Hydrogel-based engineered skeletal muscle grafts normalize heart function early after myocardial infarction

Tissue engineering represents an attractive approach for the treatment of congestive heart failure. The influence of the differentiation of myogenic graft for functional recovery is not defined. We engineered a biodegradable skeletal muscle graft (ESMG) tissue and investigated its functional effect after implantation on the epicardium of an infarcted heart segment. ESMGs were synthesized by mixing collagen (2 mg/mL), Matrigel (2 mg/mL), and rat skeletal muscle cells (10(6)). Qualitative and quantitative aspects of ESMGs were optimized. Two weeks following coronary ligation, the animals were randomized in three groups: ESMG glued to the epicardial surface with fibrin (ESMG, n = 7), fibrin alone (fibrin, n = 5), or sham operation (sham, n = 4). Echocardiography, histology, and immunostaining were performed 4 weeks later. A cohesive three-dimensional tissular structure formed in vitro within 1 week. Myoblasts differentiated into randomly oriented myotubes. Four weeks postimplantation, ESMGs were vascularized and invaded by granulation tissue. Mean fractional shortening (FS) was, however, significantly increased in the ESMG group as compared with preimplantation values (42 +/- 6 vs. 33 +/- 5%, P < 0.05) and reached the values of controlled noninfarcted animals (control, n = 5; 45 +/- 3%; not significant). Pre- and postimplantation FS did not change over these 4 weeks in the sham group and the fibrin-treated animals. This study showed that it is possible to improve systolic heart function following myocardial infarction through implantation of differentiated muscle fibers seeded on a gel-type scaffold despite a low rate of survival.

ECG-triggered skeletal muscle stimulation improves hemodynamics and physical performance of heart failure patients

BACKGROUND: Muscular counterpulsation (MCP) was developed for circulatory assistance by stimulation of peripheral skeletal muscles. We report on a clinical MCP study in patients with and without chronic heart failure (CHF). METHODS AND RESULTS: MCP treatment was applied (30 patients treated, 25 controls, all under optimal therapy) for 30 minutes during eight days by an ECG-triggered, battery-powered, portable pulse generator with skin electrodes inducing light contractions of calf and thigh muscles, sequentially stimulated at early diastole. Hemodynamic parameters (ECG, blood pressure and echocardiography) were measured one day before and one day after the treatment period in two groups: Group 1 (9 MCP, 11 no MCP) with ejection fraction (EF) above 40% and Group 2 (21 MCP, 14 no MCP) below 40%. In Group 2 (all patients suffering from CHF) mean EF increased by 21% (p<0.001) and stroke volume by 13% (p<0.001), while end systolic volume decreased by 23% (p<0.001). In Group 1, the increase in EF (6%) and stroke volume (8%) was also significant (p<0.05) but less pronounced than in Group 2. Physical exercise duration and walking distance increased in Group 2 by 56% and 72%, respectively. CONCLUSIONS: Noninvasive MCP treatment for eight days substantially improves cardiac function and physical performance in patients with CHF.

Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate

This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators

Automatic physical activity pattern recognition and meal intake modelling in type 1 diabetes for their inclusion in artificial pancreas systems

La diabetes comprende un conjunto de enfermedades metabólicas que se caracterizan por concentraciones de glucosa en sangre anormalmente altas. En el caso de la diabetes tipo 1 (T1D, por sus siglas en inglés), esta situación es debida a una ausencia total de secreción endógena de insulina, lo que impide a la mayoría de tejidos usar la glucosa. En tales circunstancias, se hace necesario el suministro exógeno de insulina para preservar la vida del paciente; no obstante, siempre con la precaución de evitar caídas agudas de la glucemia por debajo de los niveles recomendados de seguridad. Además de la administración de insulina, las ingestas y la actividad física son factores fundamentales que influyen en la homeostasis de la glucosa. En consecuencia, una gestión apropiada de la T1D debería incorporar estos dos fenómenos fisiológicos, en base a una identificación y un modelado apropiado de los mismos y de sus sorrespondientes efectos en el balance glucosa-insulina. En particular, los sistemas de páncreas artificial –ideados para llevar a cabo un control automático de los niveles de glucemia del paciente– podrían beneficiarse de la integración de esta clase de información. La primera parte de esta tesis doctoral cubre la caracterización del efecto agudo de la actividad física en los perfiles de glucosa. Con este objetivo se ha llevado a cabo una revisión sistemática de la literatura y meta-análisis que determinen las respuestas ante varias modalidades de ejercicio para pacientes con T1D, abordando esta caracterización mediante unas magnitudes que cuantifican las tasas de cambio en la glucemia a lo largo del tiempo. Por otro lado, una identificación fiable de los periodos con actividad física es un requisito imprescindible para poder proveer de esa información a los sistemas de páncreas artificial en condiciones libres y ambulatorias. Por esta razón, la segunda parte de esta tesis está enfocada a la propuesta y evaluación de un sistema automático diseñado para reconocer periodos de actividad física, clasificando su nivel de intensidad (ligera, moderada o vigorosa); así como, en el caso de periodos vigorosos, identificando también la modalidad de ejercicio (aeróbica, mixta o de fuerza). En este sentido, ambos aspectos tienen una influencia específica en el mecanismo metabólico que suministra la energía para llevar a cabo el ejercicio y, por tanto, en las respuestas glucémicas en T1D. En este trabajo se aplican varias combinaciones de técnicas de aprendizaje máquina y reconocimiento de patrones sobre la fusión multimodal de señales de acelerometría y ritmo cardíaco, las cuales describen tanto aspectos mecánicos del movimiento como la respuesta fisiológica del sistema cardiovascular ante el ejercicio. Después del reconocimiento de patrones se incorpora también un módulo de filtrado temporal para sacar partido a la considerable coherencia temporal presente en los datos, una redundancia que se origina en el hecho de que en la práctica, las tendencias en cuanto a actividad física suelen mantenerse estables a lo largo de cierto tiempo, sin fluctuaciones rápidas y repetitivas. El tercer bloque de esta tesis doctoral aborda el tema de las ingestas en el ámbito de la T1D. En concreto, se propone una serie de modelos compartimentales y se evalúan éstos en función de su capacidad para describir matemáticamente el efecto remoto de las concetraciones plasmáticas de insulina exógena sobre las tasas de eleiminación de la glucosa atribuible a la ingesta; un aspecto hasta ahora no incorporado en los principales modelos de paciente para T1D existentes en la literatura. Los datos aquí utilizados se obtuvieron gracias a un experimento realizado por el Institute of Metabolic Science (Universidad de Cambridge, Reino Unido) con 16 pacientes jóvenes. En el experimento, de tipo ‘clamp’ con objetivo variable, se replicaron los perfiles individuales de glucosa, según lo observado durante una visita preliminar tras la ingesta de una cena con o bien alta carga glucémica, o bien baja. Los seis modelos mecanísticos evaluados constaban de: a) submodelos de doble compartimento para las masas de trazadores de glucosa, b) un submodelo de único compartimento para reflejar el efecto remoto de la insulina, c) dos tipos de activación de este mismo efecto remoto (bien lineal, bien con un punto de corte), y d) diversas condiciones iniciales. ABSTRACT Diabetes encompasses a series of metabolic diseases characterized by abnormally high blood glucose concentrations. In the case of type 1 diabetes (T1D), this situation is caused by a total absence of endogenous insulin secretion, which impedes the use of glucose by most tissues. In these circumstances, exogenous insulin supplies are necessary to maintain patient’s life; although caution is always needed to avoid acute decays in glycaemia below safe levels. In addition to insulin administrations, meal intakes and physical activity are fundamental factors influencing glucose homoeostasis. Consequently, a successful management of T1D should incorporate these two physiological phenomena, based on an appropriate identification and modelling of these events and their corresponding effect on the glucose-insulin balance. In particular, artificial pancreas systems –designed to perform an automated control of patient’s glycaemia levels– may benefit from the integration of this type of information. The first part of this PhD thesis covers the characterization of the acute effect of physical activity on glucose profiles. With this aim, a systematic review of literature and metaanalyses are conduced to determine responses to various exercise modalities in patients with T1D, assessed via rates-of-change magnitudes to quantify temporal variations in glycaemia. On the other hand, a reliable identification of physical activity periods is an essential prerequisite to feed artificial pancreas systems with information concerning exercise in ambulatory, free-living conditions. For this reason, the second part of this thesis focuses on the proposal and evaluation of an automatic system devised to recognize physical activity, classifying its intensity level (light, moderate or vigorous) and for vigorous periods, identifying also its exercise modality (aerobic, mixed or resistance); since both aspects have a distinctive influence on the predominant metabolic pathway involved in fuelling exercise, and therefore, in the glycaemic responses in T1D. Various combinations of machine learning and pattern recognition techniques are applied on the fusion of multi-modal signal sources, namely: accelerometry and heart rate measurements, which describe both mechanical aspects of movement and the physiological response of the cardiovascular system to exercise. An additional temporal filtering module is incorporated after recognition in order to exploit the considerable temporal coherence (i.e. redundancy) present in data, which stems from the fact that in practice, physical activity trends are often maintained stable along time, instead of fluctuating rapid and repeatedly. The third block of this PhD thesis addresses meal intakes in the context of T1D. In particular, a number of compartmental models are proposed and compared in terms of their ability to describe mathematically the remote effect of exogenous plasma insulin concentrations on the disposal rates of meal-attributable glucose, an aspect which had not yet been incorporated to the prevailing T1D patient models in literature. Data were acquired in an experiment conduced at the Institute of Metabolic Science (University of Cambridge, UK) on 16 young patients. A variable-target glucose clamp replicated their individual glucose profiles, observed during a preliminary visit after ingesting either a high glycaemic-load or a low glycaemic-load evening meal. The six mechanistic models under evaluation here comprised: a) two-compartmental submodels for glucose tracer masses, b) a single-compartmental submodel for insulin’s remote effect, c) two types of activations for this remote effect (either linear or with a ‘cut-off’ point), and d) diverse forms of initial conditions.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

Design of artificial myocardial microtissues

Cultivation technologies promoting organization of mammalian cells in three dimensions are essential for gene-function analyses as well as drug testing and represent the first step toward the design of tissue replacements and bioartificial organs. Embedded in a three-dimensional environment, cells are expected to develop tissue-like higher order intercellular structures (cell-cell contacts, extracellular matrix) that orchestrate cellular functions including proliferation, differentiation, apoptosis, and angiogenesis with unmatched quality. We have refined the hanging drop cultivation technology to pioneer beating heart microtissues derived from pure primary rat and mouse cardiomyocyte cultures as well as mixed populations reflecting the cell type composition of rodent hearts. Phenotypic characterization combined with detailed analysis of muscle-specific cell traits, extracellular matrix components, as well as endogenous vascular endothelial growth factor (VEGF) expression profiles of heart microtissues revealed (1) a linear cell number-microtissue size correlation, (2) intermicrotissue superstructures, (3) retention of key cardiomyocyte-specific cell qualities, (4) a sophisticated extracellular matrix, and (5) a high degree of self-organization exemplified by the tendency of muscle structures to assemble at the periphery of these myocardial spheroids. Furthermore (6), myocardial spheroids support endogenous VEGF expression in a size-dependent manner that will likely promote vascularization of heart microtissues produced from defined cell mixtures as well as support connection to the host vascular system after implantation. As cardiomyocytes are known to be refractory to current transfection technologies we have designed lentivirus-based transduction strategies to lead the way for genetic engineering of myocardial microtissues in a clinical setting.

Predictive Analytics and Decision Support for Heart Failure patients

Thesis (Master's)--University of Washington, 2016-08