Speckle tracking echocardiography (STE) is a relatively new imaging modality that enables the direct measurement of active contractile myocardial tissue in an offline analysis. This is accomplished through a software algorithm that tracks collections of acoustic markers, known as 'speckles', that are unique to a given section of myocardium. By measuring the displacement of these 'speckles' as the heart contracts and relaxes, STE produces parameters of the strain, or percent change in length, exhibited by the myocardium. As multiple studies have shown, this strain data produced by tracking of the global left atrium has the ability to accurately assess the physiologic functions of the atrium as a reservoir, conduit and booster pump in the cardiac cycle. Despite these valuable correlations, there are noted problems with STE regarding acoustic cluttering and disappearance of 'speckles' that can occur as the selected region of interest moves out of the field of view or becomes obscured. These problems may be increased when tracking an extended region of myocardium. Therefore, this present study sought to test a new method of assessing left atrial function with STE strain analysis by focusing on a concise region of the atrium, specifically the interatrial septum. To test this, the echocardiograms of 37 patients were obtained and grouped according to the designation of their cardiac function as normal (n=11), abnormal (n=12), or exhibiting signs of cardiac amyloidosis (n=14). In all patients, STE strain analysis was performed on the both the global left atrium and the interatrial septum. Measurements of the mean peak strain observed in the resultant strain curves were recorded for both STE scans of each patient. The curves produced by the tracking the segments of the entire atrium (6 segments) and interatrial septum (3 segments) were compared based on the exhibited changes in strain seen in the relative shapes of the curves, as well as the spread of the segmental strain curves about the calculated mean strain curve. Additionally, the number of segments that were either unsuccessfully or incorrectly tracked was recorded as a measure of the accuracy of STE. As a final step, the interatrial strain curves of four selected patients in the various states of ventricular diastolic dysfunction were chosen and compared with data obtained from scans of mitral flow echocardiography and tissue Doppler imaging (TDI) in an attempt to correlate the exhibited changes in strain shown in the interatrial septum with the physiologic functions of the atrium during ventricular diastole. The results showed that the mean peak strain of the global atrial strain trace decreased from normal (41.32%±10.8) to abnormal (21.69%±13.8) to the amyloid group (10.41%±6.9). This trend was echoed in the mean peak strain measured in the interatrial septum, as measured in normal (64.2%±15.6), abnormal (28.37%±13.4) and amyloid groups (12.21%±12.1). When the strain curves of the entire atrium and interatrial septum were compared, they demonstrated similar patterns in the timing of changes in strain, however the strain curves of the individual interatrial septum segments showed a much more concise grouping about the mean strain curve and were less likely to exhibit discordant segmental strain curves that deviated from the pattern established by all other segments in the trace. Additionally, within the STE scans of the global atrium, the interatrial septum exhibited a higher percentage of successfully tracked segments than did the lateral atrial wall; this trend was universally exhibited in all three groups. Finally, the interatrial septum strain curves, mitral flow echocardiography and TDI scans all demonstrated similar indications of left atrial function in the four selected patients. Ultimately, STE strain analysis of the interatrial septum appears to be a more accurate method of tracking the atrial myocardium than STE tracing of the global left atrium. Furthermore, it shows viable potential as a method for assessing the global physiologic function of the left atrium, as indicated by the similarities between the trends exhibited by these STE scans and the data gathered from scans produced by mitral flow echocardiography and TDI.

L’Hypertrophie Ventriculaire Gauche (HVG) est actuellement mise en évidence par échographie. Cet examen fournit des informations anatomo-fonctionnelles mais ne permet pas de déterminer l’étiologie des HVG, ce qui engendre de graves erreurs de diagnostic et de prise en charge thérapeutique. Les HVG sont classiquement séparées en 2 catégories :1. pathologies hypertrophiques induites par la modification structurelle et fonctionnelle des cardiomyocites qui tend à compenser des insuffisances cardiaques liées par exemple à des problèmes d’hypertension artérielle, de rétrécissement aortique ou de CardioMyopathies Hypertrophiques sarcomériques ;2. pathologies infiltratives correspondant au dépôt de protéines dans la matriceextracellulaire principalement dues à différentes formes d’amyloses cardiaquesNotre hypothèse est que les différents mécanismes physiopathologiques (hypertrophique ou infiltratif) pourraient se traduire dans l’image par des propriétés spécifiques du speckle échographique. Nous avons donc développé un travail d’analyse de la texture de ces images afin de discriminer les différentes HVG.Dans cette étude, la base de données de 4795 images est divisée en une base d’apprentissage de 3770 images et une base de test de 1025 images. L’analyse de texture des images est faite par les ondelettes de Gabor avec 8 orientations, 7 tailles et 5 niveaux de décomposition. Ensuite, les caractéristiques statistiques de premier et deuxième ordre sont extraites des images. Le nombre des caractéristiques est réduit pour la base d’apprentissage en appliquant l’Analyse en Composantes Principales (ACP) suivie par l’analyse discriminante linéaire (ADL) pour une séparation supervisée des classes. Les caractéristiques extraites pour la base de test sont projetées sur les vecteurs propres sélectionnés au cours de l’apprentissage. L’ADL est appliquée à ce niveau pour la classification des données du test et la qualité de cette classification est évaluée. Les résultats obtenus sont bons (qualité totale de classification de 95,51%) et sont suivis d’une étape de cross-validation afin de vérifier la robustesse de notre méthode. A cette étape, les bases de données de l’apprentissage et du test sont mélangées et 50 combinaisons différentes sont évaluées. La même méthode décrite précédemment est appliquée. La cross-validation montre une variation de la qualité de classification (entre 30% et 99.96%) probablement due à l’hétérogénéité des caractéristiques texturelles pour les patients d’une même classe que l’on peut expliquer par des degrés différents d’avancement dans la pathologie.Ces travaux montrent qu’une analyse de texture des images échocardiographiques peut permettre de déterminer des bio-marqueurs aptes à discriminer différentes cardiopathies qui s’expriment par une HVG. Ce résultat peut avoir des retombées très importantes dans la détection précoce des amyloses cardiaques, maladies engendrant un fort taux de mortalité souvent dû à un retard de diagnostic et prise en charge des patients par un centre expert / Left-Ventricular Hypertrophy (LVH) is currently detected through echocardiography. The latter imaging modality provides anatomical and functional information. However, it does not allow the determination of the HVG etiology. This can, in turn, lead to dangerous errors in the diagnosis and treatment planning of the disease. LVH pathologies are separated into two categories:- Hypertrophic pathology caused by the structural and functional modification of cardiomyocytes that lead to cardiac failure related, for example, to arterial hypertension problems, aortic narrowing or sarcomeric hypertrophic cardiomyopathies.- Infiltrative pathologies corresponding to protein deposits on the extracellular matrix, mainly due to different forms of cardiac amyloidosisOur hypothesis is that different physiopathological mechanisms (hypertrophic or infiltrative) can be translated in the image through properties specific to echographic speckle. We have therefore developed the work of texture analysis of such images in order to discriminate the different types of LVH.In this study, the database of 4795 images is divided into a learning database of 3770 images and another testing database of 1025 images. The textural analysis of these images is done using Gabor wavelets with 8 orientations, 7 sizes and 5 decomposition levels. Next, the statistical characteristics of first and second orders are extracted from the filtered images. The number of characteristics is reduced for the learning database by applying Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) for a supervised separation of the classes. The extracted characteristics for the test database are projected on the eigenvectors selected in the learning step. LDA is applied at this level for the test data classification, and the quality of this classification is evaluated. The obtained results are good (total classification quality of 95.51%). A step of cross-validation follows in order to verify the robustness of our method. At this stage, the learning and testing databases are mixed, and 50 different combinations are evaluated. The same method described previously is then applied. The cross-validation shows a variation in the classification quality (between 30% and 99.96%) probably due to the heterogeneity of the texture characteristics for the patients of the same class explained by different disease advancement stages.This work shows that the textural analysis of echocardiographic images can permit the determination of bio-markers suitable to discriminate different LVH cardiopathies. Our results can have a very important impact on the early detection of cardiac amyloidosis, a pathology causing a considerable rate of mortality often due to a belated diagnosis and support by the centers of expertise

Amyloidosis is a term used for a whole group of diseases caused by deposition of a substance called amyloid into different tissues. Amyloid may be produced by a range of pathologic processes. Heart affliction is typical for only several types of amyloidoses. Heart involvement is then the patient`s prognosis major limiting factor. Diagnosis of heart amyloidosis is difficult especially for nonspecific symptoms and nonspecific findings obtained during common diagnostic procedures. The aim of this thesis was to evaluate usefulness of novel diagnostic methods, namely cardiac magnetic resonance with gadolinium enhancement and a simplified echocardiographic evaluation of left ventricular longitudinal strain, in diagnosing amyloid cardiomyopathy. In our first study we examined 22 patients with light chain amyloidosis by echocardiography and also with cardiac magnetic resonance with late gadolinium enhancement. We compared morphologic and functional parameters acquired by magnetic resonance examination, which is considered a gold standard for morphologic and functional measurements, with values obtained by echocardiographic measurement. Afterwards we evaluated the presence and eventually pattern of late gadolinium enhancement during cardiac magnetic resonance exam. From acquired data we conclude that the...

Proteins are the center of all biochemical reactions in living organisms. Proteins need to be present at the right time, in the right place, with the correct concentration and have the right shape to carry their designated function. Protein homeostasis is when all proteins in the proteome are in functional balance, and such balance is maintained by synthesis, folding, and degradation machinery. When protein homeostasis is lost, organisms start to age and develop diseases. To truly unveil disease mechanisms and provide more efficient means for treatment and prevention, we need a holistic understanding of the mechanism of protein homeostasis. Currently, most biomarker studies focus on the quantity aspect of the proteome. The quality aspect has been neglected because of the difficulties in measuring quality in vivo with cellular context retained. This work first proposes a kinetic model of protein homeostasis, which can provide a holistic view, including both quantity and quality aspects, as well as monitor the complex protein interactions. Using mass spectrometry, the model quantifies the quality of proteome by linking the concentration of protein, mRNA, and the rate protein synthesis, folding, unfolding, misfolding, refolding, degradation of the correctly folded protein, and degradation of protein aggregation. We then applied the ideas within the kinetic model of protein homeostasis to study several proteins in human blood serum. We reviewed the current known mechanism of transthyretin mediated amyloidosis and proposed a study approach that can measure the quality difference between different transthyretin's mutation stages, as well as monitor if the transthyretin amyloidosis has been developed at the early stage. We also used mass-spectrometry to quantify the surface accessibility differences in human serum albumin (HSA) between patients with and without rheumatoid arthritis (RA). We found certain residues are less reactive in the RA group, indicating a structural change in HSA. Such structural changes, possibly caused by ligand binding, stabilized HSA and explained the heat denature curve shift we observed. In the end, we introduced a novel assay, Iodination Protein Stability Assay (IPSA). IPSA is used to quantify protein quality by measuring protein folding stability. We applied IPSA to human serum, and it is the first in situ study, to our best knowledge, that measure the protein folding stability of proteins from human serum. We confirmed that IPSA is sensitive to measuring the differences in protein folding stability between transferrin's different iron-binding states. Together, this dissertation conveys the importance of adding quality aspects to current quantity-focused research in curing diseases and improving the quality of human life.