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Journal articles on the topic 'Cardiovascular modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

Xia, Ling, Alan Murray, Dingchang Zheng, Feng Liu, Xuesong Ye, and Gangmin Ning. "Cardiovascular System Modeling." Computational and Mathematical Methods in Medicine 2012 (2012): 1–2. http://dx.doi.org/10.1155/2012/583172.

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2

Muhlbaier, Lawrence H., and David B. Pryor. "Data for cardiovascular modeling." Journal of the American College of Cardiology 14, no. 3 (September 1989): A60—A64. http://dx.doi.org/10.1016/0735-1097(89)90166-6.

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3

Marsden, Alison L. "Optimization in Cardiovascular Modeling." Annual Review of Fluid Mechanics 46, no. 1 (January 3, 2014): 519–46. http://dx.doi.org/10.1146/annurev-fluid-010313-141341.

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4

Soares, Joao S., Salvatore Pasta, David A. Vorp, and James E. Moore. "Modeling in cardiovascular biomechanics." International Journal of Engineering Science 48, no. 11 (November 2010): 1563–75. http://dx.doi.org/10.1016/j.ijengsci.2010.06.006.

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5

Pletcher, Mark J. "Modeling Cardiovascular Disease Prevention." JAMA 303, no. 9 (March 3, 2010): 835. http://dx.doi.org/10.1001/jama.2010.188.

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6

Hingorani, Aroon D. "Modeling Cardiovascular Disease Prevention—Reply." JAMA 303, no. 9 (March 3, 2010): 835. http://dx.doi.org/10.1001/jama.2010.189.

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7

Eckberg, Dwain L. "Arterial Baroreflexes and Cardiovascular Modeling." Cardiovascular Engineering 8, no. 1 (December 15, 2007): 5–13. http://dx.doi.org/10.1007/s10558-007-9042-8.

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8

Lippi, Melania, Ilaria Stadiotti, Giulio Pompilio, and Elena Sommariva. "Human Cell Modeling for Cardiovascular Diseases." International Journal of Molecular Sciences 21, no. 17 (September 2, 2020): 6388. http://dx.doi.org/10.3390/ijms21176388.

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The availability of appropriate and reliable in vitro cell models recapitulating human cardiovascular diseases has been the aim of numerous researchers, in order to retrace pathologic phenotypes, elucidate molecular mechanisms, and discover therapies using simple and reproducible techniques. In the past years, several human cell types have been utilized for these goals, including heterologous systems, cardiovascular and non-cardiovascular primary cells, and embryonic stem cells. The introduction of induced pluripotent stem cells and their differentiation potential brought new prospects for large-scale cardiovascular experiments, bypassing ethical concerns of embryonic stem cells and providing an advanced tool for disease modeling, diagnosis, and therapy. Each model has its advantages and disadvantages in terms of accessibility, maintenance, throughput, physiological relevance, recapitulation of the disease. A higher level of complexity in diseases modeling has been achieved with multicellular co-cultures. Furthermore, the important progresses reached by bioengineering during the last years, together with the opportunities given by pluripotent stem cells, have allowed the generation of increasingly advanced in vitro three-dimensional tissue-like constructs mimicking in vivo physiology. This review provides an overview of the main cell models used in cardiovascular research, highlighting the pros and cons of each, and describing examples of practical applications in disease modeling.

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9

Han, Yanxiao, Gonzalo Hernandez-Hernandez, Pei-Chi Yang, John R. D. Dawson, Kevin R. DeMarco, Kyle C. Rouen, Khoa Ngo, et al. "Multiscale modeling of sympathetic cardiovascular stimulation." Biophysical Journal 121, no. 3 (February 2022): 286a. http://dx.doi.org/10.1016/j.bpj.2021.11.1316.

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10

Taylor, C. A., and C. A. Figueroa. "Patient-Specific Modeling of Cardiovascular Mechanics." Annual Review of Biomedical Engineering 11, no. 1 (August 2009): 109–34. http://dx.doi.org/10.1146/annurev.bioeng.10.061807.160521.

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11

Lucor, Didier, and Olivier P. Le Maître. "Cardiovascular Modeling With Adapted Parametric Inference." ESAIM: Proceedings and Surveys 62 (2018): 91–107. http://dx.doi.org/10.1051/proc/201862091.

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Computational modeling of the cardiovascular system, promoted by the advance of fluid-structure interaction numerical methods, has made great progress towards the development of patient-specific numerical aids to diagnosis, risk prediction, intervention and clinical treatment. Nevertheless, the reliability of these models is inevitably impacted by rough modeling assumptions. A strong in-tegration of patient-specific data into numerical modeling is therefore needed in order to improve the accuracy of the predictions through the calibration of important physiological parameters. The Bayesian statistical framework to inverse problems is a powerful approach that relies on posterior sampling techniques, such as Markov chain Monte Carlo algorithms. The generation of samples re-quires many evaluations of the cardiovascular parameter-to-observable model. In practice, the use of a full cardiovascular numerical model is prohibitively expensive and a computational strategy based on approximations of the system response, or surrogate models, is needed to perform the data as-similation. As the support of the parameters distribution typically concentrates on a small fraction of the initial prior distribution, a worthy improvement consists in gradually adapting the surrogate model to minimize the approximation error for parameter values corresponding to high posterior den-sity. We introduce a novel numerical pathway to construct a series of polynomial surrogate models, by regression, using samples drawn from a sequence of distributions likely to converge to the posterior distribution. The approach yields substantial gains in efficiency and accuracy over direct prior-based surrogate models, as demonstrated via application to pulse wave velocities identification in a human lower limb arterial network.

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12

Chico, Timothy J. A., Philip W. Ingham, and David C. Crossman. "Modeling Cardiovascular Disease in the Zebrafish." Trends in Cardiovascular Medicine 18, no. 4 (May 2008): 150–55. http://dx.doi.org/10.1016/j.tcm.2008.04.002.

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13

Hlatky, Mark A., and Harry P. Selker. "Projects and priorities in cardiovascular modeling." Journal of the American College of Cardiology 14, no. 3 (September 1989): A52—A56. http://dx.doi.org/10.1016/0735-1097(89)90164-2.

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14

Liu, Ning, and Eric N. Olson. "CRISPR Modeling and Correction of Cardiovascular Disease." Circulation Research 130, no. 12 (June 10, 2022): 1827–50. http://dx.doi.org/10.1161/circresaha.122.320496.

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Cardiovascular disease remains the leading cause of morbidity and mortality in the developed world. In recent decades, extraordinary effort has been devoted to defining the molecular and pathophysiological characteristics of the diseased heart and vasculature. Mouse models have been especially powerful in illuminating the complex signaling pathways, genetic and epigenetic regulatory circuits, and multicellular interactions that underlie cardiovascular disease. The advent of CRISPR genome editing has ushered in a new era of cardiovascular research and possibilities for genetic correction of disease. Next-generation sequencing technologies have greatly accelerated the identification of disease-causing mutations, and advances in gene editing have enabled the rapid modeling of these mutations in mice and patient-derived induced pluripotent stem cells. The ability to correct the genetic drivers of cardiovascular disease through delivery of gene editing components in vivo, while still facing challenges, represents an exciting therapeutic frontier. In this review, we provide an overview of cardiovascular disease mechanisms and the potential applications of CRISPR genome editing for disease modeling and correction. We also discuss the extent to which mice can faithfully model cardiovascular disease and the opportunities and challenges that lie ahead.

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15

Киселев, И. Н., and I. N. Kiselev. "Modular Modeling of the Human Cardiovascular System." Mathematical Biology and Bioinformatics 7, no. 2 (December 30, 2012): 703–36. http://dx.doi.org/10.17537/2012.7.703.

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16

Garner, Kaley H., and Dinender K. Singla. "3D modeling: a future of cardiovascular medicine." Canadian Journal of Physiology and Pharmacology 97, no. 4 (April 2019): 277–86. http://dx.doi.org/10.1139/cjpp-2018-0472.

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Cardiovascular disease resulting from atypical cardiac structures continues to be a leading health concern despite advancements in diagnostic imaging and surgical techniques. However, the ability to visualize spatial relationships using current technologies remains a challenge. Therefore, 3D modeling has gained significant interest to understand complex and atypical cardiovascular disorders. Moreover, 3D modeling can be personalized and patient-specific. 3D models have been demonstrated to aid surgical planning and simulation, enhance communication among surgeons and patients, optimize medical device design, and can be used as a potential teaching tool in medical schools. In this review, we discuss the key components needed to generate cardiac 3D models. We highlight prevalent structural conditions that have utilized 3D modeling in pre-operative planning. Furthermore, we discuss the current limitations of routine use of 3D models in the clinic as well as future directions for utilization of this technology in the cardiovascular field.

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17

Talaminos, Alejandro, Laura M. Roa, Antonio Álvarez, and Javier Reina. "Computational Hemodynamic Modeling of the Cardiovascular System." International Journal of System Dynamics Applications 3, no. 2 (April 2014): 81–98. http://dx.doi.org/10.4018/ijsda.2014040106.

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Computational methods and modeling are widely used in many fields to study the dynamic behaviour of different phenomena. Currently, the use of these models is an accepted practice in the biomedical field. One of the most significant efforts in this direction is applied to the simulation and prediction of pathophysiological conditions that can affect different systems of the human body. In this work, the design and development of a computational model of the human cardiovascular system is proposed. The structure of the model has been built from a physiological base, considering some of the mechanisms associated to the cardiovascular system. Thus, the aim of the model is the prediction, heartbeat by heartbeat, of some hemodynamic variables from the cardiovascular system, in different pathophysiological cardiac situations. A modular approach to development of the model has been considered in order to include new knowledge that could force the model's hemodynamic. The model has been validated comparing the results obtained with hemodynamic values published by other authors. The results show the usefulness and applicability of the model developed. Thus, different simulations of some cardiac pathologies and physical exercise situations are presented, together with the dynamic behaviors of the different variables considered in the model.

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18

Gueyffier, François, Ivanny Marchant, and Bo Carlberg. "Modeling the impact of cardiovascular prevention strategies." Journal of Hypertension 30, no. 1 (January 2012): 51–52. http://dx.doi.org/10.1097/hjh.0b013e32834e089d.

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19

DeMarco, Kevin R., John R. D. Dawson, Slava Bekker, Vladimir Yarov-Yarovoy, Colleen E. Clancy, and Igor Vorobyov. "Atomistic Modeling of Neuro-cardiovascular Coupling Modulation." Biophysical Journal 118, no. 3 (February 2020): 161a. http://dx.doi.org/10.1016/j.bpj.2019.11.996.

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20

Dai, Tinglong, Kelly Gleason, Chao‐Wei Hwang, and Patricia Davidson. "Heart analytics: Analytical modeling of cardiovascular care." Naval Research Logistics (NRL) 68, no. 1 (November 27, 2019): 30–43. http://dx.doi.org/10.1002/nav.21880.

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21

Vetter, R., P. Celka, J. M. Vesin, G. Thonet, E. Pruvot, M. Fromer, U. Scherrer, and L. Bernardi. "Subband Modeling of the Human Cardiovascular System: New Insights into Cardiovascular Regulation." Annals of Biomedical Engineering 26, no. 2 (March 1998): 293–307. http://dx.doi.org/10.1114/1.57.

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22

Thummerer, Tobias, Johannes Tintenherr, and Lars Mikelsons. "Hybrid modeling of the human cardiovascular system using NeuralFMUs." Journal of Physics: Conference Series 2090, no. 1 (November 1, 2021): 012155. http://dx.doi.org/10.1088/1742-6596/2090/1/012155.

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Abstract Hybrid modeling, the combination of first principle and machine learning models, is an emerging research field that gathers more and more attention. Even if hybrid models produce formidable results for academic examples, there are still different technical challenges that hinder the use of hybrid modeling in real-world applications. By presenting NeuralFMUs, the fusion of a Functional Mock-up Unit (FMU), a numerical ODE solver and an artifical neural network, we are paving the way for the use of a variety of first principle models from different modeling tools as parts of hybrid models. This contribution handles the hybrid modeling of a complex, real-world example: Starting with a simplified 1D-fluid model of the human cardiovascular system (arterial side), the aim is to learn neglected physical effects like arterial elasticity from data. We will show that the hybrid modeling process is more comfortable, needs less system knowledge and is therefore less error-prone compared to modeling solely based on first principle. Further, the resulting hybrid model has improved in computation performance, compared to a pure first principle white-box model, while still fulfilling the requirements regarding accuracy of the considered hemodynamic quantities. The use of the presented techniques is explained in a general manner and the considered use-case can serve as example for other modeling and simulation applications in and beyond the medical domain.

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23

Baselli, Giuseppe, Enrico Caiani, Alberto Porta, Nicola Montana, Maria Gabriella Signorini, and Sergio Cerutti. "Biomedical Signal Processing and Modeling in Cardiovascular Systems." Critical Reviews in Biomedical Engineering 30, no. 1-3 (2002): 55–84. http://dx.doi.org/10.1615/critrevbiomedeng.v30.i123.40.

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24

Jang, Jinah. "3D Bioprinting and In Vitro Cardiovascular Tissue Modeling." Bioengineering 4, no. 4 (August 18, 2017): 71. http://dx.doi.org/10.3390/bioengineering4030071.

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25

Hu, Sijung, Vicente Azorin-Peris, and Jia Zheng. "Opto-Physiological Modeling Applied to Photoplethysmographic Cardiovascular Assessment." Journal of Healthcare Engineering 4, no. 4 (December 2013): 505–28. http://dx.doi.org/10.1260/2040-2295.4.4.505.

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26

Rogers, Aaron J., Jessica M. Miller, Ramaswamy Kannappan, and Palaniappan Sethu. "Cardiac Tissue Chips (CTCs) for Modeling Cardiovascular Disease." IEEE Transactions on Biomedical Engineering 66, no. 12 (December 2019): 3436–43. http://dx.doi.org/10.1109/tbme.2019.2905763.

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27

Heldt, Thomas, Eun B. Shim, Roger D. Kamm, and Roger G. Mark. "Computational modeling of cardiovascular response to orthostatic stress." Journal of Applied Physiology 92, no. 3 (March 1, 2002): 1239–54. http://dx.doi.org/10.1152/japplphysiol.00241.2001.

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The objective of this study is to develop a model of the cardiovascular system capable of simulating the short-term (≤5 min) transient and steady-state hemodynamic responses to head-up tilt and lower body negative pressure. The model consists of a closed-loop lumped-parameter representation of the circulation connected to set-point models of the arterial and cardiopulmonary baroreflexes. Model parameters are largely based on literature values. Model verification was performed by comparing the simulation output under baseline conditions and at different levels of orthostatic stress to sets of population-averaged hemodynamic data reported in the literature. On the basis of experimental evidence, we adjusted some model parameters to simulate experimental data. Orthostatic stress simulations are not statistically different from experimental data (two-sided test of significance with Bonferroni adjustment for multiple comparisons). Transient response characteristics of heart rate to tilt also compare well with reported data. A case study is presented on how the model is intended to be used in the future to investigate the effects of postspaceflight orthostatic intolerance.

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28

Taylor, C. A. "Patient-specific modeling: predicting outcomes of cardiovascular interventions." Journal of Biomechanics 39 (January 2006): S207. http://dx.doi.org/10.1016/s0021-9290(06)83754-1.

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29

Weinstein, Milton C. "Methodologic issues in policy modeling for cardiovascular disease." Journal of the American College of Cardiology 14, no. 3 (September 1989): A38—A43. http://dx.doi.org/10.1016/0735-1097(89)90160-5.

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30

Teng, Evan L., and Adam J. Engler. "Mechanical influences on cardiovascular differentiation and disease modeling." Experimental Cell Research 377, no. 1-2 (April 2019): 103–8. http://dx.doi.org/10.1016/j.yexcr.2019.02.019.

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31

North, Trista E., and Leonard I. Zon. "Modeling human hematopoietic and cardiovascular diseases in zebrafish." Developmental Dynamics 228, no. 3 (October 21, 2003): 568–83. http://dx.doi.org/10.1002/dvdy.10393.

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32

Kristen A, Windoloski, Ottesen Johnny T, and Olufsen Mette S. "In Silico modeling of immune-cardiovascular-endocrine interactions." Journal of Cardiovascular Medicine and Cardiology 9, no. 4 (October 15, 2022): 037–41. http://dx.doi.org/10.17352/2455-2976.000186.

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The immune system provides an intricate, balanced response to combat the effects of inflammatory stimuli. It incorporates both positive and negative feedback from multiple physiological systems such as the cardiovascular and endocrine systems including mechanisms functioning on a variety of time scales. They have been studied individually via scientific experiments and using mathematical modeling. However, more analysis is needed to study the interactions between these three systems during an inflammatory event. We present the first dynamical systems model studying immune, cardiovascular and endocrine responses to a 2 ng/kg bolus dose of endotoxin. The model is calibrated to experimental data from two endotoxin challenge studies and we use this model to investigate the effects of endotoxin dosage, administration timing and administration method. Our model shows that most repercussions of endotoxin administration clear the system within 24 hours, but effects can linger for up to 72 hours.

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33

Hoppler, Stefan, and Frank L. Conlon. "Xenopus: Experimental Access to Cardiovascular Development, Regeneration Discovery, and Cardiovascular Heart-Defect Modeling." Cold Spring Harbor Perspectives in Biology 12, no. 6 (November 25, 2019): a037200. http://dx.doi.org/10.1101/cshperspect.a037200.

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34

WESSEL, NIELS, HAGEN MALBERG, ROBERT BAUERNSCHMITT, and JÜRGEN KURTHS. "NONLINEAR METHODS OF CARDIOVASCULAR PHYSICS AND THEIR CLINICAL APPLICABILITY." International Journal of Bifurcation and Chaos 17, no. 10 (October 2007): 3325–71. http://dx.doi.org/10.1142/s0218127407019093.

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In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular regulation. Applications of nonlinear data analysis and modeling tools are various and outlined in the second part of this tutorial: monitoring-, diagnosis-, course and mortality prognoses as well as early detection of heart diseases. We show, that these data analyses and modeling methods lead to significant improvements in different medical fields.

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35

Lee, Sang-Hyun. "NUMERICAL MODELING OF FLUID-STRUCTURE INTERACTIONS IN CARDIOVASCULAR MECHANICS." Journal of Computational Fluids Engineering 22, no. 2 (June 30, 2017): 1–14. http://dx.doi.org/10.6112/kscfe.2017.22.2.001.

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36

Cavalcanti, S., and E. Belardinelli. "Modeling of cardiovascular variability using a differential delay equation." IEEE Transactions on Biomedical Engineering 43, no. 10 (1996): 982–89. http://dx.doi.org/10.1109/10.536899.

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37

Pearson-Stuttard, Jonathan, Maria Guzman-Castillo, Jose L. Penalvo, Colin D. Rehm, Ashkan Afshin, Goodarz Danaei, Chris Kypridemos, et al. "Modeling Future Cardiovascular Disease Mortality in the United States." Circulation 133, no. 10 (March 8, 2016): 967–78. http://dx.doi.org/10.1161/circulationaha.115.019904.

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38

Tang, Dalin, Zhi-Yong Li, and Gui-Rong Liu. "Preface — Computational Modeling for Cardiovascular Disease and Biological Applications." International Journal of Computational Methods 16, no. 03 (March 17, 2019): 1802002. http://dx.doi.org/10.1142/s0219876218020024.

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39

Greiter-Wilke, Andrea, Mark Davies, Stefan Sturm, Sonia Roberts, and Liudmila Polonchuk. "CiPA-like Cardiovascular In Silico Modeling—Two Case Reports." Journal of Pharmacological and Toxicological Methods 88 (November 2017): 211–12. http://dx.doi.org/10.1016/j.vascn.2017.09.143.

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40

Clune, Rory, Denis Kelliher, James C. Robinson, and John S. Campbell. "NURBS modeling and structural shape optimization of cardiovascular stents." Structural and Multidisciplinary Optimization 50, no. 1 (January 26, 2014): 159–68. http://dx.doi.org/10.1007/s00158-013-1038-y.

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41

Liang, Ping, and Jie Du. "Human induced pluripotent stem cell for modeling cardiovascular diseases." Regenerative Medicine Research 2, no. 1 (2014): 4. http://dx.doi.org/10.1186/2050-490x-2-4.

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42

Batzel, J., F. Kappel, D. Schneditz, and T. Kenner. "Introduction: Issues in Cardiovascular Respiratory and Metabolic Control Modeling." Cardiovascular Engineering 4, no. 2 (June 2004): 125. http://dx.doi.org/10.1023/b:care.0000031636.73691.66.

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43

KADOYA, Kento, Tomoharu SHINDO, and Shigehiko KANEKO. "Modeling of a cardiovascular system for detecting sudden disease." Proceedings of the Dynamics & Design Conference 2018 (2018): 534. http://dx.doi.org/10.1299/jsmedmc.2018.534.

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44

Liu, Hao, Fuyou Liang, Jasmin Wong, Takashi Fujiwara, Wenjing Ye, Ken-iti Tsubota, and Michiko Sugawara. "Multi-scale modeling of hemodynamics in the cardiovascular system." Acta Mechanica Sinica 31, no. 4 (August 2015): 446–64. http://dx.doi.org/10.1007/s10409-015-0416-7.

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45

Porphiriev, A. O., A. A. Pugovkin, S. V. Selishchev, and D. V. Telyshev. "Development of Artificial Ventricles for Modeling the Cardiovascular System." Biomedical Engineering 49, no. 6 (March 2016): 331–34. http://dx.doi.org/10.1007/s10527-016-9560-z.

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46

Oshima, M., R. Torii, and T. E. Tezduyar. "W02-1-(4) Modeling and Simulation of Cardiovascular Flow(International Minisymposium on Challenger and Advances in Flow Simulation and Modeling,Mechanical Engineering Congress, 2005 Japan (MECJ-05))." Reference Collection of Annual Meeting 2005.8 (2005): 268. http://dx.doi.org/10.1299/jsmemecjsm.2005.8.0_268.

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47

Le, Trung Q., Satish T. S. Bukkapatnam, and Ranga Komanduri. "Real-Time Lumped Parameter Modeling of Cardiovascular Dynamics Using Electrocardiogram Signals: Toward Virtual Cardiovascular Instruments." IEEE Transactions on Biomedical Engineering 60, no. 8 (August 2013): 2350–60. http://dx.doi.org/10.1109/tbme.2013.2256423.

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48

Finol, Ender A., Elena S. Di Martino, and Seungik Baek. "Cardiovascular Biomechanics and Biofluids: A Special Issue with a Focus on Modeling of Cardiovascular Structures." Annals of Biomedical Engineering 41, no. 7 (May 31, 2013): 1309–10. http://dx.doi.org/10.1007/s10439-013-0830-6.

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49

Melchior, F. M., R. S. Srinivasan, and J. B. Charles. "Mathematical modeling of human cardiovascular system for simulation of orthostatic response." American Journal of Physiology-Heart and Circulatory Physiology 262, no. 6 (June 1, 1992): H1920—H1933. http://dx.doi.org/10.1152/ajpheart.1992.262.6.h1920.

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This paper deals with the short-term response of the human cardiovascular system to orthostatic stresses in the context of developing a mathematical model of the overall system. It discusses the physiological issues involved and how these issues have been handled in published cardiovascular models for simulation of orthostatic response. Most of the models are stimulus specific with no demonstrated capability for simulating the responses to orthostatic stimuli of different types. A comprehensive model incorporating all known phenomena related to cardiovascular regulation would greatly help to interpret the various orthostatic responses of the system in a consistent manner and to understand the interactions among its elements. This paper provides a framework for future efforts in mathematical modeling of the entire cardiovascular system.

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50

Tanaka, Atsushi, Shinsuke Yuasa, Koichi Node, and Keiichi Fukuda. "Cardiovascular Disease Modeling Using Patient-Specific Induced Pluripotent Stem Cells." International Journal of Molecular Sciences 16, no. 8 (August 12, 2015): 18894–922. http://dx.doi.org/10.3390/ijms160818894.

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