SIMBIO-M 2014, SIMulation technologies in the fields of BIO-Sciences and Multiphysics: BioMechanics, BioMaterials and BioMedicine, Marseille, France, june 2014

Proceedings de la 3ème édition de la conférence internationale Simbio-M (2014). Organisée conjointement par l'IFSTTAR, Aix-Marseille Université, l'université de Coventry et CADLM, cette conférence se concentre sur les progrès des technologies de simulation dans les domaines des sciences du vivant et multiphysiques: Biomécanique, Biomatériaux et Biomédical. L'objectif de cette conférence est de partager et d'explorer les résultats dans les techniques d'analyse numérique et les outils de modélisation mathématique. Cette approche numérique permet des études prévisionnelles ou exploratoires dans les différents domaines des biosciences

Bridging spatiotemporal scales in biomechanical models for living tissues : from the contracting Esophagus to cardiac growth

Appropriate functioning of our body is determined by the mechanical behavior of our organs. An improved understanding of the biomechanical functioning of the soft tissues making up these organs is therefore crucial for the choice for, and development of, efficient clinical treatment strategies focused on patient-specific pathophysiology. This doctoral dissertation describes the passive and active biomechanical behavior of gastrointestinal and cardiovascular tissue, both in the short and long term, through computer models that bridge the cell, tissue and organ scale. Using histological characterization, mechanical testing and medical imaging techniques, virtual esophagus and heart models are developed that simulate the patient-specific biomechanical organ behavior as accurately as possible. In addition to the diagnostic value of these models, the developed modeling technology also allows us to predict the acute and chronic effect of various treatment techniques, through e.g. drugs, surgery and/or medical equipment. Consequently, this dissertation offers insights that will have an unmistakable impact on the personalized medicine of the future.Het correct functioneren van ons lichaam wordt bepaald door het mechanisch gedrag van onze organen. Een verbeterd inzicht in het biomechanisch functioneren van deze zachte weefsels is daarom van cruciale waarde voor de keuze voor, en ontwikkeling van, efficiënte klinische behandelingsstrategieën gefocust op de patiënt-specifieke pathofysiologie. Deze doctoraatsthesis brengt het passieve en actieve biomechanisch gedrag van gastro-intestinaal en cardiovasculair weefsel, zowel op korte als lange termijn, in kaart via computermodellen die een brug vormen tussen cel-, weefsel- en orgaanniveau. Aan de hand van histologische karakterisering, mechanische testen en medische beeldvormingstechnieken worden virtuele slokdarm- en hartmodellen ontwikkeld die het patiënt-specifieke orgaangedrag zo accuraat mogelijk simuleren. Naast de diagnostische waarde van deze modellen, laat de ontwikkelde modelleringstechnologie ook toe om het effect van verschillende behandelingstechnieken, via medicatie, chirurgie en/of medische apparatuur bijvoorbeeld, acuut en chronisch te voorspellen. Bijgevolg biedt deze doctoraatsthesis inzichten die een onmiskenbare impact zullen hebben op de gepersonaliseerde geneeskunde van de toekomst

Optimization and Application of Metabolomic Assays for Analyzing Diet-induced and Gut Microbiota-derived Short-chain Fatty Acids in Mice and Humans

Introduction: In recent decades, the obesity epidemic worldwide has prompted the need for research targeting disease prevention, treatment, and maintenance. Dietary interventions are one of the primary methods to instill positive nutrition habits into one’s lifestyle. Thus, resistant starch type 4 (RS4), a prebiotic dietary fiber, has been proposed to induce beneficial immunometabolic health outcomes. Currently there is a lack of knowledge on the health outcomes of RS4 in adults with metabolic syndrome (MetS). Goal: The goal of this research was to optimize a metabolomic assay to quantify fecal short chain fatty acids (SCFAs), a byproduct of microbial fermentation in the gut, and to apply this assay to health outcomes of RS4 intervention in an adult population with MetS as well as genetically induced obese mice. Methods: An assay was optimized to extract and derivatize fecal SCFA from human stool samples followed by quantification using gas chromatography – mass spectrometry (GC-MS). Retrospective analysis of fecal samples from adults including both men (n=4) and women (n=12) with signs of MetS, collected at four time points throughout an ad libitum dietary intervention of RS42, were processed and quantified. This method was also retrospectively applied to cecum samples of KK.Cg-Ay/a, genetically induced obese mouse model, to quantify the effects of RS4 on cecum SCFA concentrations. 16S rRNA sequencing was performed to study the effect of RS4 on gut microbial composition. Blood biomarkers, glycemic, and lipid viariables, anthropometric measurements, and diet nutrient composition were also studied. Results: GC-MS analysis revealed significantly increased SCFAs following RS4 consumption including butyrate, propionate, valerate, isovallerate, and hexanoate. 16S-rRNA gene sequencing revealed a differential abundance of 71 bacterial operational taxonomic units, including the enrichment of three Bacteroides species and one each of Parabacteroides, Oscillospira, Blautia, Ruminococcus, Eubacterium, and Christensenella species in the RS4 group. RS4-specific associations were found between gut microbial composition and SCFA concentrations. Cholesterols, fasting glucose, glycosylated haemoglobin, and proinflammatory markers in the blood as well as waist circumference and % body fat were lower post intervention in the RS4 group compared with the control group. In KK.Cg-Ay/a mice, butyrate was significantly enriched in RS4 fed mice intestinal tissue. Discussion: An optimized method to quantify intestinal and fecal SCFA was created. The biological function of RS4 on gut microbiota in inidividuals with MetS was also identified. Larger studies are needed to fully understand the mechanistic action of RS4 in individuals with metabolic dysfunction for future implications on dietary guidelines

Methods and Algorithms for Cardiovascular Hemodynamics with Applications to Noninvasive Monitoring of Proximal Blood Pressure and Cardiac Output Using Pulse Transit Time

Advanced health monitoring and diagnostics technology are essential to reduce the unrivaled number of human fatalities due to cardiovascular diseases (CVDs). Traditionally, gold standard CVD diagnosis involves direct measurements of the aortic blood pressure (central BP) and flow by cardiac catheterization, which can lead to certain complications. Understanding the inner-workings of the cardiovascular system through patient-specific cardiovascular modeling can provide new means to CVD diagnosis and relating treatment. BP and flow waves propagate back and forth from heart to the peripheral sites, while carrying information about the properties of the arterial network. Their speed of propagation, magnitude and shape are directly related to the properties of blood and arterial vasculature. Obtaining functional and anatomical information about the arteries through clinical measurements and medical imaging, the digital twin of the arterial network of interest can be generated. The latter enables prediction of BP and flow waveforms along this network. Point of care devices (POCDs) can now conduct in-home measurements of cardiovascular signals, such as electrocardiogram (ECG), photoplethysmogram (PPG), ballistocardiogram (BCG) and even direct measurements of the pulse transit time (PTT). This vital information provides new opportunities for designing accurate patient-specific computational models eliminating, in many cases, the need for invasive measurements. One of the main efforts in this area is the development of noninvasive cuffless BP measurement using patient’s PTT. Commonly, BP prediction is carried out with regression models assuming direct or indirect relationships between BP and PTT. However, accounting for the nonlinear FSI mechanics of the arteries and the cardiac output is indispensable. In this work, a monotonicity-preserving quasi-1D FSI modeling platform is developed, capable of capturing the hyper-viscoelastic vessel wall deformation and nonlinear blood flow dynamics in arbitrary arterial networks. Special attention has been dedicated to the correct modeling of discontinuities, such as mechanical properties mismatch associated with the stent insertion, and the intertwining dynamics of multiscale 3D and 1D models when simulating the arterial network with an aneurysm. The developed platform, titled Cardiovascular Flow ANalysis (CardioFAN), is validated against well-known numerical, in vitro and in vivo arterial network measurements showing average prediction errors of 5.2%, 2.8% and 1.6% for blood flow, lumen cross-sectional area, and BP, respectively. CardioFAN evaluates the local PTT, which enables patient-specific calibration and its application to input signal reconstruction. The calibration is performed based on BP, stroke volume and PTT measured by POCDs. The calibrated model is then used in conjunction with noninvasively measured peripheral BP and PTT to inversely restore the cardiac output, proximal BP and aortic deformation in human subjects. The reconstructed results show average RMSEs of 1.4% for systolic and 4.6% for diastolic BPs, as well as 8.4% for cardiac output. This work is the first successful attempt in implementation of deterministic cardiovascular models as add-ons to wearable and smart POCD results, enabling continuous noninvasive monitoring of cardiovascular health to facilitate CVD diagnosis

Data driven health system

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 106-110).Effective use of data is believed to be the key to address systemic inefficiencies in health innovation and delivery, and to significantly enhance value creation for patients and all stakeholders. However, there is no definition for health data. Rather, data in health is an assortment of observations and reports varying from science to clinical notes and reimbursement claims that emerge from practice rather than design. What is health data? In this thesis we try to answer that question by looking at the system of health almost exclusively as a system that generates, transforms, and interprets data. We overview the different meanings data has throughout the health system, we analyze systematically the inefficiencies and trends as they emerge from data, and propose a new architecture for the system of health in which data is not present by accident. The result of this thesis is a new architecture for the system of health that is consistent with its present state but also consistent with a future learning system and a redefinition of value in health care that is patient and information centric.by Melissa Beth Rosen Ceruolo.S.M

Mechanisms of vascular smooth muscle contraction and the basis for pharmacologic treatment of smooth muscle disorders

The smooth muscle cell directly drives the contraction of the vascular wall and hence regulates the size of the blood vessel lumen. We review here the current understanding of the molecular mechanisms by which agonists, therapeutics, and diseases regulate contractility of the vascular smooth muscle cell and we place this within the context of whole body function. We also discuss the implications for personalized medicine and highlight specific potential target molecules that may provide opportunities for the future development of new therapeutics to regulate vascular function.Accepted manuscrip

Physiological Sensing for Affective Computing

This thesis addresses two aspects related to enabling systems to recognize the affective state of people and respond sensibly to it. First, the issue of representing affective states and unambiguously assigning physiological measurements to those is addressed by suggesting a new approach based on the dimensional emotion model of valence and arousal. Second, the issue of sensing affect-related physiological data is addressed by suggesting a concept for physiological sensor systems that live up to the requirements of adaptive, user-centred systems.In dieser Arbeit wird ein Konzept zur eindeutigen Zuordnung physiologischer Messdaten zu Emotionszuständen erarbeitet, wobei Probleme klassischer Ansätze hierzu vermieden werden. Des Weiteren widmet sich die Arbeit der Erfassung emotionsbezogener physiologischer Parameter. Es wird ein Konzept für Sensorsysteme vorgestellt, welches die zuverlässige Erfassung relevanter physiologischer Parameter erlaubt, ohne jedoch den Nutzer stark zu beeinträchtigen. Der Schwerpunkt liegt hierbei auf der alltagstauglichen Gestaltung des Systems

Precision Nutrition and Metabolic Syndrome Management

Precision nutrition is an emerging concept encompassing an integrated action considering not only the genetic/epigenetic makeup and ethnic aspects of individuals, but other personalized phenotypical features, such as family and individual clinical issues, previous diseases and therapeutic treatments, perinatal nutrition, food likes/dislikes, allergies/intolerances, lifestyle attitudes and patterns, social and cultural circumstances or religious beliefs, etc. In this context, chronic disease prevalence is a global public health problem itself, which is also accompanied by a number of complications, including insulin resistance, hypertension, hypercholesterolemia, fatty liver, inflammation, oxidative status and immunocompetence disturbances, and other adverse manifestations related to metabolic syndrome, which may need individualized nutritional approaches. Therefore, the current Special Issue attempts to provide specific nutritional strategies to prevent or treat the complications associated with metabolic syndrome features concerning diabetes, vascular events, liver diseases, dyslipemia, and cancer with a precision nutrition scope

Personalized Multi-Scale Modeling of the Atria: Heterogeneities, Fiber Architecture, Hemodialysis and Ablation Therapy

This book targets three fields of computational multi-scale cardiac modeling. First, advanced models of the cellular atrial electrophysiology and fiber orientation are introduced. Second, novel methods to create patient-specific models of the atria are described. Third, applications of personalized models in basic research and clinical practice are presented. The results mark an important step towards the patient-specific model-based atrial fibrillation diagnosis, understanding and treatment