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.

Seat interface forces, particularly shear forces, play an essential role in predicting the risk of pressure ulcers and seating discomfort. When a finite element human body model (HBM) is used for static seating simulation, the most common loading method is to put the model in a position close to the desired final posture and then 'drop' it from just above the seat by applying the gravity (DROP). This does not represent how people sit in a seat. In addition, high coefficients of friction (COF) are often used to prevent sliding, which may lead to unrealistically high tangential forces. This study aims to investigate the effects of the loading process and the COF on seating simulations with a HBM. We propose a new loading approach called 'drop and rotate' (D&R) to better mimic people sitting on a seat. With the trunk more flexed than the desired posture, the model is dropped to establish the contact between the buttocks and thighs, and the seat pan first, and then between the back and the backrest by rotating the hip. Simulations were performed using a recently developed and validated adult male model in two different seat configurations. Results show that the proposed D&R method was less sensitive to COF and gave a better prediction of contact forces, especially on the seat pan. However, its computational time is higher than the DROP method. The study highlights the importance of the loading process when simulating static seating.

The SARS-CoV-2 virus reportedly originated in Wuhan in 2019, causing the coronavirus outbreak (COVID-19), which was technically designated as a global epidemic. Numerous studies have been carried out to diagnose and treat COVID-19 throughout the midst of the disease's spread. However, the genetic similarity between COVID-19 and other types of coronaviruses makes it challenging to differentiate between them. Therefore it's essential to swiftly identify if an epidemic is brought on by a brand-new virus or a well-known disease. In the present article, the DeepCoV deep-learning (DL) approach utilizes layered convolutional neural networks (CNNs) to classify viral serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) besides other viral diseases. Additionally, various motifs linked with SARS-CoV-2 can be located by examining the computational filter processes. In identifying these important motifs, DeepCoV reveals the transparency of CNNs. Experiments were conducted using the 2019nCoVR datasets, and the results indicate that DeepCoV performed more accurately than several benchmark ML models. Additionally, DeepCoV scored its maximum area under the precision-recall curve (AUCPR) and receiver operating characteristic curve (AUC-ROC) at 98.62% and 98.58%, respectively. Overall, these investigations provide strong knowledge of the employment of deep learning (DL) algorithms as a crucial alternative to identifying SARS-CoV-2 and identifying patterns of disease in the SARS-CoV-2 genes.

Understanding the complex three-dimensional (3D) dynamic interactions between self-contained breathing apparatus (SCBA) and the human torso is critical to assessing potential impacts on firefighter health and informing equipment design. This study employed a multi-inertial sensor fusion technology to quantify these interactions. Six volunteer firefighters performed walking and running experiments on a treadmill while wearing the SCBA. Calculations of interaction forces and moments from the multi-inertial sensor technology were validated against a 3D motion capture system. The predicted interaction forces and moments showed good agreement with the measured data, especially for the forces (normal and lateral) and moments (x- and z-direction components) with relative root mean square errors (RMSEs) below 9.4%, 7.7%, 7.7%, and 7.8%, respectively. Peak pack force reached up to 150 N, significantly exceeding the SCBA's intrinsic weight during SCBA carriage. The proposed multi-inertial sensor fusion technique can effectively evaluate the 3D dynamic interactions and provide a scientific basis for health monitoring and ergonomic optimization of SCBA systems for firefighters.

This paper proposes a modified Quantum Dilated Convolutional neural network (QDCNN) to detect cancer using gene expression data. Primarily, the input gene expression data is taken from a specified dataset. Then, data transformation is done using Adaptive Box-Cox transformation and feature fusion is done by a Deep Neural Network (DNN) with Kulczynski. The refined features are then fed into the modified QDCNN, which effectively predicts cancer. The modified QDCNN attains an accuracy of 90.6%, a True Positive Rate (TPR) of 89.0%, False Negative Rate (FNR) of 0.109, and a Matthews correlation coefficient (MCC) of 89.9% when using the PANCAN dataset.

The surge in popularity of running has led to a multitude of designs in running shoe technology, notably, there is an increasing trend in toe spring elevation. However, the impact of this design on foot structures during running remains an essential exploration. To investigate the effects of toe spring on the foot during forefoot running, we employed finite element simulation to create two sole models with different toe spring heights (6.5 cm and 8 cm) and ground contact angles (5°, 10°, and 15°). We established and validated two foot-shoe coupling models and compared stress variations in metatarsal bones and the big toe under identical loading and environmental conditions. Higher toe spring resulted in lower peak stress and reduced stress concentration in metatarsal bones. The fourth and fifth metatarsals exhibited increasing stress trends with ground contact angle, with the fifth metatarsal experiencing the most significant stress concentration. In the case of low toe spring, stress on the fifth metatarsal increased from 15.917 MPa (5°) to 27.791 MPa (15°), indicating a rise of 11.874 MPa. Conversely, the first metatarsal showed lower stress, indicating relative safety but reduced functional significance. Moreover, higher toe spring running shoes exerted less pressure on the big toe, with an increasing trend in stress on the big toe with an increase in ground contact angle. Shoes with a higher toe spring design result in reduced pressure on the big toe. Therefore, it is advisable to avoid landing angles greater than 15° to prevent stress fractures resulting from repetitive loading.

The global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD $I{L}_2$ Z, is developed for the early detection and treatment of lung cancer. This model incorporates $I{L}_2$ cytokine and anti-PD-L1 inhibitors, enhancing the immune system's anticancer response within five epidemiological compartments. The TCD $I{L}_2$Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number ($R_0$), stability, and sensitivity analysis. Next-generation techniques based on $R_0$ that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with $I{L}_2$ and anti-PD-L1 inhibitors admiring the Lyapunov functions' applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler's method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with $I{L}_2$ and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes.

Purpose: To optimize scoliosis correction strategies by comparing continuous and interval pedicle screw configurations and proposing a dual-geometry screw design.

Methods: A patient-specific T11-L5 scoliotic spine model was reconstructed via finite element analysis (FEA). Continuous and interval screw placements were evaluated for biomechanical performance. A novel dual-geometry screw (tapered-cylindrical transition) was developed.

Results: Continuous configurations achieved a 43.5% reduction in displacement (1.33 mm vs. 2.36 mm) and a 29.7% decrease in screw stress (444.08 MPa vs. 631.35 MPa). The dual-geometry screw lowered drilling stress (16.5%, p < 0.05) and displacement heterogeneity (22.4%).

Conclusion: Continuous screws enhance stability through synergistic load transfer, while dual-geometry screws mitigate interfacial damage. This provides biomechanical criteria for clinical scoliosis correction.