Computer Methods in Biomechanics and Biomedical Engineering最新文献

Valvular heart disease (VHD) is a major cause of loss of physical function, quality of life and longevity, and its prevalence is growing worldwide due to increased survival rates and an aging population. The most common treatment for VHD is surgical heart valve replacement with mechanical heart valves (MHVs) and bioprosthetic heart valves (BHVs), but with different limitations. Polymeric heart valves (PHVs) exhibit promising material properties, valve dynamics and biocompatibility, representing the most feasible alternative to existing artificial heart valves. However, inadequate fatigue performance remains a critical obstacle to their clinical translation. In this case, geometry and material design are essential to obtain the best mechanical properties of the PHV. In this study, we summarized the effects of optimal design of PHVs from geometrical configuration optimization (valve height, thickness and design curve) and structural material optimization (anisotropy, fiber reinforcement, variable thickness, microstructure and asymmetric optimization), and selected the parameters including Effective Orifice Area (EOA), Regurgitant fraction (RF), and Stress Distribution to compare the performance of valves. It would provide the theoretical support for the optimal design of PHVs.

We introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose-effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets.

Recent reports have highlighted a notable prevalence of atypical hangman's fractures, yet their biomechanical aspects remain underexplored. Using a validated finite element model, this study assesses changes in rotation-moment characteristics of the upper cervical spine due to fractures involving the superior and inferior articular process, pars interarticularis, and lamina. The results revealed that fractures affecting the superior articular process and pars interarticularis led to significant instability, particularly in axial rotation and extension. However, atypical hangman's fractures did not necessarily produce greater instability than Levine-Edwards type II hangman's fractures.

Exercise-induced laryngeal obstruction (EILO) is a known cause of exertional dyspnoea, characterised by paradoxical inward collapse of laryngeal tissues. The pathophysiological mechanisms of EILO remain to be fully established, but insufficient mechanical resistance of laryngeal tissues to air-induced loads is hypothesised. It is understood that airflow and anatomic configurations of the airway play a key role in the wall pressure distribution of the larynx. While breathing is a cyclic process with directional changes of airflow, the literature is confined to steady, unidirectional airflow. It is necessary to assess the role of oscillatory airflow on the loads on the laryngeal airway. This study investigates the effect of oscillatory airflow on the laryngeal flow fields and air-induced loads. A computational fluid dynamics model of the upper respiratory tract (URT) is developed using the Reynolds-averaged Navier-Stokes equations. Five oscillatory airflow cases through a single geometry are considered, utilising sinusoidal breathing cycles with different breathing frequencies (24, 32 and 40 breaths per minute) and peak inspiratory flow rates (96, 168 and 240 L/min). Results include the airflow velocity distribution in the URT, and the air-induced pressure and forces. It is demonstrated that inspiratory velocity distribution varies with breathing frequency and intensity. The force acting on the URT walls are in-phase with the airflow rate and therefore exhibit quasi-steady behaviour. These findings are also reflected in the force vectors acting on the aryepiglottic folds and indicate that air-induced closure of the supraglottis in EILO is influenced by the breathing intensity rather than the breathing frequency.

Adult-acquired flatfoot causes various deformities. If a patient-specific foot model can be created using the finite element method, it can be used to study the appropriate surgical technique for each patient. Nine patient-specific flatfoot models were created, and loading simulations were performed. To validate the models, the patients' weight-bearing radiographs were compared with the parameters of the models. The CCC values ranged from 0.917 to 0.993 , all exceeding the moderate threshold according to the McBride criteria. Our model reproduces the biomechanics of a patient's foot under loading conditions, which may be useful for investigating patient-specific surgical procedures.

Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.

This paper introduces a mathematical model for the growth of transactive response DNA binding protein of 43 kDa (TDP-43) inclusion bodies in neuron soma. The parameter representing the accumulated neurotoxicity caused by misfolded TDP-43 oligomers is also introduced. The model's equations enable the numerical calculation of the concentrations of TDP-43 monomers, dimers, free oligomers, and oligomers deposited in inclusion bodies. By simulating the deposition of free oligomers into inclusion bodies, the model predicts the size of TDP-43 inclusion bodies. An approximate solution to the model equations is derived for the scenario where protein degradation machinery is dysfunctional, leading to infinite half-lives for TDP-43 dimers, monomers, and both free and deposited oligomers. This solution, valid at large times, predicts that the radius of the inclusion body increases proportionally to the cube root of time, whereas the accumulated neurotoxicity increases linearly with time. To the best of the author's knowledge, this study is the first to model the relationship between the size of TDP-43 inclusion bodies and time, and the first to introduce the concept of accumulated neurotoxicity caused by misfolded TDP-43 oligomers. Sensitivity analysis of the approximate solution indicates that the inclusion body radius and accumulated neurotoxicity become independent of the kinetic constants at large timescales. Unlike the case of infinite half-lives, the numerical solution for physiologically relevant (finite) half-lives demonstrates that the long-term behavior of the inclusion body radius and accumulated neurotoxicity remains dependent on the kinetic constants, converging to distinct curves over time.

This study investigates the dynamic breast response of nine representative breast types by finite element method, focusing on the effects of breast volume, running speed, and nipple spatial configuration. The simulation revealed that greater breast volume and higher speed significantly increased displacement and acceleration (p < 0.001). The volume and speed were key predictors of breast motion in both Y- and Z-directions, while nipple position type modulated lateral dynamics. Notably, middle-type breasts exhibited greater lateral stability. These findings offer biomechanical insights into breast movement, and provide basis for the design of sports bras tailored to different breast morphologies.

The regulator of G-protein signaling (RGS) family is involved in the malignant progression of several cancers, but has not been adequately studied in glioma. Six RGS genes with differential expression were evaluated using differential expression analysis. Four genes, including RGS19, which correlated with the prognosis, were screened. A nomogram was completed by the 'rms' package. The correlation between RGS19 and immune infiltration was analyzed using the TIMER website, the 'GSVA' and 'estimate' packages. The GO and KEGG annotation analyses were completed by the 'clusterProfiler' package to screen potential signaling pathways associated with RGS19.

Accurate prediction of drug-target interactions (DTI) is critical for accelerating drug discovery. In this study, we propose deep contrastive learning (DeepCL), a novel deep contrastive learning framework that significantly improves DTI prediction accuracy, particularly in low-coverage scenarios. Unlike traditional methods, DeepCL introduces a generalized sigmoid activation function to resolve numerical underflow issues and employs a margin-based contrastive loss to enforce better separation between interacting and non-interacting pairs. Extensive experiments on three benchmark datasets (Davis, BindingDB, and BIOSNAP) demonstrate that DeepCL outperforms state-of-the-art methods in both standard and zero-shot settings, achieving superior AUPR and AUROC scores. Methodologically, DeepCL constructs a dual-pathway architecture: it leverages the ESM-2 protein language models to capture rich contextual protein features and Morgan fingerprints for precise molecular structural representation. These heterogeneous features are aligned in a shared latent space via modality-specific projectors. DeepCL contributes a robust, scalable, and numerically stable solution for DTI prediction.