Browsing by Author "Fang, Xin"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    A new noninvasive and patient-specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
    (Wiley, 2022-07) Yu, Huidan; Khan, Monsurul; Wu, Hao; Du, Xiaoping; Chen, Rou; Rollins, Dave M.; Fang, Xin; Long, Jianyun; Xu, Chenke; Sawchuk, Alan P.; Surgery, School of Medicine
    Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life-threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient-specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in-house computational platform, InVascular, for image-based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with graphic processing unit parallel computing technology. The TPI-S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S = 0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in vivo measured ones and the systolic pressure is statistical equivalence to the in-vivo measurements with p < .001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient-specific hemodynamic index has the potential to aid in the clinical decision-making of interventional treatment with reduced medical cost and patient risks.
  • Thumbnail Image
    Item
    DhhP, a Cyclic di-AMP Phosphodiesterase of Borrelia burgdorferi, Is Essential for Cell Growth and Virulence
    (ASM, 2014-05) Ye, Meiping; Zhang, Jun-Jie; Fang, Xin; Lawlis, Gavin B.; Troxell, Bryan; Zhou, Yan; Gomelsky, Mark; Lou, Yongliang; Yang, X. Frank; Department of Microbiology and Immunology, IU School of Medicine
    Cyclic di-AMP (c-di-AMP) is a recently discovered second messenger in bacteria. Most of work on c-di-AMP signaling has been done in Gram-positive bacteria, firmicutes, and actinobacteria, where c-di-AMP signaling pathways affect potassium transport, cell wall structure, and antibiotic resistance. Little is known about c-di-AMP signaling in other bacteria. Borrelia burgdorferi, the causative agent of Lyme disease, is a spirochete that has a Gram-negative dual membrane. In this study, we demonstrated that B. burgdorferi BB0619, a DHH-DHHA1 domain protein (herein designated DhhP), functions as c-di-AMP phosphodiesterase. Recombinant DhhP hydrolyzed c-di-AMP to pApA in a Mn2+- or Mg2+-dependent manner. In contrast to c-di-AMP phosphodiesterases reported thus far, DhhP appears to be essential for B. burgdorferi growth both in vitro and in the mammalian host. Inactivation of the chromosomal dhhP gene could be achieved only in the presence of a plasmid-encoded inducible dhhP gene. The conditional dhhP mutant had a dramatic increase in intracellular c-di-AMP level in comparison to the isogenic wild-type strain. Unlike what has been observed in Gram-positive bacteria, elevated cellular c-di-AMP in B. burgdorferi did not result in an increased resistance to β-lactamase antibiotics, suggesting that c-di-AMP's functions in spirochetes differ from those in Gram-positive bacteria. In addition, the dhhP mutant was defective in induction of the σS factor, RpoS, and the RpoS-dependent outer membrane virulence factor OspC, which uncovers an important role of c-di-AMP in B. burgdorferi virulence.
  • Thumbnail Image
    Item
    Inlet and Outlet Boundary Conditions and Uncertainty Quantification in Volumetric Lattice Boltzmann Method for Image-Based Computational Hemodynamics
    (MDPI, 2022-01-10) Yu, Huidan; Khan, Monsurul; Wu, Hao; Zhang, Chunze; Du, Xiaoping; Chen, Rou; Fang, Xin; Long, Jianyun; Sawchuk, Alan P.; Surgery, School of Medicine
    Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.
  • Thumbnail Image
    Item
    Inlet and Outlet Boundary Conditions and Uncertainty Quantification in Volumetric Lattice Boltzmann Method for Image-Based Computational Hemodynamics
    (MDPI, 2022) Yu, Huidan; Khan, Monsurul; Wu, Hao; Zhang, Chunze; Du, Xiaoping; Chen, Rou; Fang, Xin; Long, Jianyun; Sawchuk, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.
  • Thumbnail Image
    Item
    A new noninvasive and patient-specific hemodynamic index for the severity of renal stenosis and outcome of interventional treatment
    (Wiley, 2022) Yu, Huidan; Khan, Monsurul; Wu, Hao; Du, Xiaoping; Chen, Rou; Rollins, Dave M.; Fang, Xin; Long, Jianyun; Xu, Chenke; Sawchuk, Alan P.; Mechanical and Energy Engineering, School of Engineering and Technology
    Renal arterial stenosis (RAS) often causes renovascular hypertension, which may result in kidney failure and life-threatening consequences. Direct assessment of the hemodynamic severity of RAS has yet to be addressed. In this work, we present a computational concept to derive a new, noninvasive, and patient-specific index to assess the hemodynamic severity of RAS and predict the potential benefit to the patient from a stenting therapy. The hemodynamic index is derived from a functional relation between the translesional pressure indicator (TPI) and lumen volume reduction (S) through a parametric deterioration of the RAS. Our in-house computational platform, InVascular, for image-based computational hemodynamics is used to compute the TPI at given S. InVascular integrates unified computational modeling for both image processing and computational hemodynamics with GPU parallel computing technology. The TPI-S curve reveals a pair of thresholds of S indicating mild or severe RAS. The TPI at S=0 represents the pressure improvement following a successful stenting therapy. Six patient cases with a total of 6 aortic and 12 renal arteries are studied. The computed blood pressure waveforms have good agreements with the in-vivo measured ones and the systolic pressure is statistical equivalence to the in-vivo measurements with p<0.001. Uncertainty quantification provides the reliability of the computed pressure through the corresponding 95% confidence interval. The severity assessments of RAS in four cases are consistent with the medical practice. The preliminary results inspire a more sophisticated investigation for real medical insights of the new index. This computational concept can be applied to other arterial stenoses such as iliac stenosis. Such a noninvasive and patient-specific hemodynamic index has the potential to aid in the clinical decision-making of interventional treatment with reduced medical cost and patient risks.