Determination of the difference between two complex polymer models simulating the behaviour of biological structures

• Autorzy: Kmiecik Barbara , Labowska Magdalena , Detyna Jerzy

Identyfikatory

DOI

10.1016/j.bbe.2019.04.001

• Języki publikacji: EN

Abstrakty

EN

This article presents research conducted on various polymer models imitating biological structures. Tests were conducted using a newly developed research method described in [1]. The purpose of our research was to check if the measuring system [1] is able to distinguish multilayer samples. Test materials were two different polymer models which were subjected to pressure in the central point. Marked points on the external surface of the sample were followed during the measurement. The arrangement of points on the image allowed to reconstruct the 3D surface of the sample and to determine the displacement of the analysed points. Measurements were repeated 10 times to ensure the representativeness (credibility) of the conducted research. Statistical tests and artificial neural networks (ANN) were used to classify the examined samples. We used 5-fold cross-validation for training and validating the ANN. The entire set of 75 cases was divided into 5 equinumerous subsets. The obtained results suggest that the proposed method distinguished between the tested samples on the basis of results. Analysed materials react differently to the given mechanical factor.

Słowa kluczowe

EN

polymer model; hysteresis; biological structure

PL

model polimerowy; histereza; struktura biologiczna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 2
• Strony: 503--511
• Opis fizyczny: Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Kmiecik Barbara

• Department of Mechanics, Materials Science and Engineering, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Smoluchowskiego 25, Wroclaw 50-370, Poland

autor

Labowska Magdalena

• Department of Mechanics, Materials Science and Engineering, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland

autor

Detyna Jerzy

• Department of Mechanics, Materials Science and Engineering, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland

Bibliografia

• [1] Kmiecik B, Niemczyk K, Detyna J. Observation of changes deriving on surface of polymer materials imiting biological structures – presentation of the test method. Acta Bio-Optica Inform Medica Inzynieria Biomed 2018;24.
• [2] Fung YC. Biomechanics. Mechanical properties of living tissues. 2nd ed. New York: Springer-Verlag; 1993. http://dx.doi.org/10.1007/978-1-4757-2257-4.
• [3] Puglisi G, Saccomandi G. Multi-scale modelling of rubberlike materials and soft tissues: an appraisal. Proc R Soc A Math Phys Eng Sci 2016;472:20160060. http://dx.doi.org/10.1098/rspa.2016.0060.
• [4] Kauer M, Vuskovic V, Dual J, Szekely G, Bajka M. Inverse finite element characterization of soft tissues. Lect Notes Comput Sci (Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 2001;2208:128–36. http://dx.doi.org/10.1007/3-540-45468-3_16.
• [5] Guerra A, Belinha J, Jorge RN. Modelling skin wound healing angiogenesis: a review. J Theor Biol 2018;459:1–17. http://dx.doi.org/10.1016/J.JTBI.2018.09.020.
• [6] Holzapfel GA. Biomechanics of soft tissue in cardiovascular systems. Springer; 2003. p. 109–84.
• [7] Humphrey JD, Review Paper:. Continuum biomechanics of soft biological tissues. Proc R Soc A Math Phys Eng Sci 2003;459:3–46. http://dx.doi.org/10.1098/rspa.2002.1060.
• [8] Hajirassouliha A, Kmiecik B, Taberner AJ, Nash MP, Nielsen PMF. A low-cost, hand-held stereoscopic device for measuring dynamic deformations of skin in vivo. International Conference on Image and Vision Computing New Zealand; 2016. http://dx.doi.org/10.1109/IVCNZ.2015.7761573.
• [9] Ridge MD, Wright V. A bio-engineering study of the mechanical properties of human skin in relation to its structure. Br J Dermatol 1965;77:639–49. http://dx.doi.org/10.1111/j.1365-2133.1965.tb14595.x.
• [10] Falland-Cheung L, Scholze M, Lozano PF, Ondruschka B, Tong DC, Brunton PA, et al. Mechanical properties of the human scalp in tension. J Mech Behav Biomed Mater 2018;84:188–97. http://dx.doi.org/10.1016/J.JMBBM.2018.05.024.
• [11] Oftadeh R, Connizzo BK, Nia HT, Ortiz C, Grodzinsky AJ. Biological connective tissues exhibit viscoelastic and poroelastic behavior at different frequency regimes: application to tendon and skin biophysics. Acta Biomater 2018;70:249–59. http://dx.doi.org/10.1016/J.ACTBIO.2018.01.041.
• [12] Alam F, Rahman SU, Ullah S, Gulati K. Medical image registration in image guided surgery: issues, challenges and research opportunities. Biocybern Biomed Eng 2018;38:71– 89. http://dx.doi.org/10.1016/J.BBE.2017.10.001.
• [13] Dobrowolski Z, Tadeusiewicz R, Glazar B, Dobrowolski W. Urological robotics. Kraków: AGH University of Science and Technology; 2014 [in Polish].
• [14] Pierrat B, MacManus DB, Murphy JG, Gilchrist MD. Indentation of heterogeneous soft tissue: local constitutive parameter mapping using an inverse method and an automated rig. J Mech Behav Biomed Mater 2018;78:515–28. http://dx.doi.org/10.1016/J.JMBBM.2017.03.033.
• [15] Yan W-C, Davoodi P, Vijayavenkataraman S, Tian Y, Ng WC, Fuh JYH, et al. 3D bioprinting of skin tissue: from pre-processing to final product evaluation. Adv Drug Deliv Rev 2018;132:270–95. http://dx.doi.org/10.1016/J.ADDR.2018.07.016.
• [16] Hoffman AS. Hydrogels for biomedical applications. Adv Drug Deliv Rev 2012;64:18–23. http://dx.doi.org/10.1016/J.ADDR.2012.09.010.
• [17] Kwak BS, Choi W, Jeon J, Won J-I, Sung GY, Kim B, et al. In vitro 3D skin model using gelatin methacrylate hydrogel. J Ind Eng Chem 2018;66:254–61. http://dx.doi.org/10.1016/J.JIEC.2018.05.037.
• [18] Lamers E, van Kempen THS, Baaijens FPT, Peters GWM, Oomens CWJ. Large amplitude oscillatory shear properties of human skin. J Mech Behav Biomed Mater 2013;28:462–70. http://dx.doi.org/10.1016/J.JMBBM.2013.01.024.
• [19] Geerligs M, van Breemen L, Peters G, Ackermans P, Baaijens F, Oomens C. In vitro indentation to determine the mechanical properties of epidermis. J Biomech 2011;44:1176–81. http://dx.doi.org/10.1016/J.JBIOMECH.2011.01.015.
• [20] Kang MJ, Kim B, Hwang S, Yoo HH. Experimentally derived viscoelastic properties of human skin and muscle in vitro. Med Eng Phys 2018;61:25–31. http://dx.doi.org/10.1016/J.MEDENGPHY.2018.08.001.
• [21] Haddad SMH, Dhaliwal SS, Rotenberg BW, Samani A, Ladak HM. Estimation of the Young's moduli of fresh human oropharyngeal soft tissues using indentation testing. J Mech Behav Biomed Mater 2018;86:352–8. http://dx.doi.org/10.1016/J.JMBBM.2018.07.004.
• [22] Soetens JFJ, van Vijven M, Bader DL, Peters GWM, Oomens CWJ. A model of human skin under large amplitude oscillatory shear. J Mech Behav Biomed Mater 2018;86:423– 32. http://dx.doi.org/10.1016/J.JMBBM.2018.07.008.
• [23] Kenedi RM, Gibson T, Evans JH, Barbenel JC. Tissue mechanics. Phys Med Biol 1975;20:699–717.
• [24] Iwaniec J, Iwaniec M. Heart work analysis by means of recurrence-based methods. Diagnostyka 2017;18:89–96.
• [25] Oomens CWJ, van Vijven M, Peters GWM. Skin mechanics. Biomech Living Organs 2017;347–57. http://dx.doi.org/10.1016/B978-0-12-804009-6.00016-X.
• [26] Gerhardt L-C, Schmidt J, Sanz-Herrera JA, Baaijens FPT, Ansari T, Peters GWM, et al. A novel method for visualising and quantifying through-plane skin layer deformations. J Mech Behav Biomed Mater 2012;14:199–207. http://dx.doi.org/10.1016/J.JMBBM.2012.05.014.
• [27] Malhotra D, Pan S, Rüther L, Goudoulas TB, Schlippe G, Voss W, et al. Linear viscoelastic and microstructural properties of native male human skin and in vitro 3D reconstructed skin models. J Mech Behav Biomed Mater 2019;90:644–54. http://dx.doi.org/10.1016/J.JMBBM.2018.11.013.
• [28] Abdel-Wahab AA, Alam K, Silberschmidt VV. Analysis of anisotropic viscoelastoplastic properties of cortical bone tissues. J Mech Behav Biomed Mater 2011;4:807–20. http://dx.doi.org/10.1016/J.JMBBM.2010.10.001.
• [29] Bandyopadhyay A, Dewangan VK, Vajanthri KY, Poddar S, Mahto SK. Easy and affordable method for rapid prototyping of tissue models in vitro using three-dimensional bioprinting. Biocybern Biomed Eng 2018;38:158–69. http://dx.doi.org/10.1016/J.BBE.2017.12.001.
• [30] Lim CT, Zhou EH, Li A, Vedula SRK, Fu HX. Experimental techniques for single cell and single molecule biomechanics. Mater Sci Eng C 2006;26:1278–88. http://dx.doi.org/10.1016/j.msec.2005.08.022.
• [31] Lee GYH, Lim CT. Biomechanics approaches to studying human diseases. Trends Biotechnol 2007;25:111–8. http://dx.doi.org/10.1016/j.tibtech.2007.01.005.
• [32] Ismail HM, Pretty CG, Signal MK, Haggers M, Zhou C, Chase JG. Mechanical behaviour of tissue mimicking breast phantom materials. Biomed Phys Eng Express 2017;3. http://dx.doi.org/10.1088/2057-1976/aa7992.
• [33] Fraczkowska K, Bacia M, Przybylo M, Drabik D, Kaczorowska A, Rybka J, et al. Alterations of biomechanics in cancer and normal cells induced by doxorubicin. Biomed Pharmacother 2018. http://dx.doi.org/10.1016/j.biopha.2017.11.040.
• [34] Li QS, Lee GYH, Ong CN, Lim CT. AFM indentation study of breast cancer cells. Biochem Biophys Res Commun 2008;374:609–13. http://dx.doi.org/10.1016/j.bbrc.2008.07.078.
• [35] Jelen L, Lipinski A, Detyna J. Grading breast cancer malignancy with neural networks. Bio-Algorithms Med-Syst 2011;7:47–53.
• [36] Crick SL, Yin FC-P. Assessing micromechanical properties of cells with atomic force microscopy: importance of the contact point. Biomech Model Mechanobiol 2007;6:199–210. http://dx.doi.org/10.1007/s10237-006-0046-x.
• [37] Plaza JA, Prieto VG. Neoplastic lesions of the skin. Springer Publishing Company; 2013.
• [38] Farage MA, Miller KW, Berardesca E, Maibach HI. Neoplastic skin lesions in the elderly patient. Cutan Ocul Toxicol 2008;27:213–29. http://dx.doi.org/10.1080/15569520802143600.
• [39] Han L, Noble JA, Burcher M. A novel ultrasound indentation system for measuring biomechanical properties of in vivo soft tissue. Ultrasound Med Biol 2003;29:813–23.
• [40] Hendriks FM, Brokken DV, Van Eemeren JTWM, Oomens CWJ, Baaijens FPT, Horsten JBAM. A numerical- experimental method to characterize the non-linear mechanical behaviour of human skin. Skin Res Technol 2003;9:274–83.
• [41] Delalleau A, Josse G, Lagarde JM, Zahouani H, Bergheau JM. A nonlinear elastic behavior to identify the mechanical parameters of human skin in vivo. Skin Res Technol 2008;14:152–64.
• [42] Flynn C, Taberner AJ, Nielsen PM. Characterizing skin using a three-axis parallel drive force-sensitive micro-robot. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. 2010. pp. 6481–4.
• [43] Escoffier C, de Rigal J, Rochefort A, Vasselet R, Léveque JL, Agache PG. Age-related mechanical properties of human skin: an in vivo study. J Invest Dermatol 1989;93:353–7.
• [44] Holzapfel GA. Similarities between soft biological tissues and rubberlike materials. Leiden 2005;607–17.
• [45] Tadeusiewicz R. Neural networks as a tool for modeling of biological systems. Bio-Algorithms and Med-Systems 2015;11:135–44.
• [46] Plawiak P. An estimation of the state of consumption of a positive displacement pump based on dynamic pressure or vibrations using neural networks. Neurocomputing 2014;144:471–83. http://dx.doi.org/10.1016/j.neucom.2014.04.026.
• [47] Plawiak P, Rzecki K. Approximation of phenol concentration using computational intelligence methods based on signals from the metal-oxide sensor array. IEEE Sens J 2015;15:1770–83. http://dx.doi.org/10.1109/JSEN.2014.2366432.
• [48] Plawiak P, Tadeusiewicz R. Approximation of Phenol Concentration Using Novel Hybrid Computational Intelligence Methods. Int J Appl Math Comput Sci 2014;24:165–81. http://dx.doi.org/10.2478/amcs-2014-0013.
• [49] Rzecki K, Sosnicki T, Baran M, Niedzwiecki M, Krol M, Lojewski T, et al. Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS Sensors (Basel) 2018;18:3670. http://dx.doi.org/10.3390/s18113670.
• [50] Yildirim Ö, Plawiak P, Tan R-S, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med 2018;102:411–20. http://dx.doi.org/10.1016/j.compbiomed.2018.09.009.
• [51] Yildirim Ö, Tan RS, Acharya UR. An efficient compression of ECG signals using deep convolutional autoencoders. Cogn Syst Res 2018;52:198–211. http://dx.doi.org/10.1016/j.cogsys.2018.07.004.
• [52] Plawiak P. Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol Comput 2018;39:192–208. http://dx.doi.org/10.1016/j.swevo.2017.10.002.
• [53] Rzecki K, Plawiak P, Niedzwiecki M, Sosnicki T, Leskow J, Ciesielski M. Person recognition based on touch screen gestures using computational intelligence methods Inf Sci (Ny) 2017;415–6. http://dx.doi.org/10.1016/j.ins.2017.05.041. 70-84.
• [54] Andrea TA, Kalayeh H. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chem 1991;34:2824–36. http://dx.doi.org/10.1021/jm00113a022.
• [55] Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships. J Chem Inf Model 2015;55:263–74. http://dx.doi.org/10.1021/ci500747n.
• [56] Rajesh KNVPS, Dhuli R. Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier. Biomed Signal Process Control 2018;41:242–54. http://dx.doi.org/10.1016/j.bspc.2017.12.004.
• [57] Rajesh KNVPS, Dhuli R. Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine. Comput Biol Med 2017;87:271– 84. http://dx.doi.org/10.1016/j.compbiomed.2017.06.006.
• [58] Kmiecik B, Detyna J, Panek M, Myszka W. Analysis of changes on the surface of selected polymer plates imiting biological structures under the mechanical force. Acta Bio-Optica Inform Medica Inzynieria Biomed 2018;24 (in print).

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).