The Capstone Project is intended to culminate the skills of the BME undergraduate degree. The students are required to take the course and complete the project their senior year. Below are examples of student projects from previous years.
Class of 2023
Students: Anisa Abdulhussein, Hannamarie Ecobiza, Nikhil Patel, Carter Ung
Advisor: Dr. Jerome Schultz
Students: Lilly Roelofs, Anh Tran, Dana Albishah, Hoang Tran, David Lloyd, Zuha Yousuf, Farial Rahman, Laura Rubio
Advisor: Dr. Mario Romero-Ortega
Students: Valeria Espinosa, Lediya Haider, Bao Le, and Christian Pena
Advisor: Dr. Chandra Mohan
Students: Kenneth Nguyen, Laura Rubio, Jessica Avellaneda, Juan Gonzalez
Advisor: Dr. Mario Romero-Ortega
Students: Sean Chakraborty, Tien Tran, Elizabeth Kolb, Elaine Raymond
Advisor: Dr. Jerome Schultz
Students: Mikayla Deehring, Bryan McElvy, Elizabeth Perry, William Walker
Advisor: Dr. Nuri Ince
Students: Wesley Cherry, Shanzeh Imran, Rami ElHajj, Nivriti Sabhani
Advisor: Dr. Yingchun Zhang
Students: Anaga Ajoy, Kailee Keiser, Aria Shankar, Alexa Truong
Advisor: Dr. Renita Horton
Students: Alan Luu, Raed Mohammed, Anique Siddiqui, and Brendan Wong
Advisor: Dr. Nuri Ince
Students: Neftali Garcia, Wajid Masood, Angela Soto
Advisor: Dr. Yingchun Zhang
Class of 2022
Students: Rumaisa Baig, Aliza Sajid, Kinda Aladdasi, Hira Rizvi, and Eugenia Ponte
Advisor: Dr. Jerome Schultz
Students: Ayesha Budhwani, Duc Ho, Dorothy Mwakina, Nicolas Nino
Advisor: Dr. Renita Horton
Students: Sarah Hakam, Hy Doan, Attiya Hussaini, Krishna Sarvani Deshabtotla
Advisor: Dr. Mario Romero-Ortega
Students: Fariz Nazir, Chinenye Chidomere, Bryan Choo, Jessica Chidomere
Advisor: Dr. Tianfu Wu
Students: Mautin Ashimiu, Shannen Eshelman, Amanda Reyes, Catherine Tran
Advisor: Dr. Luca Pollonini and Dr. Samuel Montero Hernandez
Students: Amie Theall, Barbora Bobakova, Zarmeen Khan, Abigail Janvier
Students: Alexandru Neagu, Dailene Torres, Loren Thompson, Dylan Creasey
Advisor: Dr. Jose Luis Contreras-Vidal
Students: Jordyn Folh, Raeedah Alsayoud, Mirren Robison, Xanthica Carmona
Students: Sarah Aldin, Rita Maduro, Patrick Calderon, Hebah Kafina
Advisor: Dr. Jerome Schultz
Students: Martin Reyes, Regan Persyn, Quynh Nguyen, Bryan Gutierrez
Advisor: Dr. Yingchun Zhang and Michael Houston
Students: Yalda Barram, Tatiana Barroso, Theresa Pham, and Amy Tang
Advisor: Dr. Joseph Francis
Students: Alma Antonette Antonio, Jose Carrion, Lindsey McGill, Sharmeen Shahid
Advisor: Dr. Metin Akay and Dr. Yasemin Akay
Class of 2021
Project 1: Vital Sign Wristband
Abstract: As most hospitals transition to a digital world in order to streamline medical procedure, our group wanted to streamline the check in process by making a wristband that measures vital signs. We wanted the wristband to measure heart rate, temperature, and blood oxygen, and for this data to be sent to an app. We first decided which sensors to use, and moved forward with the MCP9808 temperature sensor and the MAX30100 sensor for heart rate and blood oxygen. We then assured the MCP9808 worked to our standards by connecting it to a ESP32 microcontroller on a breadboard. The connection and reading of the sensor required Arduino code, which we constructed with online resources. After getting the readings that aligned with our expected values, we followed the same procedure with the MAX30100 sensor. We then ‘pushed’ the data to an app that we constructed using Blynk, an app that is used to read data from microcontrollers. After ‘pushing’ the data to our app, we were ready to start making the wristband by connecting the sensors to the ESP32s, and attaching the connections to a wristband using V elcro. With our final prototype, we were able to wirelessly read heart rate, temperature, and blood oxygen from the Blynk app. To more efficiently assist in hospital applications, a potential future direction for this project would be to add blood pressure as a parameter for the wristband. We would also like the wristband to ID the patient that is wearing it in order to track and assign the data throughout their stay.
Project 2: Development of a low cost method to evaluate mask efficiency
Abstract: Since the start of the pandemic, over 1.5 Billion single use face masks have been used across the globe. Many people have also made and using homemade masks due to convenience or necessity. At the start of the pandemic there was an acute shortage of masks and even now, with the lifting of mask mandates across the United States, we anticipate that masks will still be used by the public for the foreseeable future. Our objective was to develop a fast, low cost reusable method to evaluate the efficiency of face masks and the materials that are used to manufacture them. We believe that consumers could benefit from knowing that masks that they buy or make are useful and will protect them from COVID 19 and future diseases. To accomplish this, we built a self contained unit that works by measuring the efficiency of material by calculating the amount of light reflected by aerosolized salt solution that penetrates masks. The consumer can use their phone to take a picture of the light compartment through the device and upload the result to our website that will give them the efficiency immediately. In future versions we hope to make the process easier by using an inbuilt camera and a single switch to turn the device on and off.
Project 3: Sensor Array for COVID19 Diagnostics
Abstract: The emergence of the COVID 19 pandemic has highlighted the need for reliable and rapid diagnostic tools to aid in community wide contact tracing and monitoring efforts. Early Covid 19 tests relied on either molecular or serological assays, which had long turnaround times and required specialized equipment and personnel. Our goal was to create a diagnostic tool that could provide rapid and accurate patient feedback without the need of special equipment. To this end we employed the use of a metal oxide array, which was composed of four sensors, in order to detect endogenous Volatile Organic Compounds in the breath. These sensors were fabricated and supplied by the Nanodevices and Materials Lab. We developed a comprehensive testing setup involving a Mass Flow Controller, Gas Chamber, Multiplexor, and a Picoammeter with the creation of a Graphical User Interface (GUI) to make the data collection autonomous and efficient. We also devised a pattern recognition algorithm using Principal Component Analysis and K Means Clustering to identify our four target gases based on the sensor array’s response.
Project 4: Microcontroller Based Functional Electrical Stimulator
Abstract: Electrical stimulation is used in various therapeutic applications in medicine, ranging from neuromodulation to functional mapping of the brain. There are still many of these devices that are operated through manual tuning and pressing buttons. Having the ability to control these analog devices from a computer is critical for research and advanced therapy , but this cannot be done The aim of this Capstone Project is to develop a low cost Functional Electrical Stimulator (FES) that can be fully controlled with a microcontroller (Teensy 3.5) connected to a PC through a USB interface. In practice, the system can be used in various scenarios, but the intended application is for delivering non invasive Neuromuscular Electrical Stimulation (NMES). The hardware was developed using 9 Volt batteries connected to DC DC boosters for power supply and other primary components that include analog switches and transistors. This system is controlled through Arduino IDE and a Graphical User Interface (GUI) developed within MATLAB that allows for ease of manipulation and further development in the future. We have successfully produced a symmetrical, biphasic square wave capable of operating at 60 microsecond pulse widths. We have also demonstrated the capability of producing a biphasic sinusoidal wave with flexible frequency. One future goal of this system is to fuse it with a brain computer interface (BCI) that can drive the FES to improve the rehabilitation of the patients suffering from stroke or spinal cord injury by translating their thoughts to muscle contractions and associated movement.
Project 5: Inclusive System for Image Capture and Rheological Image Analysis for Artificial Microvascular Network
Abstract: Measuring blood flow in capillaries of an Artificial MicroVascular Network (AMVN) device is typically done using a research grade inverted microscope. Research grade microscopes can provide high resolution images but are bulky, unportable, and expensive, which significantly limits the scope of AMVN technology. As an alternative, we have developed an inclusive, portable system that contains all of the necessary hardware to perform the experiment as well as a code to analyze the perfusion rates of the AMVN channels. The system utilizes a camera and magnification lens to simulate the optics of a microscope, but in a more affordable, compact, and user friendly unit. Video captured by the system can easily be transferred to a laptop for analysis. The perfusion rate data produced using our code has yielded reproducible and accurate results comparable to values in previous literature. This inclusive system can be used to perform analysis on a variety of experiments including testing the effect of new storage conditions, additive solutions, novel drugs, and rejuvenation strategies on the rheological properties of red blood cells in vitro. Future work could entail expanding the usefulness of the system to function with various different microfluidic devices.
Project 6: Voice Activated Alarm System for Patients with Limited Mobility
Abstract: Current hospital alert systems require a mechanical input, most commonly the push of a button Patients with mobility issues such as quadriplegics are unable to perform this input Most solutions to this problem require proximity and are prone to displacement, such as clipping the button to patients’ gowns to press with their chin If these devices are displaced, the patient is unable to correct it, and must resort to yelling to alert a nurse Our device will attempt to mitigate these shortcomings by allowing the patient to speak to activate the alert system, allowing for input at a greater distance with no limb movements required The device uses a mini computer with a microphone attachment for voice input and activation, and a microcontroller connected to a solenoid for mechanical activation of the alert system. This allows for the device to be easily and selectively integrated into the existing alert system at most hospitals We assembled and programmed the device to respond to a specific key phrase amid ambient noise and were able to voice activate the solenoid, as well as demonstrate that it could generate enough force to push a button Future work could replace the external power source with a battery, and compact into a flexible attachment This device will improve accessibility and quality of life for patients with restricted limb mobility
Project 7: Biological Organism Recording and Integrated System During Rocket Launch
Abstract: Space exploration has deleterious effects on the human body and can lead to significant long term adverse effects such as muscle atrophy and bone density loss Many astronauts undergo intense training to prepare for a launch such as High G training, where they are exposed to a high amount of G force Understanding the impact the hypergravity and microgravity environments have on tissue development and function is critical to keeping humans healthy for space travel, especially with the upcoming Artemis program and Mars missions Thus, there is need for a device that can monitor the effects that high action events, such as a rocket launch, has on an organism’s tissues in real time The Biological Organism Recording and Integrated System (BORIS is a device mounted inside the payload bay of Space City Rocketry’s high powered rocket Oberon, with the aim of observing and recording the impact of high accelerative forces on a cell culture to understand how the forces of flight make changes to the structure and function of cell walls and membranes Video footage of magnified cells and interior payload temperature are recorded for analysis of cell conditions and to determine the change in cell diameter during the flight a test flight in March observed rudimentary footage during a 24 second ascent of 7514 N applied on the cells, and internal temperature varied over 1 C Increased magnification and securing the switch on the device light are the next steps to ensure video is visible for the whole flight and that clusters of
cells may be identified more easily.
Project 8: Remote Rehabilitation System
Abstract: Electromyography signals are electrical impulses generated by muscle activation. Such signals are obtained using an EMG device to analyze the muscles of interest and determine any muscular or motor dysfunction. Consequently, they can be used for rehabilitation purposes. Currently, there are only a few wireless EMG systems, and they are expensive. However, they can be highly beneficial in cases that would require patient isolation or other reasons. Inspired by this and the growing telerehabilitation, our team set a goal to build an affordable and wireless rehab system that entails building the EMG device and the mobile application necessary to transfer/receive data. The device consists of 3 MyoWare sensors that collect and transfer integrated and rectified EMG signals to the mobile app via the Bluetooth module. The app was built through a program, compatible with the device’s components, called MIT App Inventor 2, and works on Android phones only. The application receives and displays the EMG signals that can also be saved locally. Additionally, it can time the patient’s activity. Further improvements could be made to our system to provide a highly effective remote rehab system for the targeted patients.
Project 9: Blood Flowmeter for Skin
Abstract: For diabetic patients, blood circulation to extremities becomes slower and, as result, can lead to decreased healing rate and increased risk for infection. A lack of treatment can lead to the infection potentially spreading to surrounding tissue and even limb amputation. Monitoring blood flow rate is crucial in detecting the risk for such an infection. While there are other devices for measuring blood flow, such as the Laser Doppler flowmeter, the cost for these devices are often high and used mainly in a clinical setting. We proposed a design for a low cost and portable device to calculate the average energy required to keep a small region of skin at a set temperature for one minute and relate that measurement to blood flow. Our device consists of a small heating coil made from nichrome wire and has an NTC thermistor placed in the center of the coil. We used Arduino Uno as a hardware to software platform and coded for our device via MATLAB. Our software utilizes an on off temperature control system and a relay component to safely power the heating element to the set temperature. To test our device, we developed a low cost artificial vein model to mimic blood circulation and correlated varying flow rates to average energy required to keep the circulation five degrees higher than its current temperature. Our device demonstrates a potential low cost method for measuring blood circulation and for improving the lives of diabetic patients.
Project 10: A Wireless sEMG Based Robotic Rehabilitation System
Abstract: Stroke has been a huge concern throughout the years as it is known to be one of the leading causes of death in the United States For stroke patients, there are a couple of techniques such as targeted physical and technology assisted activities that would help them and serve as therapy to gain motor movement. Nevertheless, new advances in bioengineering have introduced a robotic hand named ‘Hand of Hope” (HoH) that uses real time surface electromyographic signals (sEMG) to control the robotic hand according to the patient’s muscle signals. sEMG is a procedure that measures muscle response or electrical activity based on an individual’s response to nerve stimulation and is recorded by placing electrodes on the surface of a patient’s muscle In this project, TMSi Refa Amplifier was used to amplify the signals received from the sEMG electrodes and send it to MATLAB Later, the Transmission Control Protocol/Internet Protocol (TCP/IP) communication will serve as a method of communication between the commands in MATLAB and the robotic hand motor control performance based on the classified sEMG signals The experiment included fine motor movements such as hand opening/closing and the movement of finger combination gestures. By creating a LDA classifier with 81 accuracy, we were able to have the robotic hand identify and assist in 5 different gestures We hope this stroke rehabilitation technique will help patients with reinforcement of their fine motor function through the strengthening of the nerve signal pathway
Project 11: Quantifying Peripheral Nerves using Deep Learning
Abstract: Larger neurons in the peripheral nervous system (PNS) have thick myelin sheaths which cause them to be easy to detect during transmission electron microscopy (TEM) studies. Smaller neurons that tend to be unmyelinated lack the distinct bold outline. Current methods of quantifying axons in PN tissue include manual counting, which is labor intensive and inaccurate. This project is aiming to develop an open source software using Python to automatically identify and quantify cell types (large/small
neurons) from TEM images of PN tissue. We built a basic mask region based convolutional neural network (Mask R CNN) using a pre trained object detection model to identify the presence, location, and type of cells. This program is able segment a large image, learn filter values, detect axons apart from other cells, then places a color mask over the cell depending on the thickness of the myelin sheaths. These masks are quantified. As can be seen in the image our program can detect larger, myelinated axons but has trouble with detecting smaller axons. Once we adjust our code to locate both types of axons, we will run our program with a larger dataset of TEM images then compare to manually counted images. This program can be made more beneficial for research teams by further developing it into a deep learning neural network. This will allow researchers to process larger datasets with more accurate results and less preprocessing. Another future direction is to integrate this program with an image analysis software, such as Image J, using Jython , a python java hybrid code.
Project 12: Smart Multiplex Flow Meter Sensor System
Abstract: Stress urinary incontinence (SUI) is a highly prevalent condition in women. This condition consists of weakened pelvic muscles leading to diminished bladder control; often leading to uncontrollable leakage during physical movements. Despite the inconveniences of this disorder, treatment options are limited due to safety and efficacy concerns. To study this, we created an automated metabolic cage suited for female rabbits with induced SUI. The objective of this proposal was to create an adaptable system that includes a collection apparatus and a sensor system. These are then attached to the current cages at the University of Houston to measure volume and frequency of micturition events with easy access for data retrieval. This prototype incorporates a mesh filter, a funnel, a flow rate sensor, a peristaltic pump, and an Arduino with Bluetooth capabilities. The data is wirelessly transmitted to a local PC for easy processing and data analysis. Overall, the prototype has been successful in measuring correct volumes of fluid with approximately 93% accuracy and allows for the automatic transfer of data from the Arduino to the mounted SD card for further data analysis. For the future, we plan to test our prototype with SUI-induced rabbits to ensure that the prototype is compatible, accurate for urine testing, and that the prototype can be used to study SUI. This can revolutionize the research industry by improving accuracy of urinary data from rabbits to further the understanding of SUI and other urinary disorders.
Class of 2015
Project 1: Fabrication of Immunosensing Soft Contact Lens as a POC System in Eye Infection Detection
Abstract: Rapid diagnosis of infection within the eye is an area of study that has (to date) been very limited in exploration and innovation. Differentiation between bacterial, fungal, and viral infections within the eye is a difficult process due to the similarities in symptoms in patients with a variety of ocular infections. Proposed is an ELISA-based immunosensing contact lens capable of detecting inflammatory protein markers within human aqueous tears. Soft contact lens assembly will be conducted via two primary methods: synthesis of novel hydrogel-based lens with maximum binding capabilities and improved cross-linking and surface plasma modification of commercially available soft contact lens for binding and successful detection. The lenses will be printed with anti- VCAM-1 antibodies, intended for the detection of the protein VCAM-1, an inflammatory marker. Detection will be conducted using a solution of peroxidase-labeled secondary antibodies in conjunction with a silver reagent, initiating an enzyme-catalyzed silver deposition reaction indicative of the presence of the inflammatory marker. Initial progress in development has been focused on research and acquisition of materials. Due to the limited literature available in the development of such novel diagnostic tools, extensive research has been conducted into creating a device with optimum binding and detecting capabilities. All materials have been sourced and, once received, will immediately be used for hydrogel synthesis and commercial lens plasma modification. Extensive testing will be conducted on the lenses, utilizing an artificial “tear” solution containing VCAM-1 protein for feasibility of design. Following establishment of success of this design, additional modifications will be made to test lens’ capability for differentiating between different types of inflammatory responses and viability of this diagnostic device in clinical applications.
Project 2: Modular Physiological Monitoring System
Abstract: The intended application of the project is vital monitoring during commercial space flights, home healthcare, fitness, and research. The system will measure both physiological and environmental parameters simultaneously. EKG, skin temperature, barometric pressure (altitude), ambient temperature, accelerations, and UV index are the parameters that will be measured. The centerpiece of the system is the Arduino microcontroller. All sensors and the EKG shield are connected to the Arduino boards, which extract the readings of all sensors. The extracted data will be sent to a computer through Wi-Fi thanks to the wireless capability of the Arduino Yun microcontroller. Plotly will be used for data extraction and analysis. Parameter relational plots will be constructed using physiological response to environmental stressors. At the conclusion of last semester we constructed a model on an Arduino Uno board to demonstrate system capabilities. An ambient temperature sensor was implemented in the model with on-board LED lights (green and red) that provided notification (Red LED) when the ambient temperature exceeded 21.5 degrees Celsius. An LCD monitor was also included to demonstrate continuous sensor measurements and display. At the beginning of the second semester we had completed development of the hardware prototype (Milestone 1) and the formation of the Central Hardware Interface (CHI) (Milestone 2), and were starting to work on the data extraction, analysis, and display. This was done by using Plotly to communicate sensor data wirelessly to a server. A computer then extracts this data and displays it in real-time. At the conclusion of the second semester, we had a completed system that utilized two microcontrollers to wirelessly extract and display data (Milestone 3). Although using two microcontrollers was not our original objective, it was the best way for us to integrate the serial EKG into the system. Future work can focus on the miniaturization of the system and establishing communication between the two boards. Our total expenditure for this project was $168 in parts and $6400 in labor.
Project 3: Embryo Dissection Station
Abstract: The purpose of our project was to design, improve, and develop the methods and processes used for the live embryo dissection, including, improvement to the dissection station and examination process. The specific concentration of this project was the construction of a live embryo dissection station that has the same uniform temperature throughout the apparatus that is also economical with regard to fabrication (i.e., the process is cost- and time-effective).
Project 4: Google Glass as a Diagnostic for Melanoma
Abstract: Early melanoma diagnosis is vital for the prevention of complication onsets that may compromise an individual’s life span. In order to diagnose for the presence of melanoma, patients are required to visit a medical facility, which results in the negligence of early symptoms. Our team proposed to develop a melanoma diagnostic utility using Google Glass, which would help provide a point-of-care diagnosis without having to visit a medical facility. Developing a Google Glass diagnostic presents various challenges that mandate the integration of different techniques. The Glass is only capable of capturing 2 dimensional images with its camera, but in order to enhance the diagnostic accuracy, we are developing a code based on the modification of existing algorithms that can create 3-dimensional images from 2-dimensional images. Implementing additional diagnostic criteria for existing 2-dimensional analysis will allow for a 3-dimensional melanoma analysis, which would provide definitive diagnostic results. Image acquisition and analysis will be done via servers that support the processes, and then integrated into the Google Glass. At this time, the Google Glass provides big challenges due to its relative new introduction into the technology market. Therefore, our project includes establishing a method to connect the Google Glass to a development platform, create a graphical user interface to display the diagnostic results, and integrate the servers for a comprehensive diagnosis. During this semester, we were able to establish the software development platform, create a sample melanoma diagnostic display, create a preliminary low resolution 3-dimensional image construct, and run successful 2-dimensional analysis on sample melanoma images. The sponsors covered the Google Glass cost of $1,500, and the University of Houston provides the necessary software for the development process.
Project 5: Optimization of SMFT-based Actuation System Final Report
Abstract: In our Capstone Design Project, we are tasked to optimize an actuation system based on Solid Media Flexible Transmission (SMFT). The SMFT-based system is applicable for robot-assisted surgeries within the MRI, where a very strong permanent magnetic field, fast changing magnetic field gradients and RF pulses are used. SMFT tubes have the potential to efficiently transfer force without the use of magnetically susceptible materials, making it compatible with the MRI scanner. Previously, the tubes have been used at a force transfer efficiency of 50%. Our goal is to increase the force transfer efficiency to 70%. To achieve this goal, we designed a force transfer efficiency testing system involving load cell force sensors, a testing station, and SMFT tubes (Milestones 1, 2, and 3). We also aimed to complete the actuation system by assembling an MRI-compatible needle onto it (Milestone 4). We have successfully completed Milestones 1 and 2, which involves calibrating the load cell and designing a cost-efficient stationary load cell holder to hold the load cell for force efficiency tests. In completing Milestone 3, we have successfully made more stable connections using BNC-BNC cables and interlocking connectors and collected data for the force transfer efficiency of a 1m SMFT tube. Milestone 4 involves assembling a needle holder to be attached to the actuation system and testing it on a porcine kidney suspended in a ballistic gel. The project has reliability constraints for the load cell rod, economic constraints in the 3D printing of the load cell testing station, and manufacturability constraint in the current 3D printing cost and the project’s applicability to test other force transfer systems. During the testing, standards such as the maximum load capacity and the excitation voltage of the load cells have to be determined. The load cell itself follows the accuracy standard IEC 61298-2. In conclusion, the force transfer efficiency decreases with increasing lengths of tubes, but increases at an average of 12.1% across all tubes.
Class of 2014
Project 1: Wireless ECG and Respiratory Monitoring System
Abstract: The purpose of this project is to design a Wireless ECG and Respiratory Monitoring System. The ECG signal would be collected by electrodes and then amplified and filtered by analog circuit. Next the microcontroller would convert the analog signal into digital signal and amplify it even more. The microcontroller is included in the Wireless transmitter system. Then the data will be sent through MSP430 wireless transmitter (TI wireless development tool) to be processed in a local PC. Our Respiratory monitoring system measures the airflow by using nasal cannula pressure system. This system consists of a nasal cannula (which is standard for oxygen administration) connected to a pressure transducer. Respiratory waveform signal will be generated by detecting the fluctuations in pressure caused by inspiration and expiration. The data will be sent through the same wireless transmitter to be processed in a local PC.
Project 2: Optical Projection Tomography System
Abstract: The scope of this project is to build for Baylor College of Medicine an Optical Projection Tomography system to use in function with an ongoing embryology study. The goal of this project is for the Optical Projection Tomography system to provide a method for high throughput murine embryo imaging. Our design is based on previously published work from the University of Toronto with tweaks and customizations for the specific application requested by Baylor College of Medicine. These tweaks include a differing CCD camera and lens, as well as a possible rotating stage for sequential imaging of multiple embryos at once.
Abstract: The project aims to design, test, and build a Universal Transducer Adapter (UTA) to use in conjunction with commercially available Ultrasound Systems and the Euclid™ Tier 1 Mini Access System designed by Houston Medical Robotics (HMR). The UTA is a much needed design improvement to the Euclid™ system because of the time and financial cost associated with redesigning the adapter for different commercially available ultrasound systems. Multiple design concepts will be presented and tested both in benchtop and animal models and the necessary design documentation will be completed throughout this process. Secondarily, the Euclid™ Tier 1 Mini Base will be ergonomically redesigned for customer ease of use.
Project 4: Lupus Biomarkers
Abstract: The goal of this project is to identify Lupus biomarkers that will be used in a sensor to track the progress of Lupus in a diagnosed patient. Lupus is a systemic autoimmune disease that often results in kidney failure. By tracking the proteins that are filtered through the kidney, it is possible to identify protein biomarkers that are involved in this kidney damage. In order to achieve this goal, enzyme-linked immunosorbent assays (ELISA) will be run on urine samples of Lupus patients that will identify those protein biomarkers that have a statistically higher protein concentration compared to patients who are not diagnosed with Lupus. After these biomarkers are identified, a sensor can be created that will evaluate the concentration of these proteins in a urine sample. This sensor can be used in a at home diagnostic kit that can allow a patient to track the progress of their disease without going to the doctor. If the sensor produces alarming results, the patient can then visit the doctor to reevaluate their treatment plan.