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 high levels of interest, but the excessively high biodegradation rate of the alloys used is a major challenge. An effective approach to delaying the degradation of magnesium alloys is surface modification, including the use of drug-polymer coatings. Surface smoothing is another approach to enhance the performance of Mg alloy BDSs. Smooth surfaces and rounded edges facilitate BDS trackability and deliverability. Recently, a novel mechanical polishing process to smooth the entire stent surfaces was proposed, and its feasibility was demonstrated. This REU project aims to clarify the material removal mechanism of the developed process and identify the relationship between the material removal, mechanical properties, and corrosion resistance (biodegradability) of the Mg alloy BDSs.
Training Plan: The REU Participant will be trained on how to design experiments and collect data. The student will be trained on how to use precision equipment and analytical tools, including a polishing machine, an optical profiler, and various microscopes.
Intellectual Merit: The participant will experimentally clarify the parameters influencing the material removal of Mg alloy BDSs, the material removal mechanism itself, and the relationship between the stent surface integrity and stent performance. This will result in an understanding of microstructure evolution during polishing and the effect on the mechanical properties biodegradability.
Mechanical Testing of Structure-Property Relationships of Cranial Dura Mater
Faculty Mentor: Prof. Lakiesha Williams
Project Description: Cranial dura mater is the thickest outermost layer of meninges and consists of a dense elastic membrane that keeps cerebrospinal fluid inside the cranial cavity. It provides critical mechanical, immunological, and vascular support to brain parenchyma. In most brain surgeries, the dura mater is incised, and needs to be repaired with a replacement graft. However, there is no standard on the requirements of replacement grafts as the mechanics and microstructure relationships of cranial dura are yet to be confirmed. A scanning electron microscopy (SEM) study on dura mater illustrated its complexity, identifying five uniquely different layered architectures along its 500 μm thickness. In this project, the REU Participant will investigate the complex structure and mechanical response of porcine and cadaveric cranial dura mater using systematic experiments and image analysis tools.
Training Plan: REU participants will be trained in hazardous waste management, and in working with human and animal tissues. They will participate in lab safety training. Participants will receive training on mechanical testing devices, digital image correlation cameras, nano-CT, and other microscopy tools.
Intellectual Merit: A fundamental understanding of dura mater will further uncover its role in providing protection to the brain. This proposed study correlating the complex multi-layered structure and mechanics of dura will give insight for developing suitable synthetic dura replacement grafts.
Engineered Microchannel Surfaces for Tumor Cell Isolation
Faculty Mentor: Prof. Z. Hugh Fan
Project Description: Circulating tumor cells (CTC) in the peripheral blood are potential biomarkers for cancer diagnosis and prognosis. However, CTCs are extremely rare in the bloodstream, typically a few CTCs per billions of normal blood cells, making their detection very challenging. Microfluidics is a unique platform to address this problem because (i) a decrease in the diffusion distance, results in an increase in interaction opportunities; and (ii) a higher surface-to-volume ratio in a microchannel provides a higher density of capture agents and accordingly higher capture efficiency. A variety of schemes have been integrated in microfluidic devices to enhance the capture efficiency, including micropillars, micromixers, nanoparticles, and multivalent capture agents. The objective of this project is to engineer surfaces and fabricate microfluidic devices that will enhance tumor cell capture efficiency and selectivity.
Training Plan: First, the REU Participant will be trained to fabricate microfluidic devices. The devices will then be tested by the participant while being trained on micropumps and microscopic observation. Improvement of device designs by engineering unique surfaces and various microchannel geometry will be carried out, and their effects on tumor cell capture efficiency will be investigated.
Intellectual Merit: The CTC isolation method is currently performed using macroscale beads and various microfluidic devices. The proposed project to design and fabricate biomimetic surfaces will advance the microfluidics-enabled CTC isolation, further enhancing capture efficiency.
Device Platform Development for Continuous Biochemical Monitoring
Faculty Mentor: Prof. Jing Pan
Project Description: Continuous monitoring of biomolecular concentrations in blood is a transformative technology that provides valuable insights about a patient’s health status in real-time. The development of such capability is particularly useful to tailor the treatment for critically ill patients in Intensive Care Units (ICUs). The objective of this project is to develop a microfluidic biochemical monitoring system that can report blood biomarker concentrations continuously at bedside. The project will leverage the RT-ELISA technology platform to target immune markers (e.g., interleukin), metabolites (e.g., glucose) and coagulation factors (e.g., thrombomodulin). The RT-ELISA chip will be integrated into a bedside readout system that displays the real-time biomarker concentration.
Training Plan: The REU Participant will complete the following trainings: (1) Safety trainings approved by UF EHS (Biohazard Waste Management, Hazardous Waste Management, Laser Safety, Chemical Hygiene Plan) (2) Web-lab basics training (e.g., pipetting, benchtop instrument operation, etc.) (3) Project specific training on RT-ELISA development and operation.
Intellectual Merit: This research will provide the first demonstration of a continuous monitoring device for low abundance biomarkers. The data collected from this project will inform the dynamics of immune responses in critically ill patients and guide future clinical studies of personalized treatment in ICUs.
Control of Inflammation-Mediated Coagulopathy
Faculty Mentor: Prof. Amor Menezes
Project Description: This project investigates the use of feedback control to manipulate the concentrations of proteins that are involved in inflammation-mediated coagulation disorders. Two such disorders are: (i) trauma-induced coagulopathy, which occurs after severe trauma and shock and has poor treatment outcomes, and (ii) infection (e.g., COVID-19) induced coagulopathy in the lung, which results in blood clots and contributes to lung fibrosis. Our approach permits protein concentration modulation in a patient-tailored way, thereby realizing personalized and precision medicine. This project leverages recently developed quick, control-oriented, phenomenological models from input coagulation protein concentrations using sequential system identification, with demonstration in silico.
Training Plan: This project will involve both wet lab (70%) and dry lab (30%) components. The participant will train on instruments that interrogate clotting in blood samples, manipulating protein concentrations, culturing alveolar epithelial cells, imaging cells, and designing biological control systems.
Intellectual Merit: Current trauma-induced coagulopathy resuscitation protocols use slow laboratory measurements, rules-of-thumb, and clinician gestalt to administer large volumes of blood products. In contrast, coagulation proteins are central to this control-theoretic approach as the actuators for treatment, and their concentrations can be measured in <10 minutes using coagulation assays as sensors.
Endothelial Tissue Morphogenesis in 3D Bioprinted Constructs
Faculty Mentor: Prof. Thomas Angelini
Project Description: Investigating the interaction and assembly of tissue cells within controlled 3D environments may lead to an improved understanding of how morphogenesis emerges in vivo. However, the inherent limits of using solid scaffolding or liquid spheroid culture for this purpose restrict experimental freedom in studies of cell assembly. A culture medium made from packed microgels serves as a bridge between the extremes of solid scaffolds and liquid culture and allows studying the form and function of 3D printed minimal tissue models. In this project, the REU Participant will 3D print structures into this 3D culture medium and investigate how populations of endothelial assemble and spontaneously undergo morphogenic processes.
Training Plan: The participant will learn traditional cell culture techniques as well as more advanced approaches to culturing 3D printed tissues. The participant will be trained in 3D bioprinting and confocal fluorescence microscopy. To analyze their data, the participant will learn to write image processing and analysis programs in Matlab.
Intellectual Merit: Epithelial acini represent one of the best and most studied tissue models. Traditionally, these structures are grown from single cells that spontaneously grow into spherical monolayer shells. However, there have been very few attempts to leverage this knowledge to create more complex structures using 3D bioprinting tools.
Modeling Interstitial Fluid Flow and Drug Delivery in Tumors
Faculty Mentor: Prof. Malisa Sarntinoranont
Project Description: Uneven drug coverage is a major problem encountered in chemotherapy since the clinical goal is to eliminate 100% of cancer cells. Barriers to uniform drug delivery can be traced to an abnormal fluid flow environment where leaky tumor vessels contribute to elevated pressures and tissue deformation patterns that adversely affect drug spread. The REU participant will develop new computational models that account for nonuniform vascular leakiness through magnetic resonance imaging (MRI) of contrast agents. Since, tissue flows within tumors are too slow to be measured non-invasively, these simulations provide a way to predict spread of drugs as well as cellular response to flow.
Training Plan: During the first two weeks, the participant will use examples from existing imaging data sets. They will learn to use image-processing software (FIJI) to segment structures of interest from magnetic resonance images. In the following weeks, finite element (FEBio) models will be developed that simulate flows and/or transport within the tumor.
Intellectual Merit: There is increasing clinical interest in heterogeneous tumor drug delivery and these studies will provide an understanding of flow patterns that contribute to nonuniform spread.
New Tools for Cell Mechanics Research
Faculty Mentor: Prof. Xin Tang
Project Description: The cell nucleus is a mechano-sensor that can convert biophysical signals into biochemical signals. Until recently, no method could directly apply force on the cell nucleus to study nuclear mechano-sensing. Tang et al. developed a new method to (i) deliver magnetic microbeads into the cytoplasm non-invasively, (ii) use a magnet to realize precise temporal-spatial control of the magnetic microbead, (iii) directly apply force on the nucleus by the magnetic microbead, and (iv) perform live-cell functional imaging during the force application. In this project, the REU participant help develop of a multi-pole magnetic tweezer. Specifically, this project will improve the temporal and spatial control of magnetic microbeads to create binding between the microbeads and the target organelles (nucleus, cytoskeleton, etc.) at the target location.
Training Plan: Under the mentorship of Dr. Tang and his graduate students, the REU participant will be trained in (i) live-cell optogenetic imaging of CRISPR/Cas9-engineered cells using Nikon Ti-2 confocal fluorescent microscope, (ii) imaging processing (background noise subtraction, and measurement of fluorescence signal) by ImageJ and MATLAB, and (ii) statistical analysis.
Intellectual Merit: This project will enable direct force application on the nucleus to study how the cell nucleus responds to biophysical signals. This method can bypass the conventional mechano-sensing of the cell membrane to directly study the previously unknown role and mechanism of nuclear mechano-sensing.
Molecular Modeling of Fracture in Hydrogels
Faculty Mentor: Prof. Douglas Spearot
Project Description: Hydrogels are comprised of a mechanically or chemically cross-linked hydrophilic polymer network immersed in absorbed water molecules. Many hydrogels are biocompatible and thus have been used in advanced healthcare applications such as tissue scaffolds and in repair or replacement of damaged tissue. In these applications, mechanical integrity is critical yet the understanding of mechanical failure in hydrogels across a range of strain rates is not well understood. Thus, the objective of this project is to investigate crack propagation in hydrogels using atomistic simulations. Atomistic models of hydrogels with different chemistries or water contents will be constructed and the fundamental details of crack propagation through the cross-linked network will be simulated.
Training Plan: This project is computationally focused. Starting in the first week, the REU Participant will be provided access to the University of Florida high-performance computing system (HiPerGator) and will conduct training exercises using examples developed in the Spearot laboratory on how navigate a Linux environment and how to use the atomistic simulation code LAMMPS.
Intellectual Merit: Molecular dynamics simulations will provide an understanding of solvent diffusion and stress transfer mechanisms within a network and/or between networks upon dissociation of a bond during fracture. This work will produce knowledge and data to assess viscoelastic and poroelastic dissipation contributions to fracture toughness in hydrogels.
Characterizing physical activity patterns with wearable technology
Faculty Mentor: Prof. Kerry Costello
Project Description: One major risk factor for knee osteoarthritis is mechanical loading on the knee joint. Knee loading can be modified by changing the way someone moves (biomechanics) or their types of activities. Biomechanics can be captured in detail within a lab environment; however, the lab environment does not accurately mimic the real world and lab-based studies typically do not capture the range of different types, intensities, and patterns of activities a person experiences during everyday life. Wearable technologies (e.g., a smartwatch) are often used to provide summary measures of activity, but do not provide detail about time-varying joint loads. This project will use laboratory-based motion capture and wearable technology to catalog patterns of three-dimensional acceleration, angular velocity, and knee joint loading during a range of different activities of daily living. This is part of ongoing projects that use machine learning to identify features of time-varying, multidimensional wearable technology and biomechanics data that can identify individuals at risk of knee osteoarthritis progression.
Training Plan: The REU Participant will be trained to capture and assess human movement using wearable technologies (e.g., smartwatch) and laboratory-based motion capture, computer programming and machine learning applied to movement data. The REU Participant will also receive training in data collection with human research study participants and ethical considerations for working with human data.
Intellectual Merit: Little is known about how time-varying patterns of three-dimensional acceleration and angular velocity of the body relate to mechanical loading at a specific joint (e.g., knee) or how the time-varying patterns of these signals differ among activities of daily living. This work will help characterize these patterns for a range of different activities and relate them to knee joint-specific loads.
Neuromuscular Simulations of Human Balance Control
Faculty Mentor: Prof. Jessica Allen
Project Description: Falls due to a loss of balance are a primary cause of injury in older adults and individuals with a range of neurological deficits. The underlying impairments causing the increased risk of falls can be either mechanical (e.g., reduced muscle strength) and/or neural (e.g., inappropriate muscle recruitment). Because these impairments can vary from person to person, there is no “one-sized-fits-all” approach to improve balance. The Allen laboratory is developing predictive neuromuscular simulations; these simulations use computer models of the musculoskeletal system combined with rigid-body dynamics and optimization techniques to replicate how humans move. The REU Participant will perform predictive simulations of balance control by changing the parameters of the model or its control scheme, to explore how different impairments or interventions affect balance control.
Training Plan: This project is mostly computationally, with an opportunity for experimental data collection for simulation validation. The REU Participant will be trained on rigid body dynamics and optimization techniques to generate predictive simulations of human movement and their practical implementation using the OpenSim simulation platform.
Intellectual Merit: Developing individualized interventions that reduce fall-risk is currently limited by our ability to characterize individual-specific impairments. This project has the potential for improving not only personalized rehabilitation prescription, but also rehabilitation and device design.