Chronic wounds present a significant burden on healthcare systems worldwide and have a substantial impact on the health and quality of life of affected individuals. The prevalence of chronic wounds is estimated to be 1 to 2% of the population in developed countries, with annual costs ranging from $20 to $50 billion in the United States alone. With an aging population and an increase in conditions such as diabetes, the prevalence of chronic wounds is expected to increase in the coming years. Innovative approaches are required to manage chronic wounds effectively, given the challenges faced by healthcare providers, including overburdened hospitals, inconsistent assessment and documentation by home health nurses, and patient-related barriers in the outpatient setting. One promising solution is the integration of smartphone technology that utilizes the sophisticated imaging capabilities of mobile devices to enable more precise, rapid, and reliable assessments. This project proposes the development of a smartphone-based chronic wound management system that utilizes machine learning (ML) models to gather accurate data on wound measurements and descriptors. The system leverages depth imaging technology on modern smartphones to produce photographs and 3-D scans of the wound, enabling the automated generation of accurate wound measurements and descriptors. These measurements include wound length, width, maximum depth, average depth, surface area, and volume, while descriptors include the percentage of wound epithelization, granulation, slough, and necrosis. The proposed work stems from collaboration with home nurses on a previous study of stoma patient care, with an extension of this study resulting from exposure to the NSF I-Corp grant; it aims to enhance the management of chronic wounds, with a smartphone-based system that facilitates efficient and precise documentation and assessment of patients' wounds. My main role in this project is to provide support in proposal editing, pitch deck creation, and conducting patient interviews to gather information about their current needs and identify helpful technologies; addressing patients' concerns and feedback, and working to incorporate new features into our system to better meet their needs.The system's use of depth imaging technology and ML models will enable healthcare providers to gather accurate data more efficiently and accurately, leading to better patient outcomes and reduced healthcare costs.

In the body, cells grow in the extracellular matrix (ECM), which presents biochemical and mechanical signals to the cells inside. Hydrogel biomaterials are water-laden polymer networks that can mimic the properties of the ECM, allowing controlled study of cellular behavior in vitro. Many cells are mechanosensitive, but mechanical cues other than stiffness have not been fully investigated. This project aims to develop a platform in which degradability and strain can be activated by a researcher bio-orthogonally. We have synthesized a cyclic peptide crosslinker for a synthetic poly(ethylene glycol) hydrogel that acts as a Boolean AND-gate: one half is degradable by cell-secreted enzymes, and the other half is degradable by sortase, a bacterial enzyme, added by a researcher. We quantified the degradation of hydrogels made with this crosslink via fluorescence release and demonstrated that degradation only occurs after exposure to both enzymatic inputs. We further demonstrated that cells encapsulated in this material retain strong viability. We predict that cells will be unable to spread in this material until after a researcher adds sortase. After sortase addition, we expect that contractile cells will be able to locally degrade the material, spread, and generate strain. We intend to quantify spreading and strain with encapsulated fibroblasts. We also plan to use this platform to study development, by encapsulating immature cardiac stem cells and investigating the effect of fibroblast driven strain as a model; we predict that strain will trigger further specification of these immature cells. In addition to understanding the pathways for development, this research may help identify new therapeutic targets for disease, and it will also inform new strategies to grow tissue in vitro that more closely mimic the native environment.

Across a variety of signaling pathways, soluble factors in the extracellular matrix bind to protein receptors that span the cell wall, thereby triggering an information cascade that affects cell activity or function. It follows that by controlling the binding of signaling factors to these receptors, cell behavior and activity can be guided with substantial precision. In this project, we aim to design a system that allows de novo-developed protein agonists and antagonists, referred to as binders, to be activated with a high degree of temporal and spatial control within cell-encapsulating hydrogels. Towards this end, we employ methods derived from protein semisynthesis and click chemistry to tether binders to the hydrogel polymer network and then subsequently photo-release them from the network. We expect a difference in the functionality of binders when they are bound to the network compared to when they are released through light exposure and solubilized, thus achieving light-dependent control of the binder-receptor interaction and cell activity. This system will be the first to employ de novo developed agonist and antagonist biomolecules for the interrogation and control of cellular behavior. In so doing, it will expand the tool box of biomaterial engineering to include finer control over cells grown in 3D matrices, with direct implications in fields as diverse as therapeutic development, regenerative medicine, and organ-on-a-chip engineering.

Effective rapid tests have been developed to detect COVID-19 in nasal swabs. However, nasal swabs require a trade-off in which deeper insertion of the swab allows higher accuracy but causes greater patient discomfort. On the other hand, saliva can be collected non-invasively in large volumes. Therefore, this project aims to develop a point-of-care diagnostic device (“DiagnosDisk”) for the rapid and sensitive detection of SARS-CoV-2 nucleocapsid protein (NP) in saliva. The DiagnosDisk is a circular disk (25mm diameter) consisting of three layers, from top to bottom: a plastic adhesive capping sheet with a sample port, a hydroxylated nylon detection membrane, and an absorbent pad. Analytes are efficiently enriched by binding to anti-NP antibodies conjugated to temperature-responsive polymers (poly(N-isopropylacrylamide)) which aggregate upon heating. To prepare the sample, polymer- and gold-conjugated antibodies are mixed with the sample and bind to the target antigen, forming a sandwich immunocomplex. When flowing through the heated detection membrane (>37℃), the sandwich immunocomplexes aggregate and are captured on the membrane, producing a visual signal. I quantify the signal intensity on the membrane using ImageJ software. With this design, I hypothesize that the DiagnosDisk will have higher sensitivity than the lateral flow assay (LFA) by utilizing temperature-responsive polymers for sample enrichment and using larger sample volumes which increases the number of antigens that can be captured. From my initial testing, the DiagnosDisk enabled 2mL of buffer flow-through in 7.5 minutes, demonstrating a sample volume capacity 10-times that of most LFA. Next, I tested the DiagnosDisk with buffer samples spiked with 5ng/mL NP. Heated membrane pads produced signal intensities significantly greater than unheated membranes. I plan to apply the device to saliva samples next. Ultimately, by enabling rapid and sensitive detection of COVID-19 in saliva, the DiagnosDisk can help limit community transmission of the virus.

Clearance of wound infections can be hindered by a bacterial biofilm; a complex extracellular matrix (EM) secreted by adherent bacteria that allows them to evade the host immune system and obviate antibiotics. A novel, synthetic peptide—known as an anti-α-sheet inhibitor—can disrupt biofilm stability by inhibiting the formation of amyloid fibrils, which contribute to the biofilm EM. This project aims to design and characterize alginate porous scaffolds that elute these synthetic peptides, for use as anti-biofilm wound dressings. The physical properties and peptide release kinetics of the scaffolds will be optimized for clinical applications, supported by in vitro efficacy studies with live bacteria. This project draws upon past work from the Bryers Research Group on engineering infection immunity and tissue scaffolds, in which biofilms are prevalent. Results of this project will provide an alternative approach to biofilm prevention, thus reducing the burden of biofilm-related infection complications.

Hepatocellular Carcinoma (HCC) is an aggressive primary liver cancer that can be characterized with contrast-enhanced ultrasound (CEUS) by blood flow parameters. The most important blood-flow parameters are wash-in and washout, measured by the rate of contrast flow into and out of the liver and tumor. Currently blood flow is observed by eye, assessing the brightness of the contrast agent over time during the scan, and then scored on the Liver Reporting and Data System (LI-RADS) scale. This subjectivity delays diagnosis and treatment, and increases HCC mortality rate. To aid clinicians in a timely diagnosis, we are creating a quantitative Python algorithm to extract blood flow parameters from CEUS videos. I studied the existing MATLAB code our lab has created and translated its functionality into a Python executable. Since the two languages have different syntaxes and functions, I reconfigured the entire script to run in Python. This Python script will run faster than the MATLAB code, be able to accurately compute blood flow measures from CEUS scans, and be easier to distribute outside of academia. To extract parameters from CEUS liver videos of HCC patients, we processed the video using respiratory gating and motion compensation to ensure the tumor is on the scan and not moving around due to breathing. Then we analyzed the pixel brightness of the liver and tumor over the course of the scan to create a time-intensity curve (TIC). Finally, we compared various points on the curve to quantify the blood flow within the tumor and surrounding liver. Further iterations of the algorithm will include machine learning tools available in Python to increase the level of autonomy and thus the objectivity of the parameters. Using this algorithm, HCC diagnosis using LI-RADS will be timelier, allowing patients access to more effective treatment options.