The 21st century brought tremendous changes in technologies, sweeping away every aspect of daily life, from social to private life, from education to professional world, at a breakneck pace. Medical science has not been spared by this colossal wave of changes: e-health, e-patients, e-physicians and e-medical students have already made their first appearance in the routine medical practice. Surgical robots, 3D printing, Artificial-Intelligence based imaging, smartwatches, wearable sensors are already used to improve diagnosis, personalize treatments and monitor patients’ health. Ballistocardiography (BCG) and seismocardiography (SCG) are ancient techniques which estimate the mechanical performance of the heart through measuring myocardial contraction-induced vibrations transmitted to the skin surface. They made their first appearance into clinic at the beginning of the 20th century to help in the diagnosis of cardiovascular diseases, but their popularity drastically declined in the middle of the 70s and they never had their place in clinical practice: cumbersome and complex equipment, ambiguity in the signal interpretation, unclear understanding of the physiological genesis of the signal, the advent of high-performing technologies (cardiac MRI, echocardiography) are a few of the reasons for their clinical failure. Thanks to the tremendous improvements in technologies, these techniques of the past came back to the medical world as wearable biosensors, first holding the promise of a remote and continuous monitoring of the cardiovascular status. Displacement, velocity and acceleration of blood mass flowing into cardiac chambers and main extracardiac vessels are recorded for each heartbeat along three cardinal axes and in two dimensions, a linear and a rotational one, by the renewed BCG and SCG, with 6 degrees-of-freedom (6D-BCG and 6D-SCG). By applying the Newtonian principles to the recorded signals, signal processing algorithms automatically compute the kinetic energy (KE) and its temporal integral (iK) for each cardiac cycle. This work first analyzed the influence of normal and pathological respiration as well as the effects of sympathetic overactivity on the genesis of the 6D-BCG and 6D-SCG signals and the iK parameters; secondary, it tested the usefulness of 6D-BCG and 6D-SCG techniques in the detection of cardiac dysfunction and hemodynamic impairment during acute myocardial infarction and reperfusion in an animal model for acute coronary syndrome. While breathing normally mildly affects cardiac iK parameters, pathological respiration profoundly alters them. During a sustained end-expiratory apnea, as it happens in patients suffering from central sleep apnea, the iK generated within a contractile cycle acutely increases at the end of the apnea, strictly depending on the magnitude of sympathetic activity; inspiring against a resistance, as it happens in patients suffering from obstructive sleep apnea, acutely increases the cardiac iK and this surge is related to the acute external force afterloading the left ventricle. So, whether it is a central apnea or an obstructive one, myocardial mechanical function as expressed in terms of iK is profoundly impaired, suggesting the myocardium is enduring a sustain endeavor during these pathological respirations. During an experimental acute myocardial infarction, in a context of mechanical ventilation without major respiratory events, iK parameters drastically drop during coronary occlusion and does not improve during reperfusion, along with systemic blood pressures and cardiac output, thus holding the promise to continuously monitor the cardiac contractile function and the hemodynamic profile both during acute coronary occlusion and reperfusion. Renewed and wearable 6D-BCG and 6D-SCG may prove useful in the detection and continuous monitoring of cardiac dysfunction and hemodynamic impairment in patients suffering from sleep disordered breathing and may be used in the mid-long-term follow-up of patients with myocardial dysfunction of ischemic origin. There is still a lot of work to do before validating these renewed technologies in the practice of cardiovascular medicine, but evidences are there to consider them as next generation medical devices. / Doctorat en Sciences médicales (Médecine) / info:eu-repo/semantics/nonPublished

L'objectif de cette thèse est la modélisation mathématique des mécanismes de contraction musculaire à l'échelle microscopique dans le but de proposer et d'intégrer ces modèles dans un environnement de simulation cardiaque multi-échelle.Ce travail est réalisé dans le contexte de la médecine numérique, qui propose d'améliorer le traitement des patients par l'utilisation d'outils numériques.La première contribution de cette thèse est une analyse bibliographique des travaux expérimentaux caractérisant l’interaction actine-myosine et ses régulations afin de compiler les informations sous une forme utilisable pour le développement de modèles.Cette étape est une condition préalable essentielle à la modélisation.Nous proposons ensuite une hiérarchie de modèles de contraction musculaire à partir d'un modèle stochastique raffiné existant, mais validé uniquement pour les muscles squelettiques, en appliquant des hypothèses de simplification successives.Les étapes de simplification transforment l'équation différentielle stochastique initiale en une équation aux dérivées partielles avec une description qui fait partie de la famille de modèles dérivée du modèle Huxley'57.Une simplification supplémentaire conduit ensuite à un modèle décrit par un ensemble d'équations différentielles ordinaires.La pertinence des modèles proposés, qui ciblent différentes échelles de temps, est démontrée en les comparant aux données expérimentales obtenues avec des muscles cardiaques, et leur domaine de validité est étudié.Pour intégrer ces descriptions dans un environnement de simulation cardiaque, nous avons étendu ces modèles afin de prendre en compte les mécanismes de régulation de la force qui se produisent in vivo.Cela conduit à de nouvelles équations aux dérivées partielles.Ensuite, nous lions les modèles de contraction microscopiques à un modèle d’organe macroscopique.Nous suivons pour cela une approche fondée sur les principes thermodynamiques pour traiter la nature multi-échelle en temps et en espace du tissu musculaire aux niveaux continu et discret.La validité de cet environnement de simulation est démontrée en présentant sa capacité à reproduire le comportement du coeur et en particulier les caractéristiques essentielles de l'effet Frank-Starling. / This PhD thesis deals with the mathematical description of the micro-scale muscle contraction mechanisms with the aim of proposing and integrating our models into a multiscale heart simulation framework.This research effort is made in the context of digital medicine, which proposes to improve the treatment of patients with the use of numerical tools.The first contribution of this thesis is a literature review of the experimental works characterizing the actin-myosin interaction and its regulations to compile information in a useable form for the development of models.This stage is an essential prerequisite to modeling.We then propose a hierarchy of muscle contraction models starting from a previously proposed refined stochastic model, which was only validated for skeletal muscles, and applying successive simplification assumptions.The simplification stages transform the initial stochastic differential equation into a partial differential equation with a model that is part of the Huxley'57 model family.A further simplification then leads to a description governed by a set of ordinary differential equations.The relevance of these models, targeting different time scales, is demonstrated by comparing them with experimental data obtained with cardiac muscles and their range of validity is investigated.To integrate these microscopic descriptions into a heart simulation framework, we extend the models to take into account the force regulation mechanisms that take place in vivo, leading to the derivation of new partial differential equations.Then, we link the microscopic contraction models to the macroscopic organ model.We follow for that an approach based on the thermodynamical principles to deal with the multi-scale nature in time and space of the muscle tissue at the continuous and at the discrete levels.The validity of this simulation framework is demonstrated by showing its ability to reproduce the heart behavior and in particular to capture the essential features of the Frank-Starling effect.

Although the female menopause has been extensively characterized as a well-defined symptomatic state of oestrogen deficiency, which responds relatively well to oestrogen replacement therapy, the symptomatic state of androgen deficiency in men is poorly defined and uncertainty exists whether it responds to testosterone replacement. It has been proposed that hypothalamic-pituitary-testicular (HPT)-axis function (responsible for the production of androgens) and regulation could be viewed as a ‘barometer’ of health status in older men and that potential alterations in HPT-axis function and regulation reflect subclinical and clinical deficits in function and health, which may result in an aged phenotype of human health and disease in older men. The HPT-axis constitutes a well-defined, tractable, clinically-relevant, biological system, which may permit insight into the mechanisms underlying the expression of ageing-related phenotypes of human health and disease. By using a different lens – such as the genetic background; the compensatory responses within the HPT-axis; the syndromes of androgen deficiency; the ethnic background of an individual or the life course trajectory of function and health from conception into older age – to magnify potential dysregulation in the HPT-axis will it be possible to visualize and understand the phenotypic expression of human male ageing as a gradient of functional and health outcomes. This will allow for a better understanding of the physiological mechanics underlying symptomatic expression of dysregulation in the HPT-axis.