Annals of Biomedical Engineering最新文献

Purpose: Interossei muscles of the hand, innervated primarily by the ulnar nerve, are essential for coordinated finger abduction and adduction. Quantitative strength assessment of these key actions, which support precision grip and fine motor control, may aid in the diagnosis and monitoring of neuropathic conditions including cubital tunnel syndrome. This study aimed to develop and validate a novel Hand Interossei Muscle Dynamometer (HIMDNA) for quantifying finger abduction and adduction strength for the index to little fingers and to establish normative values for healthy adults.

Methods: HIMDNA was designed based on hand morphology, incorporating a load cell within a fixed frame and a linear guide mechanism to ensure uniaxial, examiner-independent force measurements. The abduction and adduction strengths of 48 healthy adults aged 20-39 years were measured and validated against index and little finger abduction measurements obtained with a handheld dynamometer (HHD). The inter- and intra-rater reliabilities and overall usability of HIMDNA were then compared with those of HHD.

Results: HIMDNA demonstrated excellent agreement with HHD (Pearson's r = 0.927 ~ 0.967; ICC = 0.911 ~ 0.956) and superior inter- and intra-rater reliabilities (ICC = 0.963 ~ 0.983). Normative values were approximately 4.5 ~ 23.4 N, with greater strength in the radial than ulnar direction except for the little finger. Participants rated HIMDNA higher in usefulness, effectiveness, and overall satisfaction than HHD.

Conclusion: HIMDNA provided a reliable, valid, and user-friendly way of quantifying finger abduction and adduction strengths, suggesting its potential as a standardized and clinically applicable tool for evaluating the strength of interossei muscles quantitatively in patients with ulnar nerve disorders.

Purpose: Antibiotic-loaded bone cements (ALBCs) are widely used in managing prosthetic joint infection (PJI). This study aimed to compare the antibiotic elution, surface porosity, and mechanical properties of polymethylmethacrylate (PMMA) cement loaded with vancomycin using powdered and liquid incorporation methods.

Methods: High-viscosity Palacos R® PMMA was impregnated with 1-4 g vancomycin (powdered or dissolved in water) per 40 g cement. Beads, cylinders, and blocks were fabricated. Antibiotic release was quantified by high-performance liquid chromatography HPLC over 6 weeks. Porosity was assessed by micro-CT whilst compressive and bending strength were measured on an Instron® material testing system.

Results: Powder-mixed beads showed higher cumulative vancomycin release, whilst liquid-mixed beads showed greater porosity than powder-mixed beads. For both powder and liquid-mixed formulations, increasing vancomycin concentration was associated with a dose-dependent reduction in compressive and bending strength compared with control group. Differences between liquid and powder mixed specimens were formulation and loading mode-dependent and were not uniformly directional across concentrations.

Conclusion: Smaller PMMA beads and powder-mixed formulations demonstrated greater early release and higher sustained elution over time, resulting in superior cumulative antibiotic delivery compared with larger beads and liquid-mixed formulations. These findings highlight the need to balance antimicrobial efficacy and mechanical requirements when tailoring formulation of ALBC, particularly in spacer relevant clinical applications.

Purpose: Structural valve deterioration of aortic bioprosthetic heart valves (BHV) is influenced by leaflet mechanical stress, dependent on design parameters. This study provides a mathematical description of BHV geometry based on these geometric parameters and investigates their influence on valve function.

Methods: Ten BHV models with variations in geometric parameters were tested under controlled conditions in a cardiac simulator. The Trifecta valve TF-25 (Abbott) was used as a reference geometry to define the "normal" mathematical valve design and was also experimentally tested. The valves were made of silicone using 3D-printed molds. Hemodynamic performance was assessed by Doppler echocardiography. Leaflet motion and strain fields were analyzed with stereophotogrammetry and digital image correlation.

Results: There was no significant difference in hemodynamic performance between the Trifecta valve and the "normal" silicone valve (p > 0.05). Increased leaflet thickness, smaller diameters, and greater belly curvature reduced significantly (p < 0.01) the hemodynamic performance, while taller leaflets and greater free-edge angle relative to the commissural plane improved the valve performance. Strains during diastole were highest near the commissures. Increased leaflet thickness reduced leaflets deformation, whereas smaller diameters resulted in localized deformation peaks. A greater free-edge angle minimized deformation and increased spacing between leaflets caused inward pulling near stent posts. Excess leaflet height promoted leaflet pinwheeling.

Conclusion: The in vitro analysis reveals the differences between various geometries, emphasizing the importance of valve design for BHV function and durability. The development of new BHVs could be improved through in vitro testing. Furthermore, these in vitro experiments can be replicated to evaluate the geometry of native valves.

Purpose: Deformable image registration (DIR) is a crucial technique in medical image analysis and is particularly important for 4D-CT-guided lung radiotherapy, where accurate spatiotemporal alignment and deformation plausibility are required for downstream tasks such as dose accumulation. However, many existing learning-based methods are limited in modeling global spatiotemporal dependencies and in preserving fine anatomical structures under large respiratory motion.    METHODS: To address these issues, this paper proposes a wavelet transform-based regularized hybrid recursive spatiotemporal fusion registration network (W-STFNet). The proposed method incorporates SwinLSTM to effectively capture global spatiotemporal dependencies. To achieve better semantic integration of features across scales and time steps, a multi-scale spatiotemporal attention fusion (MSTAF) module is proposed, which improves the network's robustness and stability. Additionally, we design a novel frequency-domain loss function based on Discrete Wavelet Transform (DWT), which optimizes fine-grained structural matching by aligning high-frequency sub-bands, effectively improving the accuracy of high-frequency detail registration. The method is optimized in an unsupervised, patient-specific one-shot setting without anatomical annotations or multi-patient pretraining.    RESULTS: Experiments on two public 4D-CT datasets (DIR-Lab and POPI-model) show that W-STFNet achieves competitive registration accuracy and stable performance across cases with varying deformation amplitudes. On DIR-Lab, W-STFNet attains a mean TRE of     $1.13\pm 0.72\text{mm}$ , and on POPI-model a mean TRE of     $0.87\pm 0.56\text{mm}$ , substantially reducing the initial misalignment. A two-sided paired Wilcoxon signed-rank test further supports that W-STFNet differs significantly from several learning-based baselines under the reported settings, although the absolute differences should be interpreted with respect to image resolution and annotation uncertainty.    CONCLUSION: W-STFNet provides an annotation-free, patient-specific one-shot registration framework that achieves robust and competitive performance for 4D-CT lung DIR, particularly in handling image registration scenarios involving large deformations and complex temporal dynamics.

Statistical shape modelling (SSM) offers a robust framework for quantifying anatomical variability and constructing representative virtual patient cohorts of 3D anatomies that can be used as the foundation of biomechanical in silico clinical trials. In this study, we developed a SSM of the mitral valve using 72 contrast-enhanced computed tomography angiography (CTA) scans of the heart. Principal component analysis revealed dominant modes of shape variation that align with previously reported anatomical patterns in the literature, validating the model's physiological relevance. The resulting shape model effectively captures the geometric diversity of the mitral valve without making any presuppositions about the importance of landmarks or linear measurements. Our results demonstrate the utility of SSMs in generating virtual patient populations from existing scan data. These findings support the integration of SSMs into computational modelling pipelines for preclinical testing, device design, and personalised medicine.

Mechanical hemolysis remains one of the most critical complications associated with blood-contacting devices. Computational prediction of blood damage has been widely adopted as a key tool for evaluating the hemolytic potential of cardiovascular devices. Although numerical hemolysis modeling has been investigated since the 1990s, a universally accurate and predictive approach is still lacking. This review traces the evolution of computational hemolysis models, from the earliest stress-based power-law formulation to recent strain-based approaches that describe the red blood cells at cellular and molecular levels. It examines the various definitions of the power-law shear stress found in literature, including Von Mises-like formulations, extensions incorporating extensional or Reynolds stresses and those based on turbulent dissipation rate or deformation of the red blood cell. The broad range of power-law constants reported in literature is summarized, together with their development conditions. Furthermore, the Lagrangian and Eulerian approaches used for numerical hemolysis predictions are analyzed in detail. Finally, emerging trends and future directions are highlighted, offering insights into the pathways toward more reliable hemolysis modeling.

Purpose: Muscle fatigue is accompanied by coordinated changes in both neuromuscular activation and cortical activity. Traditional fatigue assessment methods based on single physiological signals often fail to capture system-level reorganization of the brain-muscle control network. This study aims to characterize upper limb muscle fatigue by analyzing parallel functional networks derived from surface electromyography (sEMG) and electroencephalography (EEG) signals.

Methods: Sixteen healthy participants performed sustained isometric elbow flexion tasks under non-fatigued and fatigued conditions. Multichannel sEMG signals from eight upper limb muscles and EEG signals from 21 scalp locations were simultaneously recorded. Functional muscle networks and functional brain networks were constructed independently using generalized partial directed coherence. Network topology was quantified using average clustering coefficient (ACC), average global efficiency (AGE), and average shortest path length (APL) in relevant frequency bands.

Results: In the muscle functional network, fatigue was associated with a significant increase in ACC and AGE, accompanied by a reduction in APL, indicating enhanced local clustering and more efficient information transfer among muscles. In the brain functional network, significant changes were observed primarily in the beta-band, with increased ACC and AGE and decreased APL following fatigue. In contrast, gamma-band network metrics showed limited or non-significant alterations. These results suggest that fatigue-related neuromuscular adaptation is reflected at the network topology level rather than through isolated signal features.

Conclusion: The proposed brain and muscle functional network framework provides a system-level characterization of upper limb muscle fatigue based on parallel EEG and sEMG network analysis. By capturing fatigue-related changes in network topology, this approach provides a system-level reference for investigating neuromuscular coordination under fatigue and lays a methodological foundation for future rehabilitation-oriented fatigue monitoring studies.

Origami robotics has emerged as a transformative paradigm in biomedical engineering, enabling compact, adaptable, and minimally invasive devices that can perform complex tasks inside the human body. The field integrates principles of origami folding with robotic actuation to address critical challenges in surgery, diagnostics, and therapeutic delivery. The objective of this review is to synthesize recent advances in origami-inspired biomedical systems, highlighting their design principles, fabrication methods, and translational potential. A narrative review approach was adopted, surveying peer-reviewed publications from 2010 to 2025 retrieved from databases such as Scopus, PubMed, and IEEE Xplore using keywords related to origami robotics, biomedical devices, minimally invasive systems, and soft robotics. Across the surveyed literature, origami-based architectures consistently enable extreme miniaturization, enhanced flexibility, and deployable geometries that improve access, localization, and functionality in minimally invasive surgery, targeted drug delivery, diagnostic platforms, and rehabilitation technologies. Key trends include the integration of smart and bioresorbable materials, programmable stiffness, and self-folding mechanisms, alongside persistent challenges in long-term biocompatibility, control precision under physiological uncertainty, and the lack of harmonized performance benchmarks and regulatory pathways. Overall, this review positions origami robotics as a cornerstone of next-generation biomedical device design and argues that future research should focus on advancing bioresponsive materials, adaptive and data-driven control strategies, and regulatory and evaluation frameworks to enable safe and reliable clinical translation.

Purpose: Osteocytes are force-sensitive cells possessing a complex lacunar-canalicular system (LCS), embedded within a piezoelectric bone matrix. TRPV4, as a key mechanosensor, plays a crucial role in osteocyte mechanotransduction and related bone disorders. However, the mechanisms underlying its force sensing, mechanical signaling pathways, and biomechanical response remain poorly understood.

Methods: To address this issue, this study established a finite element model incorporating multiple mechanosensors-including bone matrix, LCS, osteocytes, and TRPV4-based on the piezoelectric effect. By integrating the multiphysics coupling of solid mechanics, fluid mechanics, and electric fields to simulate the complex effects on osteocytes, the model calculated stress, strain, and fluid shear stress (FSS) on TRPV4.

Results: Result indicate that piezoelectricity significantly increases stress and strain in TRPV4, particularly FSS. Biomechanical parameters of TRPV4 exhibit significant variations across different locations, with the highest stress levels observed at the cell processes (Maximum increase of approximately 300%). Stress distribution patterns also differ across distinct regions, while stress concentration in TRPV4 primarily occurs in its transmembrane domain and ion channel regions. This study reveals that upon coupling with primary cilia and RhoA, the mechanical response mechanism of TRPV4 undergoes significant alteration. TRPV4 exhibits greater sensitivity to fluid shear stress, whereas Piezo1 responds more strongly to membrane stress.

Conclusions: This study elucidates the microscopic mechanical response mechanism and gating activation mechanism of TRPV4 within complex bone cell environments. It clarifies the interaction mechanisms between TRPV4 and other cellular structures, providing a research pathway for understanding bone cell mechanical transduction mechanisms and complex interactions across multiple scales. This work offers theoretical insights into the pathogenesis and therapeutic approaches for TRPV4-related bone disorders.