Annals of Biomedical Engineering最新文献

Purpose: Despite falls accounting for the greatest number of fatal and non-fatal work-related traumatic brain injuries, current standards do not evaluate safety helmets under impact conditions representative of fall scenarios. This study's objective was to develop a test method that evaluates safety helmets under impact scenarios representative of falls. A Construction STAR rating system that quantitatively compares safety helmet performance in the context of concussion and skull fracture risk is also outlined.

Methods: A multi-step approach that combined information from previous literature, Occupational Safety and Health Administration (OSHA) accident reports, and oblique impact tests were used to develop a fall-specific safety helmet test methodology. The test methodology consisting of three impact locations (front boss, rear boss, and rear), two impact velocities (5.5 and 6.8 m/s), and a 25-degree anvil was executed on a representative subset of one Type I and four Type II models. STAR scores, combining concussion and skull fracture risk, were calculated for each model and compared.

Results: STAR scores demonstrated that Type II helmets reduced concussion risk by 32.7% and skull fracture risk by 57.5% when compared to the Type I model. Large variations in Type II performance were observed, with the top-performing Type II helmets reducing concussion risk by 28.7 and 33.2% compared to bottom-performing models.

Conclusions: Type II helmets offer substantial benefits in head protection compared to Type I models for oblique fall-related impacts. By including both skull fracture and concussion risk in the STAR score, the proposed methodology can differentiate high and low-performing safety helmets.

Purpose: Physiologic closed-loop controlled (PCLC) medical devices have the potential to enhance therapeutic precision and effectiveness. However, their complexity introduces potential failure modes that may pose risks to patients if not properly evaluated. This study assesses the effectiveness of a mathematical model of the cardiovascular system in predicting PCLC performance metrics through in vivo and in silico comparisons of blood pressure responses to automated fluid infusion.

Methods: A closed-loop control system for regulating mean arterial pressure (MAP) was implemented using a custom Java-based software application on a tablet. The system operated in two control speed modes, 'slow' and 'fast.' The study was conducted in an animal laboratory using thirteen swine subjects (two were excluded due to hardware-related issues). Following intubation, anesthesia, and splenectomy, hemorrhage was induced until MAP reached 45 mmHg, followed by fluid resuscitation using the closed-loop controller targeting 70 mmHg. Arterial blood pressure waveforms were continuously recorded, and cardiac output and hematocrit measurements were taken every 15 min. The same control algorithm and speed modes were applied to in silico subjects generated with a mathematical model simulating cardiovascular responses to fluid perturbation while the same protocol was simulated.

Results: The mathematical model effectively predicted key PCLC performance metrics, including rise time, %overshoot, settling time, and divergence. The simulated results closely matched experimental data and captured differences between slow and fast control speeds.

Conclusion: The ability of the mathematical model to predict PCLC performance metrics demonstrates its value in supporting the development and evaluation of automated fluid resuscitation controllers.

High myopia (HM) is a vision-threatening ocular disorder characterized by excessive axial elongation. While previous studies have primarily focused on structural and functional impairments in the posterior segment-including the optic nerve, posterior sclera, and macular region-recent evidence indicates that adaptive remodeling also occurs in anterior segment structures, such as the cornea, anterior sclera, and lens. In this review, we examine the molecular mechanisms underlying anterior segment remodeling in high myopia, investigate the associated biomechanical alterations using ex vivo and in vivo measurement techniques, and analyze the interrelationships among these changes. Furthermore, we highlight how multimodal imaging technologies, when integrated with artificial intelligence algorithms, enable the quantitative assessment of biomechanical parameters. These advances may contribute to improved prediction of myopia progression, risk stratification for associated complications, and the development of personalized therapeutic strategies. Finally, we discuss the current challenges and translational bottlenecks in this area.

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.