Giovana Gavidia

Venous thromboembolism is a complex disease with a high heritability. There are significant associations among Factor XI (FXI) levels and SNPs in the KNG1 and F11 loci. Our aim was to identify the genetic variation of KNG1 and F11 that might account for the variability of FXI levels. The KNG1 and F11 loci were sequenced completely in 110 unrelated individuals from the GAIT-2 (Genetic Analysis of Idiopathic Thrombophilia 2) Project using Next Generation Sequencing on an Illumina MiSeq. The GAIT-2 Project is a study of 935 individuals in 35 extended Spanish families selected through a proband with idiopathic thrombophilia. Among the 110 individuals, a subset of 40 individuals was chosen as a discovery sample for identifying variants. A total of 762 genetic variants were detected. Several significant associations were established among common variants and low-frequency variants sets in KNG1 and F11 with FXI levels using the PLINK and SKAT packages. Among these associations, those of rs710446 and five low-frequency variant sets in KNG1 with FXI level variation were significant after multiple testing correction and permutation. Also, two putative pathogenic mutations related to high and low FXI levels were identified by data filtering and in silico predictions. This study of KNG1 and F11 loci should help to understand the connection between genotypic variation and variation in FXI levels. The functional genetic variants should be useful as markers of thromboembolic risk.

Incipient Alzheimer’s Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then to support early and accurate AD diagnosis. However, there is poor understanding of the initial stage of AD; seemingly healthy elderly brains lose matter in regions related to AD, but similar changes can also be found in non-demented subjects having mild cognitive impairment (MCI). By using a Linear Mixed Effects approach, we modelled the change of 166 Magnetic Resonance Imaging (MRI)-based biomarkers available at a 5-year follow up on healthy elderly control (HC, n = 46) subjects. We hypothesized that, by identifying their significant variant (vr) and quasi-variant (qvr) brain regions over time, it would be possible to obtain an age-based null model, which would characterize their normal atrophy and growth patterns as well as the correlation between these two regions. By using the null model on those subjects who had been clinically diagnosed as HC (n = 161), MCI (n = 209) and AD (n = 331), normal age-related changes were estimated and deviation scores (residuals) from the observed MRI-based biomarkers were computed. Subject classification, as well as the early prediction of conversion to MCI and AD, were addressed through residual-based Support Vector Machines (SVM) modelling. We found reductions in most cortical volumes and thicknesses (with evident gender differences) as well as in sub-cortical regions, including greater atrophy in the hippocampus. The average accuracies (ACC) recorded for men and women were: AD-HC: 94.11%, MCI-HC: 83.77% and MCI converted to AD (cAD)-MCI non-converter (sMCI): 76.72%. Likewise, as compared to standard clinical diagnosis methods, SVM classifiers predicted the conversion of cAD to be 1.9 years earlier for females (ACC:72.5%) and 1.4 years earlier for males (ACC:69.0%).

Geometric models of human body organs are obtained from imaging techniques like computed tomography (CT) and magnetic resonance image (MRI) that oallow an accurate visualization of the inner body, thus providing relevant information about their its structure and pathologies. Next, these models are used to generate surface and volumetric meshes, which can be used further for visualization, measurement, biomechanical simulation, rapid prototyping and prosthesis design. However, going from geometric models to numerical models is not an easy task, being necessary to apply image-processing techniques to solve the complexity of human tissues and to get more simplified geometric models, thus reducing the complexity of the subsequent numerical analysis. In this work, an integrated and efficient methodology to obtain models of soft tissues like gray and white matter of brain and hard tissues like jaw and spine bones is proposed. The methodology is based on image-processing algorithms chosen according to some characteristics of the tissue: type, intensity profiles and boundaries quality. First, low-quality images are improved by using enhancement algorithms to reduce image noise and to increase structures contrast. Then, hybrid segmentation for tissue identification is applied through a multi-stage approach. Finally, the obtained models are resampled and exported in formats readable by computer aided design (CAD) tools. In CAD environments, this data is used to generate discrete models using finite element methed (FEM) or other numerical methods like the boundary element method (BEM). Results have shown that the proposed methodology is useful and versatile to obtain accurate geometric models that can be used in several clinical cases to obtain relevant quantitative and qualitative information.

ResumenLos tejidos humanos son estructuras anatómicas que se caracterizan por tener una morfología compleja y solapada entre sí, razones por las cuales la generación de modelos geométricos precisos no resulta una tarea fácil. En la actualidad, esta tarea se ha beneficiado por el desarrollo de técnicas de diagnóstico por imágenes médicas, las cuales permiten visualizar el cuerpo humano en una forma confiable y no invasiva, generando cortes transversales que representan una sección de los tejidos bajo evaluación. En este trabajo, se propone el uso de un conjunto de técnicas numéricas de procesamiento digital de imágenes implementadas en una herramienta de software para la obtención de modelos geométricos a través de cinco etapas: (1) lectura y reconstrucción tridimensional (3D) inicial de los cortes originales de imágenes de tomografía computacional y resonancia magnética; (2) corrección de la baja calidad de las imágenes utilizando algoritmos de preprocesamiento para suavizar el ruido y realzar los bordes de los tejidos; (3) segmentación híbrida para obtener la geometría 3D de los tejidos de interés; (4) posprocesamiento para corregir errores de segmentación, y (5) exportación de los volúmenes en formatos legibles por otras herramientas de visualización médica y de Diseño Asistido por Computador (CAD) para verificar su utilidad para la generación de modelos discretos a través del análisis con los métodos numéricos. Las técnicas utilizadas fueron validadas calculando descriptores estadísticos en los modelos generados y los modelos proporcionados por otros medios como base de datos libres disponibles en sitios web. Los resultados demostraron que las técnicas implementadas generan modelos precisos y útiles para el análisis numérico, de manera versátil y en corto tiempo de procesamiento.AbstractThe generation of anatomical models is one the most important concern to biomedical researchers as well as to medical doctors, due to needed to understand the human tissues. Is know that the soft tissues like heart, brain, prostate and hard tissues like jaw, bones, skull, etc are structures of complex morphologies, so, the anatomical models generation is not an easy and trivial task. Currently, this task has benefited of advances of imaging diagnostic, which permit obtain cross and longitudinal sections of human body. In this research, we describe a method to obtain 3D discrete models of human body given by a dataset of medical images. Five main modules were implemented in prototype software: (1) Reading and 3D reconstruction of Computerized Axial Tomography and Magnetic Resonance Images. (2) Preprocessing techniques for improve the low medical images quality by using enhancement algorithms to reduce image noise and to increase structures contrast. (3) Combined segmentation techniques for tissue identification, which were applied through a multi-stage approach. (4) Post processing techniques to improve segmented volumes and (5) Exportation task of volumes to readable formats by Computer Aided Design (CAD) tools to be later analyzed by numerical methods. The performance of our method is shown on several medical examples and the techniques were validated using statistical descriptors to compare our models with models from free databases. Results showed that the implemented techniques generate precise and useful models for numerical analysis and medical survey, planning and surgery in a short processing time