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Koruk H, Rajagopal S. A Comprehensive Review on the Viscoelastic Parameters Used for Engineering Materials, Including Soft Materials, and the Relationships between Different Damping Parameters. SENSORS (BASEL, SWITZERLAND) 2024; 24:6137. [PMID: 39338881 PMCID: PMC11435754 DOI: 10.3390/s24186137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Revised: 09/12/2024] [Accepted: 09/20/2024] [Indexed: 09/30/2024]
Abstract
Although the physical properties of a structure, such as stiffness, can be determined using some statical tests, the identification of damping parameters requires a dynamic test. In general, both theoretical prediction and experimental identification of damping are quite difficult. There are many different techniques available for damping identification, and each method gives a different damping parameter. The dynamic indentation method, rheometry, atomic force microscopy, and resonant vibration tests are commonly used to identify the damping of materials, including soft materials. While the viscous damping ratio, loss factor, complex modulus, and viscosity are quite common to describe the damping of materials, there are also other parameters, such as the specific damping capacity, loss angle, half-power bandwidth, and logarithmic decrement, to describe the damping of various materials. Often, one of these parameters is measured, and the measured parameter needs to be converted into another damping parameter for comparison purposes. In this review, the theoretical derivations of different parameters for the description and quantification of damping and their relationships are presented. The expressions for both high damping and low damping are included and evaluated. This study is considered as the first comprehensive review article presenting the theoretical derivations of a large number of damping parameters and the relationships among many damping parameters, with a quantitative evaluation of accurate and approximate formulas. This paper could be a primary resource for damping research and teaching.
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Affiliation(s)
- Hasan Koruk
- Ultrasound and Underwater Acoustics Group, Department of Medical, Marine and Nuclear, National Physical Laboratory, Teddington, Middlesex TW11 0LW, UK;
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2
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Lambers L, Waschinsky N, Schleicher J, König M, Tautenhahn HM, Albadry M, Dahmen U, Ricken T. Quantifying fat zonation in liver lobules: an integrated multiscale in silico model combining disturbed microperfusion and fat metabolism via a continuum biomechanical bi-scale, tri-phasic approach. Biomech Model Mechanobiol 2024; 23:631-653. [PMID: 38402347 DOI: 10.1007/s10237-023-01797-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/22/2023] [Indexed: 02/26/2024]
Abstract
Metabolic zonation refers to the spatial separation of metabolic functions along the sinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepatic metabolism to physiological requirements in health and disease (e.g., metabolic dysfunction-associated steatotic liver disease/MASLD). Zonated metabolic functions are influenced by zonal morphological abnormalities in the liver, such as periportal fibrosis and pericentral steatosis. We aim to analyze the interplay between microperfusion, oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule. Therefore we developed a continuum biomechanical, tri-phasic, bi-scale, and multicomponent in silico model, which allows to numerically simulate coupled perfusion-function-growth interactions two-dimensionally in liver lobules. The developed homogenized model has the following specifications: (i) thermodynamically consistent, (ii) tri-phase model (tissue, fat, blood), (iii) penta-substances (glycogen, glucose, lactate, FFA, and oxygen), and (iv) bi-scale approach (lobule, cell). Our presented in silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion, metabolic pathways and fat accumulation. The model thus allows the prediction of fat development in the liver lobule, depending on perfusion, oxygen and plasma concentration of free fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs). The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus, we will investigate how changes at the cellular scale affect perfusion at the lobular scale and vice versa. This allows to predict the zonation of fat distribution (periportal or pericentral) depending on initial conditions, as well as external and internal boundary value conditions.
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Affiliation(s)
- Lena Lambers
- Institute of Structural Mechanics and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, Stuttgart, 70191, Germany
| | - Navina Waschinsky
- Institute of Structural Mechanics and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, Stuttgart, 70191, Germany
| | - Jana Schleicher
- Friedrich-Schiller-Universität Jena, Fürstengraben 27, Jena, 07743, Germany
| | - Matthias König
- Systems Medicine of Liver, Institute for Theoretical Biology, Institute for Biology, Humboldt-University Berlin, Philippstraße 13, 10115 Berlin, Germany
| | - Hans-Michael Tautenhahn
- Department of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, Liebigstraße 20, Leipzig, 04103, Germany
| | - Mohamed Albadry
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Drackendorfer Straße 1, Jena, 07747, Germany
- Department of Pathology, Faculty of Veterinary Medicine, Menoufia University, Shebin Elkom, Menoufia, Egypt
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Drackendorfer Straße 1, Jena, 07747, Germany
| | - Tim Ricken
- Institute of Structural Mechanics and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, Stuttgart, 70191, Germany.
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Lemine AS, Ahmad Z, Al-Thani NJ, Hasan A, Bhadra J. Mechanical properties of human hepatic tissues to develop liver-mimicking phantoms for medical applications. Biomech Model Mechanobiol 2024; 23:373-396. [PMID: 38072897 PMCID: PMC10963485 DOI: 10.1007/s10237-023-01785-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 10/17/2023] [Indexed: 03/26/2024]
Abstract
Using liver phantoms for mimicking human tissue in clinical training, disease diagnosis, and treatment planning is a common practice. The fabrication material of the liver phantom should exhibit mechanical properties similar to those of the real liver organ in the human body. This tissue-equivalent material is essential for qualitative and quantitative investigation of the liver mechanisms in producing nutrients, excretion of waste metabolites, and tissue deformity at mechanical stimulus. This paper reviews the mechanical properties of human hepatic tissues to develop liver-mimicking phantoms. These properties include viscosity, elasticity, acoustic impedance, sound speed, and attenuation. The advantages and disadvantages of the most common fabrication materials for developing liver tissue-mimicking phantoms are also highlighted. Such phantoms will give a better insight into the real tissue damage during the disease progression and preservation for transplantation. The liver tissue-mimicking phantom will raise the quality assurance of patient diagnostic and treatment precision and offer a definitive clinical trial data collection.
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Affiliation(s)
- Aicha S Lemine
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713, Doha, Qatar
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar
| | - Zubair Ahmad
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar
- Center for Advanced Materials (CAM), Qatar University, PO Box 2713, Doha, Qatar
| | - Noora J Al-Thani
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar
| | - Anwarul Hasan
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, 2713, Doha, Qatar
| | - Jolly Bhadra
- Qatar University Young Scientists Center (QUYSC), Qatar University, 2713, Doha, Qatar.
- Center for Advanced Materials (CAM), Qatar University, PO Box 2713, Doha, Qatar.
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Nair DG, Weiskirchen R. Recent Advances in Liver Tissue Engineering as an Alternative and Complementary Approach for Liver Transplantation. Curr Issues Mol Biol 2023; 46:262-278. [PMID: 38248320 PMCID: PMC10814863 DOI: 10.3390/cimb46010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/20/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Acute and chronic liver diseases cause significant morbidity and mortality worldwide, affecting millions of people. Liver transplantation is the primary intervention method, replacing a non-functional liver with a functional one. However, the field of liver transplantation faces challenges such as donor shortage, postoperative complications, immune rejection, and ethical problems. Consequently, there is an urgent need for alternative therapies that can complement traditional transplantation or serve as an alternative method. In this review, we explore the potential of liver tissue engineering as a supplementary approach to liver transplantation, offering benefits to patients with severe liver dysfunctions.
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Affiliation(s)
- Dileep G. Nair
- Institute of Molecular Pathobiochemistry, Experimental Gene Therapy and Clinical Chemistry (IFMPEGKC), Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, D-52074 Aachen, Germany
| | - Ralf Weiskirchen
- Institute of Molecular Pathobiochemistry, Experimental Gene Therapy and Clinical Chemistry (IFMPEGKC), Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, D-52074 Aachen, Germany
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Safraou Y, Krehl K, Meyer T, Mehrgan S, Jordan JEL, Tzschätzsch H, Fischer T, Asbach P, Braun J, Sack I, Guo J. The influence of static portal pressure on liver biophysical properties. Acta Biomater 2023; 169:118-129. [PMID: 37507032 DOI: 10.1016/j.actbio.2023.07.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
The liver is a highly vascularized organ where fluid properties, including vascular pressure, vessel integrity and fluid viscosity, play a critical role in gross mechanical properties. To study the effects of portal pressure, liver confinement, fluid viscosity, and tissue crosslinking on liver stiffness, water diffusion, and vessel size, we applied multiparametric magnetic resonance imaging (mpMRI), including multifrequency magnetic resonance elastography (MRE) and apparent diffusion coefficient (ADC) measurements, to ex vivo livers from healthy male rats (13.6±1.6 weeks) at room temperature. Four scenarios including altered liver confinement, tissue crosslinking, and vascular fluid viscosity were investigated with mpMRI at different portal pressure levels (0-17.5 cmH2O). Our experiments demonstrated that, with increasing portal pressure, rat livers showed higher water content, water diffusivity, and increased vessel sizes quantified by the vessel tissue volume fraction (VTVF). These effects were most pronounced in native, unconfined livers (VTVF: 300±120%, p<0.05, ADC: 88±29%, p<0.01), while still significant under confinement (confined: VTVF: 53±32%, p<0.01, ADC: 28±19%, p<0.05; confined-fixed: VTVF: 52±20%, p<0.001, ADC: 11±2%, p<0.01; confined-viscous: VTVF: 210±110%, p<0.01, ADC: 26±9%, p<0.001). Softening with elevated portal pressure (-12±5, p<0.05) occurred regardless of confinement and fixation. However, the liver stiffened when exposed to a more viscous inflow fluid (11±4%, p<0.001). Taken together, our results elucidate the complex relationship between macroscopic-biophysical parameters of liver tissue measured by mpMRI and vascular-fluid properties. Influenced by portal pressure, vascular permeability, and matrix crosslinking, liver stiffness is sensitive to intrinsic poroelastic properties, which, alongside vascular architecture and water diffusivity, may aid in the differential diagnosis of liver disease. STATEMENT OF SIGNIFICANCE: Using highly controllable ex vivo rat liver phantoms, hepatic biophysical properties such as tissue-vascular structure, stiffness, and water diffusivity were investigated using multiparametric MRI including multifrequency magnetic resonance elastography (MRE) and diffusion-weighted imaging (DWI). Through elaborate tuning of the experimental conditions such as the static portal pressure, flow viscosity, amount and distribution of fluid content in the liver, we identified the contributions of the fluid component to the overall imaging-based biophysical properties of the liver. Our finding demonstrated the sensitivity of liver stiffness to the hepatic poroelastic properties, which may aid in the differential diagnosis of liver diseases.
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Affiliation(s)
- Yasmine Safraou
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Karolina Krehl
- Department of Veterinary Medicine, Institute of Animal Welfare, Animal Behavior and Laboratory Animal Science, Freie Universität Berlin
| | - Tom Meyer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Shahryari Mehrgan
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jakob Ernst Luis Jordan
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Heiko Tzschätzsch
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Thomas Fischer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Patrick Asbach
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jürgen Braun
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Ingolf Sack
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jing Guo
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
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Mandl L, Mielke A, Seyedpour SM, Ricken T. Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem. Sci Rep 2023; 13:15566. [PMID: 37730743 PMCID: PMC10511457 DOI: 10.1038/s41598-023-42141-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/05/2023] [Indexed: 09/22/2023] Open
Abstract
Physics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network. This approach has been applied successfully to various types of differential equations. A major area of research on PINNs is the application to coupled partial differential equations in particular, and a general breakthrough is still lacking. In coupled equations, the optimization operates in a critical conflict between boundary conditions and the underlying equations, which often requires either many iterations or complex schemes to avoid trivial solutions and to achieve convergence. We provide empirical evidence for the mitigation of bad initial conditioning in PINNs for solving one-dimensional consolidation problems of porous media through the introduction of affine transformations after the classical output layer of artificial neural network architectures, effectively accelerating the training process. These affine physics-informed neural networks (AfPINNs) then produce nontrivial and accurate field solutions even in parameter spaces with diverging orders of magnitude. On average, AfPINNs show the ability to improve the [Formula: see text] relative error by [Formula: see text] after 25,000 epochs for a one-dimensional consolidation problem based on Biot's theory, and an average improvement by [Formula: see text] with a transfer approach to the theory of porous media.
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Affiliation(s)
- Luis Mandl
- Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569, Stuttgart, Germany
| | - André Mielke
- Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569, Stuttgart, Germany
| | - Seyed Morteza Seyedpour
- Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569, Stuttgart, Germany
- Biomechanics Lab, Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569, Stuttgart, Germany
| | - Tim Ricken
- Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569, Stuttgart, Germany.
- Biomechanics Lab, Institute of Structural Mechanics and Dynamics in Aerospace Engineering, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569, Stuttgart, Germany.
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Hnilicova P, Kantorova E, Sutovsky S, Grofik M, Zelenak K, Kurca E, Zilka N, Parvanovova P, Kolisek M. Imaging Methods Applicable in the Diagnostics of Alzheimer's Disease, Considering the Involvement of Insulin Resistance. Int J Mol Sci 2023; 24:3325. [PMID: 36834741 PMCID: PMC9958721 DOI: 10.3390/ijms24043325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disease and the most frequently diagnosed type of dementia, characterized by (1) perturbed cerebral perfusion, vasculature, and cortical metabolism; (2) induced proinflammatory processes; and (3) the aggregation of amyloid beta and hyperphosphorylated Tau proteins. Subclinical AD changes are commonly detectable by using radiological and nuclear neuroimaging methods such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). Furthermore, other valuable modalities exist (in particular, structural volumetric, diffusion, perfusion, functional, and metabolic magnetic resonance methods) that can advance the diagnostic algorithm of AD and our understanding of its pathogenesis. Recently, new insights into AD pathoetiology revealed that deranged insulin homeostasis in the brain may play a role in the onset and progression of the disease. AD-related brain insulin resistance is closely linked to systemic insulin homeostasis disorders caused by pancreas and/or liver dysfunction. Indeed, in recent studies, linkages between the development and onset of AD and the liver and/or pancreas have been established. Aside from standard radiological and nuclear neuroimaging methods and clinically fewer common methods of magnetic resonance, this article also discusses the use of new suggestive non-neuronal imaging modalities to assess AD-associated structural changes in the liver and pancreas. Studying these changes might be of great clinical importance because of their possible involvement in AD pathogenesis during the prodromal phase of the disease.
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Affiliation(s)
- Petra Hnilicova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Ema Kantorova
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Stanislav Sutovsky
- 1st Department of Neurology, Faculty of Medicine, Comenius University in Bratislava and University Hospital, 813 67 Bratislava, Slovakia
| | - Milan Grofik
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Kamil Zelenak
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Egon Kurca
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Norbert Zilka
- Institute of Neuroimmunology, Slovak Academy of Sciences, 845 10 Bratislava, Slovakia
| | - Petra Parvanovova
- Department of Medical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Martin Kolisek
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
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He M, Feng L, Chen Y, Gao B, Du Y, Zhou L, Li F, Liu H. Polydatin attenuates tubulointerstitial fibrosis in diabetic kidney disease by inhibiting YAP expression and nuclear translocation. Front Physiol 2022; 13:927794. [PMID: 36277194 PMCID: PMC9585250 DOI: 10.3389/fphys.2022.927794] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 09/20/2022] [Indexed: 11/25/2022] Open
Abstract
The activation of Yes-associated protein (YAP) pathway is mutually causal with the increase of extracellular matrix (ECM) stiffness. Polydatin (PD) has been proved to have anti-fibrosis effect in diabetic kidney disease (DKD), but it is still a mystery whether PD participates in YAP-related mechano-transduction. Therefore, this study intends to solve the following two problems: 1) To construct an in vitro system of polyacrylamide hydrogels (PA gels) based on the true stiffness of kidneys in healthy and DKD rats, and observe the effect of PD on pathological matrix stiffness-induced YAP expression in renal fibroblasts; 2) Compared with verteporfin (VP), a pharmacological inhibitor of YAP, to explore whether the therapeutic effect of PD on DKD in vivo model is related to the regulation of YAP. In this study, the in vitro system of PA gels with 3 kPa, 12 kPa and 30 kPa stiffness was constructed and determined for the first time to simulate the kidney stiffness of healthy rats, rats with DKD for 8 weeks and 16 weeks, respectively. Compared with the PA gels with 3 kPa stiffness, the PA gels with 12 kPa and 30 kPa stiffness significantly increased the expression of YAP, α-smooth muscle actin (α-SMA) and collagen I, and the production of reactive oxygen species (ROS) in renal fibroblasts, and the PA gels with 30 kPa stiffness were the highest. PD significantly inhibited the above-mentioned changes of fibroblasts induced by pathological matrix stiffness, suggesting that the inhibition of PD on fibroblast-to-myofibroblast transformation and ECM production was at least partially associated with regulating YAP-related mechano-transduction pathway. Importantly, the inhibitory effect of PD on YAP expression and nuclear translocation in kidneys of DKD rats is similar to that of VP, but PD is superior to VP in reducing urinary protein, blood glucose, blood urea nitrogen and serum creatinine, as well as decreasing the expression of α-SMA and collagen I, ROS overproduction and renal fibrosis. Our results prove for the first time from the biomechanical point of view that PD is a potential therapeutic strategy for delaying the progression of renal fibrosis by inhibiting YAP expression and nuclear translocation.
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Affiliation(s)
- Manlin He
- Department of Nephrology, Tangdu Hospital, Air Force Military Medical University (Fourth Military Medical University), Xi’an, China
| | - Lan Feng
- Department of Nephrology, Tangdu Hospital, Air Force Military Medical University (Fourth Military Medical University), Xi’an, China
| | - Yang Chen
- Department of Nephrology, Tangdu Hospital, Air Force Military Medical University (Fourth Military Medical University), Xi’an, China
| | - Bin Gao
- Department of Endocrinology, Tangdu Hospital, Air Force Military Medical University (Fourth Military Medical University), Xi’an, China
| | - Yiwei Du
- Department of Nephrology, Tangdu Hospital, Air Force Military Medical University (Fourth Military Medical University), Xi’an, China
| | - Lu Zhou
- Department of Nephrology, Tangdu Hospital, Air Force Military Medical University (Fourth Military Medical University), Xi’an, China
| | - Fei Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Hongbao Liu, ; Fei Li,
| | - Hongbao Liu
- Department of Nephrology, Tangdu Hospital, Air Force Military Medical University (Fourth Military Medical University), Xi’an, China
- *Correspondence: Hongbao Liu, ; Fei Li,
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Verma A, Manchel A, Melunis J, Hengstler JG, Vadigepalli R. From Seeing to Simulating: A Survey of Imaging Techniques and Spatially-Resolved Data for Developing Multiscale Computational Models of Liver Regeneration. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:917191. [PMID: 37575468 PMCID: PMC10421626 DOI: 10.3389/fsysb.2022.917191] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Liver regeneration, which leads to the re-establishment of organ mass, follows a specifically organized set of biological processes acting on various time and length scales. Computational models of liver regeneration largely focused on incorporating molecular and signaling detail have been developed by multiple research groups in the recent years. These modeling efforts have supported a synthesis of disparate experimental results at the molecular scale. Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. Noninvasive imaging methods such as ultrasound, computed tomography and magnetic resonance imaging provide morphological and physiological features including volumetric measures over time. In this review, we discuss multiple imaging modalities capable of informing computational models of liver regeneration at the organ-, tissue- and cellular level. Additionally, we discuss available software and algorithms, which aid in the analysis and integration of imaging data into computational models. Such models can be generated or tuned for an individual patient with liver disease. Progress towards integrated multiscale models of liver regeneration can aid in prognostic tool development for treating liver disease.
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Affiliation(s)
- Aalap Verma
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Alexandra Manchel
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Justin Melunis
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jan G. Hengstler
- IfADo-Leibniz Research Centre for Working Environment and Human Factors, Technical University Dortmund, Dortmund, Germany
| | - Rajanikanth Vadigepalli
- Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, United States
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Christ B, Collatz M, Dahmen U, Herrmann KH, Höpfl S, König M, Lambers L, Marz M, Meyer D, Radde N, Reichenbach JR, Ricken T, Tautenhahn HM. Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function. Front Physiol 2021; 12:733868. [PMID: 34867441 PMCID: PMC8637208 DOI: 10.3389/fphys.2021.733868] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/26/2021] [Indexed: 01/17/2023] Open
Abstract
Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.
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Affiliation(s)
- Bruno Christ
- Cell Transplantation/Molecular Hepatology Lab, Department of Visceral, Transplant, Thoracic and Vascular Surgery, University of Leipzig Medical Center, Leipzig, Germany
| | - Maximilian Collatz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
- Optisch-Molekulare Diagnostik und Systemtechnologié, Leibniz Institute of Photonic Technology (IPHT), Jena, Germany
- InfectoGnostics Research Campus Jena, Jena, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Sebastian Höpfl
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Matthias König
- Systems Medicine of the Liver Lab, Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Lena Lambers
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Daria Meyer
- RNA Bioinformatics and High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Nicole Radde
- Faculty of Engineering Design, Production Engineering and Automotive Engineering, Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
| | - Jürgen R. Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
| | - Tim Ricken
- Faculty of Aerospace Engineering and Geodesy, Institute of Mechanics, Structural Analysis and Dynamics, University of Stuttgart, Stuttgart, Germany
| | - Hans-Michael Tautenhahn
- Department of General, Visceral and Vascular Surgery, Jena University Hospital, Jena, Germany
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