Computer Methods in Biomechanics and Biomedical Engineering最新文献

Background: The incidence of cervical spondylosis is increasing, gradually affecting people's normal lives. Establishing a finite element model of the cervical spine is one of the methods for studying cervical spondylosis. MRI (Magnetic Resonance Imaging) still has certain difficulties in transitioning from human imaging to establishing muscle models suitable for finite element analysis. Medical software provides specific morphologies and can generate muscle finite element models. Additionally, there is little research on the static analysis of cervical spine finite element models with solid muscle.

Purpose: A new method is proposed for establishing a finite element model of the cervical spine based on CT (Computed Tomography) data and medical software, and the model's effectiveness is validated. Human movement characteristics based on the force distribution in various parts are analyzed and predicted.

Methods: The muscle model is reconstructed in medical software and a three-dimensional finite element model of the entire cervical spine (C0-C7) is established by combining muscle models with CT vertebral data models. 1.5 Nm of load is applied to the finite element model to simulate the cervical spine movement.

Results: The finite element model was successfully established, and effectiveness was verified. Stress variations in various parts under six movements were obtained. The effectiveness of the model was basically verified.

Conclusion: The finite element model of the cervical spine for mechanical analysis can be successfully established by using medical software and CT data. In daily life, the C2-3, C3-4, C4-C5 intervertebral discs, rectus capitis posterior major, longus colli, and obliquus capitis inferior are more prone to injury.

This study investigated how low-intensity exercise affects hemodynamics in small saccular and fusiform abdominal aortic aneurysms (AAAs). Patient-specific CFD simulations were performed under resting and exercise conditions. At rest, saccular AAAs exhibited smaller low-shear, high-oscillatory, and high-activation regions than fusiform AAAs. Exercise eliminated low-shear zones and reduced oscillatory behavior but induced high-shear jets impacting saccular walls, while fusiform AAAs showed more uniform flow. These findings suggest that low-intensity exercise improves adverse hemodynamics and achieves a better balance between shear normalization and wall stress in fusiform AAAs, whereas saccular AAAs may experience localized stress elevation and increased rupture risk.

The effective reconstruction of osteochondral biomimetic structures is a key factor in guiding the regeneration of full-thickness osteochondral defects. Due to the avascular nature of hyaline cartilage, the greatest challenge in constructing this scaffold lies in both utilizing the biomimetic structure to promote vascular differentiation for nutrient delivery to hyaline cartilage, thereby enhancing the efficiency of osteochondral reconstruction, and effectively blocking vascular ingrowth into the cartilage layer to prevent cartilage mineralization. However, the intrinsic relationship between the planning of the microporous pipe network and the flow resistance in the biomimetic structure, and the mechanism of promoting cell adhesion to achieve vascular differentiation and inhibiting cell adhesion to block the growth of blood vessels are still unclear. Inspired by the structure of tree trunks, this study designed a biomimetic tree-like tubular network structure for osteochondral scaffolds based on Murray's law. Utilizing computational fluid dynamics, the study investigated the influence of the branching angle of micro-pores on the flow velocity, pressure distribution, and scaffold permeability within the scaffold. The results indicate that when the differentiation angle exceeds 50 degrees, the highest flow velocity occurs at the confluence of tributaries at the ninth fractal position, forming a barrier layer. This structure effectively guides vascular growth, enhances nutrient transport capacity, increases flow velocity to promote cell adhesion, and inhibits cell infiltration into the cartilage layer.

This study aimed to predict the index of effectiveness (IE) and positive impulse proportion (PIP) to assess the cyclist's pedalling technique from lower limb kinematic variables. Several wrapped feature selection techniques were applied to select the best predictors. To predict IE and PIP two multiple linear regressions (MLR) composed of 11 predictors (R² = 0.81 ± 0.12, R² = 0.81 ± 0.05) and two artificial neural networks (ANN) composed of 21 and 28 predictors (R² = 0.95 ± 0.01, R² = 0.92 ± 0.02) were developed. The ANN predicts with accuracy, and the MLR shows the influence of each predictor.

Identifying appropriate biomarkers for sepsis diagnosis and treatment holds great significance. This study aims to explore potential sepsis biomarkers through bioinformatics analysis. The expression profile data of sepsis were downloaded from Gene Expression Omnibus (GEO) database. Hub genes were screened and performed functional analysis, therewith verified using external datasets. The mechanisms and potential therapeutic agents were also identified. A total of 14 Hub genes were identified in this study including PRF1, CD247, IL7R, CD27, CCR7, IL2RB, GZMB, KLRK1, GZMK, CD160, FCGR1A, RUNX3, HLA-DRB1 and PRKCQ, which may serve as potential biomarkers for diagnosis and treatment of sepsis.

Microneedles, as a new efficient and safe transdermal drug delivery technology, has a wide range of applications in drug delivery, vaccination, medical cosmetology, and diagnostics. The degree of microneedles penetration into the skin determines the reliability of the delivery dose, but its evaluation is not yet well-established, which is one of the major constraints in the commercialization of microneedles. In this paper, a novel visual simulated skin model was developed with reference to the physical properties of real skin. The simulated skin model was well-designed and its prescription was optimized to make the thickness, hardness, elasticity, and other parameters close to those of real skin. It not only meets the need to assess the degree of insertion of microneedles but also provides a visual observation of the insertion state of microneedles.

Idiopathic pulmonary fibrosis (IPF) is a fatal fibrotic lung disease. This study aimed to explore cuproptosis-related molecular clusters and build a predictive model for IPF. Using dataset GSE32537, we analyzed immune infiltration and cuproptosis regulators, followed by WGCNA and machine-learning modeling. Two cuproptosis-related clusters were identified, with C2 showing stronger immune activation. Among four algorithms, the GLM model showed the best performance (AUC = 0.992) and yielded a five-gene signature. The nomogram and calibration analyses confirmed its accuracy. This study provides a reliable IPF prediction model and insight into cuproptosis-related mechanisms.

The homogenization of plantar pressure distribution is of great significance for the treatment of diabetic foot, lower extremity sports injuries, and abnormal posture. Reasonable insole design is currently the more mainstream method to adjust the plantar pressure distribution. This research designed nine types of porous insoles with different hole types, hole distributions, and hole sizes. The finite element method is used to simulate the equilibrium state of the foot presses on the insole for obtaining the contact stress distribution of the foot sole and the deformation of the insole. The foot sole is abstracted into a rigid block with a relatively small curvature. Porous insoles with straight and larger holes are not conducive to reducing the peak pressure, while spindle-shaped porous insoles can effectively reduce the peak pressure up to 16.2% and reduce the ratio of contact surface area in high stress areas up to 50%. The influence of porosity on the peak plantar pressure is not clear when the hole types are different. When designing the stiffness distribution of an insole, the continuity of stiffness must be considered. Areas with sudden changes in stiffness may result in higher contact stresses. As the plantar pressure distribution tends to become more uniform, the compression of the insole increases, thus requiring a greater initial thickness of the insole to provide sufficient deformation allowance.

Autism is a complex psychiatric condition that needs to be diagnosed early using more objective techniques. Hence, many researchers have turned to diagnosing autism by analyzing EEG signals. However, a comprehensive framework for this has not yet been introduced, and there is room for improvement. In this study, to increase the precision of autism diagnosis from EEG signals, a new framework is introduced that includes the steps of data preprocessing, extraction of nonlinear features from EEG time series, optimization of extracted features using an innovative technique based on GA and DBSCAN algorithms, feature reduction using the LASSO technique, and classification using a fuzzy ELM classifier. This study presents a feature optimization technique that leverages a genetic algorithm informed by clustering principles. Rather than relying on random selection for forming the new generation, the approach incorporates clustering during the fitness evaluation phase to identify and exclude outliers from advancing to the next generation. The recommended scheme was examined on two EEG databases. Using only 14-channel EEG data, it was able to achieve 96.81% accuracy, 95.16% sensitivity, 97.73% specificity, and a 96.42% F1-score for autism detection using database A, as well as 97.64% accuracy, 96.55% sensitivity, 98.49% specificity, and a 97.51% F1-score using database B. This framework outperformed existing methods on two EEG databases. The practical use of low-channel systems suggests potential for real-world clinical deployment, enabling scalable and cost-effective screening. This study underscores the potential of using nonlinear dynamics of EEG signals alongside fuzzy ELM for diagnosing autism in children.

To advance the understanding of arterial diseases, it is essential to develop hemodynamic models that accurately predict pulsatile blood flow within arterial tree. This study presented a one-dimensional (1D) nonlinear analytical model (NLAM) for simulating blood flow. By decomposing pulsatile flow, NLAM addressed nonlinear terms in governing equations and derived an analytical solution for a single artery, extended to the entire system via vessel coupling. Validation comfirms its effectiveness in simulating basic hemodynamics and identifying potential locations and severity of pathological changes. This work aims to refine previous modeling approaches and provide a theoretical basis for clinical diagnosis and treatment.