In the S.M.A.R.T. Lab we are interested in using Sensors, Materials, and Analytics together for the Regeneration of Tissues. You will find that our research is interdisciplinary by nature, and we actively seek to build an interdisciplinary research team to meet these needs. Particularly we are interested in those who are excited to work well with those that think are learn differently than themselves. Diversity in research expertise and background is one of the strengths of our group! Please see our research projects below and contact us with any questions or interests you may have. -Current Research


Smart Wound Dressings for Chronic Wounds

Chronic wounds, resulting from injuries such as burns, diabetic ulcers, or non-healing surgical sites, present significant challenges for patients, clinicians, and researchers across the globe. These wounds are characterized by a frustrated inflammatory response and high likelihood of infection. The possibility of infection is especially troubling because this may lead to tissue death, amputation, or systemic infection (sepsis leading to death. The S.M.A.R.T Lab is taking a theranostic approach to addressing the chronic wound healing problem through development of smart wound dressings for diagnosis and treatment of these wounds.

Treatment of Chronic Wounds through Medical Textiles

Important factors in the management of chronic wounds include:
1. Infection Management
2. Moisture Regulation
3. Ingrowth of Granulation Tissue

Our lab approaches these three areas through fabricating biomaterial-based wound dressings based on textile technology. We produce micro and nano-scale monofilaments and yarns through an array of polymer spinning techniques (i.e. solution blow spinning, electrospinning, melt spinning) and arrange these fibers into wound dressings via knitting or weaving. Arranging fibers using these textile methods offers the unique opportunity to control mechanical properties, geometry, and surface characteristics. Each of these variables can be modulated to effect wound healing in one or more of the areas mentioned above.

Biosensing the Wound Environment

Diagnosing chronic wounds is currently a highly subjective process in which infection likelihood, wound severity, and prognosis for healing are generally determined through visible inspection and clinician experience. Our aim is to use conductive nanofiber-based biosensors to characterize the state of chronic wounds in real time. In this way, clinicians will be equipped with a quantitative measure of wound state, enabling a consistency of treatment strategies across all clinical practice.


Real-time Image Processing for In-vitro Drug Testing

The current paradigm in in-vitro testing of drugs is to culture cells in contact with the particular drug of interest and to observe effects related to efficacy based on periodic microscopic imaging or end-point biochemical assays. While in-vitro testing does offer limited information regarding drug efficacy in-vivo this testing is still an important step across many contexts in life science and engineering. Given its importance, it is critical that we extract as much information as possible from ¬in-vitro experiments. The S.M.A.R.T. Lab is focused on developing ways to incorporate real-time, continuous image acquisition and processing into ¬in-vitro testing to uncover more details from these experiments. We aim to use innovations in both hardware (low-cost incubator-compatible imaging) and software (automated, machine learning for processing) to uncover information previously not accessible with traditional observation methods.


Multivariate Analysis of CHO Cell Culture for Biomanufacturing Optimization

The optimization of manufacturing of cell-based drugs is currently an important area of research for the U.S. as we attempt to reduce the cost of prescription medicines, stream line the development process for new drugs, and increase the efficacy of these medicines. Chinese Hamster Ovary (CHO) cells have been an important component of biomanufacturing for the last 30 years. However, due to genomic variation, complex culture requirements and processes, and challenges with research at industrially relevant scales, research in the optimization of these cells as producers of therapeutic or diagnostic proteins has been mainly accomplished through trial-and-error at the industry scale. As a part of an NSF EPSCoR Track II project – Genomes to Phenomes we are focused on applying machine learning techniques for multivariate analysis of CHO cell cultures in bioreactors. Our goal is to relate bioreactor process control variables, CHO genomic information, and phenotypic outcomes for productivity.