Annals of Biomedical Engineering最新文献

Purpose: Airway management is critical in lifesaving care, and effective airway clearance in emergency and combat settings requires portable suction devices that provide strong suction yet remain easy to carry. Existing options are either powerful but too large or portable but lack sufficient performance for field use. To address this gap, the Suction Capability for Emergencies: Portable Technology for Responders (SCEPTRE)-a compact, lightweight suction device-was developed.

Methods: SCEPTRE's design and prototyping was guided by end-user feedback. Performance testing, defined a priori using published standards, characterized vacuum pressure, air flow rate, and liquid flow rate using water, blood simulant, and vomit simulant. SCEPTRE was compared to the SSCOR Quickdraw (battery-powered competitor) and Laerdal V-VAC (manually-powered competitor), with liquid flow normalized to device weight and volume.

Results: SCEPTRE achieved a vacuum pressure of 266.7 ± 2.1 mmHg and an average air flow rate of 3.44 ± 0.04 L/min. Liquid flow testing across water, blood, and vomit simulants showed low variability, with flow rates of 1.65 ± 0.05, 1.65 ± 0.04, and 0.70 ± 0.04 L/min, respectively. For water, blood, and vomit, SSCOR reached 5.51 ± 0.13, 5.21 ± 0.22, and 1.78 ± 0.17 L/min, respectively, while V-VAC achieved 2.17 ± 0.23, 2.26 ± 0.32, and 1.14 ± 0.06 L/min, respectively. When normalized by weight and volume, SCEPTRE (0.206 kg, 241 cm³) had a flow-to-weight ratio of 8.25 L/min/kg and the highest flow-to-volume ratio (7.05 × 10^-3 L/min/cm³). In comparison, V-VAC reached 8.84 L/min/kg with a lower flow-to-volume of 1.03 × 10⁻³ L/min/cm³, and SSCOR showed 4.78 L/min/kg and 2.12 × 10⁻³ L/min/cm³.

Conclusion: SCEPTRE achieved satisfactory performance for suction across physiologically relevant fluids and demonstrated high weight- and volume-normalized efficiency. These results support its potential use in combat and prehospital care and reinforce the value of weight- and volume-based metrics for portable suction devices.

Purpose: This study aimed to simulate accidental ankle inversion in sports and compare the effects of high-top and low-top shoes on rearfoot kinematics and plantar pressure distribution to understand the ankle protection mechanism of high-top shoes.

Methods: Eight pairs of shoes (four high-top and four low-top shoes) were custom-made from four brands. The high-top shoes were 3 cm higher than the low-top ones, whereas the rest of their structure remained the same as that of the low-top shoes. Twenty male amateur basketball players (age 19 ± 1 years, height 180 ± 5 cm) participated. An extreme inversion of 30° was simulated using an electromagnetically controlled platform. The rearfoot angles and velocities were recorded using high-speed cameras, and plantar pressure was measured using a three-channel pressure sensor.

Results: High-top shoes significantly reduced the peak rearfoot inversion angles (A brand, 7.56%; B brand, 19.61%; C brand, 9.15%; D brand, 8.36%; p < 0.05) and peak angular velocities (A brand, 4.48%; B brand, 14.92%; C brand, 3.67%; D brand, 4.70%; p < 0.05). Peak angular velocity occurred 0.005-0.01 s earlier in high-top shoes. No significant differences were observed in plantar pressure distribution.

Conclusion: High-top shoes reduce ankle inversion range and speed through mechanical support, potentially aided by neuromuscular regulation, thus decreasing the load on lateral ligaments. This study provides evidence for optimizing shoe collar height in sports shoe design and highlights the importance of collar support in reducing the risk of ankle inversion injuries during sports.

Purpose: Clinical failure rates associated with in-stent restenosis are difficult to predict and manage, particularly at the patient-specific level. Studies have linked biomechanical factors to focal disease development and progression, suggesting that physics-based simulations using finite element (FE) approaches hold potential to mitigate stent failure rates. However, insufficient validation to assess the accuracy of model predictions limit model credibility for clinical translation. Herein, we established a computational framework to validate vascular stent deployment by integrating robust simulation and rigorous experimental approaches.

Methods: Experimental testing characterized the transient deformation of a commercially available balloon-expandable stent system, and high-resolution image data were post-processed to create a representative FE model. Non-linear material behaviors and physical boundary conditions were varied to create mixed-fidelity models that assessed the effects of modeling assumptions on stent deformation metrics.

Results: Qualitative comparisons of stent deployment stages showed that high-fidelity FE models captured the characteristic burst opening of the stent edges, followed by the central stent region. Quantitative metrics determined from pressure-diameter curves showed strong agreement, with root mean square error and concordance correlation coefficient values for the proximal, central, and distal diameters ranging from 0.31 mm and 0.96, respectively (lowest fidelity) to 0.21 mm and 0.99 (highest fidelity). Analysis of higher-order metrics (i.e., dog-boning, foreshortening) further demonstrated strong agreement.

Conclusion: This framework successfully established a validation plan for vascular stent deployment, analyzed errors in model development, and demonstrated the utility of quantitative assessments, potentially improving the translatability of in silico tools and reducing device failure rates.

Purpose: Spinal cord injury (SCI) is a devastating condition with limited therapeutic options owing to poor intrinsic regeneration and the formation of glial scars. Platelet-rich plasma (PRP)-based biomaterials have emerged as promising candidates for neural repair; however, their application in complete SCI models with rigorous multimodal validation has not yet been investigated. This study aimed to extend the current knowledge on SCI-PRP based study, by developing and comprehensively validating a clinically translatable PRP-derived fibrin scaffold for spinal cord regeneration.

Methods: PRP-fibrin scaffolds were synthesized from donor-derived plasma and extensively characterized in terms of their physicochemical properties, degradation profiles, protein release dynamics, and cellular compatibility. Its regenerative potential was evaluated using an integrative pipeline that included in ovo chorioallantoic membrane (CAM) assays, a complete spinal cord transection rat model, and multimodal outcome measures, including magnetic resonance imaging (MRI), retrograde neuronal tract tracing, electrophysiological recordings, and gene expression analyses.

Results: The scaffold exhibited favorable structural and biochemical characteristics, supported angiogenesis in the CAM assay, and promoted tissue integration in vivo. In an SCI model, the scaffold significantly enhanced neovascularization, reduced glial scarring, and facilitated axonal regeneration. Functional improvements were observed 30 days' post-implantation. In silico docking further demonstrated stable interactions between scaffold proteins and key neuroregenerative signaling molecules.

Conclusion: This study provides multimodal validation of the derived fibrin scaffold, establishing it as a robust ECM-mimetic platform for spinal cord repair. These findings lay the groundwork for future clinical translation of SCI therapeutics.

Background: Accurate non-invasive localization of right ventricular (RV) arrhythmia origins remains a challenge in electrophysiology. This study investigates the feasibility of using machine learning models based on 12-lead ECG QRS integrals to localize early RV activation sites.

Methods: A generic RV mesh was constructed from a CT scan, consisting of 277 triangular elements. Two cohorts were used: a development cohort with 8 patients and 227 known pacing sites, and a validation cohort with 3 patients and 34 pacing sites. Each pacing site was assigned to the centroid of a mesh element. QRS integrals (∫QRS) were computed from eight ECG leads and used as input features for support vector regression (SVR) models with radial basis function (RBF) and linear kernels. ∫QRS values were trimmed over varying durations (30-160 ms in 10 ms increments) to identify the optimal QRS integration window through bootstrapped cross-validation. Localization accuracy was assessed using Euclidean distance, RMSE, and R2 across both cohorts.

Results: The RBF SVR trained on the initial 60 ms QRS interval yielded the lowest mean localization error - 9.5 mm in the development set and 14.4 mm in the independent test set. In contrast, the linear SVR yielded a mean localization error of 16.6 mm on the development set and 17.0 mm on the independent test set, with more stable performance across QRS durations. Axis-specific analysis revealed superior predictive accuracy along the y- and z-axes, while the x-axis-corresponding to the septal-free wall direction-showed reduced performance. In a validation cohort (n = 34), the mean localization errors were similar between kernels, and the difference was not statistically significant (p = 0.31).

Conclusion: QRS-integral-based SVR models enable millimeter-scale localization of RV pacing sites from surface ECGs. While nonlinear models provide greater accuracy in anatomically complex regions, linear models offer robustness and simplicity. These findings support the clinical potential of ECG-driven machine learning for guiding RV arrhythmia localization and complementing traditional mapping approaches.

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.