Annals of Biomedical Engineering最新文献

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.

Purpose:  Breast-conserving surgery (BCS) is the standard of care for early-stage breast cancer, offering recurrence and survival rates comparable to mastectomy while preserving healthy breast tissue. However, surgical cavity healing post-BCS often leads to highly variable tissue remodeling, including scar tissue formation and contracture, leading to visible breast deformation or asymmetry. These outcomes significantly impact patient quality of life but are difficult to predict due to the complex interplay between biologic healing processes and individual patient variability. To address this challenge, we extended our calibrated computational mechanobiological model of post-BCS healing by incorporating diagnostic imaging data to evaluate how patient-specific breast and tumor characteristics influence healing trajectories and deformation.

Methods:  The model captured multi-scale biologic and biomechanical processes, including fibroblast activity, collagen remodeling, and nonlinear tissue mechanics, to simulate time-dependent tissue remodeling. Patient-specific breast and tumor geometries from preoperative magnetic resonance imaging (MRI) were integrated into finite element simulations of cavity healing, whose outputs trained Gaussian process surrogate models for rapid prediction of healing dynamics and breast surface deformation across diverse patient profiles.

Results:  These models revealed how factors including breast density, cavity volume, breast volume, and cavity depth influence post-surgical cavity contraction and measures of breast surface deformation.

Conclusion: This framework has the potential to provide a personalized, predictive tool for surgical planning and decision-making, enabling clinicians and patients to anticipate healing trajectories and cosmetic outcomes, with the goal of optimizing surgical results and enhancing patient quality of life.

Tooth and endodontic disease pose important challenges, and traditional therapies are dependent upon mechanical restoration rather than biological regeneration. Progress in tissue engineering, biofabrication, and biomaterials science is increasingly supporting regenerative endodontics and bioengineered tooth construction through the use of stem cell-based methods, functionalized scaffolds, and 3D bioprinting technologies. This article summarizes advances in bioengineered dental tissue, emphasizing scaffold design, regulation of induced pluripotent stem cell and dental pulp stem cell differentiation, and solutions for vascularization and innervation. Such important biomaterials as PLGA, alginate, bioactive ceramics, and nanomaterials have been evaluated for their ability to regulate cell behavior and tissue integration. The new role of artificial intelligence in scaffold design and growth factor delivery is presented. There are persisting challenges of mimicking native tooth complexity, obtaining functional vasculature, and satisfying regulatory requirements for bioengineered tooth models. Future directions are expected to emphasize novel biofabrication technologies, the use of high-throughput screening for optimizing biomaterials, and the development of scalable production processes. This article provides a roadmap for translating bioengineered dental constructs from preclinical models to clinical-scale regenerative solutions, with implications for broader tissue engineering fields beyond dentistry.

Purpose: Most injuries in mountain biking (MTB), including traumatic brain injuries (TBIs), are caused by falls. Risk for TBI during a fall depends largely on whether impact occurs to the head. There is a lack of evidence on the frequency and risk factors for head impact during MTB falls. We addressed this knowledge gap by analyzing videos of real-life falls in recreational and competitive MTB.

Methods: Publicly available video footage was collected from the internet of falls in recreational MTB (n = 150) and falls in competitive MTB (n = 150). Each video was analyzed using a structured questionnaire. Logistic regression was used to test whether the probability for head impact depended on demographic, situational, and environmental factors.

Results: The head was impacted in 52.0% of falls. The probability for head impact did not differ between recreational and competitive MTB. The odds for head impact increased by falling forward (OR 4.9; 95%CI:1.7-14.1), falling due to wheel slippage (3.1;1.6-6.0) and falling while executing a jump (2.2;1.1-4.5). The odds for head impact decreased by dismounting from the bicycle during descent (0.11; 0.02-0.59), and with increased rider age (0.96; 0.93-0.99 per year).

Conclusion: Over half of falls in MTB resulted in impact to the head, and risk for head impact depended on characteristics of the rider, and characteristics of the fall. By identifying the types of falls in MTB that are most likely associated with head impact, our results can guide skills training and improvements in the design of protective equipment and MTB trails.

Purpose: First-pass peripheral intravenous catheter (PIVC) insertion failure rates are up to 40%, with vein localization often critical for success in subsequent attempts. This study aimed to evaluate a novel ultrasound-based device with coronal pathway visualization for improving PIVC accuracy compared to point localization (single ultrasound frame) in untrained users.

Methods: We conducted a benchtop study using n = 14 untrained investigators and observed PIVC insertion into tissue and blood vessel mimicking phantoms using pathway visualization and point localization assistance. Key metrics such as first attempt insertion success rate, cannula tip-to-vessel distance, and angle alignment were collected. Secondary aims included assessing user factors such as cannulation familiarity, handedness, and grip technique to guide prototype development. Reliability of calibration and analysis measurements was calculated, and grip types were retrospectively analyzed for their influence on accuracy.

Results: Fourteen investigators achieved non-significantly higher first-pass success rates with pathway visualization (85.7%) compared to point localization (71.4%), with failure rates halving (28.6% vs. 14.3%, n = 14, p = .625). Pathway visualization also demonstrated superior accuracy compared to point localization in critical metrics, including cannula tip-to-vessel distance (1.91 ± 1.47 mm vs. 4.21 ± 1.88 mm, p = .004) and angle alignment (3.49 ± 1.89° vs. 12.50 ± 5.73°, p < .001).

Conclusion: Pathway visualization improves PIVC placement accuracy, success rates, and alignment compared to point localization. The findings highlight its potential to improve cannulation in difficult intravenous access (DIVA) patients. Future studies comparing the efficacy and accuracy of insertion with the use of transverse visualization (traditional handheld ultrasound) versus coronal pathway visualization provided by the prototype device are required.