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Davey NA, Chase JG, Zhou C, Murphy L. Preserving multi-dimensional information: A hypersphere method for parameter space analysis. Heliyon 2024; 10:e28822. [PMID: 38601671 PMCID: PMC11004565 DOI: 10.1016/j.heliyon.2024.e28822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 03/25/2024] [Indexed: 04/12/2024] Open
Abstract
Background Physiological modelling often involves models described by large numbers of variables and significant volumes of clinical data. Mathematical interpretation of such models frequently necessitates analysing data points in high-dimensional spaces. Existing algorithms for analysing high-dimensional points either lose important dimensionality or do not describe the full position of points. Hence, there is a need for an algorithm which preserves this information. Methods The most-distant uncovered point (MDUP) hypersphere method is a binary classification approach which defines a collection of equidistant N-dimensional points as the union of hyperspheres. The method iteratively generates hyperspheres at the most distant point in the interest region not yet contained within any hypersphere, until the entire region of interest is defined by the union of all generated hyperspheres. This method is tested on a 7-dimensional space with up to 35.8 million points representing feasible and infeasible spaces of model parameters for a clinically validated cardiovascular system model. Results For different numbers of input points, the MDUP hypersphere method tends to generate large spheres away from the boundary of feasible and infeasible points, but generates the greatest number of relatively much smaller spheres around the boundary of the region of interest to fill this space. Runtime scales quadratically, in part because the current MDUP implementation is not parallelised. Conclusions The MDUP hypersphere method can define points in a space of any dimension using only a collection of centre points and associated radii, making the results easily interpretable. It can identify large continuous regions, and in many cases capture the general structure of a region in only a relative few hyperspheres. The MDUP method also shows promise for initialising optimisation algorithm starting conditions within pre-defined feasible regions of model parameter spaces, which could improve model identifiability and the quality of optimisation results.
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Affiliation(s)
| | | | - Cong Zhou
- University of Canterbury, New Zealand
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Buchner T, Zajdel M, Pȩczalski K, Nowak P. Finite velocity of ECG signal propagation: preliminary theory, results of a pilot experiment and consequences for medical diagnosis. Sci Rep 2023; 13:4716. [PMID: 36949077 PMCID: PMC10033722 DOI: 10.1038/s41598-023-29904-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/13/2023] [Indexed: 03/24/2023] Open
Abstract
A satisfactory model of the biopotentials propagating through the human body is essential for medical diagnostics, particularly for cardiovascular diseases. In our study, we develop the theory, that the propagation of biopotential of cardiac origin (ECG signal) may be treated as the propagation of low-frequency endogenous electromagnetic wave through the human body. We show that within this approach, the velocity of the ECG signal can be theoretically estimated, like for any other wave and physical medium, from the refraction index of the tissue in an appropriate frequency range. We confirm the theoretical predictions by the comparison with a direct measurement of the ECG signal propagation velocity and obtain mean velocity as low as v=1500 m/s. The results shed new light on our understanding of biopotential propagation through living tissue. This propagation depends on the frequency band of the signal and the transmittance of the tissue. This finding may improve the interpretation of the electric measurements, such as ECG and EEG when the frequency dependence of conductance and the phase shift introduced by the tissue is considered. We have shown, that the ECG propagation modifies the amplitude and phase of signal to a considerable extent. It may also improve the convergence of inverse problem in electrocardiographic imaging.
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Affiliation(s)
- Teodor Buchner
- Faculty of Physics, Warsaw University of Technology, Warsaw, Poland.
| | - Maryla Zajdel
- Faculty of Physics, Warsaw University of Technology, Warsaw, Poland
| | | | - Paweł Nowak
- Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland
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Cushway J, Murphy L, Chase JG, Shaw GM, Desaive T. Modelling patient specific cardiopulmonary interactions. Comput Biol Med 2022; 151:106235. [PMID: 36334361 DOI: 10.1016/j.compbiomed.2022.106235] [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: 07/28/2022] [Revised: 09/19/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Mechanical ventilation is well known for having detrimental effects on the cardiovascular system, particularly when using high positive end-expiratory pressure. High positive end-expiratory pressure levels cause a decrease in stroke volume, which, under normal conditions, usually bring about a decrease in stressed blood volume. Stressed blood volume, defined as the total pressure generating volume of the cardiovascular system, has been shown to be a potential index of fluid responsiveness, making it a potentially important diagnostic tool. Generally, respiratory and haemodynamic care are provided independently of one another. However, that positive end-expiratory pressure alters both stroke volume and stressed blood volume suggests both the pulmonary and cardiovascular state should be conjointly optimised and used to guide positive end-expiratory pressure. However, the complex and patient-specific nature of cardiopulmonary interactions which occur during mechanical ventilation presents a challenge for accurate modelling of respiratory and cardiovascular interactions required to better optimise care. Previous models attempting to incorporate cardiopulmonary interactions have suffered from poor reliability at higher PEEP levels, largely due to an exaggerated effect of intrathoracic pressure on the cardiovascular system. A new parameter, alpha, is added to a previously validated cardiopulmonary model, to modulate the percentage of intrathoracic pressure applied to the vena cava and left ventricle. The new parameter aims to increase reliability under high PEEP conditions as well as provide a patient specific solution to modelling cardiopulmonary interactions. The results from the identified optimal alpha are compared to the original model to investigate how this new parameter may be used to create a more patient-specific cardiopulmonary model, which would be better suited for guidance of care in the ICU.
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Affiliation(s)
- James Cushway
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand; University of Liège (ULg), GIGA-Cardiovascular Sciences, Liège, Belgium.
| | - Liam Murphy
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand
| | - J Geoffrey Chase
- University of Canterbury, Department of Mechanical Engineering, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Thomas Desaive
- University of Liège (ULg), GIGA-Cardiovascular Sciences, Liège, Belgium
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Bienroth D, Nim HT, Garkov D, Klein K, Jaeger-Honz S, Ramialison M, Schreiber F. Spatially resolved transcriptomics in immersive environments. Vis Comput Ind Biomed Art 2022; 5:2. [PMID: 35001220 PMCID: PMC8743310 DOI: 10.1186/s42492-021-00098-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/24/2021] [Indexed: 12/13/2022] Open
Abstract
Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.
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Affiliation(s)
- Denis Bienroth
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany.,Cell Biology, Murdoch Children's Research Institute, Parkville, Melbourne, VIC, Australia
| | - Hieu T Nim
- Cell Biology, Murdoch Children's Research Institute, Parkville, Melbourne, VIC, Australia.,Australian Regenerative Medicine Institute, Monash University, Clayton, Melbourne, VIC, Australia.,Systems Biology Institute Australia, Clayton, Melbourne, VIC, Australia
| | - Dimitar Garkov
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Sabrina Jaeger-Honz
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Mirana Ramialison
- Cell Biology, Murdoch Children's Research Institute, Parkville, Melbourne, VIC, Australia. .,Australian Regenerative Medicine Institute, Monash University, Clayton, Melbourne, VIC, Australia. .,Systems Biology Institute Australia, Clayton, Melbourne, VIC, Australia.
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany. .,Faculty of Information Technologies, Monash University, Melbourne, Australia.
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Viceconti M, Pappalardo F, Rodriguez B, Horner M, Bischoff J, Musuamba Tshinanu F. In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods 2020; 185:120-127. [PMID: 31991193 PMCID: PMC7883933 DOI: 10.1016/j.ymeth.2020.01.011] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/10/2019] [Accepted: 01/14/2020] [Indexed: 02/03/2023] Open
Abstract
Regulators now consider also evidences produced in silico. We need accepted methods to evaluate the credibility of models. In this paper we describe the use of the ASME V&V-40 technical standard. We also discuss its application to various types of modelling methods.
Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered “qualified” by the regulatory agency. This involves the assessment of the overall “credibility” that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as “biophysical” models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.
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Affiliation(s)
- Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Italy; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | | | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, UK
| | | | - Jeff Bischoff
- Corporate Research Department, Zimmer Biomet, Warsaw, IN, USA
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