Literature DB >> 18539301

A model for the formation, growth, and lysis of clots in quiescent plasma. A comparison between the effects of antithrombin III deficiency and protein C deficiency.

M Anand1, K Rajagopal, K R Rajagopal.   

Abstract

A mathematical model comprised of 23 reaction-diffusion equations is used to simulate the biochemical changes and transport of various reactants involved in coagulation and fibrinolysis in quiescent plasma. The growth and lysis of a thrombus, as portrayed by the model equations, is governed by boundary conditions that include the surface concentration of TF-VIIa, the generation of XIa by contact activation (in vitro), and the secretion of tPA due to endothelial activation. We apply the model to two clinically relevant hypercoagulable states, caused by deficiency of either antithrombin III or protein C. These predictions are compared with published experimental data which validate the utility of the developed model under the special case of static conditions. The incorporation of varying hemodynamic conditions in to the current fluid static model remains to be performed.

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Year:  2008        PMID: 18539301     DOI: 10.1016/j.jtbi.2008.04.015

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  25 in total

1.  Is there value in kinetic modeling of thrombin generation? Yes.

Authors:  K G Mann
Journal:  J Thromb Haemost       Date:  2012-08       Impact factor: 5.824

2.  The impact of uncertainty in a blood coagulation model.

Authors:  Christopher M Danforth; Thomas Orfeo; Kenneth G Mann; Kathleen E Brummel-Ziedins; Stephen J Everse
Journal:  Math Med Biol       Date:  2009-05-18       Impact factor: 1.854

3.  A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms.

Authors:  Chubin Ou; Wei Huang; Matthew Ming-Fai Yuen
Journal:  Med Biol Eng Comput       Date:  2016-04-22       Impact factor: 2.602

4.  Modelling of platelet-fibrin clot formation in flow with a DPD-PDE method.

Authors:  A Tosenberger; F Ataullakhanov; N Bessonov; M Panteleev; A Tokarev; V Volpert
Journal:  J Math Biol       Date:  2015-05-24       Impact factor: 2.259

5.  Transport dissipative particle dynamics model for mesoscopic advection-diffusion-reaction problems.

Authors:  Zhen Li; Alireza Yazdani; Alexandre Tartakovsky; George Em Karniadakis
Journal:  J Chem Phys       Date:  2015-07-07       Impact factor: 3.488

6.  Accelerating availability of clinically-relevant parameter estimates from thromboelastogram point-of-care device.

Authors:  Michelle A Pressly; Robert S Parker; Matthew D Neal; Jason L Sperry; Gilles Clermont
Journal:  J Trauma Acute Care Surg       Date:  2020-05       Impact factor: 3.313

7.  A mathematical model to quantify the effects of platelet count, shear rate, and injury size on the initiation of blood coagulation under venous flow conditions.

Authors:  Anass Bouchnita; Kirill Terekhov; Patrice Nony; Yuri Vassilevski; Vitaly Volpert
Journal:  PLoS One       Date:  2020-07-29       Impact factor: 3.240

8.  A numerical study of blood flow using mixture theory.

Authors:  Wei-Tao Wu; Nadine Aubry; Mehrdad Massoudi; Jeongho Kim; James F Antaki
Journal:  Int J Eng Sci       Date:  2014-03-01       Impact factor: 8.843

Review 9.  Modeling thrombin generation: plasma composition based approach.

Authors:  Kathleen E Brummel-Ziedins; Stephen J Everse; Kenneth G Mann; Thomas Orfeo
Journal:  J Thromb Thrombolysis       Date:  2014-01       Impact factor: 2.300

Review 10.  Global assays of hemostasis.

Authors:  Kathleen E Brummel-Ziedins; Alisa S Wolberg
Journal:  Curr Opin Hematol       Date:  2014-09       Impact factor: 3.284

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