renal failure and dyslipidaemia in the development of calcific aortic valve disease in rats

OBJECTIVE

Calcific aortic valve disease (CAVD) is currently the most common heart valve disease worldwide and is known to be an active process. Both renal failure and dyslipidaemia are considered to be promoting factors for the development of valvular calcifications. The aim of this study is to prospectively evaluate the respective contribution and interaction of renal failure and dyslipidaemia on CAVD in a rat model, using echocardiography and compared with histology.

METHODS AND RESULTS

Sixty-eight male Wistar rats were prospectively divided in eight groups, each fed a different diet to induce renal failure alone and combined with hyperlipidaemia or hypercholesterolemia. CAVD was detected and quantified by calibrated integrated backscatter of ultrasound (cIB) and compared with the histological calcium score. The study follow-up was 20 weeks. At the end of the study, the cIB value and the calcium score of the aortic valve were significantly increased in the group with isolated renal failure but not with dyslipidaemia. The combination of renal failure with high cholesterol or high-fat diet did not significantly increase calcifications further.

CONCLUSIONS

Renal failure alone does induce aortic valve calcifications in a rat model of CAVD, whereas dyslipidaemia alone does not. The combination of renal failure with dyslipidaemia does not increase calcification further. These findings suggest that a combination of atherosclerotic and calcifying factors is not required to induce aortic valve calcifications in this model.

Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors

Assessment of tumor tissue heterogeneity via ultrasound has recently been suggested as a method for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting the local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine to coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the purpose of ultrasound tissue characterization. The fractal dimension (self-similarity measure) was used to characterize the spatial distribution of scatterers, whereas the lacunarity (sparsity measure) was applied to determine scatterer number density. Performance was assessed based on 608 cross-sectional clinical ultrasound radiofrequency images of liver tumors (230 and 378 representing respondent and non-respondent cases, respectively). Cross-validation via leave-one-tumor-out and with different k-fold methodologies using a Bayesian classifier was employed for validation. The fractal properties of the backscattered echoes based on the Nakagami model (Nkg) and its extend four-parameter Nakagami-generalized inverse Gaussian (NIG) distribution achieved best results-with nearly similar performance-in characterizing liver tumor tissue. The accuracy, sensitivity and specificity of Nkg/NIG were 85.6%/86.3%, 94.0%/96.0% and 73.0%/71.0%, respectively. Other statistical models, such as the Rician, Rayleigh and K-distribution, were found to not be as effective in characterizing subtle changes in tissue texture as an indication of response to treatment. Employing the most relevant and practical statistical model could have potential consequences for the design of an early and effective clinical therapy.