RAREST REU Site

REU Site: Rural Appalachia REsearch in bioSensing Technology (RAREST)

This NSF funded REU Site: Rural Appalachia Research in biosensing Technology (RAREST) hosted by West Virginia University boasts strong intellectual merits by advancing scientific understanding of critical health disparities in rural Appalachian communities. It aims to generate new knowledge about the factors contributing to several diseases such as tick-borne infections, cardiovascular diseases, ischemic stroke, cancers in this underserved region, addressing a significant gap in regional public health research. The research outcomes are expected to have broad implications for improving health interventions and policies tailored to rural populations, contributing to the scientific community’s knowledge base.

For students involved, this project offers valuable hands-on research experience in understanding complex health issues within a real-world, underserved setting. They will have opportunities to develop skills in epidemiological research, data collection, and analysis, as well as to participate in interdisciplinary collaborations. Such experiences enhance their academic training and prepare them for careers in public health, biomedical sciences, and community-based research, fostering the next generation of researchers dedicated to health equity and rural health challenges.

This RAREST site is directed by Dr. Soumya Srivastava (PI) and co-directed by Dr. Srinivas Palanki (co-PI)

2026 RAREST REU-Site Projects

^{1) AI for Microfluidic Biosensor Design and Optimization} ^M^entor: ^{Dr. Yuxin Liu}

^{Microfluidic-based biosensors are important because they allow fast, low-cost, and
accurate testing using tiny amounts of samples. Building and testing each design
in the lab are costly and time-consuming because small changes in shape or size
can affect how well they work. This project investigates how artificial intelligence
(AI) can accelerate the design and optimization of microfluidic devices for biosensing
applications. AI has been rapidly adopted in many fields, however its use in microfluidics
remains relatively limited. By integrating AI-based design tools with computational
simulations, the project aims to efficiently generate and evaluate new channel
geometries without extensive laboratory testing. The objective is to develop an
AI-guided workflow that improves mixing performance and detection efficiency, contributing
to the design of faster and more effective biosensors. Student Task: learn how microfluidic devices work, use AI and programming languages
to design microfluidic devices, run computer simulations and apply data analysis
for design selection and comparison. By the end of the project, the student will
have a basic understanding of how AI can help design microfluidic biosensors, and
will gain hands-on experience with coding, simulations, and data analysis.}

^{2) Designing impedance-based sensors for detecting Lyme disease} ^{Mentor: Dr. Soumya Srivastava}

The MESA lab focuses on developing point-of-care impedance-based sensingbiomedical devices for abnormal or infectious cell detection. For this REU project, we propose to develop an impedance sensor to detect Lyme disease. Molecular diagnostic tests (PCR and IHC) can detect Borrelia spp. in the acute phase of illness; however, the cost and required expertise preclude their use in many endemic settings. We propose to develop an accessible, field-deployable, and cost-effective detection platform using impedance sensors without requiring cells to be treated with fluorescently tagged antibodies or using a differentiation marker-driven green fluorescent protein (GFP) on a microfluidic chip. Student Task: The REU student will design a microfluidic impedance sensor to detect cells. The participant will learn protocols of a biosafety level 2 lab, fabrication of amicrofluidic chip, sample preparation, manipulation of cells under an electric field, design experiments considering cell variabilities, and data interpretation. They will be trained to perform a literature survey,justify the significance, develop a detailed plan and milestones for the research project, microscopy, image processing, and simulating the diagnostic tool. The participant will present at the Summer Undergraduate Symposium held at WVU and will be included in any resulting publications.

^{3) EIS} ^{to Understand Neuroinflammation Following Ischemic Stroke} ^{Mentor: Dr. Moriah Katt}

Ischemic strokes impact nearly one million individuals in the United States annually with complex pathophysiological downstream effects that are not well understood. Blood-brain barrier (BBB) disruption and heightened proinflammatory signaling are seen following ischemic stroke. Disruption of the BBB allows blood components to enter the brain, disrupting the tightly controlled microenvironment in the brain necessary for neuronal function and survival increasing neuroinflammation, where proinflammatory factors are known to decrease barrier function, causing a self-perpetuating cycle of worsening phenotype. Electrical Impedance Spectroscopy (ECIS) can be used to measure barrier function of brain endothelial cells (BMECs) allowing for a real-time assessment of barrier integrity in response to therapeutic intervention. Utilizing FDA approved complement inhibitor treatments to attenuate neuroinflammation the impact on barrier function will be assessed. Human induced pluripotent stem cell (hiPSC) derived brain endothelial-like and astrocyte-like cells will be used in an in vitro model to model neuroinflammation following ischemic stroke. Cells will be exposed to simulated stroke using oxygen and glucose deprivation and treated inhibitors of proinflammatory cascades to investigate their utility as treatments for ischemic stroke. Student Task: Students will gain experience culturing human induced pluripotent stem cells (hiPSCs) toward BMEC and astrocyte phenotypes. They will culture these cells in plates containing electrodes and stimulate them with inflammatory cytokines, simulated ischemic stroke, and pharmacological inhibitors to decrease the inflammatory cascade. They will evaluate ECIS measurements to determine the impact on barrier functions.

^{4) Souping Up NEMO: Engineering Rapid-Response MRI Nanoparticles} ^{Mentors: Dr. Margaret Bennewitz & Dr. Sharan Bobbala}

Although mammography is the gold standard method to detect breast cancer at an early stage, the cancer is often missed in younger women with dense breasts. Additionally, a benign breast tumor can be mistaken for a malignant one in half the women screened annually for 10 years. Magnetic resonance imaging (MRI) detects more breast cancers but also experiences false positive readings from the clinically used contrast agent (e.g., gadolinium (Gd)-chelates). Gd-chelates are always “ON”, lighting up any vascularized tissue and are not targeted so they highlight both benign and malignant tissues. We have developed Nano-Encapsulated Manganese Oxide (NEMO) particles that are targeted to cancer cells and dissolved in their low pH endosomes and lysosomes to produce cancer-specific robust MRI signal. Our preliminary animal studies show that NEMO particles are as bright as Gd-chelates in enhancing breast tumors in mice, but they are more specific. However, NEMO particles only produce MRI signal in 3-4 hours. This project will utilize a novel pH-sensitive polymer to change NEMO’s chemical composition and enhance its ability to rapidly turn “ON” MRI signal in cancer cells. Student Task: Here, students will encapsulate manganese oxide into pH-responsive acetalated dextran nanoparticles for precision cancer cell uptake and rapid intracellular release of Mn2+ to initiate MRI signal. They will characterize the nanoparticles for size, charge, stability, metal loading, controlled release of metal ions, T1 MRI signal, peptide targeting, etc. NEMO particle labeling and contrast generation in cancer cells will be evaluated in static cell studies and a dynamic tumor-on-a-chip platform using confocal microscopy and MRI. Students will present at WVU’s Summer Undergraduate Research Experience (SURE) Symposium to disseminate their results.

^{5) Microgravity‑Induced Electrical Fingerprints of Pancreatic Cancer Cells Using Microfluidic Dielectrophoresis}^{Mentor: Dr. Soumya Srivastava}

^{This REU project will investigate how simulated microgravity alters the biophysical and bioelectric properties of pancreatic cancer cells and whether these changes can be detected using a microfluidic dielectrophoresis biosensor. Student Task: Students will culture pancreatic cancer cells under normal and simulated microgravity conditions, then run them through an existing DEP microfluidic platform in the WVU lab to measure changes in crossover frequency, trapping patterns, or impedance signatures. By comparing the DEP response of microgravity‑exposed and control cells, the student will assess whether microgravity‑induced phenotypic shifts in pancreatic ductal adenocarcinoma can be captured as distinct “electrical fingerprints,” supporting future space‑inspired strategies for pancreatic cancer diagnosis and therapy.}

^{6) Using data from biosensors to add context to measures of real-world biomechanics} ^{Mentor: Dr. Stephen Cain}

Wearable inertial measurement units (IMUs), which measure linear acceleration and angular velocity, make it possible to capture high resolution human movement data (sampling frequency 100 Hz) in the real world for extended periods of time (7+ days). Using these data, we can calculate biomechanically relevant measures such as joint angles for the arms and legs, walking speed and stride lengths (for walking), and wheelchair speed and propulsion style (for manual wheelchair users). Because the data collections are unobserved, we unfortunately do not understand the context (e.g., walking inside or outside, pushing a wheelchair on a smooth or rough surface, fatigued or not fatigued) of each type of movement we capture and therefore are unable to account for how context may be influencing the measured biomechanics. The aim of this project is to explore how data from biosensors, such as heart rate, heart rate variability, skin temperature, blood oxygenation, and skin conductance, can be used to add context to measures of real-world biomechanics. Student Task: The student will work to quantify how biosensor measurements change due to context. To accomplish this, the student will 1) explore the biosensor measurements available using consumer-grade wearables (e.g., Apple Watch, Garmin, Fitbit), 2) create a study using an existing health research software platform (Avicenna Research) to capture data from select consumer-grade wearables, 3) design a small pilot study to capture biosensor measures during a few well-defined contexts/conditions, 4) collect data on a small sample (n < 5), and 5) quantify changes in biosensor measurements due to context.

^{7)

Precision Multichannel Sensing Leads for Advanced Peripheral Nerve Electrode
Interfaces} ^{Mentor: Dr. Loren Rieth}

^{Neural electrodes used to interface peripheral nerves such as the vagus nerve
require higher density electrodes and robust leads. The leads must tolerate
repetitive loadings and fatigue to enable safe and effective interfaces with
these nerves. Developing micro-scale compliant leads with many channels is
a critical unmet need for the next generation of advanced peripheral nerve
interfaces. These interfaces will allow recording and analysis of
electromyography (EMG) and electroneurography (ENG) signals, and stimulate
the nerve with more precision as shown with vagus nerve stimulation in animal
models. Student Task:}^{Students will train in microfabrication, device packaging, integration, and
medical device development. The students will develop lead integration approaches
to allow high-density neural electrodes to be reliably and robustly integrated
with electrophysiological electronics. This work in work with flexible neural
interface device based on microfabricated polyimide devices that use IrOx
electrode materials. Techniques to integrate these devices with robust electrical
leads and characterize their mechanical and electrical properties and performance
are two thrusts for this work.}

^{8)

Real‑Time Tooth Thermography Sensing for Endodontic Diagnosis and Thermal
Protection} ^{Mentor: Dr. Loren Rieth}

Precise, rapid, local, and robust measurements of tooth temperature are a critical need to prevent thermal injury to the jaw and gums during bone drilling procedures used for dental implants. Additionally, thermal testing by cooling or heating a tooth and measuring the patients response is an important diagnostic test for health of the pulp and nerve of a tooth. This is a standard diagnostic test to determine the need for a root canal procedure. Reliable, economical, and robust tools to perform these measurements and provide rapid visual feedback to the endodontist in the surgical field would improve the workflow and outcome for these procedures. ^{Student Task:}^{Students will design, fabricate, and test a flexible, small-scale, fully digital
temperature probe array that can be quickly and effectively adhered to a
tooth. The response time, precision, localization of the sensing array will
be evaluated using a custom-design flexible PCB and Arduino microcontroller.
Additionally, the flex PCB will stream temperature data to displays and control
an integrated micro-LED display that allows temperature evaluation in the
surgical field. Devices will be tested on cadaveric animal teeth and jaws
to evaluate their performance in pre-clinical testing in collaboration with
the School of Dentistry.}

^{9)
Design and Evaluation of a Low-cost Sensor for Remote Diagnosis of Respiratory
Diseases} ^{Mentor: Dr. Srinivas Palanki}

^{Lung diseases such as restrictive ventilatory abnormalities kill about 4 million
people annually in the U.S. These chronic diseases require repeated doctor
or hospital visits for proper disease management. Current diagnostic devices
include spirometry, costing $1,000-$3,000, typically available only in the
doctor’s office. Thus, there is a need to develop a low-cost biomedical device
that patients can use at home instead of making frequent (and expensive) trips
to the doctor’s office. Furthermore, remote diagnosis of respiratory diseases
is possible if this device can send breathing pattern data to the doctor’s
office remotely. An inexpensive device that is easily affordable while offering
accurate measurements would improve the lives of people.
Student Task}^{: Students will fabricate a device using a pressure sensor that produces a
measurable voltage response to human breath. They will simulate a breathing
disorder and see if their developed device can produce a voltage pattern
distinctly different from the one observed during normal breathing. Then,
they will write a program in Python in a Raspberry Pi that will send the
voltage versus time data from the device to a remote email address (that
can be accessed at a doctor’s office). The objective is to keep the cost
of the overall device to less than $100. Students will have the chance to
learn how point-of-care devices are developed and commercialized. The
participant will present at the Summer Undergraduate Symposium held at WVU and
will be included in any resulting publications.}

Program Dates:

Summer 2026: May 18-July 24, 2025 (10 weeks in duration)

Participant Benefits:

Stipend of $7,000 ($700/week for 10 weeks), lodging, meal expenses, travel reimbursement to/from REU Site (limited to ~$250/participant), and comprehensive training to move participants toward intellectual and research independence.

Eligibility

U.S. citizens, U.S. nationals, or permanent residents of the United States.
Rising juniors & seniors majoring in biomedical engineering, bioengineering, and related disciplines.
Students from any higher-education institution in the U.S. are eligible, but students from institutions in the Appalachian region are especially encouraged to apply.
Have a grade point average of 3.0 or above in their undergraduate coursework
Passion for translational biomedical research and innovation

Application: APPLY HERE

Opens on December 15, 2025, and closes February 15, 2026 . Applications will be evaluated as soon a received and on a rolling basis. Applications will be accepted until all positions are filled.