Lee, Rodriguez, Rosenstein earn seed funding awards as principal investigators for future physical/life and medical sciences projects

We propose developing artificial intelligence (AI) solutions to improve stroke patient care by addressing two key challenges: (1) detecting severe strokes before patients arrive at the hospital and (2) predicting long term recovery outcomes more accurately. We propose developing artificial intelligence (AI) solutions to improve stroke patient care by addressing two key challenges: (1) detecting severe strokes before patients arrive at the hospital and (2) predicting long term recovery outcomes more accurately. In the first part of our project, we will create AI models that can quickly identify large-vessel occlusion (LVO) strokes, which require triage to a comprehensive stroke center equipped for specialized surgical intervention (e.g., only Rhode Island Hospital in Rhode Island). These AI tools will analyze brain activity using portable EEG devices in ambulances, helping emergency teams make faster decisions about patient care. This could lead to quicker treatments, improving patient outcomes and reducing the risk of disability. The second part focuses on predicting a patient’s recovery using brain scans and other medical data. We will develop AI models that analyze this data to provide more precise recovery predictions, offering better clinical guidance and improving resource allocation. This project combines the strengths of engineering, neurology, and AI research at Brown University to address critical needs in stroke care. By improving early detection and outcome prediction, our work could have a significant impact on how stroke patients are treated, ultimately leading to better recovery and quality of life for many individuals globally. 
PI: Jonghwan Lee, Associate Professor of Engineering, Assistant Professor of Brain Science 
Co-PI: Shadi Yaghi, Associate Professor of Neurology 
Key Personnel: Liqi Shu, Instructor in Neurology (Research)

We hypothesize that mantis shrimp telsons have evolved properties that reduce the damage of cavitation. In particular, given the basic physics of cavitation damage and the broad evolutionary trends in mantis shrimp telson geometry and material properties (especially between species that fight or do not fight), our central hypothesis is that telson geometry, the combination of raised carina with troughs between, and material properties, the alternation of stiff and flexible materials along this geometry, reduce the damage that may be caused by cavitation. The central aim of this collaborative proposed work is to test our hypotheses and predictions by harnessing the vast diversity of mantis shrimp worldwide, combining analyses of telson geometry and material properties, theoretical modeling of cavitation dynamics, and high speed video based analyses of cavitation. Here, we focus on a single species (the Caribbean mantis shrimp Neogonodactylus bredini), that is known to receive cavitation-forming strikes during contests, and for which we can gather information on basic physical principles like geometry and material properties. We have strategically organized a combined theoretical, experimental, and modeling research approach to understand whether geometry (why three carina?) and material properties (why alternation of stiff and flexible materials?) affect cavitation damage. 
PI: Mauro Rodriguez, Assistant Professor of Engineering
Co-PI: Patrick Green, Assistant Professor of Ecology, Evolution, and Organismal Biology

Coordinated motility of human cells is essential to shape organ formation in the embryo, to repair wounds, and to respond to infection. Historically, cellular morphology and dynamics have been imaged using bulky and expensive optical microscopes using fluorescent labeling, which often limits accessibility in the clinic or low resource settings. Moreover, detecting and tracking features in time-lapse images remains labor-intensive and time-consuming. First, we propose to develop a capacitance imaging platform for label-free sensing of mammalian cell cultures at single-cell resolution, combined with deep learning to virtually stain for cell states, biomarkers, and features of interest. Second, we propose to investigate how directed cell migration can be guided by highly localized electric fields. The effect of electric fields on collective cell migration is currently poorly understood due in part to prior work being limited to coarse spatial control with large electrodes. Third, we propose to explore new architectures for mechanically flexible electrochemical imaging and stimulation chips, based on a robust commercially-available thin film transistor (TFT) technology. As a case study, we will investigate how adherent epithelial cells transition to a more motile mesenchymal phenotype, which is relevant for collective migration and wound healing. The preliminary results enabled by this Research Seed Award will make this collaborative team competitive for future projects at the interfaces of semiconductors, biology, and medicine.

PI: Jacob Rosenstein, Associate Professor of Engineering
Co-PI: Ian Y. Wong, Associate Professor of Engineering, Associate Professor of Pathology and Laboratory Medicine
Co-I: Ritambhara Singh, John E. Savage Assistant Professor of Computer Science and Data Science, Brown University
Co-I: Jonathan S. Reichner, Professor of Surgery (Research), Rhode Island Hospital and Brown University

Breast cancer is the most commonly diagnosed cancer in women in the United States. In 2021, more than 250,000 new breast cancer cases were reported in females in the United States, while in 2022, more than 40,000 women died from breast cancer. Outcomes vary dramatically depending on the stage of the disease, with the five-year survival rate for distant metastasized breast cancer reported as 32.4% vs ~99% for localized breast lesions. Understanding the biology underlying the progression of this disease and the mechanisms for its spread are therefore critically important. While the extracellular environment is recognized as a critical regulator of breast cancer progression and spread, the interaction between its architectural and biochemical properties remains unclear, a critical hindrance to progress in fundamental understanding of these processes. This proposal will therefore develop a cross-disciplinary high throughput screening platform to enable the development of 3D synthetic extracellular matrix systems that accurately recreate the cellular behaviors of healthy and various cancerous mammary tissue states and further enable the independent manipulation of key physical and biochemical properties. This platform will uniquely bridge the critical gap between material development and biological validation, accelerating the research progress in the etiology and treatment of breast cancer.

PI: Benjamin McDonald, Assistant Professor of Chemistry
Co-PI: Michelle Dawson, Associate Professor of Molecular Biology, Cell Biology and Biochemistry, Assistant Professor of Engineering