Project Summary/Abstract Point of Care Diagnostics for Liver Disease using Fluorescent Nanosensors Liver fibrosis/cirrhosis is a major driver of mortality and morbidity worldwide. Rapid diagnosis of liver fibrosis is crucial to optimizing patient outcome and minimizing economic impact of this disease. Current strategies for detecting liver damage (fibrosis/cirrhosis) use biomarker and mechanical strategies that are expensive and difficult to translate into Point of Care (PoC) platforms. Robust PoC liver diagnostics would enable regular monitoring of chemotherapy patients and game-changing diagnostics for the developing world. In preliminary studies we have demonstrated that polymer-based sensor arrays on paper substrates can generate serum ‘signatures’ that can diagnose liver fibrosis with clinical relevance. In our proposed research we will build on this foundation to create effective lateral flow device (LFD) diagnostics for liver fibrosis. In our multi-pronged strategy, we will: Aim 1: Synthesize engineered polymer conjugates (Rotello) and use these as sensor elements to provide multi-channel outputs serum sensing. Protein/serum selectivity will be guided by integration of synthesis (Rotello) with computational/machine learning tools (Van Lehn) in a feedback-driven cycle. Thesse studies will be performed in solution to facilitate sensor optimization. Aim 2: Fabricate LFD devices and immobilize polymers downselected from Aim 1 onto surfaces to provide prototype sensing systems suitable for PoC use. (Rotello) These sensors will be tested and optimized using model sera generated by spiking healthy serum. Aim 3: Apply LFDs downselected from Aim 2 to profile liver fibrosis using pathological samples and liver diagnostics insight provided by Rosenberg and Peveler. These studies will focus on detection and staging of liver fibrosis, using statistical methods developed by C. Rotello. Aim 4: Effective sensor systems identified in Aim 3 will be explored using proteomics by Vachet. These studies characterize protein binding to the polymer sensors, providing insight into how the sensor works and potentially new biomarkers for fibrosis. The key driver of the proposed research is the development of PoC systems for diagnosis of liver fibrosis; effective achievement of this goal would provide strategies that could be translated to numerous disease states. The focus on polymer-protein affinity and selectivity will provide new insight into fundamental aspects of these interactions, and the integration of machine learning into this process will develop new polymer design strategies biomedical applications.