Annals of Biomedical Engineering最新文献

Purpose: While gait analysis is essential for assessing neuromotor disorders like cerebral palsy (CP), capturing accurate ground reaction force (GRF) measurements during natural walking presents challenges, particularly due to variations in gait patterns. Previous studies have explored GRF prediction using machine learning, but specific focus on patients with CP is lacking. This research aims to address this gap by predicting GRF using joint angles derived from marker data during gait in patients with CP, thereby suggesting a protocol for gait analysis without the need for force plates.

Methods: The study employed an extensive dataset comprising both typically developed (TD) subjects (n = 132) and patients with CP (n = 622), captured using motion capture systems and force plates. Kinematic data included lower limb angles in three planes of motion, while GRF data encompassed three axes. A one-dimensional convolutional neural network model was designed to extract features from kinematic time series, followed by densely connected layers for GRF prediction. Evaluation metrics included normalized root mean squared error (nRMSE) and Pearson correlation coefficient (PCC).

Results: GRFs of patients with CP were predicted with nRMSE values consistently below 20.13% and PCC scores surpassing 0.84. In the TD group, all GRFs were predicted with higher accuracy, showing nRMSE values lower than 12.65% and PCC scores exceeding 0.94.

Conclusion: The predictions considerably captured the patterns observed in the experimentally obtained GRFs. Despite limitations, including the absence of upper extremity kinematics data and the need for continuous model evolution, the study demonstrates the potential of machine learning in predicting GRFs in patients with CP, albeit with current prediction errors constraining immediate clinical applicability.

The objective of the study was to determine the specific roles of decorin and biglycan in the early and late phases of tendon healing in aged mice. Aged (300 day-old) female wildtype (WT), Dcn^flox/flox (I-Dcn^-/-), Bgn^flox/flox (I-Bgn^-/-), and compound Dcn^flox/flox/Bgn^flox/flox (I-Dcn^-/-/Bgn^-/-) mice with a tamoxifen (TM) inducible Cre underwent a bilateral patellar tendon injury (PT). Cre excision of the conditional alleles was induced at 5 days (samples collected at 3 and 6 weeks) or 21 days post-injury (samples collected at 6 weeks). Scar tissue area, collagen architecture, gene expression and mechanical properties were assessed during re-establishment of tendon architecture after injury. Fibril diameter distribution was impacted by both decorin and biglycan knockdown at 3 and 6 weeks compared to WT. Although early healing appeared to be delayed in the I-Bgn^-/- tendons (larger scar tissue area at 3 weeks), no differences in failure properties were detected. By 6 weeks, in the I-Dcn^-/- tendons, we observed a better recovery of viscoelastic properties compared to the WT tendons (reduced stress relaxation and increased dynamic modulus) when the knockdown was induced early. This could be explained by the increased expression of other matrix proteins, such as elastin whose gene expression was increased at 3 weeks in the I-Dcn^-/- tendons. Despite an impact on collagen fibrillogenesis, decorin and/or biglycan knockdown did not produce a detectable effect on quasi-static properties after patellar tendon injury. However, early decorin knockdown resulted in better recovery of viscoelastic properties. Mechanisms underlying this result remained to be clarified in further studies.

The effectiveness of neuromuscular electrical stimulation (NMES) depends on electrode placement, with placement over the motor points (MPs) being the most effective. This study aimed to determine the MP-distribution and establish a heatmap indicating the probability of finding a MP in different areas of the hamstring (H) muscles. Additionally, inter-individual variations in the number of MPs were investigated. Thirty-one healthy participants (15 females, 16 males), aged 18-65 years, were included. The individual anatomy of the H-muscles, including the biceps femoris (BF), semitendinosus (ST), and semimembranosus (SM), was determined using ultrasound. MPs were located using a MP-search pen. The thigh anatomy was divided into 70 3x3cm areas, and the probability of finding a MP in each area was calculated using the Clopper-Pearson test to create a heatmap. Regression analysis was employed to determine if patient characteristics were associated with the number of MPs. The two best areas, found over BF and ST, exhibited a 39% probability of containing a MP and were significantly more likely to contain a MP compared to 85% of the remaining areas (p < 0.05). Two areas over SM had a 29% probability of containing a MP. BF exhibited a significantly higher number of MPs compared to SM and ST (p < 0.001). Male sex and higher physical activity were independent explanatory factors positively correlated with the number of MPs over BF in the multiple linear regression (R² = 0.38, p = 0.001). The MP-heatmap of the H-muscles can effectively facilitate NMES application by highlighting areas with a higher probability of finding a MP. Large inter-individual variations in location and number of MPs were demonstrated.

This study determined the insertion angle at the porcine anterior cruciate ligament (ACL) enthesis under joint loading to provide information on the structure and mechanical function of the enthesis. Ten intact porcine knee joints were harvested, and an anterior tibial load was applied using a robotic testing system. After dissecting a portion of the ACL enthesis along ligament fibers, the remaining enthesis was imaged using a digital microscope while reproducing the three-dimensional intact knee motion. Fiber orientation angles (FOAs) in the enthesis region (0-300 µm from the ligament-bone boundary) and the ligament region (500-2000 µm from the ligament-bone boundary) were analyzed in the femoral and tibial entheses of the anteromedial bundle (AMB) of the ACL under loading. On the femoral side, the FOA in the enthesis region was significantly higher than that in the ligament region by approximately 10 degrees under loading (n = 5, p < 0.05 in paired t-test). In contrast, the FOAs in the enthesis and ligament regions on the tibial side were nearly equal under loading, with no significant difference (n = 5, p > 0.15 in paired t-test). Histological examination indicated that uncalcified fibrocartilage (UF) was abundant in the enthesis region of the AMB femoral enthesis while the UF was not observed in the enthesis region of the AMB tibial enthesis. Thus, the current data suggest that the regional dependence and independence in FOA are caused by the presence or absence of UF and contributes to a moderate and subtle load-transduction in the ACL enthesis.

Despite progress, osteochondral (OC) tissue engineering strategies face limitations in terms of articular cartilage layer development and its integration with the underlying bone tissue. The main objective of this study is to develop a zonal OC scaffold with native biochemical signaling in the cartilage zone to promote articular cartilage development devoid of cells and growth factors. Herein, we report the development and in vivo assessment of a novel gradient and zonal-structured scaffold for OC defect regeneration. The scaffold system is composed of a mechanically supportive 3D-printed template containing decellularized cartilage extracellular matrix (ECM) biomaterial in the cartilage zone that possesses bioactive characteristics, such as chemotactic activity and native tissue biochemical composition. OC scaffolds with a bioactive cartilage zone were implanted in vivo in a rabbit osteochondral defect model and assessed for gross morphology, matrix deposition, cellular distribution, and overall tissue regeneration. The scaffold system supported recruitment and infiltration of host cells into the cartilage zone of the graft, which led to increased ECM deposition and physiologically relevant articular cartilage tissue formation. Semi-quantitative ICRS scoring (overall score double for OC scaffold with bioactive cartilage zone compared to PLA scaffold) further confirm the bioactive scaffold enhanced articular cartilage engineering. This strategy of designing bioactive scaffolds to promote endogenous cellular infiltration can be a much simpler and effective approach for OC tissue repair and regeneration.

Osteoporosis represents a major healthcare concern. The development of novel treatments presents challenges due to the limited cost-effectiveness of clinical trials and ethical concerns associated with placebo-controlled trials. Computational models for the design and assessment of biomedical products (In Silico Trials) are emerging as a promising alternative. In this study, a novel In Silico Trial technology (BoneStrength) was applied to replicate the placebo arms of two concluded clinical trials and its accuracy in predicting hip fracture incidence was evaluated. Two virtual cohorts (N = 1238 and 1226, respectively) were generated by sampling a statistical anatomy atlas based on CT scans of proximal femurs. Baseline characteristics were equivalent to those reported for the clinical cohorts. Fall events were sampled from a Poisson distribution. A multiscale stochastic model was implemented to estimate the impact force associated to each fall. Finite Element models were used to predict femur strength. Fracture incidence in 3 years follow-up was computed with a Markov chain approach; a patient was considered fractured if the impact force associated with a fall exceeded femur strength. Ten realizations of the stochastic process were run to reach convergence. Each realization required approximately 2500 FE simulations, solved using High-Performance Computing infrastructures. Predicted number of fractures was 12 ± 2 and 18 ± 4 for the two cohorts, respectively. The predicted incidence range consistently included the reported clinical data, although on average fracture incidence was overestimated. These findings highlight the potential of BoneStrength for future applications in drug development and assessment.

Purpose: In treating prostate cancer, distinguishing alpha and beta therapies is vital for efficient radiopharmaceutical delivery. Our study introduces a 3D image-based spatiotemporal computational model that utilizes MRI-derived images to evaluate the efficacy of ²²⁵Ac-PSMA and ¹⁷⁷Lu-PSMA therapies. We examine the impact of tumor size, diffusion, interstitial fluid pressure (IFP), and interstitial fluid velocity (IFV) on the absorbed doses.

Methods: An MRI-based geometric model of the tumor and its surrounding environment is initially developed. Subsequently, COMSOL Multiphysics software is utilized to solve convection-diffusion-reaction equations and conduct numerical analyses of blood pressure distribution. This computational methodology provides valuable insights into interstitial fluid patterns and the spatiotemporal distribution of extracellular and intracellular concentrations of ²²⁵Ac-PSMA and ¹⁷⁷Lu-PSMA. In addition, our study investigates the impacts of increasing tumor size on absorbed doses and mechanisms involved in radiopharmaceutical transport and delivery.

Results: Larger tumors have diminished absorbed doses, highlighting the need for customized treatments according to tumor size. Diffusion significantly influences the transport and delivery of radiopharmaceuticals. Additionally, alpha therapy was observed to consistently yield higher absorbed doses within the tumor than beta therapy.

Conclusions: This study reveals the complex interplay between radiopharmaceutical properties, the tumor microenvironment, and treatment outcomes. It highlights the potential of ²²⁵Ac-PSMA in prostate cancer treatment, advocating for personalized treatment strategies tailored to the specific characteristics of each patient and their tumor.

Subject-specific knee joint models are widely used to predict joint contact mechanics for individuals but may not capture the variance in knee joint geometry across a population. Statistical shape modeling uses the dataset of a cohort to encapsulate population-wide variability. The present study aimed to develop a shape modeling procedure for poromechanical finite element models of knee joint to account for population diversity in the creep response of knees. Shape models of right knee joints were created from MRI of 31 healthy male subjects using principal component analysis. Creep analysis was performed for 13 shape models in total, i.e., the average model, plus six models for both the first and second principal modes. For a given loading, the contact and fluid pressures varied substantially within these mathematically produced models but compared reasonably well to that of three subject-specific models that were constructed from individual knees, representing approximately the smallest, median and largest knees of the 31 right knees. While the joint size variation, generally represented by the first principal component, predominantly influenced the magnitudes of contact and fluid pressures, the joint shape variation characterized by the second principal component further affected the pressure distribution, and load sharing between the lateral and medial compartments. The present study evaluated a workflow for the statistical shape modeling of poromechanical behavior of knee joints with sample results based on a small population. However, the workflow can be readily used for a large population to address the challenge of interpatient variability in joint contact mechanics, particularly in contact and fluid pressures in articular cartilage, and variable creep behaviors of the joint associated with individual anatomical variations.

Purpose: The impact of Aortic Stenosis (AS) on the left ventricle (LV) extends beyond the influence of the pressure drop across the stenotic valve, but also includes the additional serial afterload imposed by the vascular system. Aortic input impedance is the gold standard for comprehensively studying the contribution of the vascular system to total myocardial afterload, but in the past measurement has been challenging arising from the need for invasive catheterization or specialized equipment to precisely record time-resolved blood pressure and flow signals. The goal of this work was to develop and validate a novel simulation-based method for determining aortic input impedance using only clinically available echocardiographic data and a simple blood pressure measurement.

Methods: A simulation-based method to determine vascular impedance was developed using echocardiographic data and a brachial blood pressure measurement. Simulation-based impedance was compared to impedance calculated from echocardiographic flow data and pressure data from a non-invasive central pressure measurement device.

Results: In validation analysis comparing patient-specific simulation-based vascular impedance to non-invasively measured impedance, correlation between methods across a range of vascular parameters varied between R² = 0.40 and 0.99. A tendency was seen toward underestimation of pressure waveforms in point-by-point comparison of measured and simulated waveforms with an overall mean difference of 4.01 mmHg.

Conclusions: Requiring only non-invasive clinical data that are widely available, simulation-based vascular impedance has the potential to allow for easier, more widespread, and larger-scale investigation of the effect of vascular impedance on total LV afterload.