Annals of Biomedical Engineering最新文献

Introduction

Personalized in silico models offer great potential for improving our understanding of atrial arrhythmia mechanisms and for testing therapeutic strategies. However, the level of complexity that can be achieved in these models is often limited by the availability of clinical data. Therefore, evaluating how variations in model detail affect electrophysiological simulation outcomes is essential for evaluating how accurate these models need to be.

Purpose

To evaluate how substrate conditions (electrical remodeling and diffusion) and the inclusion of fiber orientation and ionic heterogeneities provide significant differences in simulation outcomes.

Methods

N = 320 simulations were performed in a realistic 3D model of the human atria considering different conditions: the presence and absence of fiber orientation, regional ionic heterogeneities and different levels of electrical remodeling and electrical diffusion, simulating disease progression. Their arrhythmic behavior was evaluated as the percentage of cases that sustained atrial arrhythmias and the classification of these according to their type as either functional or anatomical.

Results

Fiber orientation and electrical diffusion were the parameters that most strongly influenced the simulation outcomes. Including fiber orientation significantly altered both the incidence (44% vs. 67%, p = 0.02) and type of reentries (31% vs. 57% functional reentries, p = 0.055). In terms of electrical diffusion, reduced conduction velocity led to a significantly higher arrhythmogenicity (83% vs. 33%, p = 0.008) and an increased proportion of functional reentries (59% vs. 29%, p = 0.08). Electrical remodeling and ionic heterogeneities did not provide statistically significant differences. However, ionic heterogeneities proved to play an important role in generating different proarrhythmic patterns.

Conclusion

Fiber orientation and electrical diffusion play a relevant role in personalized simulations, since these parameters produced significant effects in simulation behavior. Therefore, they should be considered for personalized simulations. Electrical remodeling and tissue heterogeneity have a minor impact in terms of arrhythmia inducibility, but they should be considered to evaluate specific mechanisms and/or target regions in personalized simulations.

Purpose

Neonatal jaundice is a common condition that can lead to serious complications if left untreated. Although phototherapy is the standard treatment, existing devices are often costly, energy-intensive and fail to deliver the required intensity or provide uniform irradiance, particularly in low-resource settings. This study aims to design and optimize a low-cost, energy-efficient phototherapy bed that meets clinical requirements for irradiance intensity and spatial uniformity.

Methods

A multi-objective photometric optimization approach was applied to design the phototherapy bed, focusing on balancing irradiance intensity, glare control, and dark spot avoidance. Simulations were performed using a Lambertian emission model to determine the ideal LED configuration. Different LED grid layouts with varying LED spacing were simulated. The optical properties of all materials, including the LEDs, diffuser, bed flat, and mattress, were tested. The device design and assembly are described.

Results

The optimal LED configuration, a hexagonal grid with approximately 9 cm spacing, yielded the best performance. The measurement verification for the Lambertian emission model showed strong agreement with the simulation results. Prototype testing achieved a mean irradiance of 44.78 μW/cm²/nm with a relative standard deviation of 0.36. The diffuser and mattress positively influenced irradiance by improving uniformity, avoiding dark spots, and reducing glare.

Conclusions

This system, with a material cost of approximately $80, offers an affordable and clinically effective solution for neonatal jaundice therapy in low-resource settings. It supports home treatment and promotes mother-infant bonding. The proposed optimization methodology can serve as a design reference for future light-based therapeutic systems.

Graphical Abstract

Purpose

Bicuspid aortic valve (BAV), the most common congenital heart defect, affects ~ 2% of the population but accounts for nearly half of aortic stenosis (AS) cases, typically emerging 10–20 years earlier than in tricuspid aortic valve (TAV) patients. Current tissue-based transcatheter aortic valve replacement (TAVR) devices are designed for TAV anatomy and merely approved for BAV, leading to poor annular fit, paravalvular leak, thrombosis, and limited durability—especially problematic for younger BAV patients. This study presents PolyV-B, a first-of-its-kind polymeric TAVR device tailored to BAV anatomy, aimed at improving durability, hemodynamics, and outcomes through advanced computational modeling.

Methods

PolyV-B features a sutureless, eccentric valve with asymmetric xSIBS polymeric leaflets and a fatigue-optimized nitinol stent with a centerline groove. The 29-mm device incorporates a rhombic stent profile and large cells to enhance anchoring and coronary access. Developed on the 3DExperience platform, it was virtually deployed in six patient-specific BAV anatomies. Its performance was compared to the Evolut R using finite element modeling and fluid–structure interaction (FSI) simulations.

Results

Simulations demonstrated that PolyV-B offers a 50% strain reduction during crimping, improved annular fit, enhanced sealing, and reduced thrombogenic risk. Flow simulations confirmed superior hemodynamics and a larger orifice area compared to Evolut R.

Conclusion

The PolyV-B TAVR device introduces design innovations that enhance durability, hemodynamics, and usability. Simulations in six patient-specific BAV anatomies demonstrated advantages over the Evolut R device. Optimization enabled efficient feasibility assessment, supporting the development of a tailored, clinically impactful solution for the BAV patient population facing unmet clinical needs.

Purpose

Surgical decision-making for Adolescent Idiopathic Scoliosis (AIS) relies on geometrical rather than biomechanical properties, such as the spine’s in vivo load characteristics. While both in vivo and in vitro spinal loading experiments can provide valuable insights, a standardized method to compare motion for the Functional Spinal Unit (FSU) is lacking. This work aims to establish a systematic motion parametrization to unambiguously characterize FSU pose changes suitable for a robotic in vivo spinal loading application.

Method

In this work, we propose an FSU motion parameterization using robotic rigid-body-tree modelling, deploying a virtual six-degree-of-freedom joint in the intervertebral space. To demonstrate the importance of the parameterization, we analysed the effect of different joint definitions on the produced displacement considering i) preoperative or intraoperative FSU reference poses, obtained from CT imaging of an AIS patient, and ii) one or both vertebral coordinate systems of the FSU. Additionally, we compared the required wrench capabilities of an actuation device to achieve pure spinal loading conditions in a pedicle screw-mounted scenario.

Results

Applying identical virtual motions resulted in differences of up to 0.38 mm in translation and (24.19^{circ }) in rotation, depending on the joint definition. Corresponding required wrench capabilities showed maximum force and torque errors of up to 34.97(%) in virtual pure translation and bending experiments.

Conclusion

Our findings underline the importance of a robust FSU kinematic framework, critical for ensuring safe and reliable FSU manipulation and for obtaining comparable and reproducible in vivo biomechanical data.

Purpose

The neuroprotective effect of hypothermia for mitigation of ischemic and hypoxic damage to the retina is well documented, yet technology to achieve targeted, controlled ocular hypothermia in vivo is lacking. This study evaluated controlled cooling of ocular tissues using a novel scleral contact eye cooler designed to be practical in a clinical setting.

Methods

Excised fresh adult porcine eyes (n = 5) were imaged (at 9.4 T MRI) to document gross anatomy, instrumented with temperature sensors at five key locations, and partially lowered into a warm oil bath (37 °C) to represent surrounding extraocular tissues. A scleral contact ring (SCR) interfaced with an active heat pump was lowered to contact the eye. The SCR was brought to 4 °C and maintained at that temperature using feedback control while monitoring sensor temperatures. After the eye tissues reached thermal equilibrium in the cooled state, the experiment was terminated, and a micro-CT image was obtained to verify the location of each temperature sensor.

Results

Average equilibrium temperatures of the anterior sclera and optic nerve sensors were 10.7 and 30.2 °C, achieved within 3.2 and 11.7 min, respectively. These temperatures have been shown to be neuroprotective against hypoxic damage.

Conclusion

In the non-perfused eye model, therapeutically relevant temperatures could be induced throughout the eye and maintained indefinitely. Demonstration of targeted and controlled cooling of eye tissues using a minimally invasive scleral contact ring will enable in vivo therapeutic hypothermia research using a design amenable to clinical translation.

Background

The natural aortic heart valve exhibits an exceptional balance of durability and efficiency, enabling over two billion cycles during a human lifespan. Designing a prosthetic valve that replicates these attributes presents significant challenges. The development of polymeric heart valves offers a promising alternative to existing biologic and mechanical options, aiming to improve durability and hemodynamic performance. This study focuses on the optimized design of the Foldax TRIA polymeric heart valve, leveraging computational modeling to minimize strain energy and enhance structural integrity.

Methods

A fully three-dimensional computational model of the TRIA valve was developed using LS-Dyna to simulate its behavior across a full cardiac cycle and optimize for fully open and fully closed configurations. The model incorporated an explicit finite element formulation without symmetry constraints, ensuring accurate representation of valve dynamics. The leaflets, composed of LifePolymer™ (a proprietary silicone urethane-urea), and the frame, made from Solvay Zeniva® PEEK, were analyzed for strain energy distribution. A perturbation analysis was conducted by varying leaflet width to assess its impact on strain distribution, durability, and kinematic efficiency. Additionally, hydrodynamic performance was evaluated using a pulse duplicator system.

Results

The computational analysis identified an optimal leaflet width that minimized strain energy and provided uniform stress distribution, reducing the potential for long-term material fatigue. Leaflets that deviated from this optimal width exhibited excessive strain at critical points, leading to potential durability concerns. Hydrodynamic testing demonstrated that the TRIA valve exhibited a low pressure gradient and an efficient equivalent orifice area (EOA) compared to a leading bioprosthetic control valve. Long-term durability testing indicated stable valve performance over 600 million cycles, equivalent to nearly 20 years of use.

Conclusion

The optimized design of the Foldax TRIA polymeric heart valve successfully minimizes strain energy while maximizing hydrodynamic efficiency. Computational and experimental results suggest that this novel polymeric valve provides a viable, long-lasting alternative to traditional heart valve prostheses. Future studies should focus on in vivo validation to further establish clinical efficacy and longevity.

Purpose

Digital twin (DT) cohorts are collections of models where each member represents an individual real-world asset. DT cohorts can be used for in-silico trials, outlier detection and forecasting, and are used across engineering, industry, and increasingly in personalised medicine. To increase the scalability of DT cohorts, researchers often train emulators to be used as cheap surrogates of computationally expensive mathematical models. Frequently, each cohort member is emulated individually, without reference to other members. We propose that instead, we can treat each DT as a thread in a larger network, and that these threads can be woven together into a digital tapestry using cohort learning methods.

Methods

We propose two statistical approaches for transferring knowledge between threads. The first method, ‘latent-feature emulators’, utilises a latent representation of individual cohort members to generate a single emulator for the entire cohort. The second method, ‘discrepancy emulators’, learns the discrepancy between a new cohort member and existing members.

Results

In two cardiac DT case studies, we show that these methods can reduce computational costs by more than 50% compared to the standard approach of training individual emulators, even in small cohorts.

Conclusions

We find that by transferring information between meshes, the cohort methods improve both the computational efficiency and the accuracy of emulators when compared to the standard approach of individually emulating each cohort member. As cohort size increases, the computational savings grow further. We focus on the use of Gaussian process emulators, but the transfer methods are applicable to other surrogate approaches such as neural networks.

Purpose

This paper investigates the well-known Empirical Wavelet Transform (EWT) and the recently introduced Empirical Fourier Decomposition (EFD) for the early diagnosis of Alzheimer's disease (AD). Both synthetic signals and real EEG data are decomposed and reconstructed, particularly under noisy conditions.

Methods

EWT and EFD were applied to decompose non-stationary EEG signals into five sub-bands (Delta, Theta, Alpha, Beta, and Gamma). From each sub-band, eight features were extracted and used to classify subjects into AD and Mild Cognitive Impairment (MCI) groups. Among the five classifiers tested, Random Forest (RF) yielded the best performance for both EWT and EFD. In addition to conventional evaluation metrics, Dynamic Time Warping (DTW) and the Kolmogorov–Smirnov (KS) statistic were used for algorithm assessment.

Results

The results show that EFD outperforms EWT and achieves competitive performance compared to state-of-the-art approaches.

Conclusion

EFD is a novel decomposition method that demonstrates robust performance on both synthetic and real EEG signals, supporting its potential use in the early diagnosis of Alzheimer's disease.

Oscillations of aortic bioprosthetic heart valve (BHV) leaflets during systole are known as leaflet fluttering (LF). LF may be relevant for assessing valvular function and could play a role in structural valve deterioration. However, a quantitative characterization of LF and its underlying physical processes is still missing. The objectives of this study are to systematically characterize LF for a BHV in vitro and to investigate the associated flow structures using a computational model. Leaflet motion of a bovine BHV was captured with high-speed cameras in an in vitro flow loop under varying experimental conditions (cardiac output (CO), inflow, and valve and aortic root orientation). A fluid-structure interaction (FSI) study was conducted for the same BHV for one condition to examine the blood flow patterns associated with LF. In vitro, LF presents in two different modes: either as high-frequency (150–380 Hz), low-amplitude (0.2–0.8 mm) vibrations of the leaflet tips (V-mode) or low frequency (30–90 Hz), high-amplitude (0.4–2.6 mm) waves travelling from leaflet base to tip (T-mode). We observed that LF depends on individual leaflet properties, is more likely to occur, and increases in amplitude and frequency with higher CO, and is also affected by the inflow. The FSI study confirms the presence of the same two modes. We identified large-scale vortex shedding related to the T-mode, superimposed with small-scale vortex shedding connected to vibrations of the leaflet tip (V-mode). Both identified LF modes are potential factors in BHV degeneration and should be considered in the BHV’s design.

Spinal cord injury (SCI), commonly resulting from sudden trauma such as traffic or sports accidents, leads to severe disruption of axonal connections and loss of sensory and motor function below the injury site. Despite numerous therapeutic efforts, effective strategies for neural repair remain limited. Tissue engineering has emerged as a promising approach for axonal regeneration, particularly through the design of three-dimensional (3D) polymeric scaffolds that can restore the structural and functional integrity of the injured spinal cord. This review focuses on recent advances in biomaterials and scaffold designs developed for SCI repair, emphasizing the role of nanocomposite systems that combine graphene oxide (GO), synthetic polymers such as PLGA–PEG, and bioactive ceramics like hydroxyapatite (HA). These hybrid materials offer improved biocompatibility, mechanical matching with spinal tissue, and enhanced cellular adhesion and guidance cues for axonal growth. The synergistic integration of these components enables the fabrication of multifunctional scaffolds capable of supporting stem cell differentiation and neurotrophic factor delivery. By critically summarizing the key parameters influencing scaffold performance, such as microarchitecture, surface modification, and mechanical compliance, this work outlines a framework for developing next-generation 3D nanocomposite scaffolds for SCI regeneration. The proposed approach highlights how GO/PLGA–PEG/HA systems can bridge the gap between experimental tissue engineering and clinically translatable neuroregenerative therapies.