This study focuses on understanding the impact of chemotherapy on ovarian cancer stem cells (CSCs), which contribute to cancer recurrence and metastasis due to their resistance to treatment. The research will compare the biomechanical properties and cell aggression of CSCs and ovarian bulk cancer cells (BCCs) after exposure to chemotherapy agents like Cisplatin. Using atomic force microscopy and aggression/migration assays, the project aims to correlate changes in biomechanical properties to increased cell aggression post-therapy. The goal is to determine whether CSCs become more aggressive than BCCs, which may lead to recurrence and metastasis. Ultimately, the project seeks to identify targetable biomechanical changes in CSCs, potentially improving treatment options.
This project aims to enhance pre-surgical planning for dynamic cardiac conditions like hypertrophic obstructive cardiomyopathy (HOCM) by integrating 4D (time-sequential) 3D modeling into clinical decision-making. While 3D printing has advanced surgical planning, it fails to capture the dynamic nature of conditions like HOCM, where the heart muscle obstructs blood flow during contraction. The project seeks to develop an automated segmentation algorithm for cardiac myocardium from CT scans across multiple phases of heart contraction, creating a 4D model. Additionally, it will explore clinically relevant file formats for time-sequential data and implement this model in virtual reality software, allowing surgeons to interact with dynamic, patient-specific cardiac models during surgery preparation.
The Pulmonary Acoustic Sensor Telemetry Array (PASTA) project focuses on developing a compact, wearable device to improve remote lung auscultation, a method for evaluating respiratory function and diagnosing conditions like asthma and pneumonia. Current auscultation tools are limited and ineffective, especially in pediatric environments. PASTA aims to create a multi-point monitoring system that is comfortable, cost-effective and delivers high-quality audio to support algorithm-based diagnoses. The first phase will involve designing a device with eight small microphones to capture lung sounds from various body points, using edge computing devices like Raspberry Pi. This device will provide accurate, real-time data to enhance remote lung assessments in diverse medical settings.
This project aims to address breast cancer health disparities among ethnic minority women in the Peoria Tri-County area. Despite efforts to improve cancer screening access, minority populations continue to experience higher mortality rates due to inadequate health literacy. The project, led by Bradley faculty and OSF Mission Partners, focuses on improving breast cancer awareness and prevention. Three objectives guide the project: