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Kolokotroni E, Abler D, Ghosh A, Tzamali E, Grogan J, Georgiadi E, Büchler P, Radhakrishnan R, Byrne H, Sakkalis V, Nikiforaki K, Karatzanis I, McFarlane NJB, Kaba D, Dong F, Bohle RM, Meese E, Graf N, Stamatakos G. A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin. J Pers Med 2024; 14:475. [PMID: 38793058 PMCID: PMC11122096 DOI: 10.3390/jpm14050475] [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: 03/03/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| | - Daniel Abler
- Department of Oncology, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland;
- Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Eleftheria Tzamali
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - James Grogan
- Irish Centre for High End Computing, University of Galway, H91 TK33 Galway, Ireland;
| | - Eleni Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
- Biomedical Engineering Department, University of West Attica, 12243 Egaleo, Greece
| | | | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK;
| | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | | | - Djibril Kaba
- Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;
| | - Feng Dong
- Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK;
| | - Rainer M. Bohle
- Department of Pathology, Saarland University, 66421 Homburg, Germany;
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany;
| | - Norbert Graf
- Department of Paediatric Oncology and Haematology, Saarland University, 66421 Homburg, Germany;
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
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Antonopoulos M, Dionysiou D, Stamatakos G, Uzunoglu N. Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study. BMC Bioinformatics 2019; 20:442. [PMID: 31455206 PMCID: PMC6712764 DOI: 10.1186/s12859-019-2997-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/16/2019] [Indexed: 01/10/2023] Open
Abstract
Background Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. Results This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. Conclusion In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.
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Affiliation(s)
- Markos Antonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.
| | - Dimitra Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Nikolaos Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
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Forster JC, Marcu LG, Bezak E. Approaches to combat hypoxia in cancer therapy and the potential for in silico models in their evaluation. Phys Med 2019; 64:145-156. [PMID: 31515013 DOI: 10.1016/j.ejmp.2019.07.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/17/2019] [Accepted: 07/09/2019] [Indexed: 02/07/2023] Open
Abstract
AIM The negative impact of tumour hypoxia on cancer treatment outcome has been long-known, yet there has been little success combating it. This paper investigates the potential role of in silico modelling to help test emerging hypoxia-targeting treatments in cancer therapy. METHODS A Medline search was undertaken on the current landscape of in silico models that simulate cancer therapy and evaluate their ability to test hypoxia-targeting treatments. Techniques and treatments to combat tumour hypoxia and their current challenges are also presented. RESULTS Hypoxia-targeting treatments include tumour reoxygenation, hypoxic cell radiosensitization with nitroimidazoles, hypoxia-activated prodrugs and molecular targeting. Their main challenges are toxicity and not achieving adequate delivery to hypoxic regions of the tumour. There is promising research toward combining two or more of these techniques. Different types of in silico therapy models have been developed ranging from temporal to spatial and from stochastic to deterministic models. Numerous models have compared the effectiveness of different radiotherapy fractionation schedules for controlling hypoxic tumours. Similarly, models could help identify and optimize new treatments for overcoming hypoxia that utilize novel hypoxia-targeting technology. CONCLUSION Current therapy models should attempt to incorporate more sophisticated modelling of tumour angiogenesis/vasculature and vessel perfusion in order to become more useful for testing hypoxia-targeting treatments, which typically rely upon the tumour vasculature for delivery of additional oxygen, (pro)drugs and nanoparticles.
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Affiliation(s)
- Jake C Forster
- SA Medical Imaging, Department of Nuclear Medicine, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia; Department of Physics, University of Adelaide, North Terrace, Adelaide SA 5005, Australia
| | - Loredana G Marcu
- Faculty of Science, University of Oradea, Oradea 410087, Romania; Cancer Research Institute and School of Health Sciences, University of South Australia, Adelaide SA 5001, Australia.
| | - Eva Bezak
- Department of Physics, University of Adelaide, North Terrace, Adelaide SA 5005, Australia; Cancer Research Institute and School of Health Sciences, University of South Australia, Adelaide SA 5001, Australia
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Kolokotroni E, Dionysiou D, Veith C, Kim YJ, Sabczynski J, Franz A, Grgic A, Palm J, Bohle RM, Stamatakos G. In Silico Oncology: Quantification of the In Vivo Antitumor Efficacy of Cisplatin-Based Doublet Therapy in Non-Small Cell Lung Cancer (NSCLC) through a Multiscale Mechanistic Model. PLoS Comput Biol 2016; 12:e1005093. [PMID: 27657742 PMCID: PMC5033576 DOI: 10.1371/journal.pcbi.1005093] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 08/01/2016] [Indexed: 11/30/2022] Open
Abstract
The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs’ cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions. Less than 14% of medically treated patients with locally advanced and metastatic non-small cell lung cancer are expected to be alive 5 years after diagnosis. Standard therapeutic strategies include the administration of two drugs in combination, aiming at shrinking the tumor before surgery and improving overall survival. Knowing the sensitivity profile of each patient to different treatment strategies at diagnosis may help choose the most appropriate ones. We develop a methodology for the quantitative estimation of the cytotoxic efficacy of cisplatin-based doublets on cancer cells by applying a simulation model of cancer progression and response. The model incorporates the proliferation cycle, quiescence, differentiation and loss of tumor cells. We evaluate the effect of in vivo microenvironment of real tumors, as expressed by measurable tumor proliferation kinetics, such as how fast the tumor grows, the percentage of cells that are actively dividing, the resistance of stem cells, etc. on treatment outcome so as to derive more accurate estimates. A literature survey guides the selection of values. The methodology is applied to a real clinical dataset of patients. Correlation studies between the derived cytotoxicities and the patients’ molecular profile could lead to predictions of treatment response at the time of diagnosis.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Dimitra Dionysiou
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christian Veith
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | - Yoo-Jin Kim
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | | | | | - Aleksandar Grgic
- Department of Nuclear Medicine, University of Saarland, Homburg (Saar), Germany
| | - Jan Palm
- Department of Radiotherapy and Radiation Oncology, University of Saarland, Homburg (Saar), Germany
| | - Rainer M. Bohle
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
- * E-mail:
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Ouzounoglou E, Dionysiou D, Stamatakos GS. Differentiation resistance through altered retinoblastoma protein function in acute lymphoblastic leukemia: in silico modeling of the deregulations in the G1/S restriction point pathway. BMC SYSTEMS BIOLOGY 2016; 10:23. [PMID: 26932523 PMCID: PMC4774111 DOI: 10.1186/s12918-016-0264-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 01/31/2016] [Indexed: 12/13/2022]
Abstract
BACKGROUND As in many cancer types, the G1/S restriction point (RP) is deregulated in Acute Lymphoblastic Leukemia (ALL). Hyper-phosphorylated retinoblastoma protein (hyper-pRb) is found in high levels in ALL cells. Nevertheless, the ALL lymphocyte proliferation rate for the average patient is surprisingly low compared to its normal counterpart of the same maturation level. Additionally, as stated in literature, ALL cells possibly reside at or beyond the RP which is located in the late-G1 phase. This state may favor their differentiation resistant phenotype. A major phenomenon contributing to this fact is thought to be the observed limited redundancy in the phosphorylation of retinoblastoma protein (pRb) by the various Cyclin Dependent Kinases (Cdks). The latter may result in partial loss of pRb functions despite hyper-phosphorylation. RESULTS To test this hypothesis, an in silico model aiming at simulating the biochemical regulation of the RP in ALL is introduced. By exploiting experimental findings derived from leukemic cells and following a semi-quantitative calibration procedure, the model has been shown to satisfactorily reproduce such a behavior for the RP pathway. At the same time, the calibrated model has been proved to be in agreement with the observed variation in the ALL cell cycle duration. CONCLUSIONS The proposed model aims to contribute to a better understanding of the complex phenomena governing the leukemic cell cycle. At the same time it constitutes a significant first step in the creation of a personalized proliferation rate predictor that can be used in the context of multiscale cancer modeling. Such an approach is expected to play an important role in the refinement and the advancement of mechanistic modeling of ALL in the context of the emergent and promising scientific domains of In Silico Oncology and more generally In Silico Medicine.
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Affiliation(s)
- Eleftherios Ouzounoglou
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografou, 15780, Athens, Greece.
| | - Dimitra Dionysiou
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografou, 15780, Athens, Greece.
| | - Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografou, 15780, Athens, Greece.
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Stamatakos GS, Kolokotroni E, Dionysiou D, Veith C, Kim YJ, Franz A, Marias K, Sabczynski J, Bohle R, Graf N. In silico oncology: exploiting clinical studies to clinically adapt and validate multiscale oncosimulators. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:5545-9. [PMID: 24110993 DOI: 10.1109/embc.2013.6610806] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents a brief outline of the notion and the system of oncosimulator in conjunction with a high level description of the basics of its core multiscale model simulating clinical tumor response to treatment. The exemplary case of lung cancer preoperatively treated with a combination of chemotherapeutic agents is considered. The core oncosimulator model is based on a primarily top-down, discrete entity - discrete event multiscale simulation approach. The critical process of clinical adaptation of the model by exploiting sets of multiscale data originating from clinical studies/trials is also outlined. Concrete clinical adaptation results are presented. The adaptation process also conveys important aspects of the planned clinical validation procedure since the same type of multiscale data - although not the same data itself- is to be used for clinical validation. By having exploited actual clinical data in conjunction with plausible literature-based values of certain model parameters, a realistic tumor dynamics behavior has been demonstrated. The latter supports the potential of the specific oncosimulator to serve as a personalized treatment optimizer following an eventually successful completion of the clinical adaptation and validation process.
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Stamatakos G, Dionysiou D, Lunzer A, Belleman R, Kolokotroni E, Georgiadi E, Erdt M, Pukacki J, Rueping S, Giatili S, d'Onofrio A, Sfakianakis S, Marias K, Desmedt C, Tsiknakis M, Graf N. The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context. IEEE J Biomed Health Inform 2014; 18:840-54. [DOI: 10.1109/jbhi.2013.2284276] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, In Silico Oncology Group, 9 Iroon Polytechniou, Zografos, Greece
| | - Dimitra Dionysiou
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Eleni Kolokotroni
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | - Eleni Georgiadi
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | - Juliusz Pukacki
- Poznan Supercomputing and Networking Center (PSNC), Poznan, Poland
| | - Stefan Rueping
- Fraunhofer IAIS, Schloss Birlinghoven, St. Augustin, Germany
| | - Stavroula Giatili
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Kostas Marias
- Foundation for Research and Technology Hellas, Heraklion, Greece
| | | | - Manolis Tsiknakis
- Department of Informatics Engineering, TEI Crete and the Computational Medicine Laboratory, Institute of Computer Science, FORTH , Heraklion, Greece
| | - Norbert Graf
- University Hospital of the Saarland, Pediatric Haematology and Oncology, Homburg, Germany
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Georgiadi EC, Dionysiou DD, Graf N, Stamatakos GS. Towards in silico oncology: adapting a four dimensional nephroblastoma treatment model to a clinical trial case based on multi-method sensitivity analysis. Comput Biol Med 2012; 42:1064-78. [PMID: 23063290 DOI: 10.1016/j.compbiomed.2012.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2012] [Revised: 07/31/2012] [Accepted: 08/24/2012] [Indexed: 11/28/2022]
Abstract
In the past decades a great progress in cancer research has been made although medical treatment is still widely based on empirically established protocols which have many limitations. Computational models address such limitations by providing insight into the complex biological mechanisms of tumor progression. A set of clinically-oriented, multiscale models of solid tumor dynamics has been developed by the In Silico Oncology Group (ISOG), Institute of Communication and Computer Systems (ICCS)-National Technical University of Athens (NTUA) to study cancer growth and response to treatment. Within this context using certain representative parameter values, tumor growth and response have been modeled under a cancer preoperative chemotherapy protocol in the framework of the SIOP 2001/GPOH clinical trial. A thorough cross-method sensitivity analysis of the model has been performed. Based on the sensitivity analysis results, a reasonable adaptation of the values of the model parameters to a real clinical case of bilateral nephroblastomatosis has been achieved. The analysis presented supports the potential of the model for the study and eventually the future design of personalized treatment schemes and/or schedules using the data obtained from in vitro experiments and clinical studies.
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Affiliation(s)
- Eleni Ch Georgiadi
- In Silico Oncology Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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