MBI Publications

MBI Publications for Brad Rovin (6)

  • P. Grajdeanu, R. Schugart, A. Friedman, C. Valentine and B. Rovin
    A mathematical model of venous neointimal hyperplasia
    Theoretical Biology and Medical ModellingVol. 5 (2008)

    Abstract

  • P. Grajdeanu, R. Schugart, A. Friedman, D. Birmingham, D. Birmingham and B. Rovin
    The dynamics of SLE nephritis under immunosuppressive therapy: A mathematical model
    (2008) (Submitted)

    Abstract

  • P. Grajdeanu, R. Schugart, A. Friedman, C. Valentine, A. Agarwal and B. Rovin
    A mathematical model of venous neointimal hyperplasia formation
    Theoretical Biology and Medical ModellingVol. 5 No. 2 (2008)

    Abstract

  • P. Grajdeanu, R. Schugart, A. Friedman, C. Valentine, A. Agarwal and B. Rovin
    A mathematical model of venous neointimal hyperplasia formation
    Theoretical Biology and Medical ModellingVol. 5 No. 2 (2008)

    Abstract

    In hemodialysis patients, the most common cause of vascular access failure is neointimal hyperplasia of vascular smooth muscle cells at the venous anastomosis of arteriovenous fistulas and grafts. The release of growth factors due to surgical injury, oxidative stress and turbulent flow has been suggested as a possible mechanism for neointimal hyperplasia.
    In this work, we construct a mathematical model which analyzes the role that growth factors might play in the stenosis at the venous anastomosis. The model consists of a system of partial differential equations describing the influence of oxidative stress and turbulent flow on growth factors, the interaction among growth factors, smooth muscle cells, and extracellular matrix, and the subsequent effect on the stenosis at the venous anastomosis, which, in turn, affects the level of oxidative stress and degree of turbulent flow. Computer simulations suggest that our model can be used to predict access stenosis as a function of the initial concentration of the growth factors inside the intimal-luminal space.
    The proposed model describes the formation of venous neointimal hyperplasia, based on pathogenic mechanisms. The results suggest that interventions aimed at specific growth factors may be successful in prolonging the life of the vascular access, while reducing the costs of vascular access maintenance. The model may also provide indication of when invasive access surveillance to repair stenosis should be undertaken.
  • P. Grajdeanu, R. Schugart, A. Friedman, D. Birmingham and B. Rovin
    Mathematical framework for human SLE Nephritis: disease dynamics and urine biomarkers
    Theoretical Biology and Medical ModellingVol. 7 No. 14 (2010)

    Abstract

    Although the prognosis for Lupus Nephritis (LN) has dramatically improved with aggressive immunosuppressive therapies, these drugs carry significant side effects. To improve the effectiveness of these drugs, biomarkers of renal flare cycle could be used to detect the onset, severity, and responsiveness of kidney relapses, and to modify therapy accordingly. However, LN is a complex disease and individual biomarkers have so far not been sufficient to accurately describe disease activity. It has been postulated that biomarkers would be more informative if integrated into a pathogenic-based model of LN.
    This work is a first attempt to integrate human LN biomarkers data into a model of kidney inflammation. Our approach is based on a system of differential equations that capture, in a simplified way, the complexity of interactions underlying disease activity. Using this model, we have been able to fit clinical urine biomarkers data from individual patients and estimate patient-specific parameters to reproduce disease dynamics, and to better understand disease mechanisms. Furthermore, our simulations suggest that the model can be used to evaluate therapeutic strategies for individual patients, or a group of patients that share similar data patterns.
    We show that effective combination of clinical data and physiologically based mathematical modeling may provide a basis for more comprehensive modeling and improved clinical care for LN patients.
  • P. Budu-Grajdeanu, R. Schugart, A. Friedman, D. Birmingham and B. Rovin
    Predicting renal interstitial inflammation levels using urine biomarkers and artificial neural networks
    (2012) (In Preparation)

    Abstract

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