Biology and Biomedical Engineering

Project Summary

Alzheimer’s Disease and other Dementias are thought to arise from the aggregation of proteins, particularly Tau. Additionally, Tau aggregation into neurofibrillary tangles also results in the loss of Tau’s native function: stabilizing microtubules. Microtubule stability is critical for axon maintenance and function. As protein aggregation has been the central research area, there has been considerably less focus on the cellular and molecular impacts of modulating microtubule stability in neurodegenerative disease models. Here, we use a “humanized” C. elegans tauopathy model for Frontotemporal Dementia. We aim to uncover the genetic and molecular underpinnings of degenerative phenotypes, using the mechanosensory neurons to look for both developmental and degenerative effects. This project focuses on the relationship between tauopathy and the microtubule-binding protein, PTRN-1/CAMSAP, which functions in axon termination and synapse formation. In this study, we will 1) identify the role of PTRN-1 in degeneration and 2) assess the effects of modulating microtubule stability on tau-related degeneration.

Project Summary

A phenomenon involving a density-dependent change in phenotype is known as quorum sensing (QS) and is induced by an accumulation of molecules known as autoinducers (AIs). AIs are produced and released by the QS organism. In Chlamydomonas reinhardtii, a unicellular algae, a QS-like behavior involving faster swim speeds at higher cell densities was found in the common lab strain CC-124. However, the mechanism of QS in this organism remains unknown. To investigate this system, the related strains CC-4414, CC-125, and CC-503 were studied. CC-4414 has an arm of chromosome 16 that is distinct from many strains, including the ones investigated here. Additionally, CC-4414 has been previously characterized as a QS-negative (QS-) strain. We hypothesize that Chlamydomonas reinhardtii strain CC-4414 is insensitive to the QS system shared by other closely related strains. ​If the hypothesis is true, bioinformatics analysis of CC-4414 and related strains could provide information on the molecular mechanisms of QS in Chlamydomonas. To test the hypothesis, CC-125 and CC-503 were distinguished as QS-positive (QS+) based on swimming speeds at low cell density (LCD) and high cell density (HCD). Next, media swaps both within-strains and between-strains were conducted to determine if AI-containing media of HCD cultures induce a QS response in LCD cultures. This confirmed the QS+ classification of both CC-125 and CC-503. Additionally, CC-125 LCD sped up in CC-124 HCD media but not CC-4414 HCD media. Furthermore, CC-4414 LCD did not speed up in CC-125 HCD media. We concluded that CC-4414 either does not QS or does not have the same QS signal and phenotype as CC-124 and CC-125.​

Project Summary

Self-assembly of proteins leads to the formation of amyloid fibers, which can further aggregate to form bundles and hydrogels. This leads us to believe that plaques/tangles in Alzheimer's Disease brains are hydrogels. Protein hydrogels synthesized in the laboratory contain a variety of structural features, including soluble protein monomers, oligomers (colloids), amyloid fibers, fiber bundles, and fractals. When the amyloid fiber hydrogels are produced under vigorous agitation, two distinctive layers are revealed – an opaque top layer and a transparent bottom layer. This contrasts with the homogenous transparent gels synthesized with less vigorous shaking or without shaking. Quantitative analysis suggests that the two hydrogel layers vary in their distribution of different-sized aggregates. From the top to the bottom of the hydrogel, an increasing concentration of protein monomers and a decreasing concentration of amyloid fibers were observed. The opaque appearance within the top layer is likely a result of increased light scattering by the large aggregates, fiber bundles, and fractals. The enrichment of large particles in the top layer can be attributed to the Brazil Nut Effect – agitation at high speed leads to large particles rising to the top and smaller ones settling to the bottom. Without large aggregates, the bottom layer scatters less light and remains largely transparent. Subsequent work will involve introducing a new method to analyze the concentration of fiber bundles, a dominant structural feature of AD plaques/tangles, produced in each hydrogel layer. Methods established here benefit AD research and other diseases involving amyloid fiber deposition.

Project Summary

The ability of an organism to change its phenotype based on environmental variations is crucial to the survival and reproductive success of the species. Chlamydomonas reinhardtii has been studied as a model organism for algal biotechnology due to its prevalence in various habitats, ease of growth, and as a genetically tractable organism. C. reinhardtii has been identified to exhibit the microbial communication system known as quorum sensing (QS) that enables these unicellular organisms to coordinate a population-wide phenotypic change in their behavior. Specifically, an increased swimming speed at higher cell densities. Studying QS in this model algae can give us a better understanding of the molecular mechanisms at play. To determine if this quorum sensing molecule (QSM) is conserved across multiple lab strains, we conducted a media swap for numerous different strains of C. reinhardtii. The media swap consisted of growing two cultures of the same strain at various time points so that one culture crossed the HCD threshold and another culture simultaneously stayed at a low cell density (LCD). We then swapped the cells and the media, so that the media with the LCD cells was given to the HCD cells, and the HCD cells were exposed to the LCD media. Videos were taken from both cultures after a period of incubation. Videos were taken to analyze their swim speeds using Image J. What we found is that just like the standard lab strain, other strains of this species exhibit QS-like behavior, which tells us that even though they have slight differences in how fast they swim or physical characteristics, they still undergo this phenomenon. In addition to providing insight into QS in the relatively large genus Chlamydomonas, this study may provide insight into other marine quorum-sensing systems that remain undiscovered and understudied.

Project Summary

Congenital heart disease (CHD) is the most common cause of birth defects. Hypoplastic left heart syndrome (HLHS) is a CHD that narrows the aortic valve and the left ventricle. Fetal echocardiography, a key screening tool, is not performed until the 20 week screening during the second trimester, limiting early detection. Our team aims to enable CHD detection as early as the first trimester.

Project Summary

LIFE (Live Intensive Field Evaluator) is an medical armband device that utilizes various sensors for real-time, continuous data collection for skin temperature, heart rate, and blood pressure. Unlike current blood pressure watches or armbands that rely on manual blood pressure cuffs, which inflate around the arm to read one time measurements of systolic and diastolic pressures, our device aims to provide clinically accurate readings in real-time without the discomfort of inflation. This allows clinicians to continuously monitor important patient vitals during disaster relief triage, providing them with the tools they need for rapid response to patient status changes. Our device ultimately minimizes the impact of poor triage accuracy in determining patient status due to human error and a lack of continuous monitoring in disaster triage settings.

Project Objective

- Develop an accurate, cost-effective, smart medical armband for real-time vital monitoring. - Employ a cuffless BP measurement method accurate for clinical use. - Improve triage efficiency by reducing EMS load.

Manufacturing Design Methods

Our wearable device integrates ECG, PPG, and thermistor sensors to enable real-time monitoring of vital signs, including heart rate, temperature, and blood pressure. A GPS module provides clinician tracking for remote monitoring applications. ECG and PPG signals are processed using peak and trough detection algorithms to extract Pulse Transit Time (PTT), while heart rate (HR) is calculated from R-peak intervals in the ECG signal. These features—PTT and HR—are input into a machine learning model trained on reference blood pressure data to estimate live blood pressure values. Signal acquisition is handled via Arduino, with data streamed to the ThingSpeak IoT platform for initial visualization. The data is then processed in MATLAB, and results are displayed through a custom interface built in App Designer.

Analysis

The LIFE device was evaluated for its blood pressure estimation accuracy by comparing it against a standard BP cuff and a commercial BP watch. The Mean Absolute Error (MAE) and Standard Error (SE) were used as key metrics. Results show that the LIFE device achieved an MAE of 8.61 mmHg and an SE of 4.10 mmHg, outperforming the BP watch and meeting the clinical standard for SE (

Future Works

Future improvements will focus on enhancing blood pressure estimation accuracy through the use of larger datasets and more advanced machine learning models. Additional efforts will be made to increase device durability, improve wireless connectivity, and refine the user interface for clinical use. Continued validation and testing will be conducted to ensure clinical reliability and support potential regulatory approval.

Project Summary

StabiliKnee is a cost-effective smart rehabilitation sleeve designed to provide both patients and clinicians a quantitative method to enhance recovery and prevention of knee injuries, particularly anterior cruciate ligament (ACL) tears, by seamlessly combining real‑time muscle activity feedback through electromyography (EMG) sensors with adaptive pneumatic compression support. The sleeve continuously monitors quadriceps and hamstring activation patterns in locations critical to ACL load. It shows clinicians and athletic trainers early signs of muscle fatigue or imbalance before they lead to compensatory movement or re‑injury. Ultimately, StabiliKnee serves as a supplemental tool in improving recovery timelines and long‑term joint stability, and it gives both patients and care representatives the confidence that rehabilitation is guided by objective, continuous insight.

Project Objective

Our project aims to redefine ACL rehabilitation by integrating wireless EMG sensors, pneumatic pump compression, and a dedicated control circuit board into a single wearable sleeve called StabiliKnee. The primary objective is to restore knee stability after ACL injury by continuously quantifying muscle activity and using those real‑time signals to drive targeted pneumatic compression. Current technology and inventions fail to provide a cost-effective, portable sleeve and accessible tool for clinicians and athletic trainers to objectively assess patients' muscle performance. StabiliKnee monitors quadriceps and hamstring activation in locations critical to ACL load and applies support precisely when fatigue or imbalance is detected, enabling clinicians and trainers to tailor therapy and prevent compensatory movements.

Manufacturing Design Methods

The sleeve is designed with housings for two micro air pumps, the printed circuit board with all the key circuit components and batteries that control the pump delivery and EMG sensing, and attachment sites for the wireless EMG sensors to detect muscle signals. This novel sleeve is designed to be controlled through a microcontroller, Nano ESP32, which receives and delivers information from and to the pump delivery and EMG sensing systems. This allows the opportunity to communicate via Bluetooth with four EMG wireless sensors, which is directly translated onto a user interface, illustrating the clinician/trainer the results obtained. Two LED lights (green and red) in the board housing inform the user when the sleeve collects data and offers pneumatic compression to detect muscle imbalance and fatigue. A MOSFET transistor is integrated within the custom printed circuit board to regulate the pump delivery accordingly to the microcontroller readings from the EMG sensors. The EMG sensors are attached to the sleeve in distinct pockets at four muscle locations: two quadriceps (vastus medialis and vastus lateralis) and two hamstrings (biceps femoris and semitendinosus) locations. Through subject testing, algorithmic thresholds are established through post-processing methods to identify the ideal threshold voltage to activate pneumatic compression. This creates a feedback mechanism with the EMG sensors providing the readings to the microcontroller, and compression supplied from the microcontroller.

Analysis

The collected data from the subject testing revealed a clear pattern of quadriceps under‑activation alongside compensatory hamstring over‑activation in subjects following ACL or other relevant knee injury or surgery, reflecting greater atrophy in the quadriceps. Hamstring over‑activation appears to stem from the relatively preserved muscle mass in those muscles compared to the weakened quadriceps. When compared to uninjured controls, injured participants exhibited lower overall muscle activation and an earlier onset of fatigue across the same set of exercises. Introducing targeted pneumatic compression to the fatigued muscle groups helped restore their activation levels, supporting the sleeve’s role in rebalancing muscle function during rehabilitation.

Future Works

Optimize design for a compact/efficient system Power source management Improved sizing, sleeve-fitting, comfort, and electrode positioning Implement an automated decompression mechanism Machine learning integration for improved threshold sensing across more subject data

Acknowledgement

Mohammed Ahmed & Yasith Weerasinghe, PhD Tylor Bene, JoVaun Wooden, & Larimee Heh, DPT Garrett Gnemier, Computer Science Undergrad

Project Summary

SmartStride is a wearable technology designed to support the rehabilitation of individuals with Idiopathic Toe Walking (ITW), a condition where individuals habitually walk on their toes without a clear neurological or orthopedic cause. If left untreated, ITW can lead to long-term gait abnormalities and muscle issues. The SmartStride system integrates Electromyography (EMG), Inertial Measurement Units (IMUs), and machine learning to track and analyze gait patterns. Data collected from sensors embedded in a compression sock is transmitted via Bluetooth, and then the data is run through a machine learning model on the Raspberry Pi 5 to classify severity, and displayed and stored using Amazon Web Services (AWS). This platform allows clinicians to monitor patient progress remotely, view session data, and make informed treatment decisions based on objective biomechanical insights. Our team developed a functioning prototype that includes modular sensor housing, wireless communication, and an intuitive web interface for both patients and clinicians. By enabling session-based monitoring and data-driven analysis, SmartStride provides a more accessible and personalized approach to ITW rehabilitation.

Project Objective

The objective of this project is to design and develop a wearable rehabilitation system for individuals with ITW that enables monitoring, quantitative gait analysis, and remote clinician access. The system uses Electromyography (EMG) and Inertial Measurement Units (IMUs) to collect muscle and motion data, which is processed using machine learning algorithms to identify abnormal gait patterns. A secure web-based platform supports clinician-patient interaction by displaying session data and progress metrics, facilitating personalized, data-driven treatment plans aimed at improving rehabilitation outcomes.

Manufacturing Design Methods

The manufacturing of this device was focused on the ease of production, interchangeability, and consumer/patient use. This was accomplished through the leveraging of additive manufacturing and commercially available parts. First, to allow a single device for multiple age ranges, a soft spandex compression sock was used to stretch and expand, allowing for various sizes of feet to work with this device. To account for the changing dimensions permitted by the compression sock, Velcro was used to reposition sensors to ensure dimensional accuracy between trials. From a manufacturing standpoint, additive technologies were used to create the individual sensor cases. This allowed for rapid production at a minimized cost standpoint. Additionally, it allowed for easy changes to develop the final sensor case design. Finally, using commercially available parts allows for easy swap and integration. Due to the simplified electronic design, the cost stayed low and can be easily repaired.

Analysis

We successfully developed a wearable rehabilitation device that combines Electromyography (EMG) and Inertial Measurement Unit (IMU) sensors to monitor gait in individuals with Idiopathic Toe Walking (ITW). The system offers a data-driven approach to assess rehabilitation progress by capturing and analyzing muscle activity and movement patterns. To support clinician engagement, we created a user-friendly web interface that visualizes patient data and tracks progress over time. Additionally, the device was designed with customizable sensor placement, allowing it to adapt to various users and ensure accurate data collection.

Future Works

Incorporate haptic feedback into the device for faster rehabilitation. As well as collaborate with the Scott Center on campus to integrate the project with patients suffering from ITW.

Acknowledgement

A sincere thank you to Dr.Linxia Gu, Mohamed Ahmed, and Michael Grillo for their support and help throughout our project creation.