GELBOX: OPEN-SOURCE SOFTWARE TO IMPROVE RIGOR AND REPRODUCIBILITY WHEN ANALYZING GELS AND IMMUNOBLOTS.

GelBox is open-source software that was developed with the goal of enhancing rigor, reproducibility, and transparency when analyzing gels and immunoblots. It combines image adjustments (cropping, rotation, brightness, and contrast), background correction, and band-fitting in a single application. Users can also associate each lane in an image with metadata (for example, sample type). GelBox data files integrate the raw data, supplied metadata, image adjustments, and band-level analyses in a single file to improve traceability. GelBox has a user-friendly interface and was developed using MATLAB. The software, installation instructions, and tutorials, are available at https://campbell-muscle-lab.github.io/GelBox/.

A deep learning application to approximate the geometric orifice and coaptation areas of the polymeric heart valves under time - varying transvalvular pressure.

Machine learning and deep learning frameworks have been presented as a substitute for lengthy computational analysis, such as finite element analysis, computational fluid dynamics, and fluid-structure interaction. In this study, our objective was to apply a deep learning framework to predict the geometric orifice (GOA) and the coaptation areas (CA) of the polymeric heart valves under the time-varying transvalvular pressure. 377 different valve geometries were generated by changing the control coordinates of the attachment and the belly curve. The GOA and the CA values were obtained at the maximum and the minimum transvalvular pressure, respectively. The results showed that the applied framework can accurately predict the GOA and the CA despite being trained with a relatively smaller data set. The presented framework can reduce the required time of the lengthy FE frameworks.

The effect of fundamental curves on geometric orifice and coaptation areas of polymeric heart valves.

Valvular diseases, such as aortic stenosis, are considered a common condition in the US. In severe cases, either mechanical or prosthetic heart valves are employed to replace the diseased native valve. The prosthetic heart valve has been a focal point for researchers to gain a better understanding of the mechanics, which will lead to improved longevity. In this study, our objective was to evaluate the effect of fundamental curves on the geometric orifice area and the coaptation area by implementing a two-level Taguchi Orthogonal Array (OA) design (Analysis of Variance (ANOVA) technique) and the interaction plots to investigate the individual contributions. The leaflet geometry was represented with the attachment curve, the free edge, and the belly curve. A total of three varying control coordinates were used to form different leaflet surfaces. With two different biocompatible polymers, 16 finite element models were prepared. Each model was subjected to time-varying transvalvular pressure. The results showed that the control coordinate for the belly curve has the highest impact on the coaptation area of the valve models with higher average 100% modulus. The geometric orifice area was affected by both control points of the attachment curve and the belly curve. A similar effect was also observed for the valve models with lower average 100% modulus.

Finite Element Driven Design Domain Identification of a Beating Left Ventricular Simulator.

Almost ten percent of the American population have heart diseases. Since the number of available heart donors is not promising, left ventricular assist devices are implemented as bridge therapies. Development of the assist devices benefits from both in-vivo animal and in-vitro mock circulation studies. Representation of the heart is a crucial part of the mock circulation setups. Recently, a beating left ventricular simulator with latex rubber and helically oriented McKibben actuators has been proposed. The simulator was able to mimic heart wall motion, however, flow rate was reported to be limited to 2 liters per minute. This study offers a finite element driven design domain identification to identify the combination of wall thickness, number of actuators, and the orientation angle that results in better deformation. A nonlinear finite element model of the simulator was developed and validated. Design domain was constructed with 150 finite element models, each with varying wall thickness and number of actuators with varying orientation angles. Results showed that the combination of 4 mm wall thickness and 8 actuators with 90 degrees orientation performed best in the design domain.