An unresolved issue in patient-specific models of cardiac mechanics is the choice of an appropriate constitutive law, able to accurately capture the passive behavior of the myocardium, while still having uniquely identifiable parameters tunable from available clinical data. In this paper, we aim to facilitate this choice by examining the practical identifiability and model fidelity of constitutive laws often used in cardiac mechanics. Our analysis focuses on the use of novel 3D tagged MRI, providing detailed displacement information in three dimensions. The practical identifiability of each law is examined by generating synthetic 3D tags from in silico simulations, allowing mapping of the objective function landscape over parameter space and comparison of minimizing parameter values with original ground truth values. Model fidelity was tested by comparing these laws with the more complex transversely isotropic Guccione law, by characterizing their passive end-diastolic pressure-volume relation behavior, as well as by considering the in vivo case of a healthy volunteer. These results show that a reduced form of the Holzapfel-Ogden law provides the best balance between identifiability and model fidelity across the tests considered.
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http://dx.doi.org/10.1007/s10237-014-0638-9 | DOI Listing |
Sensors (Basel)
January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
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December 2024
Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.
Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.
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December 2024
KLEEMANN Group, 61100 Kilkis, Greece.
Timely damage detection on a mechanical system can prevent the appearance of catastrophic damage in it, as well as allow for better scheduling of its maintenance and repair process. For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Vascular Surgery, Cardio-Thoracic and Vascular Department, University-Hospital of Parma, 43126 Parma, Italy.
This study aims to develop and validate a standardized methodology for creating high-fidelity, custom-made, patient-specific 3D-printed vascular models that serve as tools for preoperative planning and training in the endovascular treatment of peripheral artery disease (PAD). Ten custom-made 3D-printed vascular models were produced using computed tomography angiography (CTA) scans of ten patients diagnosed with PAD. CTA images were analyzed using Syngo.
View Article and Find Full Text PDFEur J Pediatr
January 2025
Section of Hygiene, University Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Unlabelled: Effective leadership is essential in neonatal intensive care units (NICUs), where complex, high-stakes environments require coordinated multidisciplinary teamwork. Strong leadership improves clinical outcomes, team performance, and staff well-being. This systematic review assesses various leadership models and interventions in NICUs to identify best practices and areas for future research.
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