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The physics of shear waves traveling through matter carries fundamental insights into its structure, for instance, quantifying stiffness for disease characterization. However, the origin of shear wave attenuation in tissue is currently not properly understood. Attenuation is caused by two phenomena: absorption due to energy dissipation and scattering on structures such as vessels fundamentally tied to the material’s microstructure. Here, we present a scattering theory in conjunction with magnetic resonance imaging, which enables the unraveling of a material’s innate constitutive and scattering characteristics. By overcoming a three-order-of-magnitude scale difference between wavelength and average intervessel distance, we provide noninvasively a macroscopic measure of vascular architecture. The validity of the theory is demonstrated through simulations, phantoms, in vivo mice, and human experiments and compared against histology as gold standard. Our approach expands the field of imaging by using the dispersion properties of shear waves as macroscopic observable proxies for deciphering the underlying ultrastructures. 

To broaden efforts for improving diversity, equity, and inclusion (DEI) in biomedical engineering (BME) education—a key area of emphasis is the integration of inclusive teaching practices. While BME faculty generally support these efforts, translating support into action remains challenging. This project aimed to address this need through a 3-phase inclusive teaching training, consisting of graduate students, faculty, and engineering education consultants. In Phase I, graduate students and faculty participated in a 6-week learning community on inclusive teaching (Foundational Learning). In Phase II, graduate students were paired with faculty to modify or develop new inclusive teaching materials to be integrated into a BME course (Experiential Learning). Phase III was the implementation of these materials. To assess Phases I & II, graduate student participants reflected on their experiences on the project. To assess Phase III, surveys were administered to students in IT-BME-affiliated courses as well as those taking other BME-related courses. Phases I & II: graduate students responded positively to the opportunity to engage in this inclusive teaching experiential learning opportunity. Phase III: survey results indicated that the incorporation of inclusive teaching practices in BME courses enhanced the student learning experience. The IT-BME project supported graduate students and faculty in learning about, creating, and implementing inclusive teaching practices in a collaborative and supportive environment. This project will serve to both train the next class of instructors and use their study of inclusive teaching concepts to facilitate the creation of ideas and materials that will benefit the BME curriculum and students. 

Abstract Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) allow investigations in a human cardiac model system, but disorganized mechanics and immaturity of hPSC-CMs on standard two-dimensional surfaces have been hurdles. Here, we developed a platform of micron-scale cardiac muscle bundles to control biomechanics in arrays of thousands of purified, independently contracting cardiac muscle strips on two-dimensional elastomer substrates with far greater throughput than single cell methods. By defining geometry and workload in this reductionist platform, we show that myofibrillar alignment and auxotonic contractions at physiologic workload drive maturation of contractile function, calcium handling, and electrophysiology. Using transcriptomics, reporter hPSC-CMs, and quantitative immunofluorescence, these cardiac muscle bundles can be used to parse orthogonal cues in early development, including contractile force, calcium load, and metabolic signals. Additionally, the resultant organized biomechanics facilitates automated extraction of contractile kinetics from brightfield microscopy imaging, increasing the accessibility, reproducibility, and throughput of pharmacologic testing and cardiomyopathy disease modeling.