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Research Projects

The RCMI Projects promote the development of faculty members and advance their research programs. Areas of research include autoimmune diseases, cancer, multiple sclerosis, health disparities, nanoparticles, protein biomarkers, and high-performance computing.

 

Photo-Induced Unfolding of Cancer-Specific Membrane Receptors

Principal Investigator: Lorenzo Brancaleon, Ph.D.

Dr. Brancaleon’s project deals with photodynamic therapy of cancerous tissues. The project investigates the novel aspect of using laser light and light-activated drugs to target proteins that can only be found on the surface of tumors. The project may be able to provide a fundamental advancement in the phototherapy of cancerous tissues. An anticipated outcome of this project is a road-map to improve protocols of phototherapy and to develop new strategies that target other cancer specific receptors. While this project is directly related to cancer phototherapy, the research may also have applications in better understanding the function of certain surface proteins in cells.

 

Biomarker Discovery in Glucocorticoid Resistance in EAE

Principal Investigator: Thomas G. Forsthuber, M.D., Ph.D.

Dr. Forsthuber is addressing the urgent need for biomarkers to monitor and optimize clinical efficacy of glucocorticoid (GC) treatment and to help better understand the mechanisms underlying GC resistance. His project seeks to identify biomarkers for GC treatment efficacy and GC-resistance in multiple sclerosis that will provide information on the mechanism(s) underlying GC-resistance in autoimmune diseases.

 

Advanced Data Processing for Capillary LC/MS Data

Principal Investigator: Jianqiu Michelle Zhang, Ph.D.

Dr. Zhang’s research focuses on improving the detection, sensitivity, and specificity of low abundance protein biomarkers through advanced signal processing algorithms based on capillary liquid chromatography-mass spectrometry (LC/MS). By investigating LC/MS noise and data models, and the utilization of advance signal processing techniques, low abundance protein signals buried in noise can be identified and quantified with greater accuracy. Also, based on accurate protein identification and quantification information, Dr. Zhang will design context-based feature selection algorithms that consider hidden contexts to uncover unique context based features. Dr. Zhang will increase the sensitivity and specificity of detecting biologically meaningful protein markers. This will advance the discovery of protein biomarkers useful for diagnosing diseases.