Computer Methods in Biomechanics and Biomedical Engineering最新文献

Atherosclerosis in the carotid arteries increases stroke risk, yet current treatment decisions rely mainly on stenosis degree, which poorly reflects individual vulnerability. We present an ultrasound-based computational fluid dynamics (CFD) framework for patient-specific hemodynamic assessment. Using tracked 2D ultrasound and automated segmentation, we reconstructed carotid geometries for five healthy subjects and three patients with severe stenoses. CFD simulations quantified TAWSS, OSI, RRT, and helicity, visualized through risk maps. Healthy arteries showed localized risk near bifurcations, whereas stenosed geometries exhibited extensive disturbed flow and altered helicity patterns. This approach demonstrates the feasibility of ultrasound-driven CFD for personalized risk mapping and highlights helicity's potential as a diagnostic marker.

The defense against microbial pathogens can be functionalized by leukocytes because via singling immune response to enhance Inflammation. In this manuscript, a dynamical analysis for the concentration of circulating white blood cells is functionalized by fractional differential operators. The mathematical investigations for fractionalized and non-fractionalized concentration of circulating white blood cells have been traced out. The comparative analysis of circulating white blood cells has been discussed for delay between white blood cell productions. Finally, our results suggested that the hemogram reflects blood-clotting disorders and infection on the basis of fractionalized and non-fractionalized concentration of circulating white blood cells.

This study investigated an upper-extremity exoskeleton for machine learning-based discrimination of orthopedic shoulder pathology and identification of discriminative temporal features. Twelve patients with shoulder impairments and thirty healthy controls performed eight standardized tasks. Logistic regression with stratified 5-fold cross-validation was used for classification. Temporal effect sizes were computed using pointwise Cohen's d, and permutation-based phase ablation quantified the contribution of movement phases to AUROC. Classification performance ranged from 0.70 to 1.00, with six tasks achieving AUROC ≥ 0.90. Mid-cycle phases dominated in flexion and abduction tasks, whereas early and late phases were most informative for rotational movements, supporting interpretable, phase-aware ML models.

Magnetic fluid hyperthermia and magnetic drug delivery depend on accurate prediction of ferrofluid transport and heat transfer within tumour-surrounded blood vessels. Motivated by the need for physiologically realistic modelling of such therapies, the current study develops a mathematical model of ferrofluid flow and heat transfer in an inclined cylindrical vessel immersed in tumour tissue, taking into account temperature-dependent thermal conductivity and viscosity, as well as magnetic-field-induced body forces. This novel study integrates inclined flow within tumour-surrounded vessels, non-constant thermophysical properties, and magneto-thermal coupling. With the one-dimensional axisymmetric form of coupled momentum and energy equations, in tumour tissue, we describe a nonlinear thermal and flow response to excitation by a magnetic field. This creates a resulting boundary value problem that is non-dimensionalised using a similarity transformation and solved numerically with MATLAB's bvp4c, allowing for a parametric study over the inclination angle, ferromagnetic interaction parameter, and nanoparticle concentration. The results show that temperature-dependent properties influence velocity gradients, skin friction, and heat transfer, particularly near the vessel tumour interface. Thermal transport is further intensified by radiative effects and internal heat generation, leading to a notable enhancement of the Nusselt number, while inclination and curvature introduce secondary but non-negligible modifications. Overall, the study provides quantitative insight into magneto-thermal interactions in ferrofluid-based therapies and offers a theoretical basis for improving magnetic hyperthermia and targeted drug delivery strategies.

This study proposes a mandibular fixation technique based on mortise-and-tenon construction. Mandibular defect models with varying interlocking angles were established using the fibula and iliac as grafting materials, and comparative analyses of static stress distribution, displacement control, and fatigue life were conducted. Optimal performance was achieved with a fibular tenon width of 0.8 cm, a length of 1 cm, and an angle of 75°. Fatigue analyses indicated that the system satisfied clinical requirements. The proposed method shows potential for clinical application in mandibular reconstruction.

In this work, a novel framework is proposed which includes Hjorth parameters as features from time and time-frequency domain (Multi-Domain) and attention-enhanced temporal modeling, to classify epileptic seizure stages, namely normal, inter-ictal, and ictal. Three different approaches are compared, i.e. Hjorth parameters in time domain, time-frequency domain, and multi-domain. In time-frequency domain, Hjorth parameters are derived from the wavelet coefficients obtained using Discrete Wavelet Transform (DWT). The extracted features are then fed to a 1D Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and attention mechanism. The performance of the proposed framework is evaluated on Bonn EEG dataset using different performance evaluation metrics namely precision, recall, F1-score, and accuracy. The binary, three-class, and five-class seizure classification are examined using the proposed framework. The validation of the model is performed through the 10-fold cross-validation with sample level partitioning. Experimental findings show that the proposed framework with multi-domain features has given outstanding performance with 98.40, 98.00, and 85.40% test classification accuracy for binary, three-class, and five-class discrimination, respectively.

Diabetes, one of the most serious diseases in the world, however, early detection can prevent diabetes. This work proposes a novel approach to identifying early signs of diabetes based on deep learning methods. First, the input data is pre-processed and the features are selected using an improved Cheetah Optimization (ICO). Finally, diabetes is classified using a dual attention-based deep cat convolutional stacked sparse autoencoder model (DA_DCC_SSAE). The proposed study improves the results and proves that the proposed method produces better results in terms of accuracy (98.4% - dataset-1, 98% - dataset-2, 97.4% - dataset-3, and 96.8% - dataset-4.

This study constructed a prognostic and immunotherapy predictive model for gastric cancer based on amino acid metabolism-related genes. Using data from TCGA and GEO databases, the model was built via Cox and Lasso regression and validated in independent cohorts. It effectively predicts patient survival and shows significant correlations with the tumor immune microenvironment, immune cell infiltration, and immune checkpoint expression. Drug sensitivity analysis suggests potential therapeutic options. This model may serve as a potential biomarker for predicting prognosis and immunotherapy efficacy in gastric cancer patients.

This study established a canine model of non-transport disc distraction osteogenesis (NTDDO) to reconstruct segmental mandibular defects and evaluated its impact on temporomandibular joint (TMJ) biomechanics. Cone Beam computed tomography (CBCT) tracked new bone regeneration in the distraction gap and condylar changes. Three-dimensional finite element analysis (FEA) models were developed to assess the stress changes of condyles, articular discs and distractor at different time points. Condyles and articular discs histological changes were observed. The results showed that the newly formed bone increased in density with prolonged consolidation. On the healthy side, the lateral pole of the condylar head translated forwards and downwards, and the condyle underwent clockwise rotation in both the orbital-auricular and coronal planes. On the distracted side, the medial pole of the condylar head moved downwards, with the condyle rotating clockwise in the coronal plane postoperatively. However, comparisons of the overall condylar positions preoperatively, at the end of distraction, and after eight weeks of consolidation revealed no statistically significant changes. At the postoperative period, FEA revealed a concentrated area of stress on both condyles and articular discs, whereas the stress distribution was relatively uniform preoperatively and after 8 weeks of consolidation. The maximum stress of the distractor occurred at the joint between the distractor wing and the bar. Histological analysis of the condyles and articular discs harvested from stress concentration zones showed intact cartilage structure. The established NTDDO model effectively repairs segmental mandibular defects while inducing temporary TMJ biomechanical alterations without causing irreversible joint damage.

Prostate cancer (PCa) is a leading male malignancy. This study explores the anti-PCa mechanism of Qianlie Xiaozheng decoction (QLXZD) using network pharmacology. From 34 ingredients and 23 potential therapeutic targets, 3 hub ingredients (baicalein, kaempferol, quercetin) and 4 hub targets (CCNB1, CDK1, EGFR, TOP2A) were prioritized. Enrichment analysis of the 23 targets linked them to cell cycle and kinase signaling. Molecular docking confirmed strong binding of the hub ingredients to the hub targets, comparable to known inhibitors. Molecular dynamics simulations supported baicalein-TOP2A complex stability. These findings reveal QLXZD exerts anti-PCa effects via a multi-component, multi-target mechanism, supporting its clinical application.