Reducing the amount of error and introduced data variability increases the accuracy of Western blot results. In this study, different methods of normalization for loading differences and data alignment were explored with respect to their impact on Western blot results. GAPDH was compared to the LI-COR Revert total protein stain as a loading control. The impact of normalizing data to a control condition, which is commonly done to align Western blot data distributed over several immunoblots, was also investigated. Specifically, this study addressed whether normalization to a small subset of distinct controls on each immunoblot increases pooled data variability compared to a larger set of controls. Protein expression data for NOX-2 and SOD-2 from a study investigating the protective role of the bradykinin type 1 receptor in angiotensin-II induced left ventricle remodeling were used to address these questions but are also discussed in the context of the original study. The comparison of GAPDH and Revert total protein stain as a loading control was done by assessing their correlation and comparing how they affected protein expression results. Additionally, the impact of treatment on GAPDH was investigated. To assess how normalization to different combinations of controls influences data variability, protein data were normalized to the average of 5 controls, the average of 2 controls, or an average vehicle and the results by treatment were compared. The results of this study demonstrated that GAPDH expression is not affected by angiotensin-II or bradykinin type 1 receptor antagonist R-954 and is a less sensitive loading control compared to Revert total protein stain. Normalization to the average of 5 controls tended to reduce pooled data variability compared to 2 controls. Lastly, the results of this study provided preliminary evidence that R-954 does not alter the expression of NOX-2 or SOD-2 to an expression profile that would be expected to explain the protection it confers against Ang-II induced left ventricle remodeling.

2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers, but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.