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Yu Chen Obtains Two Pilot Grants from Center for Clinical and Translational Science

Yu Chen

Yu Chen

Associate Professor Yu Chen of the Biomedical Engineering (BME) Department is a member of two research teams that each received a $50,000 pilot grant from the UMass Center for Clinical and Translational Science (UMCCTS).

As part of the two UMCCTS pilot grants, Chen’s Biophotonics Imaging Lab will be awarded $16,000 for a project titled "Automatic Wild-field Optical Imaging for Transplant Organ Viability Assessment" and $15,000 for research on "Dissecting Optical Coherence Tomography Features to Predict Risk of Future Advanced Age-related Macular Degeneration."

Chen is also an adjunct associate professor in the Department of Radiology at the UMass Chan Medical School in Worcester.

As Chen explains about the research in his lab, “My research interests include development of novel quantitative optical imaging and sensing devices for clinical translation, and preclinical and clinical applications of optical technologies in imaging brain function, renal physiology, cancer therapy, and tissue engineering. Specific imaging modalities include: optical coherence tomography, laminar optical tomography, and multiphoton microscopy.”

Chen’s imaging expertise and technology will be put to good use in these two pilot projects funded by UMCCTS.

For the project that is assessing the viability of transplant organs, Chen is a co-principal investigator (PI) with Professor Babak Movahedi, M.D./Ph.D., in the Department of Surgery at the UMass Chan Medical School and Worcester Polytechnic Institute’s BME Assistant Professor Haichong Zhang.

The team will help address the worldwide shortage of solid organs for transplanting, especially kidneys and livers, by answering the clinical needs for pioneering assessment methods to determine the viability of organs available for transplant.

As the three researchers explain their approach, “Recent advances in optical imaging and spectroscopy have enabled ‘optical biopsy’ of tissues with high resolution and molecular specificity, while without the need of tissue removal. This proposal seeks to develop a new multi-modal optical imaging device to evaluate donor-organ viability before transplant.”

The researchers add that “Our optical technology platform includes both optical coherence tomography and Raman spectroscopy.”

To translate the technology into clinical practice, the team will also develop robot-assisted automatic scanning of the entire organ surface. “If successful,” the researchers explain, “our technology can increase the number of organs from deceased donors that are available for transplantation and decrease failed transplants by providing more precise and comprehensive data….”

Chen is also a co-investigator on the second project, attempting to predict the risk of future macular degeneration problems. He will be working with PI Assistant Professor Tianxiao Huan and co-PI Professor Johanna Seddon, M.D., who are both in the Department of Ophthalmology and Visual Sciences at the UMass Chan Medical School.

As the three researchers explain, age-related macular degeneration (AMD) is the leading cause of irreversible central vision impairment and blindness in the world. The number of people suffering from AMD reached 196 million worldwide in 2020 and is expected to increase to 288 million by 2040. At present, the biology and treatment of the disease are not clear.

This research project answers the vital need, as the researchers say, “to identify sensitive and specific markers for early detection of AMD. AMD is characterized by reduced function of retinal pigment epithelium and loss of photoreceptors in the center of the retina (called the macula), leading to a gradual loss of central vision.”

The approach of this project is to use automated computer-aided diagnosis methods to pave the way for more efficient, fast, and economical screening and detection. 

As the researchers sum up their research goal, “Currently, there is no comprehensive retinal layer segmentation and morphologic feature analysis performed on large samples to achieve a better understanding of the correlation of retinal morphology with genetic and non-genetic risk factors of AMD. Our application aims to help fill this void.”