In Vitro Mitral Valve Model with Unrestricted Ventricular Access: Using Vacuum to Close the Valve and Enable Static Trans-Mitral Pressure.

Current in vitro models of the left heart establish the pressure difference required to close the mitral valve by sealing and pressurizing the ventricular side of the valve, limiting important access to the subvalvular apparatus. This paper describes and evaluates a system that establishes physiological pressure differences across the valve using vacuum on the atrial side. The subvalvular apparatus is open to atmospheric pressure and accessible by tools and sensors, establishing a novel technique for experimentation on atrioventricular valves. Porcine mitral valves were excised and closed by vacuum within the atrial chamber. Images were used to document and analyze closure of the leaflets. Papillary muscle force and regurgitant flow rate were measured to be 4.07 N at 120 mmHg and approximately 12.1 ml/s respectively, both of which are within clinically relevant ranges. The relative ease of these measurements demonstrates the usefulness of improved ventricular access at peak pressure/force closure.

The myocardial and coronary histopathology and pathogenesis of hypoplastic left heart syndrome.

Hypoplastic left heart syndrome has the greatest mortality rate among all CHDs and without palliation is uniformly fatal. Despite noble efforts, the aetiology of this syndrome is unknown and a cure remains elusive. The genetic and anatomic heterogeneity of hypoplastic left heart syndrome supports a rethinking of old hypotheses and warrants further investigation into the histological and vascular variations recognised with this syndrome. In an effort to elucidate the pathogenesis of hypoplastic left heart syndrome, this review will focus on its unique myocardial and coronary pathology as well as evaluate the association of hypoplastic left heart syndrome with the endocardial fibroelastosis reaction.

Congenital heart disease linked to maternal autoimmunity against cardiac myosin.

Structural congenital heart disease (CHD) has not previously been linked to autoimmunity. In our study, we developed an autoimmune model of structural CHD that resembles hypoplastic left heart syndrome (HLHS), a life-threatening CHD primarily affecting the left ventricle. Because cardiac myosin (CM) is a dominant autoantigen in autoimmune heart disease, we hypothesized that immunization with CM might lead to transplacental passage of maternal autoantibodies and a prenatal HLHS phenotype in exposed fetuses. Elevated anti-CM autoantibodies in maternal and fetal sera, as well as IgG reactivity in fetal myocardium, were correlated with structural CHD that included diminished left ventricular cavity dimensions in the affected progeny. Further, fetuses that developed a marked HLHS phenotype had elevated serum titers of anti-ß-adrenergic receptor Abs, as well as increased protein kinase A activity, suggesting a potential mechanism for the observed pathological changes. Our maternal-fetal model presents a new concept linking autoimmunity against CM and cardiomyocyte proliferation with cardinal features of HLHS. To our knowledge, this report shows the first evidence in support of a novel immune-mediated mechanism for pathogenesis of structural CHD that may have implications in its future diagnosis and treatment.

Identifying the potential loss of monitoring wells using an uncertainty analysis.

From the mid-1940s through the 1980s, large volumes of waste water were discharged at the Hanford Site in southeastern Washington State, causing a large-scale rise (>20 m) in the water table. When waste water discharges ceased in 1988, ground water mounds began to dissipate. This caused a large number of wells to go dry and has made it difficult to monitor contaminant plume migration. To identify monitoring wells that will need replacement, a methodology has been developed using a first-order uncertainty analysis with UCODE, a nonlinear parameter estimation code. Using a three-dimensional, finite-element ground water flow code, key parameters were identified by calibrating to historical hydraulic head data. Results from the calibration period were then used to check model predictions by comparing monitoring wells' wet/dry status with field data. This status was analyzed using a methodology that incorporated the 0.3 cumulative probability derived from the confidence and prediction intervals. For comparison, a nonphysically based trend model was also used as a predictor of wells' wet/dry status. Although the numerical model outperformed the trend model, for both models, the central value of the intervals was a better predictor of a wet well status. The prediction interval, however, was more successful at identifying dry wells. Predictions made through the year 2048 indicated that 46% of the wells in the monitoring well network are likely to go dry in areas near the river and where the ground water mound is dissipating.