REU 2021 Has Been Postponed Until 2022 – Learn More Here
Please Review the REUs and Select Your Choice Below for an REU to Apply For
Novel Nanotherapeutics for Cancer treatment
Primary PI: Yupeng Chen
Our lab designs and develops DNA nanotechnology enabled Janus base nanotubes. We assemble them with drugs and therapeutic RNAs or DNAs to generate non-covalent nanodevices, named “Nanopieces“. Superior than conventional drug delivery vehicles, these tiny, nano-rod shaped Nanopieces can penetrate into deep tissues with dense extracellular matrix such as cartilage, brain and solid tumors.
Pursuit of a Smart Cerebral Shunt
Primary PI: Kazunori Hoshino
We study an implantable MEMS (micro-electro-mechanical-systems)-based small flow sensor that can monitor the functionality of a cerebral shunt. Cerebral shunt is a device to treat hydrocephalus, which is a prevalent condition, affecting 4-6 people per 1000 of all ages. Over 65,000 shunts are placed, resulting in a total of $2.275 billion spent yearly in the U.S. for surgery-related hydrocephalus. However, currently there are no devices to monitor the viability of an implanted cerebral shunt. Anytime a patient has a headache and doubts if the shunt functions properly, it may result in an unnecessary ER visit. Our smart MEMS sensor will eliminate unnecessary medical workups, and will significantly reduce the cost for the care of hydrocephalus. Through the summer project, the summer researchers will design, fabricate, and test the implantable device and seek the commercial opportunity.
Electromyography Data Collection with Mobile Application
Primary PI: Krystyna Gielo-Perczak
This project aims to investigate the potential of a mobile application that uses new wearable technology to promote engagement between the patient and the physical therapist throughout the rehabilitation process. The app captures real-time data during physical therapy sessions and uses this data to teach a new generation of physical therapists and patients about available measured and calculated results of the rehabilitation process. The main goal of the REU project is to investigate the validity and accuracy of the app’s results. The app records each session, allowing the therapist to monitor the patient’s rehabilitation progress over time. The app also includes a view for the patient; the purpose of the patient view is to provide biofeedback to help prevent injury, maintain comfort and guide/validate the patient on improving stability during the rehabilitation process.
Lower Extremity Assistive Exoskeleton Data Collection and Analysis
Primary PI: Krystyna Gielo-Perczak
This project aims to evaluate the effects of a lower extremity assistive exoskeleton device for Sit-To-Stand (STS). The purpose of this study is to evaluate changes in movement variability utilizing electromyography (EMG) while using the developed exoskeleton prototype. The exoskeleton records each STS maneuver, and assists the user through the maneuver. The main goal of the REU project is to assist in the development of the adaptive assistant algorithm, as well as to assist with prototype testing, data recording, and data analysis.
Novel Assistive Device for Post Anterior Cruciate Ligament Rehabilitation
Primary PI: Kristin Morgan
Over 250,000 anterior cruciate ligament (ACL) injuries occur every year in the United States alone, yet despite extensive rehabilitation neuromuscular deficits often remain. In response to their deficits, individuals adopt altered movement strategies that lead to the development of early onset osteoarthritis (OA) and other associated comorbidities. To address this issue, our project will integrate experimental gait analysis and computational modeling to develop a novel assistive device to help restore healthy muscle function in post ACL reconstruction individuals. The student will learn how to operate motion capture technology and musculoskeletal software to collect and analyze human movement data during dynamic tasks. This data will be integrated into a personalized machine learning algorithm to work in conjunction with the novel assistive device to provide real-time feedback that properly engages healthy muscle function.
Related Websites
https://chip.uconn.edu/person/kristin-morgan-phd/
https://www.bme.uconn.edu/faculty-staff/core-faculty/morgan-kristin-2/
Designing Supramolecular Protein Materials from the Bottom Up: A Computational Approach
Primary PI: Anna Tarakanova
A myriad of degenerative diseases has been linked to the formation of fibrilar amyloid aggregates by protein and polypeptide self-assembly. Recently, it was discovered that small-molecule metabolites associated with metabolic disorders can form similar supramolecular amyloid-like structures. The molecular mechanisms by which these nanostructures form and the specific molecular determinants of the resulting structural and functional features of the aggregates that may contribute to pathologies are not fully characterized. In addition, despite the ubiquity of amino acids in biology, there are no comprehensive studies to date to characterize the nanostructure and mechanical properties of aggregates assembled from single amino acids and other small molecule metabolites. This research will open avenues for developing supramolecular structures based on single amino acids, to be used as mesoscale building blocks for engineering pristine hierarchical materials for a variety of biomedical engineering applications. In this project, the student will gain experience in molecular model development, atomistic modeling, coarse-graining approaches, molecular simulation setup and implementation on supercomputers, molecular visualization software, MATLAB/Python scripting, and scientific writing. The student will have a chance to participate in a collaborative project with an experimental group, and if successful, contribute to a scientific publication.
Computational Drug Design for Non-Addictive Alternatives to Pain Management
Primary PI: Anna Tarakanova
Opioid-based analgesic approaches to chronic pain management, including in the musculoskeletal system, have been implicated as significant risk factors of addiction responsible for the opioid crisis. At present, effective, non-addictive treatment alternatives to opioids to manage chronic pain in the musculoskeletal system are lacking, despite a widespread need. Our long-term goal is to develop alternative, non-addictive analgesic approaches, integrating in silico predictions of commercially available compounds to target multiple pain pathways simultaneously. In this project, we will work with collaborators in the School of Pharmacy and UConn Health Center to test these compounds for efficacy and addiction propensity in mouse models, to ultimately improve treatment outcomes in chronic pain patients. The student will be involved in the development of an in silico framework to virtually screen a library of commercially available molecules as potential drug targets for two pain pathways simultaneously. In this project, the student will gain experience in molecular model development, atomistic modeling, molecular simulation setup and implementation on supercomputers, molecular visualization software, MATLAB/Python scripting, and scientific writing. The student will have a chance to participate in a collaborative project with experimental groups, and if successful, contribute to a scientific publication.
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