Despite the increasing sophistication of cardiovascular modeling techniques, a significant gap persists between advanced hemodynamic simulations and clinically accessible measurements. In this work, we propose a measurement-augmented Neural Ordinary Differential Equation (Neural ODE) framework that reconstructs time-resolved arterial waveforms from a reduced set of non-invasive scalar clinical variables, including systolic and diastolic blood pressure (SBP/DBP), heart rate (HR), pulse wave velocity (PWV), age, and ascending aortic diameter. The clinically measurable scalar vector constitutes the primary measurement reference of the framework. The Pulse Wave Database (PWDB) is used exclusively as a controlled in-silico validation environment to evaluate reconstruction consistency under known parameter conditions. Arterial wave propagation is formulated as a continuous-time dynamical process using a Neural Ordinary Differential Equation approach, enabling the reconstruction of arterial pressure, flow velocity, and luminal area signals from non-invasive measurements. Physiologically meaningful parameters—such as heart rate, arterial compliance, peripheral resistance, and pulse wave velocity—are embedded within the model to constrain learning and improve robustness across individuals. The framework is trained on arterial waveform data and employs adjoint sensitivity methods to efficiently estimate model parameters. Additionally, the same input vector enables clustering-based vascular phenotype stratification. The proposed framework acts as a measurement augmentation tool, bridging clinically accessible scalar measurements and time-resolved arterial dynamics, with potential applications in personalized cardiovascular assessment.

Introducción: Se han descripto varios parámetros de medición del estado de la función arterial y, de ellos, la velocidad de la onda de pulso (VOP) es el único que permite una medición directa de la rigidez arterial.A medida que aumenta la edad del individuo se va produciendo una pérdida de la capacidad elástica, conocida como envejecimiento vascular, proceso este que estaría acelerado en los pacientes con hipertensión arterial.Objetivos: Normalizar por décadas los valores de la VOP en pacientes normotensos (NT), hipertensos esenciales (HT) e hipertensos limítrofes (LT) y diferenciar los efectos del envejecimiento sobre la VOP de los vinculados a la hipertensión arterial.Material y métodos: Se estudiaron 221 pacientes consecutivos, de ambos sexos, que fueron clasificados, por sus valores de presión arterial (PA), en tres grupos: NT (n = 120, 46 ± 13 años): PA < 135/85 mm Hg; HT (n = 60, 50 ± 13 años): PA > 140/90 mm Hg; y LT (n = 41, 47 ± 12 años): PA = 135-139/85-89 mm Hg.Posteriormente fueron divididos en cuatro grupos por edades: GI ≤ 39 años, GII = 40-49 años, GIII = 50-59 años y GIV > 60 años.La VOP se midió con transductores mecanográficos y cálculo computarizado.Se utilizaron las pruebas de ANOVA, de Newman-Keuls y de regresión lineal multivariada.Resultados: La VOP aumentó significativamente (p < 0,05) con la edad en todos los grupos etarios (m/seg): GI: NT (n = 42): 8,6 ± 1,1, HT (n = 16): 9,5 ± 1,3, LT (n = 10): 9,0 ± 0,5; GII: NT (n = 24): 9,5 ± 1,2, HT (n = 16): 10,7 ± 1,2, LT (n = 14): 9,8 ± 0,8; GIII: NT (n = 30): 10,3 ± 1,5, HT (n = 12): 12,1 ± 1,5, LT (n = 11): 11,0 ± 1,3; y GIV: NT (n = 24): 11,4 ± 1,8, HT (n = 16): 14,1 ± 2,4, LT (n = 6): 13,3 ± 1,1.Las ecuaciones de regresión halladas fueron: en NT, VOP = 0,08 × edad + 0,04 × presión arterial sistólica (PAS) + 1,07 (r = 0,71); en HT, VOP = 0,12 × edad + 0,06 × PAS -2,51 (r = 0,81); y en LT, VOP = 0,10 × edad + 0,02 × PAS + 0,02 (r = 0,73) (p < 0,05).Conclusiones: La VOP aumentó con la edad, siendo el incremento mayor en HT para cada grupo etario.Los sujetos LT tuvieron valores intermedios entre los otros dos grupos.Ello sugiere un deterioro de la función arterial adicional inducido por la hipertensión arterial sobre el envejecimiento.Este efecto adicional puede estimarse con la ecuación de regresión obtenida para cada grupo.

Early detection of subclinical vascular alterations is critical for effective cardiovascular risk stratification and prevention. Arterial stiffness (AS), a hallmark of vascular aging and hypertension, is a strong, independent predictor of cardiovascular morbidity and mortality. While carotid-femoral Pulse Wave Velocity (cfPWV) is the non-invasive gold standard for AS assessment, it requires skilled operators and involves some procedural risks (e.g. plaque disturbance), limiting its broader clinical and ambulatory application. Optical timing-based approaches offer safer and more practical alternatives. This study presents a comprehensive evaluation of Pulse Arrival Time (PAT) at five peripheral arterial sites—carotid, temporal, radial, digital, and tibial—to explore their potential for non-invasive vascular assessment of AS using photoplethysmography waveforms (PPGW). Three complementary aims are addressed: assessing the feasibility of acquiring PAT at these sites using PPGW, conducting an in-vivo study to assess the reliability of using PPGW and electrocardiographic (ECG) signals acquired from a demographically diverse cohort, and comparing obtained values and estimating age-related changes in estimated PAT from a validated in-silico database. The temporal arterial consistently demonstrated favorable characteristics, including high-quality signals and clear associations with age and disease status. These findings position the temporal site as a particularly favorable candidate for the development of non-invasive diagnostic tools for early vascular aging detection, broader cardiovascular risk screening, and the expansion of the cerebrovascular assessment frontier.

The analysis of the left ventricular (LV) Pressure-Volume (PV) loop provides valuable information about the cardiac energetics, particularly regarding how ventricular-arterial coupling defines ventricular performance in physiological and pathophysiological states. In a recent work, the simultaneous interaction between LV and Systemic Arterial Tree (SAT) PV-loops was exposed, as a novel framework of arterial-ventricular interaction. The enclosed area of the SAT PV-loop is associated with an energy dissipating phenomenon (not all the work done by pressure is recovered), due to the viscous role of smooth muscle vasomotor tone throughout the entire vascular network. Since it was demonstrated that energy dissipation during the conversion of cardiac pulsatile energy into arterial elastic energy is enhanced in trained athletes, as the higher cardiac pulsatile energy induced by training leads to functional and histological adaptations, the main objective of this work was to obtain a ‘global arterial viscous modulus’ from the analysis of SAT PV-loop, through the application of computational lumped (0D) and distributed (1D) blood flow models.

This study introduces the cardiac hemodynamic status (CHS), an interpretable index combining cardiac output, systemic vascular resistance, and arterial compliance into a single polar representation. CHS separates arterial stiffness orientation from overall hemodynamic load. The angle (CHS θ ) discriminates healthy versus pathological states (AUC = 0.866), whereas the magnitude reflects flow-resistance balance. This Windkessel-consistent index offers a practical tool for cardiovascular screening and longitudinal monitoring, with potential for bedside estimation from common biosignals.

Assessing aortic stiffness (AS) is key for early cardiovascular disease detection, as it reflects arterial wall mechanics and predicts adverse events. Carotidfemoral Pulse Wave Velocity (cfPWV) is the gold standard for non-invasive AS assessment, while Central Aortic Systolic Pressure (CASP) provides complementary information on cardiac afterload. This study explores the predictive value of Pulse Transit Time (PTT) measured at five peripheral sites (carotid, temporal, radial, digital, tibial) using a synthetic in-silico dataset. The temporal site showed the highest correlation with cfPWV and CASP and was selected for feature extraction from its photoplethysmography waveform. A fully-connected neural network was trained to estimate both cfPWV and CASP from these features. On a held-out test set, the model achieved high accuracy for cfPWV (R ^2 = 0.99, MAE = 0.10 m/s, RMSE = 0.13 m/s) and for CASP (R ^3 = 0.84, MAE = 2.89 mmHg, RMSE = 3.78 mmHg). These results highlight the diagnostic relevance of the temporal artery and support its use in non-invasive cardiovascular monitoring and potential cerebrovascular applications.

Preparticipation cardiovascular examination (PPCE) aims to prevent sudden cardiac death (SCD) by identifying athletes with structural or electrical cardiac abnormalities. Anthropometric measurements, such as waist circumference, limb lengths, and torso proportions to detect Marfan syndrome, can indicate elevated cardiovascular risk. Traditional manual methods are labor-intensive, operator-dependent, and challenging to scale. We present a fully automated deep-learning approach to estimate five key anthropometric measurements from 2D synthetic human body images. Using a dataset of 100,000 images derived from 3D body meshes, we trained and evaluated VGG19, ResNet50, and DenseNet121 with fully connected layers for regression. All models achieved sub-centimeter accuracy, with ResNet50 performing best, achieving a mean MAE of 0.668 cm across all measurements. Our results demonstrate that deep learning can deliver accurate anthropometric data at scale, offering a practical tool to complement athlete screening protocols. Future work will validate the models on real-world images to extend applicability.

Urethral reconstruction is crucial for preserving urinary function and overall quality of life. Advances in medical technology and surgical techniques have expanded treatment options, enabling personalized approaches, such as tissue engineering, grafting procedures, endoscopic interventions, and open surgery. This study investigates the fabrication of biomimetic scaffolds based on poly(epsilon-caprolactone) (PCL), poly(ethylene oxide) (PEO), and hyaluronic acid (HA) for potential use in urethral reconstruction. Tubular scaffolds comprised PCL nanofibers, co-electrospun PCL/HA-PEO fibers, and porous PCL scaffolds infiltrated with HA. Scanning electron microscopy revealed homogeneous, randomly oriented, defect-free fibers across all scaffold types. FTIR-ATR analysis confirmed the characteristic PCL bands, with HA-related signals detected after infiltration. In vitro studies demonstrated cytocompatibility, supporting successful fibroblast culture on the constructs. Additionally, mechanical properties were evaluated using a continuous circulation biodynamic simulator. Notably, the pressure-diameter profiles mimicked a biological response, with the compliance and elasticity of PCL and hybrid scaffolds closely approximating those of the natural human urethra.

Arterial network topologies exhibit a hierarchical and self-similar organization, characterized by progressive changes in geometry and stiffness that define an arterial stiffness gradient (ASG). Aging and vascular diseases—such as hypertension and atherosclerosis—can disrupt the ASG, thereby altering hemodynamic behavior. This study investigates the impact of ASG alterations on arterial pressure waveform (APW) morphology using a generic one-dimensional hierarchical vascular model. The network incorporates variations in the constitutive arterial parameters to simulate both healthy and pathological conditions. Results demonstrate that changes in ASG significantly affect APW across arterial levels, highlighting the interplay between vascular structure and pressure dynamics. Given the clinical accessibility of APW at peripheral sites (e.g., carotid, radial), these findings support the utility of waveform-derived metrics for vascular assessment and monitoring.

This review examines the figure of Dr. René Favaloro, a pioneer in cardiovascular surgery and advocate for social justice, who devoted his life to making advanced medical care accessible to underserved communities. Despite the increasing incidence of coronary artery disease (CAD), particularly in Asian and Latin American countries, Favaloro envisioned a healthcare system where innovative technology benefits everyone. Building on his ideals, we explore the democratization of healthcare access through innovative tools for cardiovascular risk assessment, specifically Pulse Wave Velocity (PWV) and its association with Coronary Artery Calcium Score (CACs). PWV, a non-invasive and cost-effective method, shows promise as a practical screening tool for CAD, particularly when combined with Computational Intelligence (CI) and the Internet of Medical Things (IoMT). The integration of PWV into a Point-of-Care Testing (POCT) framework could enhance preventive care, especially in underserved populations. By aligning with Favaloro's vision of equitable healthcare, this approach seeks to support CAD screening and risk assessment in low-resource settings, aiming to overcome socio-economic barriers and improve access to preventive cardiac care.

Objective.Understanding cardiac hemodynamic status (CHS) is essential for accurate cardiovascular health assessment, as it is governed by key parameters such as cardiac output (CO), systemic vascular resistance (SVR), and arterial compliance (AC). This study aims to develop a non-invasive method using digital photoplethysmography (PPGD) signals and deep learning techniques to predict these biomarkers for a comprehensive CHS evaluation.Approach.A dataset of 4374 virtual subjects was used. Nonlinear features were extracted from PPGD signals to capture their inherent complexity and irregularity. A parallel convolutional neural network (PCNN) was implemented to process both raw signals and nonlinear features concurrently. Model performance was evaluated usingR2, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE).Main results.The PCNN demonstrated satisfactory predictive performance withR2, RMSE, MSE, and MAE values of 0.872, 0.086, 0.008, and 0.068 for CO; 0.851, 0.074, 0.006, and 0.058 for SVR; and 0.938, 0.049, 0.003, and 0.038 for AC. The proposed PCNN-based method offers a novel, non-invasive approach for predicting key cardiovascular biomarkers, providing an accurate CHS assessment.Significance.This method advances non-invasive cardiovascular diagnostics by combining PPGD signals and deep learning. Future work will focus on validating this findings in real-world settings for improved clinical applicability.

Cardiovascular system parameters in general provide information linked to normal physiological functioning and can be used to predict the singularities of diseases. In general, electrical recordings were studied, and in particular, biomechanical parameters related to the integrated cardio-respiratory-vascular system. Computational analysis methods and tools were applied to a group of signals acquired simultaneously invasively as a collection of random variables, applying computational processing and subsequent analysis of variability in blood pressure, aortic diameter, respiration, and heart rate. In addition, power spectral densities in the systemic vascular network and their probability distributions over time were examined.

Pressure-Volume (PV) loops are commonly utilized for left ventricular cardiac assessment, and have been extensively studied. However, the PV relationship in the systemic arterial system (SAS) domain remains poorly understood. The main objective of this work was to evaluate the SAS PV-loops dynamic behavior, in terms of the distributed nature of SAS and the impact of vascular smooth muscle tone (VSMT). A One-dimensional (1D) model of arterial vasculature was used to SAS PV-loop assessment. The results were analyzed in terms of those obtained from the generalization of the Windkessel model, together with an increase of arterial wall viscosity (VSMT augmentation). A similar behavior was observed between the two approaches, indicating the consistency of the 1D model in revealing the morphology of SAS PV-loop. Additionally, the enclosed area of the SAS PV-loop was influenced by increased VSMT, with an elevation of aortic pulse wave velocity. The use of a complete human vascular 1D model allowed the assessment of the SAS PV-loop, enabling the study of its dynamic nature as well as its relationship with SAS energy dissipation.

Since its beginnings, cardiovascular (CV) medicine has relied on professionals' judgment to diagnose various types of studies carried out on patients. Today, with the support of technologies such as machine learning (ML) and artificial neural networks, medical professionals can automate, speed up or improve their diagnoses by obtaining complementary information on the detection of diseases and CV conditions. This work proposes an online platform based on ML methods, applied to the analysis of echocardiographic recordings. The aim is to obtain dynamic measurements of an individual left ventricle behavior, and contributing to the detection of different types of heart conditions, thereby improving diagnostic precision.

René Gerónimo Favaloro was a creator of cardiovascular bypass surgery, for which he has received little recognition and about whom little has been written or internationally acknowledged [1] . He was also a rural doctor, a committed citizen, and a teacher, researcher, and historian [2] . He was known not only for his scientific brilliance, but also for fighting for equality and social justice and for his commitment to education ( Figure 1 ). This article was written to celebrate the 100th anniversary of Favaloro’s birth and his legacy in education.

Aging contributes as a major nonreversible risk factor for cardiovascular disease. This underscores the emergence of Vascular Age (VA) as a promising alternative metric to evaluate an individual’s cardiovascular risk and overall health. This study explores the use of a Convolutional Neural Network to estimate the VA group, as a surrogate of chronological age, utilizing Recurrence Plot as a robust tool for feature enhancement and image visualization from the Arterial Pulse Waveform (APW). The APW was obtained from an in-silico database of a one-dimensional cardiovascular model. The CNN demonstrated a robust performance, achieving an accuracy of 83% and 81.3%, an F1-score of 83.3% and 81.7% and an AUC of 0.96 and 0.95 for training and testing respectively. These findings may have potential implications for clinical applications, offering a noninvasive approach to cardiovascular risk assessment. The results contribute to the ongoing dialogue in cardiovascular research, highlighting the potential for innovative methodologies to enhance patient care and health outcomes. Further research will be essential to validate these methods for applications in real-world healthcare scenarios.

Introduction: The left ventricle (LV) serves as the primary source of mechanical energy in circulation, subsequently converted into kinetic energy throughout the arterial system (AS). Analyzing the specific area within an LV pressure-volume (PV) loop helps characterize the function of the cardiac pump. Simultaneously, the AS functions as a blood reservoir, receiving the blood ejected by the LV. The enclosed area in the AS PV-loop is linked to an energy dissipation process (where not all pressure work is regained), attributed to the viscous function of smooth muscle cells. Objective: The main objective of this work was to investigate wall energy dissipation in the vascular bed in terms of LV and AS PV-loop evaluation, to determine if different types of training could lead to differentiated levels of wall energy dissipation. The ‘coupling concept’ is proposed to be conceived from LV and AS loop energy interaction, quantified by a ‘ventricular-arterial damping factor’ (VADF). Material and Methods: Data from subjects with different kinds of training (soccer players and ballet dancers) were collected noninvasively and compared with a control group of untrained individuals to analyze the differentiating characteristics of the subjects, especially in terms of Stroke Work Dissipation (WDIS). To this end, a lumped parameters Windkessel (WK) model was proposed for the assessment of LV and AS loops, through an interactive process. Changes in wall energy dissipation were observed under training routines. Both soccer players and ballet dancers showed increased WDIS and VADF compared to the untrained individuals (p<0.05). However, elastic work (WEL), defined as the difference between LV stroke work and WDIS, was found to remain constant in ballet dancers, unlike in soccer players. Conclusion: The WK model enabled the simulation of the interaction between LV and AS based on a ventricular-arterial coupling framework. The findings suggest that higher WDIS values, together with the preservation of WEL, may indicate an enhanced protective effect by vascular smooth muscle cells and other wall components, involved in the AS dissipation phenomenon.

It is well known that many people experience cardiovascular events in their daily lives. However, few people are aware of the appropriate techniques or procedures to provide adequate assistance. This paper describes the development of 'RCP Ya!', a mobile application designed to guide people with no knowledge of the techniques or procedures to manage the situation in such cases. It also has the particularity of having a network of trained volunteers, who are alerted immediately, so that they can go to the event (or communicate with the user) to provide additional support.

El envejecimiento es un factor de riesgo no reversible importante para las enfermedades cardiovasculares. Esto subraya la importancia emergente de la Edad Vascular (EV) como una métrica alternativa prometedora para evaluar el riesgo cardiovascular y la salud general de un individuo. Este estudio explora el uso de una Red Neuronal Convolucional (CNN) para estimar el grupo de EV, como un sustituto de la edad cronológica, utilizando el Diagrama de Recurrencia como una herramienta robusta para la mejora de características y la visualización de imágenes de la Onda de Pulso Arterial (OPA). La OPA se obtuvo de una base de datos in-silico de un modelo cardiovascular unidimensional. La CNN demostró un rendimiento robusto, logrando una precisión del 83% y 81.3%, un F1-Score de 83.3% y 81.7% y un AUC de 0.96 y 0.95 para el entrenamiento y la evaluación respectivamente. Estos hallazgos pueden tener implicaciones potenciales para aplicaciones clínicas, ofreciendo un enfoque no invasivo para la evaluación del riesgo cardiovascular. Los resultados contribuyen al diálogo en curso en la investigación cardiovascular, destacando el potencial de metodologías innovadoras para mejorar la atención al paciente y los resultados de salud. Se necesitará más investigación para validar estos métodos para aplicaciones en escenarios de atención médica del mundo real.

The Generalized Transfer Function (GTF), defined as the harmonic relationship between peripheral blood pressure and aortic pressure, is typically utilized for the non-invasive estimation of Central Aortic Pressure (CAP). Accordingly, one-dimensional (1D) models have gained significant prominence in recent years due to their versatility in accurately reproducing the behavior of arterial waveforms and their low computational cost. On this basis, GTF morphology and its potential variations with age were studied, in terms of a set of digitally simulated individuals. Although the obtained GTFs were in agreement with published real measurements, the functions corresponding to age groups younger than 40 years behaved differently than those of older groups. This could indicate the convenience of considering differentiated GTFs functions in terms of the subject age (a 'patient specific' approach).