Molecular diagnosis and pathogenesis of Marfan syndrome and related heritable diseases associated with thoracic aortic aneurysms and dissections

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Mitochondrial implication in intrauterine growth restriction and cardiovascular remodelling

[eng] Intrauterine growth restriction (IUGR) is an obstetric complication characterized by placental insufficiency and secondary cardiovascular remodeling that may lead to cardiomyopathy in adulthood. Its etiology and potential therapeutics are poorly understood. Mitochondrial bioenergetics pathways are mainly regulated by nuclear effectors such as sirtuins and are essential for embryonic development and cardiovascular function. Members of our group developed a rabbit model of IUGR and cardiovascular remodeling, in which heart, mitochondrial alterations were observed by microscopic and transcriptomic analysis. We aimed to evaluate if such alterations are translated at a functional mitochondrial level to establish the ethiopathology and potential therapeutic targets for this obstetric complication. For that aim, heart and placenta from the rabbit model was included as well as placenta from human pregnancies together with maternal and neonatal blood. At delivery, peripheral blood and cord blood mononuclear cells (PBMC and CBMC, respectively) were isolated. For the mitochondrial characterization, we assessed: oxygen consumption of the mitochondrial respiratory chain (MRC) by polarography using endogen cellular substrates and substrates for complex I. Also, enzymatic activity of complex I, II, IV, I+III and II+III of MRC, subunit protein expression of some of the MRC complexes (CII-SDHA, CII-SDHB and CIV-COX5A), Coenzyme Q levels, mitochondrial content (through citrate synthase activity, Tom20 expression or mitochondrial DNA (mtDNA) levels), oxidative stress (by lipid peroxidation and SOD2 activity) and ATP levels. Finally, Sirtuin 3 protein expression was measured by Western Blot. In the IUGR offspring from the rabbit model, we found a significant decrease of MRC function: enzymatic activity of complexes II, IV and II+III in IUGR hearts (p<0.05) and complexes II and II+III in IUGR placentas (p<0.05 and p<0.01, respectively). This was occurring with a not significant reduction in CI-stimulated oxygen consumption in both tissues and a significant decrease of complex II SDHB subunit expression in placenta (p<0.001). Additionally, levels of mitochondrial content, Coenzyme Q and cellular ATP were conserved. Lipid peroxidation significantly decreased in IUGR hearts (p<0.001), but not significantly increased in IUGR placentas. Finally, Sirtuin3 protein expression significantly increased in IUGR hearts (p<0.05). In human pregnancies, IUGR placental tissue showed an altered mitochondrial phenotype with a significant decrease of CI-stimulated oxygen consumption (p<0.05) and MRC complex I enzymatic activity (p<0.05). The enzymatic activities of the others MRC complexes and CS were preserved. In blood cells, conserved cellular oxygen consumption and trends to decrease CI-stimulated oxygen consumption was observed in maternal PBMC, but trends to decrease both cellular and CI-stimulated oxygen consumption were evidenced in neonatal CBMC, pointing out that IUGR newborns presented higher mitochondrial deficits compared to mothers. Moreover, no differences in MRC enzymatic activities in maternal PBMC or in neonatal CBMC were observed. Conserved CS activity was present in maternal PBMC but was significantly decreased in neonatal CBMC. So, in front of unaltered mtDNA levels in neonatal CBMC, alterations in neonatal CS would be related to Krebs cycle imbalances rather than to mitochondrial content. All these changes did not affect oxidative stress or ATP production in any tissue. Finally, Sirtuin3 protein expression also showed a relevant increase in human IUGR placenta (p=0.05). The relevance of this thesis relies on the description of mitochondrial impairment in the offspring of a rabbit model of IUGR but also in newborns from pregnancies complicated by IUGR. This mitochondrial imbalance is widely present in the different studied tissues, including the heart and the placenta from the rabbit model and the placenta and neonatal blood cells from human pregnancies. The mitochondrial characterization of this obstetric complication could help to greater understand the pathophysiologic mechanisms underlying cardiac remodelling and IUGR.[cat] Els nounats amb creixement intrauterí restringit (CIR) desenvolupen un remodelat cardiovascular fetal i idiopàtic que pot portar a cardiopatia durant l’etapa adulta. La bioenergètica mitocondrial, essencial pel desenvolupament embrionari i la funció cardíaca, està regulada per diferents proteïnes, entre elles la Sirtuina 3. Es tracta d’una proteïna deacetilasa d’alt interès terapèutic, ja que es pot modular a través de la dieta. Els cors de cries amb CIR d’un model animal de conill mostren alteracions transcriptòmiques i ultraestructurals a nivell mitocondrial. L’objectiu de l’estudi ha sigut determinar la implicació d’una possible disfunció mitocondrial i de la Sirtiuna 3 en el CIR. Les troballes demostren una alteració mitocondrial de la cadena respiratòria en el cor i la placenta de les cries amb CIR del model animal (sobretot a nivell de l’activitat enzimàtica dels complexes II i IV; p<0.05) i també a la placenta de gestants humanes amb CIR (especialment del complex I; p<0.05). A més a més, aquesta alteració mitocondrial s’ha evidenciat en els nounats amb CIR a través de la reducció de l’activitat de l’enzim citrat sintasa (p<0.05), suggerint alteracions a nivell del cicle de Krebs. L’ATP cel·lular i el dany oxidatiu es troba preservat en tots els teixits estudiats, excepte en el cor de les cries del model animal de CIR, on el trobem disminuït significativament (p<0.001). Aquest desajust mitocondrial va acompanyat d’un augment significatiu de l‘expressió de la proteïna Sirtuina 3 en el cor de les cries del model animal de CIR i també a la placenta de les gestants humanes amb CIR (p<0.05). Les troballes derivades d’aquest estudi permeten associar la disfunció mitocondrial al desenvolupament del CIR i el remodelat cardiovascular associat, donant lloc al disseny d’estratègies dietètiques destinades a modular l’esmentat desbalanç bioenergètic a través de la regulació de la Sirtuina 3

Effect of audiovisual stimulation on adult memory performance based electroencephalography wavelet analysis

Human memory stores various information and events that can be retrieved when needed. Many factors can influence memory performance which either provide positive or negative feedback. This research investigates the effect of audiovisual stimulation on adult memory based on electroencephalography (EEG) analysis. Sixty college students are participating in this experimental study. They must memorize visual assessment at two different levels in Mozart's Sonata music and white noise stimulation. During memorizing duration, the EEG machine records brain electrical activity based on 10–20 electrode placement. The collected raw brain signals are processed using the wavelet-based method. The stationary wavelet transform (SWT) is used for artifact elimination, whereas discrete wavelet transform (DWT) is applied to obtain alpha, beta, theta, and gamma rhythms. The time–frequency domain features are collected from the EEG signals to discover the influence of audiovisual stimulation. The findings showed a different increasing and decreasing trend of mean, standard deviation, and peak-to-peak EEG signal amplitude before and after audiovisual stimulation exposure. The theta and alpha rhythms showed the most influence with the highest relative power. Suppression of relative gamma and beta power is vital for improving visual information processing and attention level. Memorizing in audio stimulation has suppressed the relative alpha, beta, theta, and gamma power, leading to better visual memorizing ability. The white noise stimulation provides more influence on adult visual memory

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Artificial Intelligence Tools for Facial Expression Analysis.

Inner emotions show visibly upon the human face and are understood as a basic guide to an individual’s inner world. It is, therefore, possible to determine a person’s attitudes and the effects of others’ behaviour on their deeper feelings through examining facial expressions. In real world applications, machines that interact with people need strong facial expression recognition. This recognition is seen to hold advantages for varied applications in affective computing, advanced human-computer interaction, security, stress and depression analysis, robotic systems, and machine learning. This thesis starts by proposing a benchmark of dynamic versus static methods for facial Action Unit (AU) detection. AU activation is a set of local individual facial muscle parts that occur in unison constituting a natural facial expression event. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. For this research, AU occurrence activation detection was conducted by extracting features (static and dynamic) of both nominal hand-crafted and deep learning representation from each static image of a video. This confirmed the superior ability of a pretrained model that leaps in performance. Next, temporal modelling was investigated to detect the underlying temporal variation phases using supervised and unsupervised methods from dynamic sequences. During these processes, the importance of stacking dynamic on top of static was discovered in encoding deep features for learning temporal information when combining the spatial and temporal schemes simultaneously. Also, this study found that fusing both temporal and temporal features will give more long term temporal pattern information. Moreover, we hypothesised that using an unsupervised method would enable the leaching of invariant information from dynamic textures. Recently, fresh cutting-edge developments have been created by approaches based on Generative Adversarial Networks (GANs). In the second section of this thesis, we propose a model based on the adoption of an unsupervised DCGAN for the facial features’ extraction and classification to achieve the following: the creation of facial expression images under different arbitrary poses (frontal, multi-view, and in the wild), and the recognition of emotion categories and AUs, in an attempt to resolve the problem of recognising the static seven classes of emotion in the wild. Thorough experimentation with the proposed cross-database performance demonstrates that this approach can improve the generalization results. Additionally, we showed that the features learnt by the DCGAN process are poorly suited to encoding facial expressions when observed under multiple views, or when trained from a limited number of positive examples. Finally, this research focuses on disentangling identity from expression for facial expression recognition. A novel technique was implemented for emotion recognition from a single monocular image. A large-scale dataset (Face vid) was created from facial image videos which were rich in variations and distribution of facial dynamics, appearance, identities, expressions, and 3D poses. This dataset was used to train a DCNN (ResNet) to regress the expression parameters from a 3D Morphable Model jointly with a back-end classifier

Deep Learning and parallelization of Meta-heuristic Methods for IoT Cloud

Healthcare 4.0 is one of the Fourth Industrial Revolution’s outcomes that make a big revolution in the medical field. Healthcare 4.0 came with more facilities advantages that improved the average life expectancy and reduced population mortality. This paradigm depends on intelligent medical devices (wearable devices, sensors), which are supposed to generate a massive amount of data that need to be analyzed and treated with appropriate data-driven algorithms powered by Artificial Intelligence such as machine learning and deep learning (DL). However, one of the most significant limits of DL techniques is the long time required for the training process. Meanwhile, the realtime application of DL techniques, especially in sensitive domains such as healthcare, is still an open question that needs to be treated. On the other hand, meta-heuristic achieved good results in optimizing machine learning models. The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT technologies are crucial in enhancing several real-life smart applications that can improve life quality. Cloud Computing has emerged as a key enabler for IoT applications because it provides scalable and on-demand, anytime, anywhere access to the computing resources. In this thesis, we are interested in improving the efficacity and performance of Computer-aided diagnosis systems in the medical field by decreasing the complexity of the model and increasing the quality of data. To accomplish this, three contributions have been proposed. First, we proposed a computer aid diagnosis system for neonatal seizures detection using metaheuristics and convolutional neural network (CNN) model to enhance the system’s performance by optimizing the CNN model. Secondly, we focused our interest on the covid-19 pandemic and proposed a computer-aided diagnosis system for its detection. In this contribution, we investigate Marine Predator Algorithm to optimize the configuration of the CNN model that will improve the system’s performance. In the third contribution, we aimed to improve the performance of the computer aid diagnosis system for covid-19. This contribution aims to discover the power of optimizing the data using different AI methods such as Principal Component Analysis (PCA), Discrete wavelet transform (DWT), and Teager Kaiser Energy Operator (TKEO). The proposed methods and the obtained results were validated with comparative studies using benchmark and public medical data

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Mitochondrial and autophagic alterations in human-derived cell models of Parkinson's disease related to LRRK2 (G2019S) and GBA (N370S) mutations

[eng] Parkinson's disease (PD) is the second most common neurodegenerative disease, and the most common movement disorder in the world population. In most cases its aetiology is still unknown, however, mitochondrial alterations and autophagy deregulations are some of the molecular mechanisms that are altered in this disease. These molecular alterations of PD are not limited only to the destruction of dopaminergic neurons in the substantia nigra pars compacta, but they have also been described in the peripheral nervous system and the organs that it innervates. There is also evidence of the presence of other molecular alterations in diverse tissues, such as dysfunction of Complex I of the mitochondrial respiratory chain and accumulation of alpha synuclein in fibroblasts of patients with PD. One of the great difficulties the research and understanding of the mechanisms that lead to PD is the inaccessibility of the target tissue of the disease. In the best of cases, autopsy tissue from patients with advanced PD is available, leaving a question mark about the molecular processes of prodromal and early stages of the disease. Animal models have helped to unravel some questions, but the development of accessible and replicable cell models, preferably at low cost, is much needed. It is in this context that the cellular models obtained from PD patients and from asymptomatic carriers of genes associated with the disease are of great importance and require validation. The present thesis consists of the study of two cell models obtained from patients with PD associated with the LRRK2 mutation (G2019S), asymptomatic carriers of LRRK2 (G2019S) and homozygous and heterozygous carriers of GBA (N370S); which are the genes most frequently associated with familial PD and the most important genetic risk factor for PD, respectively. First, the mitochondrial and autophagic profile of fibroblasts derived from the skin of asymptomatic carriers of the LRRK2 (G2019S) mutation and with PD were analysed. The analysis was carried out under two conditions, keeping the fibroblasts in a standard culture medium (DMEM with 25mM glucose) and after subjecting them to a mitochondrial challenge for 24 hours (DMEM with 10mM galactose), in order to simulate the oxidative environment of neurons. dopaminergic. In this study, a genotype-phenotype correlation was confirmed in fibroblasts obtained from asymptomatic carriers of the LRRK2 (G2029S) mutation and patients with PD linked to this same mutation, and it was demonstrated that a mitochondrial and autophagic function profile allows to differentiate between groups. The second study explored the genotype-phenotype correlation in a cellular model characterized by neurospheres, a conglomerate of cells obtained from the dedifferentiation of human adipocytes into neuronal stem cells, and its relationship with the onset of macroautophagy in subjects carrying the mutation GBA (N370S). The main finding of this study is that mitochondrial dysfunction preceded alterations of macroautogphagic flux in subjects carrying the GBA (N370S) mutation. In conclusion, the study of asymptomatic subjects carrying mutations associated with PD represents a relevant study method that shows initial molecular alterations and the presence of compensatory mechanisms that can be studied for the development of preventive strategies and treatments in early stages of the disease.[spa] La enfermedad de Parkinson (EP) es el trastorno de movimiento más frecuente en la población mundial. Considerada mayoritariamente idiopática y multifactorial, alteraciones mitocondriales y en la regulación autofagica son algunos de los mecanismos moleculares que se han encontrado alterados en la etiopatología de la enfermedad. El descubrimiento de genes relacionados a formas familiares de EP, del cual LRRK2 es el más frecuente, y los genes que aumentan el riesgo de padecer la enfermedad, como GBA, han abierto un campo de estudio en el cual se pueden analizar los mecanismos moleculares que llevan a la neurodegeneración en formas genéticas de la EP. La presente tesis consiste en el estudio de dos modelos celulares obtenidos a partir de portadores asintomáticos de LRRK2(G2019S) (NMLRRK2(G2019S)), pacientes con EP asociada a la mutación LRRK2(G2019S) (PDLRRK2(G2019S)), así como de portadores homozigotos y heterozigotos de GBA(N370S). El primer estudio analizó el perfil mitocondrial y autofágico de fibroblastos NMLRRK2(G2019S) y PDLRRK2(G2019S). El análisis se realizó en dos condiciones, en un medio de cultivo estándar (DMEM, glucosa 25mM) y tras someterlos 24 horas a un reto mitocondrial (DMEM, galactosa 10mM), simulando el ambiente oxidativo de las neuronas dopaminérgicas. En este estudio se confirmó una correlación genotipo-fenotipo en fibroblastos obtenidos de ambos grupos y una función mitocondrial y autofágica que permite diferenciarlos entre ellos. El segundo estudio exploró la correlación genotipo-fenotipo en un modelo celular caracterizado por neuroesferas, un conglomerado de células obtenido a partir de la desdiferenciación de adipocitos humanos en células madres neuronales, y su relación con el inicio de la macroautofagia en sujetos portadores de la mutación GBA(N370S). El hallazgo principal de este segundo estudio es que la disfunción mitocondrial precede a las alteraciones del flujo macroautofágico en sujetos portadores de la mutación GBA(N370S). El estudio de sujetos asintomáticos portadores de mutaciones asociadas a PD representa un relevante método de estudio que evidencia alteraciones moleculares iniciales y la presencia de mecanismos compensatorios que pueden ser estudiados para el desarrollo de estrategias preventivas y tratamientos en lateabas tempranas de la enfermedad