Annals of Biomedical Engineering最新文献

Purpose: Developing bone substitute materials that mimic both trabecular and cortical bone remains a major challenge due to the trade-off between bio- and mechano-compatibility, particularly in naturally derived materials. While composites of calcined bone powder and silane-crosslinked alginate exhibit good biocompatibility and mechanical properties resembling those of trabecular bone, their mechanical properties remain insufficient for cortical bone applications.

Methods: This study explores a strategy to address this limitation by optimizing the composite formulation through blending ratio adjustment and nanoparticulation of calcined bone powder. Cylindrical composites (φ 15 mm × h 8 mm) were fabricated by varying the ratios of calcined bone powder (average particle size 246 μm), alginate, and silane cross-linking agent.

Results: Increasing the alginate ratio 10-fold (B/A10-Si) relative to the original formulation (B/A-Si) led to significant increases in elastic modulus, maximum stress, and strain energy which were further improved with the addition of a reduced amount of silane agent (B/A10-Si1/10). Additional enhancement was achieved using nanoparticulated bone powder (average particle size 651 nm), leading to further increases in modulus, strength, and energy by factors of 2.4, 1.7, and 1.4 respectively, compared to B/A10-Si1/10. Overall, the elastic modulus, maximum stress, and strain energy improved 8.4-fold, 18-fold, and 11-fold compared to B/A-Si, approaching values characteristic of cortical bone.

Conclusion: These findings suggest that combining blending optimization with nanoparticulation is a promising strategy to enhance the mechanical performance of naturally derived composites and may expand their applicability to cortical bone replacement.

Accurate identification of gastrointestinal endoscopic anatomical structures is critical for improving diagnostic accuracy and reducing missed detection rates. However, endoscopic image quality may be compromised by various factors including lesion interference and inadequate bowel preparation, while the morphological similarity of certain anatomical structures further complicates recognition in low-quality images. To address these challenges, we propose a Structural Information-Guided Cascaded Feature Fusion Network (SIG-CFFNet). Our approach leverages anatomical prior knowledge to guide the cascaded fusion of CNN and Transformer branch features, while incorporating Depthwise Over-parameterized Convolutional Layer (DO-Conv) to enhance feature representation and computational efficiency during the fusion process. Comprehensive experimental results demonstrate the superior performance of our method across multiple evaluation scenarios: it achieves classification accuracy of 73.47% and 87.05% for normal and pathological endoscopic anatomical structures, respectively; attains 99.61% and 87.83% accuracy on the Kvasir-Capsule and HyperKvasir public datasets; and maintains robust performance with 84.46% and 80.21% accuracy in cross-domain evaluations (COVID19-CT and ISIC2018). Notably, our model demonstrates highly competitive or near state-of-the-art recall rates across multiple test scenarios, confirming its clinical applicability and robustness for real-world implementation.

Purpose: The study examines the concentration of cryoprotectant (CPA) in an articular cartilage sample during cryopreservation by computing the effective diffusion coefficient using different material models-homogeneous and porous.

Methods: The mass transfer phenomenon is coupled to the effective diffusion coefficient, which is determined by three different approaches. The first and second models, based on the Einstein-Stokes equation and the Arrhenius expression, respectively, treat the sample as a homogeneous material, whilst the third considers it as a porous medium. The effective diffusion coefficient is additionally weakly coupled to the heat transfer phenomenon described by the Fourier equation, and the third variant is also strongly coupled to the concentration of CPA.

Results: The final section of the article presents example calculations for the selected cryopreservation method, and the results are compared with the experimental results. Depending on the method applied to estimate the effective diffusion coefficient, the maximal relative errors in relation to experimental results are equal to 15.82%, 5.20%, and 24.96%, respectively.

Conclusion: A decrease in temperature and an increase in the concentration of dimethyl sulfoxide (DMSO) cause a reduction of the effective diffusion coefficient. Moreover, in the model considering the porosity of the sample, the lowest values of the effective diffusion coefficient were obtained. This study's novelty lies in its comparative analysis of homogeneous and porous models, as well as its explicit coupling of temperature, concentration, and diffusion processes during cryopreservation.

Endothelial-to-mesenchymal transition (EndMT) is the process of endothelial cells undergoing molecular changes that shift their phenotype from that of endothelial cells to that of mesenchymal-like cells. It is a crucial developmental process that has been implicated in various physiological and pathological conditions. EndMT has gained attention as a potential therapeutic target for cardiovascular disease processes, including atherosclerosis, myocardial fibrosis, and vascular calcification. In addition to the assessment of endothelial and mesenchymal markers, the behavioral mechanics of endothelial cells, such as migration and invasion, are often used to identify endothelial cells that have undergone EndMT. However, whether cell chirality may be another mechanobiological marker of EndMT remains unclear. In this study, we aimed to develop an accessible micropatterning platform and created a stereolithography (SLA) 3D printing-based polydimethylsiloxane (PDMS) protein-stamp fabrication platform to create customized patterns of ECM proteins to study endothelial cell chirality during EndMT. Human aortic endothelial cells (HAECs) were treated with the inflammatory cytokine tumor necrosis factor-α (TNF-α), which resulted in the downregulation of the endothelial marker ENOS3 and the upregulation of the mesenchymal markers N-cadherin and transgelin, supporting the induction of EndMT. HAECs were seeded onto fibronectin stripe micropatterns, and cell chirality was measured using custom cell-profiling software. HAECs treated with TNF-α exhibited a shift in cell orientation by approximately 18°, supporting altered cell chirality during TNF-α-induced EndMT. Our work innovates novel methods of studying EndMT by developing a flexible and cost-effective protein-stamp fabrication and image analysis pipeline. This pipeline can be used by researchers to study the endothelial cell chirality in response to EndMT induction.

Purpose: This study aims to reveal the mechanical microenvironment of trabecular bones during gait by characterizing two key mechanical signals [strain and wall shear stress (WSS)], and to clarify their relationships with the bone volume fraction (BV/TV).

Methods: Forty trabecular bone cubes with varying BV/TV were cropped from three femoral heads and reconstructed by microcomputed tomography images. Subsequently, a Fourier's series-based gait-loading curve was applied on the reconstructed trabecular bones immersed in a bone marrow domain to perform a fluid-structure interaction analysis. Importantly, the two key mechanical signals (i.e., strain and WSS) and BV/TV were correlated, and also the correlation between the strain and WSS was clarified for the first time.

Results: The strain and WSS in trabecular bones exhibited alternating peak patterns during a gait cycle, and they were strongly correlated to the BV/TV. Moreover, the smaller BV/TV yielded lower strain and WSS. Interestingly, when the BV/TV was small, the strain was weakly correlated to the WSS; otherwise, the strain was strongly correlated to the WSS.

Conclusion: From the perspective of the biomechanics, the correlations between the mechanical environment and BV/TV indicate that the deteriorated bone structure and decreased mechanical stimuli might interplay in the development of osteoporosis. In addition, the revealed mechanical microenvironment provides a loading clue for in vitro mechanobiological experiment to understand the bone adaptive remodeling.

Purpose: Bone surface registration in current computer-assisted surgical navigation primarily relies on the manual selection of anatomical landmarks and invasive bone surface sampling using mechanical probes. This approach is traumatic, highly dependent on operator expertise, and difficult to automate. Although ultrasound-based registration methods have been explored and demonstrated certain application potential, most existing techniques still rely on manual feature extraction. Moreover, their accuracy is generally limited by the structural and geometric constraints of the probes. To address these limitations, we propose and develop a real-time, noninvasive bone surface depth acquisition system based on A-mode ultrasound, aiming to replace traditional mechanical probing and provide a high-precision, automated, and noninvasive alternative for bone surface registration.

Methods: We optimized the probe geometry using finite-difference time-domain simulations and fabricated a four-element A-mode probe with integrated control and data acquisition systems. An automatic bone-echo detection algorithm based on Gaussian filtering and Hilbert transform was developed. System performance was validated through simulations and benchtop experiments on 3D-printed tibia and spine phantoms.

Results: Simulation and experimental results demonstrate the system achieves millimeter-level bone surface sampling accuracy within a 40 mm depth range, with multiple measurement errors below 1 mm. In registration tests using tibia and spine models, the system achieved average registration errors of 1.426 ± 0.300 mm and 1.262 ± 0.283 mm, respectively.

Conclusion: This study proposes and builds an A-mode ultrasound system that, through optimization of a miniature probe and development of an automatic bone echo recognition algorithm, establishes a novel and potentially clinically valuable approach for achieving millimeter-level accuracy in automated, noninvasive registration.

Bicuspid aortic valve (BAV) is a common congenital cardiovascular defect characterized by two, rather than three, cusps. Many BAV patients prematurely develop calcification and aortic stenosis by age 35, which is more severe with fusion of the right and noncoronary (RC/NC) cusps. The mechanisms underlying calcification observed within the coaptation, attachment, and fusion regions of BAV patients are unknown. While abnormal mechanical stimuli induced by the bicuspid anatomy likely plays a role, little is known about regionalized mechanical stimuli in these susceptible cusp regions of young patients prior to calcification. Strongly coupled fluid-structure interaction simulations were conducted using physiologic boundary conditions derived from data of a 23-year-old patient with RC/NC BAV and an age-matched tricuspid aortic valve control. Cusp material properties were implemented for the first time from biaxial testing of non-calcific BAV tissue. Additional simulations elucidated the independent and collective contributions of cusp fusion, material properties and boundary conditions. Results show BAV cusps experience higher time-averaged wall shear stress (TAWSS) over the coaptation region (2.92-fold increase), decreased oscillatory shear index (OSI) within the free edge (1.6-fold) and coaptation (1.4-fold) regions, and an increase in von Mises stress in the coaptation (5.72-fold), belly (6.79-fold), and attachment (5.18-fold) regions of the fused and nonfused cusps. These results reveal putative regions susceptible to calcification in BAV patients experience differences in mechanical stimuli that may contribute to the onset of calcification and provide insight for future in vitro and ex vivo studies focusing on mechanosensitive pathways involved in BAV-induced calcification.