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Atehortúa A, Gkontra P, Camacho M, Diaz O, Bulgheroni M, Simonetti V, Chadeau-Hyam M, Felix JF, Sebert S, Lekadir K. Cardiometabolic risk estimation using exposome data and machine learning. Int J Med Inform 2023; 179:105209. [PMID: 37729839 DOI: 10.1016/j.ijmedinf.2023.105209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/11/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. OBJECTIVE Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. METHODS From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. RESULTS The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. CONCLUSIONS We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.
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Affiliation(s)
- Angélica Atehortúa
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain.
| | - Polyxeni Gkontra
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Marina Camacho
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | - Oliver Diaz
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
| | | | | | - Marc Chadeau-Hyam
- Department of Epidemiology and Biostatistics, MRC-HPA Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Karim Lekadir
- BCN-AIM laboratory, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
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Raisi-Estabragh Z, Salih A, Gkontra P, Atehortúa A, Radeva P, Boscolo Galazzo I, Menegaz G, Harvey NC, Lekadir K, Petersen SE. Estimation of biological heart age using cardiovascular magnetic resonance radiomics. Sci Rep 2022; 12:12805. [PMID: 35896705 PMCID: PMC9329281 DOI: 10.1038/s41598-022-16639-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/13/2022] [Indexed: 11/08/2022] Open
Abstract
We developed a novel interpretable biological heart age estimation model using cardiovascular magnetic resonance radiomics measures of ventricular shape and myocardial character. We included 29,996 UK Biobank participants without cardiovascular disease. Images were segmented using an automated analysis pipeline. We extracted 254 radiomics features from the left ventricle, right ventricle, and myocardium of each study. We then used Bayesian ridge regression with tenfold cross-validation to develop a heart age estimation model using the radiomics features as the model input and chronological age as the model output. We examined associations of radiomics features with heart age in men and women, observing sex-differential patterns. We subtracted actual age from model estimated heart age to calculate a "heart age delta", which we considered as a measure of heart aging. We performed a phenome-wide association study of 701 exposures with heart age delta. The strongest correlates of heart aging were measures of obesity, adverse serum lipid markers, hypertension, diabetes, heart rate, income, multimorbidity, musculoskeletal health, and respiratory health. This technique provides a new method for phenotypic assessment relating to cardiovascular aging; further studies are required to assess whether it provides incremental risk information over current approaches.
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Affiliation(s)
- Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.
| | - Ahmed Salih
- Department of Computer Science, University of Verona, 37134, Verona, Italy
- Dept. de Matematiques I Informatica, University of Barcelona, 95P7+JH, Barcelona, Spain
| | - Polyxeni Gkontra
- Dept. de Matematiques I Informatica, University of Barcelona, 95P7+JH, Barcelona, Spain
| | - Angélica Atehortúa
- Dept. de Matematiques I Informatica, University of Barcelona, 95P7+JH, Barcelona, Spain
| | - Petia Radeva
- Dept. de Matematiques I Informatica, University of Barcelona, 95P7+JH, Barcelona, Spain
| | | | - Gloria Menegaz
- Department of Computer Science, University of Verona, 37134, Verona, Italy
| | - Nicholas C Harvey
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Karim Lekadir
- Dept. de Matematiques I Informatica, University of Barcelona, 95P7+JH, Barcelona, Spain
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
- Health Data Research UK, London, UK
- Alan Turing Institute, London, UK
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Atehortúa A, Romero E, Garreau M. Characterization of motion patterns by a spatio-temporal saliency descriptor in cardiac cine MRI. Comput Methods Programs Biomed 2022; 218:106714. [PMID: 35263659 DOI: 10.1016/j.cmpb.2022.106714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 02/03/2022] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Abnormalities of the heart motion reveal the presence of a disease. However, a quantitative interpretation of the motion is still a challenge due to the complex dynamics of the heart. This work proposes a quantitative characterization of regional cardiac motion patterns in cine magnetic resonance imaging (MRI) by a novel spatio-temporal saliency descriptor. METHOD The strategy starts by dividing the cardiac sequence into a progression of scales which are in due turn mapped to a feature space of regional orientation changes, mimicking the multi-resolution decomposition of oriented primitive changes of visual systems. These changes are estimated as the difference between a particular time and the rest of the sequence. This decomposition is then temporarily and regionally integrated for a particular orientation and then for the set of different orientations. A final spatio-temporal 4D saliency map is obtained as the summation of the previously integrated information for the available scales. The saliency dispersion of this map was computed in standard cardiac locations as a measure of the regional motion pattern and was applied to discriminate control and hypertrophic cardiomyopathy (HCM) subjects during the diastolic phase. RESULTS Salient motion patterns were estimated from an experimental set, which consisted of 3D sequences acquired by MRI from 108 subjects (33 control, 35 HCM, 20 dilated cardiomyopathy (DCM), and 20 myocardial infarction (MINF) from heterogeneous datasets). HCM and control subjects were classified by an SVM that learned the salient motion patterns estimated from the presented strategy, by achieving a 94% AUC. In addition, statistical differences (test t-student, p<0.05) were found among groups of disease in the septal and anterior ventricular segments at both the ED and ES, with salient motion characteristics aligned with existing knowledge on the diseases. CONCLUSIONS Regional wall motion abnormality in the apical, anterior, basal, and inferior segments was associated with the saliency dispersion in HCM, DCM, and MINF compared to healthy controls during the systolic and diastolic phases. This saliency analysis may be used to detect subtle changes in heart function.
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Affiliation(s)
- Angélica Atehortúa
- Universidad Nacional de Colombia, Bogotá, Colombia; Univ Rennes, Inserm, LTSI UMR 1099, Rennes F-35000, France
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Xue W, Li J, Hu Z, Kerfoot E, Clough J, Oksuz I, Xu H, Grau V, Guo F, Ng M, Li X, Li Q, Liu L, Ma J, Grinias E, Tziritas G, Yan W, Atehortúa A, Garreau M, Jang Y, Debus A, Ferrante E, Yang G, Hua T, Li S. Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data. IEEE J Biomed Health Inform 2021; 25:3541-3553. [PMID: 33684050 PMCID: PMC7611810 DOI: 10.1109/jbhi.2021.3064353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Automatic quantification of the left ventricle (LV) from cardiac magnetic resonance (CMR) images plays an important role in making the diagnosis procedure efficient, reliable, and alleviating the laborious reading work for physicians. Considerable efforts have been devoted to LV quantification using different strategies that include segmentation-based (SG) methods and the recent direct regression (DR) methods. Although both SG and DR methods have obtained great success for the task, a systematic platform to benchmark them remains absent because of differences in label information during model learning. In this paper, we conducted an unbiased evaluation and comparison of cardiac LV quantification methods that were submitted to the Left Ventricle Quantification (LVQuan) challenge, which was held in conjunction with the Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop at the MICCAI 2018. The challenge was targeted at the quantification of 1) areas of LV cavity and myocardium, 2) dimensions of the LV cavity, 3) regional wall thicknesses (RWT), and 4) the cardiac phase, from mid-ventricle short-axis CMR images. First, we constructed a public quantification dataset Cardiac-DIG with ground truth labels for both the myocardium mask and these quantification targets across the entire cardiac cycle. Then, the key techniques employed by each submission were described. Next, quantitative validation of these submissions were conducted with the constructed dataset. The evaluation results revealed that both SG and DR methods can offer good LV quantification performance, even though DR methods do not require densely labeled masks for supervision. Among the 12 submissions, the DR method LDAMT offered the best performance, with a mean estimation error of 301 mm 2 for the two areas, 2.15 mm for the cavity dimensions, 2.03 mm for RWTs, and a 9.5% error rate for the cardiac phase classification. Three of the SG methods also delivered comparable performances. Finally, we discussed the advantages and disadvantages of SG and DR methods, as well as the unsolved problems in automatic cardiac quantification for clinical practice applications.
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Affiliation(s)
- Wufeng Xue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China; Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
| | - Jiahui Li
- Beijing University of Post and Telecommunication, Beijing, China
| | | | - Eric Kerfoot
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - James Clough
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King’s College London, UK
| | - Hao Xu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Fumin Guo
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Matthew Ng
- Sunnybrook Research Institute, Department of Medical Biophysics, University of Toronto, Canada
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital, Boston, USA
| | - Lihong Liu
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Jin Ma
- Pingan Technology (Shenzhen) Co.Ltd. Elias Grinias and Georgios Tziritas are with Department of Computer Science, University of Crete, Heraklion, Greece
| | - Elias Grinias
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Georgios Tziritas
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Wenjun Yan
- Department of Electrical Engineering, Fudan University, Shanghai, China
| | - Angélica Atehortúa
- LTSI UMR 1099, F-35000 Rennes, France; Universidad Nacional de Colombia, Bogotá, Colombia
| | | | - Yeonggul Jang
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University
| | - Alejandro Debus
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Enzo Ferrante
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
| | - Guanyu Yang
- Centre de Recherche en Information Biomédicale Sino-Français (CRIBs), Southeast University, Nanjing, China; LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China
| | - Tiancong Hua
- Centre de Recherche en Information Biomedicale Sino-Francais (CRIBs), Southeast University, Nanjing, China
| | - Shuo Li
- Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada
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Atehortúa A, Garreau M, Simon A, Donal E, Lederlin M, Romero E. Fusion of 3D real-time echocardiography and cine MRI using a saliency analysis. Int J Comput Assist Radiol Surg 2019; 15:277-285. [PMID: 31713090 DOI: 10.1007/s11548-019-02087-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 11/04/2019] [Indexed: 10/25/2022]
Abstract
PURPOSE This paper presents a novel 3D multimodal registration strategy to fuse 3D real-time echocardiography images with cardiac cine MRI images. This alignment is performed in a saliency space, which is designed to maximize similarity between the two imaging modalities. This fusion improves the quality of the available information. METHODS The method performs in two steps: temporal and spatial registrations. A temporal alignment is firstly achieved by nonlinearly matching pairs of correspondences between the two modalities using a dynamic time warping. A temporal registration is then carried out by applying nonrigid transformations in a common saliency space where normalized cross correlation between temporal pairs of salient volumes is maximized. RESULTS The alignment performance was evaluated with a set of 18 subjects, 3 with cardiomyopathies and 15 healthy, by computing the Dice score and Hausdorff distance with respect to manual delineations of the left ventricle cavity in both modalities. A Dice score and Hausdorff distance of [Formula: see text] and [Formula: see text], respectively, were obtained. In addition, the deformation field was estimated by quantifying its foldings, obtaining a 98% of regularity in the deformation field. CONCLUSIONS The 3D multimodal registration strategy presented is performed in a saliency space. Unlike state-of-the-art methods, the presented one takes advantage of the temporal information of the heart to construct this common space, ending up with two well-aligned modalities and regular deformation fields. This preliminary study was evaluated on heterogeneous data composed of two different datasets, healthy and pathological cases, showing similar performances in both cases. Future work will focus on testing the presented strategy in a larger dataset with a balanced number of classes.
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Affiliation(s)
- Angélica Atehortúa
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France. .,Universidad Nacional de Colombia, Bogotá, Colombia.
| | - Mireille Garreau
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
| | - Antoine Simon
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
| | - Erwan Donal
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
| | - Mathieu Lederlin
- Univ Rennes, CHU Rennes, Inserm, LTSI - UMR 1099, 35000, Rennes, France
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Giraldo D, Atehortúa A, García-Arteaga JD, Díaz-Jiménez DP, Romero E, Rodríguez J. Modelo para el análisis de la mortalidad en Colombia 2000-2012. Rev Salud Publica (Bogota) 2017; 19:241-249. [DOI: 10.15446/rsap.v19n2.66239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2016] [Accepted: 01/19/2017] [Indexed: 11/09/2022] Open
Abstract
Objetivo Proponer y evaluar un modelo para el ajuste y predicción de la mortalidad en Colombia que permita analizar tendencias por edad, sexo, Departamento y causa.Metodología Los registros de defunciones no fetales fueron utilizados como fuente primaria de análisis. Estos datos se pre-procesaron recodificando las causas y redistribuyendo los códigos basura. El modelo de predicción se formuló como una aproximaciónlineal de un conjunto de variables de interés, en particular la población y el producto interno bruto departamental.Resultados Como caso particular de estudio se tomó la mortalidad de menores de 5 años, se observó una disminución sostenida a partir del año 2000 tanto a nivel nacional como departamental, con excepción de tres departamentos. La evaluación del poderpredictivo de la metodología propuesta se realizó ajustando el modelo con los datos de 2000 a 2011, la predicción para el 2012 fue comparada con la tasa observada, estos resultados muestran que el modelo es suficientemente confiable para la mayor parte de las combinaciones departamento-causa.Conclusiones La metodología y modelo propuesto tienen el potencial de convertirse en un instrumento que permita orientar las prioridades del gasto en salud utilizando algún tipo de evidencia.
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Atehortúa A, Zuluaga MA, García JD, Romero E. Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis. Med Phys 2016; 43:6270. [PMID: 27908177 DOI: 10.1118/1.4966133] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
PURPOSE Accurate measurement of the right ventricle (RV) volume is important for the assessment of the ventricular function and a biomarker of the progression of any cardiovascular disease. However, the high RV variability makes difficult a proper delineation of the myocardium wall. This paper introduces a new automatic method for segmenting the RV volume from short axis cardiac magnetic resonance (MR) images by a salient analysis of temporal and spatial observations. METHODS The RV volume estimation starts by localizing the heart as the region with the most coherent motion during the cardiac cycle. Afterward, the ventricular chambers are identified at the basal level using the isodata algorithm, the right ventricle extracted, and its centroid computed. A series of radial intensity profiles, traced from this centroid, is used to search a salient intensity pattern that models the inner-outer myocardium boundary. This process is iteratively applied toward the apex, using the segmentation of the previous slice as a regularizer. The consecutive 2D segmentations are added together to obtain the final RV endocardium volume that serves to estimate also the epicardium. RESULTS Experiments performed with a public dataset, provided by the RV segmentation challenge in cardiac MRI, demonstrated that this method is highly competitive with respect to the state of the art, obtaining a Dice score of 0.87, and a Hausdorff distance of 7.26 mm while a whole volume was segmented in about 3 s. CONCLUSIONS The proposed method provides an useful delineation of the RV shape using only the spatial and temporal information of the cine MR images. This methodology may be used by the expert to achieve cardiac indicators of the right ventricle function.
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Affiliation(s)
| | - Maria A Zuluaga
- Universidad Nacional de Colombia, Bogotá 111321, Colombia and Translational Imaging Group, Centre for Medical Image Computing, University College London, NW1 2PS, United Kingdom
| | - Juan D García
- Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Eduardo Romero
- Universidad Nacional de Colombia, Bogotá 111321, Colombia
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