REU: Undergraduate Research Opportunities in Biomedical Engineering Devices

The student working with Dr. Kievit will be introduced to nanoparticle drug delivery vehicles as well as various mechanisms to gain entry into the brain through shared relevant literature before they arrive on campus. By the end of the first week on campus, the student will be trained on the nanoparticle synthesis and characterization strategies for the type of nanoparticles they will be working with such as magnetic and polymeric nanoparticles that can be imaged using both MRI and fluorescence imaging. Once this is complete, the student should be able to formulate a hypothesis on how to achieve the project goal as well as methods to test this hypothesis. Advanced students will also learn how to image the nanoparticles using our 9.4T MRI and fluorescence microscopes. During the middle of the summer, the student will use their developed skills to test their hypothesis by generating and interpreting characterization and imaging results. During the final third of the summer the student will reformulate their hypothesis based on their preliminary results and use their methods to re-test this hypothesis (search and re-search!). During this project, the student is expected to gain an understanding of the limitations of neurological disease treatment and how nanoparticle engineering strategies can be used to overcome these limitations. The student will get hands-on experience in nanoparticle synthesis strategies, characterization methods, and use of different imaging systems. The student will also get the experience of processing, analyzing, and interpreting experimental data, as well as data presentation in written and oral form.

Prior to arriving on campus, the student working with Dr. Nama will be provided with relevant literature and COMSOL software training. At the end of week 1, the student will be able to able perform preliminary simulations on the forces experienced by microswimmers under acoustic actuation and integrate this data to design preliminary microswimmer trajectories. In weeks 2-4, the student will receive extensive training on MATLAB concerning basic programming and plotting techniques. This training will also enable the student to plot their results and prepare their final presentation. Subsequent three weeks will focus on studying the effects of acoustic frequency and power by conducting several simulations for different frequencies. The last three weeks will be used to compare the computational data against the available experimental data, and design and test novel microswimmer designs.

The student working with Dr. Pannier will create methods to produce DNA-OMV NCs and evaluate their abilities to protect DNA cargo, and mediate transfection. Prior to arriving on campus, the student will read relevant background literature and receive web-based training materials to expediate lab work once they arrive. During the first week, the student will begin with training on specific assays, including mammalian and cell culture and OMV isolation. Over the course of the summer they will also learn how to prepare plasmid DNA, load plasmid DNA into OMVs, and then deliver DNA-loaded OMVs to both intestinal epithelial cells and macrophages. The student will be responsible for determining and testing optimization parameters to maximize DNA loading to achieve high levels of transfection with low toxicity. These findings will guide our design of OMVs as oral gene delivery carriers. At the end of the summer the student will present their work, and have the opportunity to prepare an abstract for the BMES meeting.

Prior to arriving on campus, the student working with Dr. Riggan will have user accounts created on the lab’s high performance computing environment and receive essential training and resources to access data effectively and safely. By the end of the first week on campus, the REU student will be introduced to project specific sensors, sensor phenomenology, and sensor data. In weeks 2-4, the student will complete a semi-automated data curation process using our AI-enabled data annotation tools and will sketch out design principles for CV algorithms, such as physical characteristics, texture, and other patterns of interests. In weeks 5-7, they will create a new CV algorithm to detect, cluster, track, or identify anomalies in biomedical imagery. Then, students will develop their data analysis plans in collaboration with graduate students. In weeks 8-10, they will finalize algorithm designs, execute their data analysis plans, and prepare and present their work at the REU symposium.