264 research outputs found

    Advanced glycation end products and age-related diseases in the general population

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    In this thesis, epidemiological, nutritional, and gut microbiome related studies are presented to illustrate the relation of advanced glycation end products (AGEs) with age-related diseases. The studies are embedded in the Rotterdam Study, a cohort of the Dutch general population of middle-aged and elderly adults. The amount of skin AGEs measured as SAF was used as a representative of the long-term AGE burden. Chapter 1 gives an overview of the whole thesis (Section 1.1) and gives a brief introduction to AGEs and their implications in disease pathophysiology. Chapter 2 focuses on the interplay of AGEs in the skin and clinical and lifestyle factors, and Chapter 3 concerns the link of skin and dietary AGEs with age-related diseases. Chapter 4 discusses the interpretations and implications of the findings, major methodological considerations, and pressing questions for future research

    Advanced glycation end products and age-related diseases in the general population

    Get PDF
    In this thesis, epidemiological, nutritional, and gut microbiome related studies are presented to illustrate the relation of advanced glycation end products (AGEs) with age-related diseases. The studies are embedded in the Rotterdam Study, a cohort of the Dutch general population of middle-aged and elderly adults. The amount of skin AGEs measured as SAF was used as a representative of the long-term AGE burden. Chapter 1 gives an overview of the whole thesis (Section 1.1) and gives a brief introduction to AGEs and their implications in disease pathophysiology. Chapter 2 focuses on the interplay of AGEs in the skin and clinical and lifestyle factors, and Chapter 3 concerns the link of skin and dietary AGEs with age-related diseases. Chapter 4 discusses the interpretations and implications of the findings, major methodological considerations, and pressing questions for future research

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    30th European Congress on Obesity (ECO 2023)

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    This is the abstract book of 30th European Congress on Obesity (ECO 2023

    Precision mapping of gene expression and proteins in the brain using gene editing and barcoded viral vectors

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    The human brain is a masterpiece of intricate design and impeccable functionality. It serves as the ultimate command center for our thoughts, sensations, and actions, which define our very existence. This organ operates flawlessly, with billions of neurons working in perfect harmony to process information, create memories, and regulate our emotions. The brain's neural network is composed of trillions of connections, consisting of interconnected cells that communicate through electrical impulses and chemical signals at remarkable speeds. These connections, also known as synapses, serve as the means of communication that allow for information to travel uninterrupted throughout the brain. This intricate network enables us to think, learn, reason, and react to our surroundings. However, neurological disorders have the potential to disrupt this delicate balance, leading to a range of manifestations. These can include gradual memory erosion in Alzheimer's disease to the slow progression of motor and cognitive impairment in Parkinson's disease. Each condition presents a unique puzzle for scientists and researchers to decipher. The intricate interactions of genes, proteins, and neural circuits create a complex landscape that holds the key to understanding these disorders' origins and potential treatments.In this thesis, we worked on understanding a new type of neuronal communication based on the retrotransposon protein of Arc. The investigation was conducted using a gene editing technique based on the CRISPR/Cas9 system, next-generation sequencing technologies, and refined immunohistochemistry protocol. Using a mouse animal model, our findings reinforced the hypothesis that Arc has the capacity for inter-neuronal transport, as previously proposed in vitro studies. An additional objective of the thesis has been the investigation of molecular changes occurring within the Substantia Nigra throughout the progression of Parkinson's disease. At the core of this disorder's pathophysiology lies the alpha-synuclein protein. With this objective in mind, we developed a single- cell methodology to effectively investigate modifications in gene expression provoked by an overload of alpha- synuclein in animal models of rodents. From this data set, the overarching goal is to train a machine learning able to predict the disease course and to establish possible therapeutic interventions

    2023 Summer Experience Program Abstracts

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    https://openworks.mdanderson.org/sumexp23/1130/thumbnail.jp

    A Bayesian Methodology for Estimation for Sparse Canonical Correlation

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    It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study. Canonical Correlation Analysis (CCA) is a statistical procedure for identifying relationships between such data sets. In that context, Structured Sparse CCA (ScSCCA) is a rapidly emerging methodological area that aims for robust modeling of the interrelations between the different data modalities by assuming the corresponding CCA directional vectors to be sparse. Although it is a rapidly growing area of statistical methodology development, there is a need for developing related methodologies in the Bayesian paradigm. In this manuscript, we propose a novel ScSCCA approach where we employ a Bayesian infinite factor model and aim to achieve robust estimation by encouraging sparsity in two different levels of the modeling framework. Firstly, we utilize a multiplicative Half-Cauchy process prior to encourage sparsity at the level of the latent variable loading matrices. Additionally, we promote further sparsity in the covariance matrix by using graphical horseshoe prior or diagonal structure. We conduct multiple simulations to compare the performance of the proposed method with that of other frequently used CCA procedures, and we apply the developed procedures to analyze multi-omics data arising from a breast cancer study
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