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  • br I doubt the numbers will be startling enough

    2019-11-11


    I doubt the numbers will be startling enough to signif-icantly change practice patterns. However, efforts to minimize Cytochalasin D exposure during EVAR need to be continuously emphasized and improved. Because of the nature of the administrative database, the authors are not able to quantify the amount of radiation received during EVAR; however, given the stochastic nature of 
    radiation-induced malignant neoplasms, it is reasonable to assume that there is no safe dose and that every effort to minimize radiation exposure should be employed. This is increasingly relevant as EVAR becomes more complicated and potentially time-consuming with the expansion of fenestrated and branched EVAR. The authors mention some measures, such as C-arm angula-tion, frame rate, table position, and use of protective materials. Other important adjuncts include expanded use of intravascular ultrasound, two-dimensional and three-dimensional fusion imaging, and use of newer imaging systems with radiation-reducing software packages.1,2
    Whereas this article focuses on the patient risk, risks to the operating surgeons and other operating room personnel cannot be ignored. Radiation risk, both deter-ministic and stochastic, should also be a standard part of the informed consent process. Anecdotally, I have noted that our younger trainees are often the most aggressive in terms of using protective radiation shielding during procedures. This is encouraging and clearly should become endemic practice in our profession.
    The opinions or views expressed in this commentary are those of the author and do not necessarily reflect the opinions or recommendations of the Journal of Vascular Surgery or the Society for Vascular Surgery.
    REFERENCES
    1. Hertault A, Maurel B, Midulla M, Bordier C, Desponds L, Saeed Kilani M, et al. Minimizing radiation exposure during endo-vascular procedures: basic knowledge, literature review, and reporting standards. Eur J Vasc Endovasc Surg 2015;50:21-36.
    2. Mohapatra A, Greenburg RK, Mastracci TM, Eagleton MJ,
    Thornsberry B. Radiation exposure to operating room personnel and patients during endovascular procedures. J Vasc Surg 2013;58:702-9.
    APPENDIX (online only).
    Pancreatic cancer: C25
    Ovarian cancer: C56
    Testicular cancer: C62
    Adrenal cancer: C74  Journal of Vascular Surgery
    Codes for all cancer.
    Metastatic solid tumor: C77-C79
    Codes for EVAR and open surgery.
    Open
    Journal of Vascular Surgery
    Supplementary Table I (online only). Cox regression model for abdominal cancer
    Effect DF Wald c2 Pr > c2
    HR
    CL, Confidence limit; DF, degrees of freedom; EVAR, endovascular aneurysm repair; HR, hazard ratio; Pr, probability.
    Supplementary Table II (online only). Main effects for the model used for the G-computation formula
    Effect DF Wald c2 Pr > c2
    Age
    CL, Confidence limit; DF, degrees of freedom; EVAR, endovascular aneurysm repair; OR, odds ratio; Pr, probability; SHA, strategic health authority.
    Contents lists available at ScienceDirect
    Journal of Computational and Applied
    Mathematics
    journal homepage: www.elsevier.com/locate/cam
    A positivity preserving adaptive moving mesh method for cancer cell invasion models
    M. Sulman ∗, T. Nguyen Department of Mathematics & Statistics, Wright State University, OH 45435, USA
    Article history:
    Received in revised form 22 February 2019
    Keywords:
    Adaptive moving mesh method
    Positivity preserving scheme
    Chemotaxis
    Reaction–diffusion
    Nonlinear partial differential equations 
    In this paper, we present an efficient adaptive moving mesh finite difference method for the modeling of early stage cancer cell invasion of tissue or extracellular matrix (ECM). The cancer cell invasion model is a nonlinear system of reaction–diffusion-taxis partial differential equations (PDEs) describing the time evolution of the cancer cells density and concentrations of proteins of the ECM. The solution of the model exhibits very rapid variations at the boundary of the healthy and cancer cells. Thus, using a uniform grid method to solve the cancer cell invasion model requires a very large number of grid points in order to resolve the steep gradients of the solution precisely. Cytochalasin D As a result, the computation can become prohibitively expensive and inefficient. In this effort, we propose a positivity preserving scheme for the spatial discretization of the model on an adaptive mesh to accurately capture the sharp structures of the solution. The adaptive mesh is generated by a coordinate transformation obtained as the solution of the optimal mass transfer problem. Several numerical experiments are presented to demonstrate the performance of the adaptive mesh method for solving the cancer cell invasion model. The numerical results show that the proposed adaptive mesh method is effective in capturing and predicting the correct behavior of the cancer cells movement within the ECM.