Dr. Zand is a Professor of Medicine in the Division of Nephrology at the University of Rochester. He is the Director of the Rochester Center for Health Informatics, and co-director of the Center for Biodefense Immune Modeling.
Dr. Zand graduated from Northwestern University with an undergraduate degree in Biomedical Engineering, a PhD in Cell, Molecular and Structural Biology, and an MD. He completed his residency in Internal Medicine and Fellowship in Nephrology at Beth Israel Medical Center and Harvard University in Boston. He joined University of Rochester Medical Center faculty in July 1998 as Medical Director of the Kidney and Pancreas Transplant Programs, which he led until 2014, when he became Director of the Rochester Center for Health Informatics.
Dr. Zand has a clinical practice in Transplant Nephrology and Transplant Medicine, and sees patients with kidney and other solid organ transplants. He is an internationally recognized expert in B cell immunobiology in solid organ transplantation. He directs an active NIH funded research program in B cell immunobiology, vaccine biology, and computational modeling of immune responses. He leads the Rochester Center for Health Informatics, which is focused on using advanced data science methods to analyze population health data to create a "living healthcare laboratory" which improves community health and healthcare delivery.
My research is focused in two areas: Understanding how B cells respond to vaccines and organ transplants to produce antibodies, and using graph theory to understand how we can understand and improve population health and healthcare delivery. My groups use a similar core set of analytic methods in both areas, including high dimensional clustering methods, graph theory, differential equation and stochastic branching process modeling.
My laboratory studies how B cells and plasma cells participate in the adaptive immune response, through antibody production, antigen presentation, and modulation of immune responses by other immune cells. Our goal is to understand how B cell differentiation and antibody production is regulated if we are to enhance B cell anti-viral and vaccine responses, or to prevent transplant rejection. We use innovative mathematical modeling and statistical analyses to gain insights from complex and large experimental data sets. Our experiments often involve daily analysis of gene expression and cellular phenotypes from single subjects over 5-10 days after vaccination. The scientific questions we are focused on include:
1. What is the timing of B cell differentiation and antibody secretion, and how can we alter the molecular and cellular events to improve vaccine responses?
2. What are the molecular gene expression signatures of a successful B cell vaccine response?
3. How can we use mathematical models to create individualized treatment protocols for kidney transplant patients with antibody mediated rejection or allosensitization?
My Health Informatics Group uses advanced data science analytics to understand how people make their way through the healthcare system, and how larger factors such as employment, mental health, community engagement, and socio-economic status contribute to population health. We use network theory to investigate connections between these factors, and turn those insights into population health based research initiatives that we refer to as the "living healthcare laboratory". We collaborate with groups in the Institute for Data Science, the University of Rochester Medical Center, and numerous community and population health groups within the greater Rochester community.
2016 Feb 5
Shen S, Li J, Hilchey S, Shen X, Tu C, Qiu X, Ng A, Ghaemmaghami S, Wu H, ZAnd M, Qu J. "Ion-Current-Based Temporal Proteomic Profiling of Influenza-A-Virus-Infected Mouse Lungs Revealed Underlying Mechanisms of Altered Integrity of the Lung Microvascular Barrier." Journal of proteome research. 2016 Feb 5; 15(2):540-53. Epub 2016 Jan 08.
Yang H, Baker SF, González ME, Topham DJ, Martínez-Sobrido L, Zand M, Holden-Wiltse J, Wu H. "An improved method for estimating antibody titers in microneutralization assay using green fluorescent protein." Journal of biopharmaceutical statistics. 2016 26(3):409-20. Epub 2015 May 26.
2015 May 1
Wu H, Miao H, Xue H, Topham DJ, Zand M. "Quantifying Immune Response to Influenza Virus Infection via Multivariate Nonlinear ODE Models with Partially Observed State Variables and Time-Varying Parameters." Statistics in biosciences. 2015 May 1; 7(1):147-166.
Chen W, Kayler LK, Zand MS, Muttana R, Chernyak V, DeBoccardo GO. "Transplant renal artery stenosis: clinical manifestations, diagnosis and therapy." Clinical kidney journal. 2015 Feb; 8(1):71-8. Epub 2014 Dec 09.
Sandal S, Zand MS. "Rational clinical trial design for antibody mediated renal allograft injury." Frontiers in bioscience (Landmark edition). 2015 20:743-62. Epub 2015 Jan 01.