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Se presentan resultados obtenidos de la predicción de varios parámetros claves, como la velocidad de onda de pulso, relacionados a la salud cardiovascular utilizando un modelo unidimensional (1-D) como parte del entrenamiento de los modelos de aprendizaje automático, así como también la verificación de dichas predicciones utilizando señales reales obtenidas de un protocolo de medición.

PurposeIn times when digitized and blended learning paradigms are getting more profuse, the COVID-19 pandemic substantially changed the dynamics of this program, forcing all the courses to migrate to virtual modality. This study highlights the biological engineering courses at the University of the Republic (Universidad de la República) in Uruguay pertaining to the adaptations to virtual learning environments during the COVID-19 pandemic and analyzing its impact through the courses taught in the virtual setting.Design/methodology/approachGlobal education has seen a significant paradigm shift over the last few years, changing from a specialized approach to a broader transdisciplinary approach. Especially in life sciences, different fields of specializations have started to share a common space in the area of applied research and development. Based on this transdisciplinary approach, the Biological Engineering program was designed at the University of the Republic (Universidad de la República), Uruguay.FindingsThe new challenges posed by the virtual modality on the pedagogical areas like course design, teaching methodologies and evaluations and logistical aspects like laboratory-setting have sparked a considerable change in different aspects of the courses. However, despite the changes to virtual modality in this year, the student-performance showed an overall improvement compared to the last year.Originality/valueWith the changing direction of pedagogy and research in biological engineering across the world, it is quintessential to adapt university courses to the same, promoting an environment where the scientific and engineering disciplines merge and the learning methodologies lead to a dynamic and adaptive ubiquitous learning environment.

En estos últimos años se han implementado nuevas formas de pedagogía en el área de ingeniería, habida cuenta que los desafíos reales que enfrentará el futuro ingeniero deberán ser abordados de manera interdisciplinaria. Es por ello que desde los primeros años de su carrera y ya desde las ciencias básicas, es conveniente que el estudiante pueda enfrentar problemas procedentes de otros escenarios y así poder establecer puentes entre las distintas ciencias y potenciar los aportes de cada una de ellas. El objetivo principal del presente trabajo es describir el resultado de una experiencia interdisciplinar en la que se involucra al estudiante en la aplicación de conceptos matemáticos a una disciplina biológica. La experiencia se llevó a cabo con estudiantes cursantes de la asignatura Análisis Matemático I, correspondiente al primer año de las distintas carreras de Ingeniería de la Facultad Buenos Aires en la Universidad Tecnológica Nacional, a quienes se les presentó una actividad relacionada con el concepto de “ingeniería aplicada al modelado del sistema cardiovascular”. Para abordarla, 58 estudiantes debieron aplicar conceptos estudiados en la asignatura e incorporar aquellos ligados a la fisiología humana. Asimismo, participaron tanto en la recolección general de datos como en la asistencia para la adquisición de los mismos, que luego se analizaron en trabajos grupales. Se consideró una metodología descriptiva a partir de un cuestionario individual, con el objeto de completar la evaluación de la experiencia. Los resultados obtenidos evidenciaron un elevado interés por parte de los estudiantes en el abordaje de situaciones problemáticas, con un 89% de aprobación en relación a la importancia de establecer relaciones entre las distintas áreas de conocimiento. La actividad fue diseñada en el marco del proyecto de investigación y desarrollo: "Empleo de problemas interdisciplinarios en asignaturas de matemática en carreras de ingeniería" conjuntamente con el Grupo de Investigación y Desarrollo en Bioingeniería, perteneciente a la misma institución y en virtud de la experticia de sus integrantes en tópicos relacionados con la salud cardiovascular. Efectivamente, los resultados obtenidos demuestran que la implementación de estrategias interdisciplinares de enseñanza basadas en situaciones problemáticas propicia un incremento en la atención por parte del estudiante, una mayor participación y proporciona una aplicación directa de las herramientas matemáticas estudiadas.

The central axis of the work has been to empower the figure of Leonardo da Vinci 500 years after his death. With him, we wanted to infect our students with their tireless desire to know and learn. We plant the possibility of carrying out a project of educational innovation giving a push to the process of teaching and learning by making it more participatory, cooperative and interdisciplinary, as well as the desire to involve more the students encouraging their interest in learning and work. To honor Leonardo’s legacy and the spirit of the Renaissance, it is necessary to teach to stimulate the critical spirit and to encourage imagination and creativity. Leonardo da Vinci was a prolific hybrid innovator to unlock the secrets of human complexity for the first time in the history of mankind amalgamating Science, Engineering, Biology and Engineering; so, we define him as the first bioengineer of humanity.

INTRODUCTION: Global education has seen a paradigm shift in the recent years; especially in life sciences, specializations have started sharing common space in applied research and development. Extending the transdisciplinary approach to undergraduate programs, a case study on the introductory course of Biological Engineering program at University of the Republic (Universidad de la República) Uruguay is presented. OBJECTIVES: The COVID-19 pandemic has led to shifting the biological engineering course to virtual modality, changing the pedagogical dynamics. This study aims at analyzing the adaptation to the new model of virtual learning. METHODS: Different course metrics over time has been analyzed along with surveys on students and professors of the course. RESULTS: Despite several new challenges posed by the virtual modality, the overall student-performance didn't decline. CONCLUSION: The biological engineering course presents interesting contents especially in its course design and student engagements, remodeled especially during its virtual mode. Departamento de Ingeniería Biológica, Universidad de la República, Uruguay

INTRODUCTION: Arterial-ventricular coupling (AVC) has been recognized as a key determinant of global cardiovascular performance. Diastolic dysfunction (DD) occurs when inadequate filling of the ventricles is related to an abnormal elevation of intracardiac filling pressures. In some cases, DD is evidenced during cardiac stress, provoked by exercise. OBJECTIVE: To evaluate AVC in individuals with stress evidenced DD, in relation to controls. MATERIALS AND METHODS: Stress echocardiography was applied to assess cardiac function during exercise. Arterial-ventricular coupling was evaluated, based on the assessment of left ventricular and arterial elastances. RESULTS: AVC showed a significant difference at peak exercise compared to controls, basically due to a loss of cardiac contractility. CONCLUSION: The manifestation of AVC coupling imbalance could act as a complementary parameter to support the diagnosis of DD.

El objetivo de este trabajo fue analizar en el plano Entropía-Complejidad (HxC) un conjunto de series de tiempo provenientes de ECG de seres humanos con el objetivo de discriminar registros de dos grupos diferentes de pacientes: los de ritmo sinusal normal, y los de arritmias cardíacas.Las series de tiempo utilizadas en este artículo se obtuvieron de la base de registros de ECG de PhysioNet. El set estuco compuesto por 47 señales de pacientes con arritmias cardíacas diagnosticadas y 18 señales de pacientes con ritmo sinusal normal.El plano HxC se construyó con la entropía de Shannon como eje X, y con la complejidad estadística como eje Y. Dado que el contenido de información de las series de tiempo se transmite en forma de una función de distribución de probabilidad, FDP, ésta se obtuvo por medio de la metodología propuesta por Bandt & Pompe (2002).Los valores medios de complejidad estadística y entropía de Shannon normalizada se calcularon y analizaron en el plano HxC para cada serie de tiempo, donde los valores medios de complejidad de las series de tiempo de ECG de pacientes con arritmias diagnosticadas fueron mayores que el grupo de ritmo sinusal normal. Por otro lado, los valores medios de la entropía de Shannon para los pacientes con arritmias fueron más bajos que los del grupo de ritmo sinusal normal. Estas notorias características hicieron posible la discriminación de los diferentes espacios para ambos grupos de señales en el plano HxC. Los resultados se analizaron mediante una hipótesis de prueba estadística multivariada.La metodología propuesta tiene una notable simplicidad conceptual, velocidad computacional, y robustez al ruido, mostrando además una promisoria eficacia en la detección de patologías cardiovasculares.

Los últimos años han visto un cambio fenomenal en los paradigmas de atención médica y el análisis de datos con inteligencia computacional ha sido un jugador clave en este campo. Uno de los principales objetivos de incorporar inteligencia computacional en el análisis de la salud es obtener una mejor comprensión de los pacientes y ofrecer un tratamiento más eficiente. Este estudio se basa en pacientes de trasplante hepático bajo el Programa Nacional de Trasplante Hepático de Uruguay, considerando en detalle los parámetros de salud de los pacientes. El objetivo principal se constituyó en aplicar la inteligencia computacional para separar la población estudiada en grupos, facilitando así analizar eficientemente los grupos de riesgo de los pacientes evaluados bajo el programa de trasplante hepático con respecto a sus parámetros de salud correspondientes, en una perspectiva predictiva previa al trasplante. En este aspecto, se aplicaron algoritmos de agrupamiento en la población considerando sus datos de salud y se analizaron los grupos obtenidos. Los grupos mostraron propiedades distintivas, mostrando que un grupo tiene un riesgo más alto que el otro. Además, esto marca la base de los sistemas de soporte a la decisión clínica (clinical decision support system) en el trasplante hepático, que actúan como una herramienta de asistencia para el personal médico para obtener una visión más profunda de la salud del paciente y gracias a la visualización holística del escenario de la atención médica, también ayudan a elegir una estrategia de tratamiento eficiente y personalizada.

Recent years have seen a phenomenal change in healthcare paradigms and data analytics clubbed with computational intelligence has been a key player in this field. One of the main objectives of incorporating computational intelligence in healthcare analytics is to obtain better insights about the patients and proffer more efficient treatment. This work is based on liver transplant patients under the National Liver Transplant Program of Uruguay, considering in detail the health parameters of the patients. Applying computational intelligence helped to separate the cohort into clusters, thereby facilitating the efficient risk-group analysis of the patients assessed under the liver transplantation program with respect to their corresponding health parameters, in a predictive pre-transplant perspective. Also, this marks the foundation of Clinical Decision Support Systems in liver transplantation, which act as an assistive tool for the medical personnel in getting a deeper insight to patient health data and thanks to the holistic visualization of the healthcare scenario, also help in choosing a more efficient and personalized treatment strategy. Agencia Nacional de Investigación e Innovación (ANII), Uruguay Universidad Tecnológica Nacional, Buenos Aires, Argentina Universidad de la República, Uruguay Dirección Nacional de Sanidad de la Fuerzas Armadas, Montevideo, Uruguay

Introduction: Laser Doppler flowmetry (LDF) provides a noninvasive real-time quantification of relative changes in cutaneous perfusion. Assessment of the microvascular function can provide information on the development of various cardiometabolic disorders. In recent years, non-invasive measurements have gain attention, due to the potential complications associated to invasive procedures. Objective: To differentiate the microvascular function between subjects with cardiometabolic disorders and healthy controls. Methods: Evaluation through LDF during post-occlusive reactive hyperemia (PORH) in patients with or without history of cardiometabolic disorders. Results: Peak values of microvascular flow during post-occlusive reactive hyperemia and both upload and recovery slopes were lesser in the pathological group. Conclusion: A novel approach to characterize LDF during PORH was useful to differentiate patients with cardiometabolic disorders from healthy subjects.

Healthcare paradigms have always focused into the domain of early prediction of diseases. Especially in field of chronic diseases, the spotlight is always on the aspect of early detection and prevention by controlling the key risk factors in a comprehensive and integrated manner. In this endeavor the colossal power of health data comes into consideration; clubbed with the advanced techniques of computational intelligence to harvest the health data in the best possible way, the aim lies at the prediction of risks or deciphering interesting patterns and early signs of the diseases hidden in the health data. The output obtained from the intelligent analysis of the health data provides seminal insights to the design of more efficient treatment strategies. This work highlights some aspects of artificial intelligence in healthcare, illustrating through a case study of liver transplantation program, where the patient cohort could be interestingly separated into contrasting groups in a pretransplant scenario using machine learning, evincing a relationship with their respective posttransplant risks. In addition to relating the risk groups before liver transplantation with cardiometabolic risks through vascular age, this study accentuates the foundation of Clinical Decision Support System in transplantations, an assistive tool for the medical personnel to computationally analyze and visualize the comprehensive health situation of patients from the perspective of risks. Agencia Nacional de Investigación e Innovación (ANII), Uruguay Universidad Tecnológica Nacional, Buenos Aires, Argentina Universidad de la República, Uruguay

To enhance the efficiency of patient-specific risk stratification and diagnosis, an assessment of arterial structural and functional changes associated to a vascular disease in both early and advanced stages have been proposed, with the objective of limiting the progression or revert vascular alterations. In this connection, an interdisciplinary international partnership made up by research institutions from France, Argentina, Uruguay and Spain was established, with the objective of contributing to the evaluation and follow-up of factors involved in the physiopathology of cardiometabolic diseases and human aging. Several studies, such as the effect of hypertension in large arteries, alterations in arterial wall viscosity, stiffness and inertia, endothelial function and vascular reactivity, cardiovascular risk improvement, vascular age assessment and cryografts vascular response evaluation were carried out as a result of this international collaboration during the last twenty-five years.

The aim of this work was to analyze in the Entropy–Complexity plane (HxC) time series coming from ECG, with the objective to discriminate recordings from two different groups of patients: normal sinus rhythm and cardiac arrhythmias. The HxC plane used in this study was constituted by Shannon’s Entropy as one of its axes, and the other was composed using statistical complexity. To compute the entropy, the probability distribution function (PDF) of the observed data was obtained using the methodology proposed by Bandt and Pompe (2002). The database used in the present study was the ECG recordings obtained from PhysioNet, 47 long-term signals of patients with diagnosed cardiac arrhythmias and 18 long-term signals from normal sinus rhythm patients were processed. Average values of statistical complexity and normalized Shannon entropy were calculated and analyzed in the HxC plane for each time series. The average values of complexity of ECG for patients with diagnosed arrhythmias were bigger than normal sinus rhythm group. On the other hand, the Shannon entropy average values for arrhythmias patients were lower than the normal sinus rhythm group. This characteristic made it possible to discriminate the position of both signals’ groups in the HxC plane. The results were analyzed through a multivariate statistical test hypothesis. The methodology proposed has a remarkable conceptual simplicity, and shows a promising efficiency in the detection of cardiovascular pathologies.

We are really interested in improving engineering education by applying an original paradigm. For this reason, the undergraduate course of Signal and Systems was selected as our first field of work, to begin the implementation of this new idea and their promotion within our university community. In this regard, the “active learning approach” (AL) is made up of a series of tools that allow the students to become “actively” involved in the course, getting engaged in the activities proposed to enhance their skills rather than trying to assimilate unilaterally imparted information. The idea is to fight against that sort of teaching which does not take into account that each student learns in a singular way and at a specific pace, and not necessarily in a rigid division of levels. We attempt to foster an active discussion about motivation and commitment of the engineering student, analyzing their performance in challenges that stimulate their problem-solving capacity and that are not based on traditional activities.

Aims: To analyze the early vascular aging (EVA) in end-stage renal disease (ESRD) patients, attempting to determine a potential association between EVA and the etiology of ESRD, and to investigate the association of hemodialysis and EVA in ESRD patients during a 5-year follow-up period. Methods: Carotid-femoral pulse wave velocity (cfPWV) was obtained in 151 chronically hemodialyzed patients (CHP) and 283 control subjects, and in 25 CHP, who were followed-up after a 5-year lapse. Results: cfPWV increased in ESRD patients compared to control subjects. The cfPWV-age relationship was found to have a steeper increase in ESRD patients. The highest cfPWV and EVA values were observed in patients with diabetic nephropathy. Regression analysis demonstrated a significant reduction of the EVA in HD patients on a 5-year follow-up. Conclusion: Patients in ESRD showed higher levels of EVA. cfPWV and EVA differed in ESRD patients depending on their renal failure etiology. CHP showed an EVA reduction after a 5-year follow-up period.

Nowadays the continuous growing in global population and the related increase of life expectancy lead to explore new ways of making the most of the limited resources humanity has. This endeavor challenges especially the current health care of elderly population, which is particularly associated with a marked prevalence of chronic neurological disorders such as Parkinson’s Disease. Internet of Things and wearable technologies have opened up a new revolution in the domain of healthcare. Minimizing the response time in diagnosis and treatment, Internet of Things thrives towards omnipresence of the healthcare services. Using wearable devices, the lifestyle data is collected from multifarious sources, which is then accumulated, analyzed and acted upon. The emerging technological area of Wearable Sensors and the Internet of Things seems to provide a smart and intelligent way of catering ubiquitous healthcare services to the elderly population, taking healthcare facilities to a higher dimension of omnipresence.