Patients with end-stage heart failure (HF) benefit from the implantation of left ventricular assist devices (LVADs). The two primary functions of these devices are first to restore cardiac output by active propulsion of blood from the left ventricle to the aorta and second to produce mechanical unloading of the left ventricle. Several studies demonstrated that a significant number of patients (‘responders’) with end-stage dilated cardiomyopathy and end-stage HF can recover substantial cardiac function following left ventricular unloading.
Patients with chronic HF that rely on implanted LVADs are usually placed on a list of individuals destined to receive heart transplants. This list includes responders as well as non-responders. Clearly it would be desirable that potential responders undergo clinical protocols, which might lead to cardiac recovery and thus help to preserve hearts for other patients.
A critical barrier to the treatment of end-stage HF patients exists because, until now, it has not been possible to predict at time of LVAD implantation if a patient will respond to unloading with sustained cardiac recovery. Our prior studies suggest that we have a criterion that will allow us to decide whether a patient is likely to be a responder. The criterion is derived from microscopic images of cardiac tissue that are analyzed with methods of image processing.
The stipend for this SPUR project is funded by an American Heart Association grant awarded to Dr. Stavros Drakos, MD, PhD. The stipend for this project is $4,000 instead of $5,000 due to grant funding limitations.
The student will be responsible for cryosectioning, immunolabeling and three-dimensional microscopy of cardiac tissues. The project activities will involve image processing, visualization and statistical analysis of tissue microstructure to quantify differences between control, responders and non-responders at pre and post-LVAD unloading.
The student will be required to take online courses for working with human subjects and be vaccinated before he/she can assist with myocardial tissue processing and imaging.
Student Learning Outcomes & Benefits
The student will gain exceptional experiences in different areas of basic and translational biomedical research. She/he will have an opportunity to gain insights in the operating room and be able to observe the operations performed during the LVAD implantation and heart transplant/LVAD explant in the process of samples acquisition. The student will have plenty of hands-on involvement in conducting laboratory research and learn a great deal about tissue remodeling in severe human disease.
This research opportunity is well suited for student who would like to pursue a medical career and research in biomedical science (MD/PhD). The learning experience from this project will help and encourage the undergraduate student to prepare for a research related career and professional development to become a scientist and/or physician.
College of Engineering
We will develop the student’s research capabilities with several measures:
- Scientific Training. Our technical training of the student in this project will focus on confocal microscopy, image processing and 3D reconstruction of tissue microstructure. We will also train the student in statistical methods and basics of cardiac remodeling in disease.
- Presentation Skills. The student will present her/his research in the weekly lab meetings with fellow students and post-doctoral associates. She/he will report on her/his progress and present relevant publications of others on a regular basis. Also, the student will participate in the monthly Research in Progress Seminar Series at the CVRTI.
- Increasing Personal Responsibility. We will execute an iterative approach to increase the personal responsibility of the student. Initially, we will provide rapid feedback on all aspects of this project. We will guide design and preparation of publications and presentations. After this phase, the student will be increasingly enabled to independently perform experimental work, analyze scientific data and prepare publications.