Surface Finishing of Magnesium-Alloy-Based Biodegradable Stents
Faculty Mentor: Prof. Hitomi Greenslet
Project Description: A biodegradable stent (BDS) opens a narrowed arterial vessel and then dissolves after the vessel is formed into the desired shape. Magnesium-alloy-based BDSs attract much interest, but the excessively high biodegradation rate of the alloys used is a major challenge. An effective approach to delay the degradation of magnesium alloys is surface smoothing. This also enhances the performance of magnesium-alloy BDSs, as smooth surfaces and rounded edges facilitate BDS trackability and deliverability. Recently, a novel mechanical polishing process to smooth the entire stent was proposed, and its feasibility was demonstrated. In this project, the REU participant will learn (1) how to design and perform polishing experiments using magnesium-alloy BDSs, (2) how to use equipment to evaluate surface roughness of the stents, (3) measure the stent surface/edge roughness, and (4) analyze the acquired data. The REU participant will use the acquired data to identify parameters influencing polishing characteristics.
Mechanics of Multi-layered Cranial Dura Mater
Faculty Mentor: Prof. Lakiesha Williams
Project Description: Cranial dura mater is the tough, protective membrane that covers the brain. Its primary role is to keep the cerebrospinal fluid inside of the cranial cavity. It provides critical mechanical, immunological, and vascular support to the brain. In most brain surgeries dura needs to be repaired with a replacement graft. Currently, there is no gold standard for these grafts because the properties that make the dura mater so unique are not fully understood. It isn’t a simple sheet, but a complex, multi-layered material. In this project, the REU Participant will investigate the link between the dura’s intricate structure and its mechanical strength. By testing how the tissue stretches and responds to force and using advanced microscopes to see its structure up close, the student will help build a fundamental understanding of this critical tissue. The results will provide a blueprint for engineers to design safer and more effective replacement grafts for use in brain surgery. Furthermore, the student will gain experience working in interdisciplinary environments and develop enhanced professional skills including communication, teamwork, and problem-solving.
Development of Hand-Held Devices for Pathogen Detection
Faculty Mentor: Prof. Z. Hugh Fan
Project Description: Viruses are a global public health problem, evidenced from the pandemic of coronavirus disease 2019 (COVID-19). It is highly desirable to have a device that can detect viruses for transmission control and differentiate various viruses for disease-specific clinical care. To meet the need, we are developing a rapid and cost-effective platform for detecting pathogens at the point of care. The platform will disintegrate pathogens (a process called lysis) and perform isothermal amplification of enriched nucleic acids, followed by colorimetric detection by naked eye or a smartphone camera. Innovation is needed in the device miniaturization, sequential reagent delivery, and operation in the field without power and laboratory equipment. The project is a part of the lab’s efforts on microfluidics, which is an interdisciplinary field involving engineering, sciences, and medicine. The participant will be trained to fabricate devices, including 3D printing, lamination, and integration. The devices will then be tested by the student while being trained on valving control, biological assays, and detection. Various detection schemes and conditions will be investigated, and their effects on detection sensitivities will be studied. The participant is encouraged to test their own ideas after training and understanding the challenges associated with the project.
Blood Biomarker Monitoring Using Continuous Biosensing Technology
Faculty Mentor: Prof. Jing Pan
Project Description: Blood contains thousands of biomolecules that reflect a person’s health status. Measuring these molecules continuously rather than through occasional blood draws could transform how doctors monitor and treat patients, especially those in critical care. In this project, the REU Participant will help develop a biosensor system that continuously measures key biomarkers in blood, such as immune signals (e.g., interleukins), metabolites (e.g., glucose), and coagulation factors (e.g., thrombin). The system is based on an innovative biochemical monitoring platform, which uses miniature fluid channels to detect biomolecules as they flow through. Working with the research team, the REU Participant will learn how to design, operate, and analyze data from these biosensors. The goal is to integrate the chip into a bedside readout device that displays real-time biomarker concentrations, paving the way for smarter, faster, and more personalized medical care in hospitals.
Control of Inflammation-Mediated Coagulopathy
Faculty Mentor: Prof. Amor Menezes
Project Description: Hemostasis, the process of blood coagulation, is impaired in many disorders resulting in excessive bleeding or clotting. Dysregulated blood coagulation, called coagulopathy, occurs after trauma, space travel, infections like COVID-19, nuclear radiation, chemical exposure, and burns; and during hemophilia, von Willebrand disease, factor V Leiden, pulmonary embolism, deep vein thrombosis, myocardial infarction, stroke, sickle cell disease, and cancer. Treating coagulopathy is challenging due to complex molecular and cellular mechanisms. Because inflammation triggers coagulopathy, the long-term vision is to engineer cells with genetic controllers to sense and treat inflamed cells, tissues, and organs during coagulopathy. Toward this vision, the REU Participant will investigate how best to manipulate the protein concentrations that are involved in coagulopathy: either trauma-induced, space-induced, or infection-induced in the lung. The REU Participant will be trained to use instruments to interrogate clotting in blood samples; adjust protein concentrations; culture lung epithelial cells; image cells; model the biomolecular interactions that cause coagulation; and/or use feedback control theory to design physical or biological controllers. The REU Participant will then apply these skills to experimentally or computationally simulate and mitigate coagulopathy. Project results may be leveraged for personalized clinician decision support software that runs on a phone or similar hand-held device.
Burrowing Tumor Models
Faculty Mentor: Prof. Thomas Angelini
Project Description: Cancer cell invasion and remodeling of healthy host tissue is known to occur in several ways. One long studied mode of cancer spreading is metastasis, the process in which cancer cells detach from a primary tumor and enter the bloodstream or the lymphatic system to colonize new tissue in different parts of the body. It is also known that groups of cells move collectively to invade surrounding tissue. However, collective modes of cancer invasion into surrounding environments are poorly understood. Due to the challenges of studying cancer invasion in the bodies of living organisms, it is critical to create model tumors and investigate how they invade their surrounding environments. In this project, the REU Participant will 3D print tumor models from living cells in a 3D support material that serves as a surrogate for surrounding tissue. Using time lapse microscopy and digital image analysis, the REU Participant will test our current hypothesis that entire tumors can translocate through surrounding tissues by a process of mechanical burrowing. The new discoveries made in this project will aid in developing totally new approaches to treating cancer and understanding the details of its pathogenesis.
Modeling Interstitial Fluid Flow and Drug Delivery in Tumors
Faculty Mentor: Prof. Malisa Sarntinoranont
Project Description: Uneven coverage is a major problem encountered in cancer drug therapy (chemotherapy) since the clinical goal is to eliminate 100% of cancer cells. Barriers to uniform drug delivery can be traced to an abnormal fluid dynamic environment introduced by leaky tumor vessels and uneven clearance. Medical imaging methods such as magnetic resonance imaging (MRI) are known to capture certain aspects of these delivery barriers. Our lab is using MRI data to generate 3D models of brain tumors that simulate spatial flow and drug delivery patterns that are customized to an individual patient. In the future, we plan to use these models to test treatment plans and optimize coverage. The REU Participant will work with MRI data sets and learn image processing and developing 3D geometric models of brain tumors. The goal is to contribute to the development of a computational model that simulates tissue flow and transport.
Bringing Living Cells’ Symphony to Light
Faculty Mentor: Prof. Xin Tang
Project Description: To maintain life, living cells, including bacteria, yeast, plant, and mammalian cells, utilize diverse signals to regulate multiscale cell-cell interactions. Proper cell-cell interactions orchestrate healthy development, homeostasis, immune defenses, and evolution. Improper cell-cell interactions cause diseases, such as cancer, infection, neurological dysfunction, and cardiac arrhythmia. Thus, a deeper understanding of how living cells interact in tissues will bring key insights into fundamental life science, bioengineering innovations, and healthcare applications. In this project, the REU Participants will perform optical imaging, mechanical characterization, and AI (artificial intelligence) programming to shed new light on multiscale cell-cell interactions. Optical imaging allows non-invasive and rapid visualization of 3D signals that regulate cell-cell interactions. Mechanical characterization and AI programming allow quantitative understanding of how tissue mechanics coordinates and modulates the cellular circuits and switches efficiently. By establishing these experimental-computational frameworks, the REU Participants will reveal life’s intricate processes and decode the design principles of biological systems. The results can help innovate new applications in biotechnology, medicine, and environmental sustainability.
Molecular Modeling of Fracture in Hydrogels
Faculty Mentor: Prof. Douglas Spearot
Project Description: Hydrogels are soft materials that are made from a network of long polymer chains and water. The polymer chains are either physically entangled or chemically bonded. Because hydrogels are mostly comprised of water, they are very flexible materials and behave a lot like living tissue. Thus, they are used in many healthcare applications, such as in soft contact lenses, wound-care dressings, and tissue engineering scaffolds. In these uses, the hydrogel must be strong enough to retain its shape and not fail under different loading conditions. In this project, the REU Participant will perform atomistic simulations on the UF supercomputer to model crack propagation in hydrogels. Atomistic simulations allow a nanoscale view of how a material responds to an applied load. By building virtual hydrogels that contain different amounts of water, the REU Participant will simulate how cracks grow through the polymer network. The results can help design stronger, more reliable hydrogels for future healthcare applications.
Characterizing Physical Activity Patterns with Wearable Technology
Faculty Mentor: Prof. Kerry Costello
Project Description: People move in many different ways during their daily lives, and those movement patterns can influence joint health. One major risk factor for knee osteoarthritis is the amount and type of mechanical loading on the knee joint. While laboratory motion capture systems can measure movement precisely, they don’t fully reflect how people actually move day to day. Small wearable sensors that measure acceleration and rotation allow us to record motion outside the lab and study real-world activity. In this project, the REU participant will collect and analyze data from wearable sensors and laboratory motion capture while people perform everyday activities. Students will gain experience processing and visualizing movement data, exploring features that describe how the body moves, and using beginner-friendly programming and machine learning tools to uncover patterns related to knee loading. This work will contribute to a larger effort to understand how movement patterns relate to osteoarthritis risk and to develop better, activity-based treatments for maintaining joint health.
Neuromuscular Simulations of Human Balance Control
Faculty Mentor: Prof. Jessica Allen
Project Description: Falls due to a loss of balance are a leading cause of injury in older adults and individuals with neurological conditions. These balance problems can stem from mechanical impairments (such as reduced muscle strength) and/or neural impairments (such as delayed or inappropriate muscle activation). Because these impairments vary widely from person to person, there is no “one-size-fits-all” solution for improving balance. Finding the right intervention often requires a time-consuming trial-and-error process. A promising way to speed up this process is to use predictive neuromuscular simulations, or computer models that represent how the nervous system controls movement. These simulations can be used to explore how different impairments affect balance and walking, and to test potential interventions virtually before trying them in real people. In this project, the REU participant will use the UF supercomputer to run simulations of walking under different balance challenges (such as slips or pushes). The student will also work with experimental data collected from human participants to help build and validate these models. By combining simulations with real-world measurements, the project will provide insights into how specific impairments impact balance control and help identify which types of interventions may work best for different individuals.