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Miller C, Konduri P, Bridio S, Luraghi G, Arrarte Terreros N, Boodt N, Samuels N, Rodriguez Matas JF, Migliavacca F, Lingsma H, van der Lugt A, Roos Y, Dippel D, Marquering H, Majoie C, Hoekstra A. In silico thrombectomy trials for acute ischemic stroke. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 228:107244. [PMID: 36434958 DOI: 10.1016/j.cmpb.2022.107244] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE In silico trials aim to speed up the introduction of new devices in clinical practice by testing device design and performance in different patient scenarios and improving patient stratification for optimizing clinical trials. In this paper, we demonstrate an in silico trial framework for thrombectomy treatment of acute ischemic stroke and apply this framework to compare treatment outcomes in different subpopulations and with different thrombectomy stent-retriever devices. We employ a novel surrogate thrombectomy model to evaluate the thrombectomy success in the in silico trial. METHODS The surrogate thrombectomy model, built using data from a fine-grained finite-element model, is a device-specific binary classifier (logistic regression), to estimate the probability of successful recanalization, the outcome of interest. We incorporate this surrogate model within our previously developed in silico trial framework and demonstrate its use with three examples of in silico clinical trials. The first trial is a validation trial for the surrogate thrombectomy model. We then present two exploratory trials: one evaluating the performance of a commercially available device based on the fibrin composition in the occluding thrombus and one comparing the performance of two commercially available stent retrievers. RESULTS The Validation Trial showed the surrogate thrombectomy model was able to reproduce a similar recanalization rate as the real-life MR CLEAN trial (p=0.6). Results from the first exploratory trial showed that the chance of successful thrombectomy increases with higher blood cell concentrations in the thrombi, which is in line with observations from clinical data. The second exploratory trial showed improved recanalization success with a newer stent retriever device; however, these results require further investigation as the surrogate model for the newer stent retriever device has not yet been validated. CONCLUSIONS In this novel study, we have shown that in silico trials have the potential to help inform medical device developers on the performance of a new device and may also be used to select populations of interest for a clinical trial. This would reduce the time and costs involved in device development and traditional clinical trials.
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Affiliation(s)
- Claire Miller
- Computational Science Laboratory, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam 1098 XH, the Netherlands
| | - Praneeta Konduri
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Sara Bridio
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Milan 20133, Italy
| | - Giulia Luraghi
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Milan 20133, Italy
| | - Nerea Arrarte Terreros
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Nikki Boodt
- Department of Radiology, Neurology, and Public Health, Erasmus Medical Centre, Erasmus University Rotterdam, Rotterdam 3015 CE, the Netherlands
| | - Noor Samuels
- Department of Radiology, Neurology, and Public Health, Erasmus Medical Centre, Erasmus University Rotterdam, Rotterdam 3015 CE, the Netherlands
| | - Jose F Rodriguez Matas
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Milan 20133, Italy
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Milan 20133, Italy
| | - Hester Lingsma
- Department of Public Health, Erasmus Medical Centre, Erasmus University Rotterdam, Rotterdam 3015 CE, the Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus Medical Centre, Erasmus University Rotterdam, Rotterdam 3015 CE, the Netherlands
| | - Yvo Roos
- Department of Neurology, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Diederik Dippel
- Department of Neurology, Amsterdam UMC, Erasmus Medical Centre, Erasmus University Rotterdam, Rotterdam 3015 CE, the Netherlands
| | - Henk Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Charles Majoie
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location University of Amsterdam, Amsterdam 1105 AZ, the Netherlands
| | - Alfons Hoekstra
- Computational Science Laboratory, Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam 1098 XH, the Netherlands.
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Stolfi P, Castiglione F. Emulating complex simulations by machine learning methods. BMC Bioinformatics 2021; 22:483. [PMID: 34772335 PMCID: PMC8588594 DOI: 10.1186/s12859-021-04354-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 08/23/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic and inflammatory processes underlying the development of type-2 diabetes in absence of familiarity. Given the very high incidence of type-2 diabetes, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of the disease for aware individuals. The high computational cost of the developed model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, makes the simulator executable only on powerful workstations but not on mobile devices. Hence the need to implement an emulator with a reduced computational cost that can be executed on mobile devices to provide real-time self-monitoring. RESULTS Similarly to our previous work, we propose an emulator based on a machine learning algorithm but here we consider a different approach which turn out to have better performances, indeed in terms of root mean square error we have an improvement of two order magnitude. We tested the proposed emulator on samples containing different number of simulated trajectories, and it turned out that the fitted trajectories are able to predict with high accuracy the entire dynamics of the simulator output variables. We apply the emulator to control the level of inflammation while leveraging on the nutritional input. CONCLUSION The proposed emulator can be implemented and executed on mobile health devices to perform quick-and-easy self-monitoring assessments.
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Affiliation(s)
- Paola Stolfi
- Institute for Applied Computing, National Research Council of Italy, Rome, Italy.
| | - Filippo Castiglione
- Institute for Applied Computing, National Research Council of Italy, Rome, Italy
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Stolfi P, Valentini I, Palumbo MC, Tieri P, Grignolio A, Castiglione F. Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices. BMC Bioinformatics 2020; 21:508. [PMID: 33308172 PMCID: PMC7733701 DOI: 10.1186/s12859-020-03763-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. RESULTS We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. CONCLUSIONS The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .
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Affiliation(s)
- Paola Stolfi
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
| | | | | | - Paolo Tieri
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
| | - Andrea Grignolio
- Research Ethics and Integrity Interdepartmental Center, National Research Council of Italy, Rome, Italy
- Medical Humanities - International MD Program, Vita-Salute San Raffaele University, Milan, Italy
| | - Filippo Castiglione
- Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy
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Prana V, Tieri P, Palumbo MC, Mancini E, Castiglione F. Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:7525834. [PMID: 30863457 PMCID: PMC6378014 DOI: 10.1155/2019/7525834] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 01/06/2019] [Indexed: 11/17/2022]
Abstract
BACKGROUND Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated. AIM In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake. RESULTS The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.
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Affiliation(s)
- V. Prana
- Institute for Applied Computing (IAC) “M. Picone”, National Research Council of Italy (CNR), Via dei Taurini, 19-00185 Rome, Italy
| | - P. Tieri
- Institute for Applied Computing (IAC) “M. Picone”, National Research Council of Italy (CNR), Via dei Taurini, 19-00185 Rome, Italy
| | - M. C. Palumbo
- Institute for Applied Computing (IAC) “M. Picone”, National Research Council of Italy (CNR), Via dei Taurini, 19-00185 Rome, Italy
| | - E. Mancini
- Institute for Advanced Study (IAS), University of Amsterdam (UvA), Oude Turfmarkt, 147-1012 GC Amsterdam, Netherlands
| | - F. Castiglione
- Institute for Applied Computing (IAC) “M. Picone”, National Research Council of Italy (CNR), Via dei Taurini, 19-00185 Rome, Italy
- Institute for Advanced Study (IAS), University of Amsterdam (UvA), Oude Turfmarkt, 147-1012 GC Amsterdam, Netherlands
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Zhao H, Sun Z, Li L, Zhi S, Zhao Y, Zhang Z, Wang R, Li J. HIV-1-derived exosomal microRNAs miR88 and miR99 promote the release of cytokines from human alveolar macrophages by binding to TLR8. EUR J INFLAMM 2019. [DOI: 10.1177/2058739219870434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The human monocytic cell line U937 and human alveolar macrophages were used as in vitro models to explore the role of miR88 and miR99 in the chronic abnormal activation of the body caused by human immunodeficiency virus (HIV). The functions and underlying mechanisms of miR88 and miR99 were studied by real-time quantitative polymerase chain reaction, transwell, and chromatin immunoprecipitation (ChIP) assays. HIV-1-infected cells released miR88 and miR99 into the extracellular space through exosomes, and miR88 and miR99 promoted the release of tumor necrosis factor alpha (TNFα), interleukin (IL)-6, and IL-12 by activating inflammatory factors, such as TLR8, on the surface of macrophages. HIV-derived microRNAs miR88 and miR99 performed these functions by binding to TLR8 and stimulating the release of pro-inflammatory factors from macrophages, such as TNFα, IL-6, and IL-12; these factors may be involved in chronic abnormal immune activation induced by HIV infection.
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Affiliation(s)
- Hui Zhao
- The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Zhenming Sun
- The Second People’s Hospital of Changshu City, Changshu, China
| | - Lifang Li
- The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Shuyin Zhi
- The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Yiru Zhao
- The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Zhihua Zhang
- The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Runyu Wang
- The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Jianqiang Li
- The Second Hospital of Shanxi Medical University, Taiyuan, China
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