NSF funds research on Fibrin Mechano-lysis

The National Science Foundation has funded our proposal on ‘Understanding Mechano-Fibrinolysis: Fiber-Scale Multiphysics Experiments and Models’.

The project, led by Dr. Manuel K. Rausch and Dr. Sapun Parekh (Parekh Lab, BME, UT-Austin), will investigate how fibrin’s state of mechanical deformation affects its rate of enzymatic digestion, i.e., its mechano-lysis. This question is a critical one to answer as enzymatic digestion is important in the regulation of many vital tissue functions such as tissue growth and remodeling as well as in tissue dysfunction such as in cancer.

Further details about the grant can be found here.

Dr Rausch receives NSF CAREER award

Dr Rausch has received the NSF CAREER award for his proposal titled “Toward a Fundamental Understanding of Why Thrombus Dissolves, Persists, or Breaks Off”.

This Faculty Early Career Development (CAREER) award will use experimental and computational strategies to quantify fundamental biophysical properties of blood clot. The research work will study why blood clot sometimes dissolves, sometimes persists, and sometimes embolizes (breaks off.)

Further details about the grant can be found here.

Dr Rausch receives Moncrief Grand Challenge Award

Dr Manuel Rausch has received an Oden Institute 2020 W. A. "Tex" Moncrief Grand Challenge Award for his proposal titled ‘A Machine-Learning Based Training Tool for Tricuspid Valve Repair: A Prototype’.

The objective of this proposal is to develop a prototype learning tool that incorporates all complexities of a human tricuspid valve and provides in-depth didactic insight into the effects of repair and device implantation on valve function. The outcome of this project will be a machine-learning based surrogate model that has been trained via high-fidelity finite element simulations. The finite element model itself will be built around a detailed cadaver study that includes all valvular and sub-valvular complexities. The surrogate educational model will be able to visualize the kinematics (i.e., competence) of the valve at minimal computational cost in comparison to a full simulation. Thus, the user will be able to change key valve parameters and learn their effect on valve function near instantaneously. This prototype will be a showcase for the potential of machine-learning based virtual training tools. It thereby holds the promise of aiding clinical training and reducing training-related morbidity and mortality.

Further information on the Moncrief Grand Challange Award program can be found here.