1
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Sharifi H, Lee LC, Campbell KS, Wenk JF. A multiscale finite element model of left ventricular mechanics incorporating baroreflex regulation. Comput Biol Med 2024; 168:107690. [PMID: 37984204 PMCID: PMC11017291 DOI: 10.1016/j.compbiomed.2023.107690] [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: 06/22/2023] [Revised: 10/11/2023] [Accepted: 11/06/2023] [Indexed: 11/22/2023]
Abstract
Cardiovascular function is regulated by a short-term hemodynamic baroreflex loop, which tries to maintain arterial pressure at a normal level. In this study, we present a new multiscale model of the cardiovascular system named MyoFE. This framework integrates a mechanistic model of contraction at the myosin level into a finite-element-based model of the left ventricle pumping blood through the systemic circulation. The model is coupled with a closed-loop feedback control of arterial pressure inspired by a baroreflex algorithm previously published by our team. The reflex loop mimics the afferent neuron pathway via a normalized signal derived from arterial pressure. The efferent pathway is represented by a kinetic model that simulates the net result of neural processing in the medulla and cell-level responses to autonomic drive. The baroreflex control algorithm modulates parameters such as heart rate and vascular tone of vessels in the lumped-parameter model of systemic circulation. In addition, it spatially modulates intracellular Ca2+ dynamics and molecular-level function of both the thick and the thin myofilaments in the left ventricle. Our study demonstrates that the baroreflex algorithm can maintain arterial pressure in the presence of perturbations such as acute cases of altered aortic resistance, mitral regurgitation, and myocardial infarction. The capabilities of this new multiscale model will be utilized in future research related to computational investigations of growth and remodeling.
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Affiliation(s)
- Hossein Sharifi
- Department of Mechanical and Aerospace Engineering, University of Kentucky, Lexington, KY, USA
| | - Lik Chuan Lee
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA
| | - Kenneth S Campbell
- Division of Cardiovascular Medicine and Department of Physiology, University of Kentucky, Lexington, KY, USA
| | - Jonathan F Wenk
- Department of Mechanical and Aerospace Engineering, University of Kentucky, Lexington, KY, USA; Department of Surgery, University of Kentucky, Lexington, KY, USA.
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2
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Rabbani A, Gao H, Lazarus A, Dalton D, Ge Y, Mangion K, Berry C, Husmeier D. Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. Comput Med Imaging Graph 2023; 106:102203. [PMID: 36848766 DOI: 10.1016/j.compmedimag.2023.102203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/26/2022] [Accepted: 02/17/2023] [Indexed: 02/27/2023]
Abstract
In this investigation, an image-based method has been developed to estimate the volume of the left ventricular cavity using cardiac magnetic resonance (CMR) imaging data. Deep learning and Gaussian processes have been applied to bring the estimations closer to the cavity volumes manually extracted. CMR data from 339 patients and healthy volunteers have been used to train a stepwise regression model that can estimate the volume of the left ventricular cavity at the beginning and end of diastole. We have decreased the root mean square error (RMSE) of cavity volume estimation approximately from 13 to 8 ml compared to the common practice in the literature. Considering the RMSE of manual measurements is approximately 4 ml on the same dataset, 8 ml of error is notable for a fully automated estimation method, which needs no supervision or user-hours once it has been trained. Additionally, to demonstrate a clinically relevant application of automatically estimated volumes, we inferred the passive material properties of the myocardium given the volume estimates using a well-validated cardiac model. These material properties can be further used for patient treatment planning and diagnosis.
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Affiliation(s)
- Arash Rabbani
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom; School of Computing, University of Leeds, Leeds LS2 9JT, United Kingdom.
| | - Hao Gao
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Alan Lazarus
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - David Dalton
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Yuzhang Ge
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Kenneth Mangion
- School of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Colin Berry
- School of Cardiovascular & Metabolic Health, University of Glasgow, Glasgow G12 8QQ, United Kingdom
| | - Dirk Husmeier
- School of Mathematics & Statistics, University of Glasgow, Glasgow G12 8QQ, United Kingdom
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3
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Martin Cerezo ML, Raval R, de Haro Reyes B, Kucka M, Chan FY, Bryk J. Identification and quantification of chimeric sequencing reads in a highly multiplexed RAD-seq protocol. Mol Ecol Resour 2022; 22:2860-2870. [PMID: 35668693 PMCID: PMC9796921 DOI: 10.1111/1755-0998.13661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 04/29/2022] [Accepted: 05/23/2022] [Indexed: 01/07/2023]
Abstract
Highly multiplexed approaches have become common in genomic studies. They have improved the cost-effectiveness of genotyping hundreds of individuals using combinatorially barcoded adapters. These strategies, however, can potentially misassigned reads to incorrect samples. Here, we used a modified quaddRAD protocol to analyse the occurrence of index hopping and PCR chimeras in a series of experiments with up to 100 multiplexed samples per sequencing lane (639 samples in total). We created two types of sequencing libraries: four libraries of type A, where PCRs were run on individual samples before multiplexing, and three libraries of type B, where PCRs were run on pooled samples. We used fixed pairs of inner barcodes to identify chimeric reads. Type B libraries show a higher percentage of misassigned reads (1.15%) than type A libraries (0.65%). We also quantify the commonly undetectable chimeric sequences that occur whenever multiplexed groups of samples with different outer barcodes are sequenced together on a single flow cell. Our results suggest that these types of chimeric sequences represent up to 1.56% and 1.29% of reads in type A and B libraries, respectively. We also show that increasing the number of mismatches allowed for barcode rescue to above 2 dramatically increases the number of recovered chimeric reads. We provide recommendations for developing highly multiplexed RAD-seq protocols and analysing the resulting data to minimize the generation of chimeric sequences, allowing their quantification and a finer control on the number of PCR cycles necessary to generate enough input DNA for library preparation.
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Affiliation(s)
- Maria Luisa Martin Cerezo
- Department of Biological and Geographical Sciences, School of Applied SciencesUniversity of HuddersfieldHuddersfieldUK,IFM BiologyLinköping UniversityLinköpingSweden
| | - Rohan Raval
- Department of Biological and Geographical Sciences, School of Applied SciencesUniversity of HuddersfieldHuddersfieldUK
| | - Bernardo de Haro Reyes
- Department of Biological and Geographical Sciences, School of Applied SciencesUniversity of HuddersfieldHuddersfieldUK,IFM BiologyLinköping UniversityLinköpingSweden
| | - Marek Kucka
- Friedrich Miescher Laboratory of the Max Planck SocietyTübingenGermany
| | | | - Jarosław Bryk
- Department of Biological and Geographical Sciences, School of Applied SciencesUniversity of HuddersfieldHuddersfieldUK
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4
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Wu ZE, Kruger MC, Cooper GJS, Sequeira IR, McGill AT, Poppitt SD, Fraser K. Dissecting the relationship between plasma and tissue metabolome in a cohort of women with obesity: Analysis of subcutaneous and visceral adipose, muscle, and liver. FASEB J 2022; 36:e22371. [PMID: 35704337 DOI: 10.1096/fj.202101812r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/30/2022] [Accepted: 05/11/2022] [Indexed: 11/11/2022]
Abstract
Untargeted metabolomics of blood samples has become widely applied to study metabolic alterations underpinning disease and to identify biomarkers. However, understanding the relevance of a blood metabolite marker can be challenging if it is unknown whether it reflects the concentration in relevant tissues. To explore this field, metabolomic and lipidomic profiles of plasma, four sites of adipose tissues (ATs) from peripheral or central depot, two sites of muscle tissue, and liver tissue from a group of nondiabetic women with obesity who were scheduled to undergo bariatric surgery (n = 21) or other upper GI surgery (n = 5), were measured by liquid chromatography coupled with mass spectrometry. Relationships between plasma and tissue profiles were examined using Pearson correlation analysis subject to Benjamini-Hochberg correction. Plasma metabolites and lipids showed the highest number of significantly positive correlations with their corresponding concentrations in liver tissue, including lipid species of ceramide, mono- and di-hexosylceramide, sphingomyelin, phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylethanolamine, dimethyl phosphatidylethanolamine, ether-linked PC, ether-linked PE, free fatty acid, cholesteryl ester, diacylglycerol and triacylglycerol, and polar metabolites linked to several metabolic functions and gut microbial metabolism. Plasma also showed significantly positive correlations with muscle for several phospholipid species and polar metabolites linked to metabolic functions and gut microbial metabolism, and with AT for several triacylglycerol species. In conclusion, plasma metabolomic and lipidomic profiles were reflective more of the liver profile than any of the muscle or AT sites examined in the present study. Our findings highlighted the importance of taking into consideration the metabolomic relationship of various tissues with plasma when postulating plasma metabolites marker to underlying mechanisms occurring in a specific tissue.
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Affiliation(s)
- Zhanxuan E Wu
- Food Chemistry and Structure, AgResearch Limited, Palmerston North, New Zealand.,School of Health Sciences, Massey University, Palmerston North, New Zealand.,High-Value Nutrition National Science Challenge, Auckland, New Zealand
| | - Marlena C Kruger
- School of Health Sciences, Massey University, Palmerston North, New Zealand.,Riddet Institute, Massey University, Palmerston North, New Zealand
| | - Garth J S Cooper
- School of Biological Sciences, University of Auckland, Auckland, New Zealand.,Department of Medicine, University of Auckland, Auckland, New Zealand.,Centre for Advanced Discovery and Experimental Therapeutics, School of Medical Sciences, University of Manchester, Manchester, UK
| | - Ivana R Sequeira
- High-Value Nutrition National Science Challenge, Auckland, New Zealand.,School of Biological Sciences, University of Auckland, Auckland, New Zealand.,Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland, New Zealand
| | - Anne-Thea McGill
- Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland, New Zealand
| | - Sally D Poppitt
- High-Value Nutrition National Science Challenge, Auckland, New Zealand.,Riddet Institute, Massey University, Palmerston North, New Zealand.,Department of Medicine, University of Auckland, Auckland, New Zealand.,Human Nutrition Unit, School of Biological Sciences, University of Auckland, Auckland, New Zealand
| | - Karl Fraser
- Food Chemistry and Structure, AgResearch Limited, Palmerston North, New Zealand.,High-Value Nutrition National Science Challenge, Auckland, New Zealand.,Riddet Institute, Massey University, Palmerston North, New Zealand
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5
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Alidadi Shamsabadi Z, Mahdavi H, Shojaei S, Salehi H, Valiani A. Physicomechanical and cellular behavior of
3D
printed polycaprolactone/poly(lactic‐co‐glycolic acid) scaffold containing polyhedral oligomeric silsesquioxane and extracellular matrix nanoparticles for cartilage tissue engineering. POLYM ADVAN TECHNOL 2022. [DOI: 10.1002/pat.5731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
| | - Hamid Mahdavi
- Department of Novel Drug Delivery Systems Iran Polymer and Petrochemical Institute Tehran Iran
| | - Shahrokh Shojaei
- Department of Biomedical Engineering Islamic Azad University Tehran Iran
| | - Hossien Salehi
- Department of Anatomical Sciences and Molecular Biology, School of Medicine Isfahan University of Medical Sciences Isfahan Iran
| | - Ali Valiani
- Department of Anatomical Sciences and Molecular Biology, School of Medicine Isfahan University of Medical Sciences Isfahan Iran
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6
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Borowska A, Gao H, Lazarus A, Husmeier D. Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3593. [PMID: 35302293 PMCID: PMC9285944 DOI: 10.1002/cnm.3593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 03/12/2022] [Indexed: 06/14/2023]
Abstract
We consider parameter inference in cardio-mechanic models of the left ventricle, in particular the one based on the Holtzapfel-Ogden (HO) constitutive law, using clinical in vivo data. The equations underlying these models do not admit closed form solutions and hence need to be solved numerically. These numerical procedures are computationally expensive making computational run times associated with numerical optimisation or sampling excessive for the uptake of the models in the clinical practice. To address this issue, we adopt the framework of Bayesian optimisation (BO), which is an efficient statistical technique of global optimisation. BO seeks the optimum of an unknown black-box function by sequentially training a statistical surrogate-model and using it to select the next query point by leveraging the associated exploration-exploitation trade-off. To guarantee that the estimates based on the in vivo data are realistic also for high-pressures, unobservable in vivo, we include a penalty term based on a previously published empirical law developed using ex vivo data. Two case studies based on real data demonstrate that the proposed BO procedure outperforms the state-of-the-art inference algorithm for the HO constitutive law.
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Affiliation(s)
| | - Hao Gao
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Alan Lazarus
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Dirk Husmeier
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
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7
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Lazarus A, Gao H, Luo X, Husmeier D. Improving cardio‐mechanic inference by combining in vivo strain data with ex vivo volume–pressure data. J R Stat Soc Ser C Appl Stat 2022. [DOI: 10.1111/rssc.12560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Hao Gao
- University of Glasgow GlasgowUK
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8
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Guan D, Mei Y, Xu L, Cai L, Luo X, Gao H. Effects of dispersed fibres in myocardial mechanics, Part I: passive response. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3972-3993. [PMID: 35341283 DOI: 10.3934/mbe.2022183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
It is widely acknowledged that an imbalanced biomechanical environment can have significant effects on myocardial pathology, leading to adverse remodelling of cardiac function if it persists. Accurate stress prediction essentially depends on the strain energy function which should have competent descriptive and predictive capabilities. Previous studies have focused on myofibre dispersion, but not on fibres along other directions. In this study, we will investigate how fibre dispersion affects myocardial biomechanical behaviours by taking into account both the myofibre dispersion and the sheet fibre dispersion, with a focus on the sheet fibre dispersion. Fibre dispersion is incorporated into a widely-used myocardial strain energy function using the discrete fibre bundle approach. We first study how different dispersion affects the descriptive capability of the strain energy function when fitting to ex vivo experimental data, and then the predictive capability in a human left ventricle during diastole. Our results show that the chosen strain energy function can achieve the best goodness-of-fit to the experimental data by including both fibre dispersion. Furthermore, noticeable differences in stress can be found in the LV model. Our results may suggest that it is necessary to include both dispersion for myofibres and the sheet fibres for the improved descriptive capability to the ex vivo experimental data and potentially more accurate stress prediction in cardiac mechanics.
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Affiliation(s)
- Debao Guan
- School of Mathematics and Statistics, University of Glasgow, UK
| | - Yuqian Mei
- School of Medical Imaging, North Sichuan Medical College, Sichuan, China
| | - Lijian Xu
- Centre for Perceptual and Interactive Intelligence, The Chinese University of Hong Kong, Hong Kong, China
| | - Li Cai
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, China
| | - Xiaoyu Luo
- School of Mathematics and Statistics, University of Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, UK
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9
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Romaszko L, Borowska A, Lazarus A, Dalton D, Berry C, Luo X, Husmeier D, Gao H. Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artif Intell Med 2021; 119:102140. [PMID: 34531009 DOI: 10.1016/j.artmed.2021.102140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/10/2021] [Accepted: 08/03/2021] [Indexed: 12/24/2022]
Abstract
Combining biomechanical modelling of left ventricular (LV) function and dysfunction with cardiac magnetic resonance (CMR) imaging has the potential to improve the prognosis of patient-specific cardiovascular disease risks. Biomechanical studies of LV function in three dimensions usually rely on a computerized representation of the LV geometry based on finite element discretization, which is essential for numerically simulating in vivo cardiac dynamics. Detailed knowledge of the LV geometry is also relevant for various other clinical applications, such as assessing the LV cavity volume and wall thickness. Accurately and automatically reconstructing personalized LV geometries from conventional CMR images with minimal manual intervention is still a challenging task, which is a pre-requisite for any subsequent automated biomechanical analysis. We propose a deep learning-based automatic pipeline for predicting the three-dimensional LV geometry directly from routinely-available CMR cine images, without the need to manually annotate the ventricular wall. Our framework takes advantage of a low-dimensional representation of the high-dimensional LV geometry based on principal component analysis. We analyze how the inference of myocardial passive stiffness is affected by using our automatically generated LV geometries instead of manually generated ones. These insights will inform the development of statistical emulators of LV dynamics to avoid computationally expensive biomechanical simulations. Our proposed framework enables accurate LV geometry reconstruction, outperforming previous approaches by delivering a reconstruction error 50% lower than reported in the literature. We further demonstrate that for a nonlinear cardiac mechanics model, using our reconstructed LV geometries instead of manually extracted ones only moderately affects the inference of passive myocardial stiffness described by an anisotropic hyperelastic constitutive law. The developed methodological framework has the potential to make an important step towards personalized medicine by eliminating the need for time consuming and costly manual operations. In addition, our method automatically maps the CMR scan into a low-dimensional representation of the LV geometry, which constitutes an important stepping stone towards the development of an LV geometry-heterogeneous emulator.
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Affiliation(s)
- Lukasz Romaszko
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Agnieszka Borowska
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Alan Lazarus
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - David Dalton
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Colin Berry
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK.
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10
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Leong CO, Leong CN, Liew YM, Al Abed A, Aziz YFA, Chee KH, Sridhar GS, Dokos S, Lim E. The role of regional myocardial topography post-myocardial infarction on infarct extension. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3501. [PMID: 34057819 DOI: 10.1002/cnm.3501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 04/26/2021] [Accepted: 05/28/2021] [Indexed: 06/12/2023]
Abstract
Infarct extension involves necrosis of healthy myocardium in the border zone (BZ), progressively enlarging the infarct zone (IZ) and recruiting the remote zone (RZ) into the BZ, eventually leading to heart failure. The mechanisms underlying infarct extension remain unclear, but myocyte stretching has been suggested as the most likely cause. Using human patient-specific left-ventricular (LV) numerical simulations established from cardiac magnetic resonance imaging (MRI) of myocardial infarction (MI) patients, the correlation between infarct extension and regional mechanics abnormality was investigated by analysing the fibre stress-strain loops (FSSLs). FSSL abnormality was characterised using the directional regional external work (DREW) index, which measures FSSL area and loop direction. Sensitivity studies were also performed to investigate the effect of infarct stiffness on regional myocardial mechanics and potential for infarct extension. We found that infarct extension was correlated to severely abnormal FSSL in the form of counter-clockwise loop at the RZ close to the infarct, as indicated by negative DREW values. In regions demonstrating negative DREW values, we observed substantial fibre stretching in the isovolumic relaxation (IVR) phase accompanied by a reduced rate of systolic shortening. Such stretching in IVR phase in part of the RZ was due to its inability to withstand the high LV pressure that was still present and possibly caused by regional myocardial stiffness inhomogeneity. Further analysis revealed that the occurrence of severely abnormal FSSL due to IVR fibre stretching near the RZ-BZ boundary was due to a large amount of surrounding infarcted tissue, or an excessively stiff IZ.
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Affiliation(s)
- Chen Onn Leong
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Chin Neng Leong
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Yih Miin Liew
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Amr Al Abed
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Yang Faridah Abdul Aziz
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- University Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia
| | - Kok Han Chee
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | | | - Socrates Dokos
- Graduate School of Biomedical Engineering, Faculty of Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Einly Lim
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
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11
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Big data and new information technology: what cardiologists need to know. REVISTA ESPANOLA DE CARDIOLOGIA (ENGLISH ED.) 2021; 74:81-89. [PMID: 33008773 DOI: 10.1016/j.rec.2020.06.036] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 06/15/2020] [Indexed: 12/24/2022]
Abstract
Technological progress in medicine is constantly garnering pace, requiring that physicians constantly update their knowledge. The new wave of technologies breaking through into clinical practice includes the following: a) mHealth, which allows constant monitoring of biological parameters, anytime, anyplace, of hundreds of patients at the same time; b) artificial intelligence, which, powered by new deep learning techniques, are starting to beat human experts at their own game: diagnosis by imaging or electrocardiography; c) 3-dimensional printing, which may lead to patient-specific prostheses; d) systems medicine, which has arisen from big data, and which will open the way to personalized medicine by bringing together genetic, epigenetic, environmental, clinical and social data into complex integral mathematical models to design highly personalized therapies. This state-of-the-art review aims to summarize in a single document the most recent and most important technological trends that are being applied to cardiology, and to provide an overall view that will allow readers to discern at a glance the direction of cardiology in the next few years.
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12
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Baladrón C, Gómez de Diego JJ, Amat-Santos IJ. Big data y nuevas tecnologías de la información: qué necesita saber el cardiólogo. Rev Esp Cardiol 2021. [DOI: 10.1016/j.recesp.2020.06.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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13
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Cai L, Ren L, Wang Y, Xie W, Zhu G, Gao H. Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201121. [PMID: 33614068 PMCID: PMC7890479 DOI: 10.1098/rsos.201121] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 12/15/2020] [Indexed: 05/12/2023]
Abstract
A long-standing problem at the frontier of biomechanical studies is to develop fast methods capable of estimating material properties from clinical data. In this paper, we have studied three surrogate models based on machine learning (ML) methods for fast parameter estimation of left ventricular (LV) myocardium. We use three ML methods named K-nearest neighbour (KNN), XGBoost and multi-layer perceptron (MLP) to emulate the relationships between pressure and volume strains during the diastolic filling. Firstly, to train the surrogate models, a forward finite-element simulator of LV diastolic filling is used. Then the training data are projected in a low-dimensional parametrized space. Next, three ML models are trained to learn the relationships of pressure-volume and pressure-strain. Finally, an inverse parameter estimation problem is formulated by using those trained surrogate models. Our results show that the three ML models can learn the relationships of pressure-volume and pressure-strain very well, and the parameter inference using the surrogate models can be carried out in minutes. Estimated parameters from both the XGBoost and MLP models have much less uncertainties compared with the KNN model. Our results further suggest that the XGBoost model is better for predicting the LV diastolic dynamics and estimating passive parameters than other two surrogate models. Further studies are warranted to investigate how XGBoost can be used for emulating cardiac pump function in a multi-physics and multi-scale framework.
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Affiliation(s)
- Li Cai
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Lei Ren
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yongheng Wang
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Wenxian Xie
- Xi’an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi’an 710129, China
- NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi’an 710129, China
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
| | - Guangyu Zhu
- School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
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14
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Li W. Biomechanics of infarcted left ventricle: a review of modelling. Biomed Eng Lett 2020; 10:387-417. [PMID: 32864174 DOI: 10.1007/s13534-020-00159-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/06/2020] [Accepted: 05/26/2020] [Indexed: 11/26/2022] Open
Abstract
Mathematical modelling in biomechanics of infarcted left ventricle (LV) serves as an indispensable tool for remodelling mechanism exploration, LV biomechanical property estimation and therapy assessment after myocardial infarction (MI). However, a review of mathematical modelling after MI has not been seen in the literature so far. In the paper, a systematic review of mathematical models in biomechanics of infarcted LV was established. The models include comprehensive cardiovascular system model, essential LV pressure-volume and stress-stretch models, constitutive laws for passive myocardium and scars, tension models for active myocardium, collagen fibre orientation optimization models, fibroblast and collagen fibre growth/degradation models and integrated growth-electro-mechanical model after MI. The primary idea, unique characteristics and key equations of each model were identified and extracted. Discussions on the models were provided and followed research issues on them were addressed. Considerable improvements in the cardiovascular system model, LV aneurysm model, coupled agent-based models and integrated electro-mechanical-growth LV model are encouraged. Substantial attention should be paid to new constitutive laws with respect to stress-stretch curve and strain energy function for infarcted passive myocardium, collagen fibre orientation optimization in scar, cardiac rupture and tissue damage and viscoelastic effect post-MI in the future.
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Affiliation(s)
- Wenguang Li
- School of Engineering, University of Glasgow, Glasgow, G12 8QQ UK
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Davies V, Noè U, Lazarus A, Gao H, Macdonald B, Berry C, Luo X, Husmeier D. Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation. J R Stat Soc Ser C Appl Stat 2019; 68:1555-1576. [PMID: 31762497 PMCID: PMC6856984 DOI: 10.1111/rssc.12374] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized medicine. However, finding a solution to the differential equations which mathematically describe the kinematics and dynamics of the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of emulation to infer the myocardial properties of a healthy volunteer in a viable clinical timeframe by using in vivo magnetic resonance image data. Emulation methods avoid computationally expensive simulations from the left ventricular model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational efficiency at the clinic. We compare and contrast two emulation strategies: emulation of the computational model outputs and emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two interpolation methods, as well as two loss functions. The best combination of methods is found by comparing the accuracy of parameter inference on simulated data for each combination. This combination, using the output emulation method, with local Gaussian process interpolation and the Euclidean loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in the computational cost of about three orders of magnitude compared with numerical integration of the differential equations by using finite element discretization techniques.
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Affiliation(s)
| | - Umberto Noè
- German Centre for Neurodegenerative Diseases Bonn Germany
| | | | | | | | - Colin Berry
- University of Glasgow and West of Scotland Heart and Lung Centre Clydebank UK
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