Computer Methods in Biomechanics and Biomedical Engineering最新文献

Efficient and accurate multi-class classification of electroencephalogram (EEG) signals poses a significant challenge in the development of motor imagery-based brain-computer interface (MI-BCI). Drawing inspiration from the sine cosine algorithm (SCA), a widely employed swarm intelligence algorithm for optimization problems, we proposed a novel population-based classification algorithm for EEG signals in this article. To fully leverage the characteristics contained in EEG signals, multi-scale sub-signals were constructed in terms of temporal windows and spectral bands simultaneously, and the common spatial pattern (CSP) features were extracted from each sub-signal. Subsequently, we integrated the multi-center optimal vectors mechanism into the classical SCA, resulting in the development of a multi-center SCA (MCSCA) classifier. During the classification stage, the label was assigned to the test trials by evaluating the Euclidean distance between their feature vectors and each optimal vector in MCSCA. Additionally, the weights of feature vectors were exploited to select the sub-signal of specific temporal windows and spectral bands for feature reduction, thereby declining computational effort and eliminating data redundancy. To validate the performance of the MCSCA classifier, we conducted four-class classification experiments using the BCI Competition IV dataset 2a, achieving an average classification accuracy of 71.89%. The experimental results show that the proposed algorithm offers a novel and effective approach for EEG classification.

We investigated the effect of anterior disc displacement without osteoarthritis (ADDwoOA) on the morphology of the temporomandibular joint (TMJ) utilizing three-dimensional (3D) models of 23 asymptomatic individuals and 30 ADDwoOA patients. Statistical analyses between the groups were performed by measuring 10 morphological parameters. ADDwoOA patients showed significantly decreased levels of the sagittal ramus angle (SRA) and joint spaces compared with asymptomatic subjects. Moreover, the patients who had recovered exhibited normal joint spaces levels. Consequently, ADDwoOA caused the condyles to move backward and upward, approaching the articular fossa. Joint spaces can serve as an important observation during the treatment of ADD.

Three different kinds of condylar inclination were manually modelled anteriorly inclined condylar neck, vertical condylar neck, and posteriorly inclined condylar neck. Three different maxillary impactions were simulated to evaluate the effect of counterclockwise rotation. The von Misses stresses of the disc, compressive stresses of the glenoid fossa, and compressive stresses of the condyle were the highest in the models with posteriorly inclined neck and lowest in the models with vertical condylar neck design. Stresses of the temporomandibular joint increase with the counterclockwise rotation of the maxilla-mandibular complex. The posteriorly inclined neck should be considered a risk factor for condylar resorption with increased counterclockwise rotation.

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.

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.

This study aimed to create a prognostic nomogram to predict the risk of liver metastasis (LM) in thyroid cancer (TC) patients and assess survival outcomes for those with LM. Data were collected from the SEER database, covering TC patients from 2010 to 2020, totaling 110,039 individuals, including 142 with LM. Logistic regression and stepwise regression based on the Akaike information criterion (AIC) identified significant factors influencing LM occurrence: age, histological type, tumor size, bone metastasis, lung metastasis, and T stage (p < 0.05). A nomogram was constructed using these factors, achieving a Cindex of 0.977, with ROC curve analysis showing an area under the curve (AUC) of 0.977. For patients with TCLM, follicular TC, medullary TC, papillary TC, and examined regional nodes were associated with better prognosis (p < 0.001, HR < 1), while concurrent brain metastasis indicated poorer outcomes (HR = 2.747, p = 0.037). In conclusion, this nomogram effectively predicts LM risk and evaluates prognosis for TCLM patients, aiding clinicians in personalized treatment decisions.

Aiming to simplify the data acquisition process for balance diagnosis and focused on muscle, a direct factor affecting balance, to assess and judge postural stability. Utilizing a publicly available kinematic dataset, the research retained 3D coordinates and mechanical data for 8 markers on the lower limbs. By integrating this data with the musculoskeletal model in OpenSim, inverse kinematic calculations were performed to derive muscle forces. These forces, alongside the coordinates, were split into an 8:2 training and test set ratio. A neural network was then developed to predict muscle forces using normalized coordinate data from the training set as input, with corresponding muscle force data as training labels. The model's accuracy was confirmed on the test set, achieving coefficients of determination ($R^2$) above 0.99 for 276 muscle forces. Furthermore, the Force Maximum Percentage Difference (FMPD) was introduced as a novel criterion to evaluate and visualize lower limb balance, revealing significant discrepancies between the patient and control groups. This study successfully demonstrates that the neural network model can precisely predict lower limb muscle forces using reduced markers and introduces FMPD as an effective tool for assessing limb balance, providing a robust framework for future diagnostic and rehabilitative applications.

Background: Psoriasis, a chronic inflammatory dermatosis, profoundly affects patients' well-being. Although exosomes are key in disease etiology, diagnostic potentials of associated genes are unclear. Our research targeted bioinformatics-based characterization of exosome-related genes and the development of a diagnostic model for psoriasis.

Methods: Within GSE30999 dataset, an exosome-centric diagnostic model was formulated. Its diagnostic capability was appraised in GSE30999 and GSE14905 cohorts. Human keratinocytes (HaCaT) were used to construct psoriasis cell model. qRT-PCR was used to detect expression of diagnostic genes in the model. Construction of a protein-protein interaction network was undertaken, complemented by enrichment analyses. Comparative evaluation of immunological microenvironments between healthy controls and disease cohort was executed. Prospective miRNAs and transcription factors (TFs) were prognosticated using online prediction tools.

Results: A distinctive diagnostic model with superior diagnostic performance, evidenced by an AUC value greater than 0.88, was unveiled. The model featured seven exosome-related biomarker genes (CCNA2, NDC80, CCNB1, CDCA8, KIF11, CENPF, and ASPM) interwoven in a complex network and chiefly linked in the regulation of Cell Cycle and Cellular Senescence. These genes were significantly overexpressed in psoriasis cell models. Immune infiltration analysis distinguished profound discrepancies (p < 0.05) in immunological microenvironment between disease and control groups with enrichment of T cells CD4 memory activated, Macrophages M1, and Neutrophils in the disease group. 11 miRNAs and 27 TFs were identified.

Conclusion: The study introduces a new and potent diagnostic model for psoriasis, with selection of credible exosome-associated biomarker genes. These discoveries aid in clinical diagnostics and research on exosome involvement in psoriasis.

This paper proposes an optimized Long Short-Term Memory (LSTM+) model for predicting cumulative confirmed cases of COVID-19 in Germany, the UK, Italy, and Japan. The LSTM+ model incorporates two key optimizations: (1) fine-adjustment of parameters and (2) a 're-prediction' process that utilizes the latest prediction results from the previous iteration. The performance of the LSTM+ model is evaluated and compared with that of Backpropagation (BP) and traditional LSTM models. The results demonstrate that the LSTM+ model significantly outperforms both BP and LSTM models, achieving a Mean Absolute Percentage Error (MAPE) of less than 0.6%. Additionally, two illustrative examples employing the LSTM+ model further validate its general applicability and practical performance for predicting cumulative confirmed COVID-19 cases.

Total knee arthroplasty (TKA) is an end-stage treatment for knee osteoarthritis that relieves pain and loss of mobility, but patient satisfaction and revision rates require improvement. One cause for TKA revision is joint instability, which may be due to improper ligament balancing. A better understanding of the relationship between prosthesis design, alignment, and ligament engagement is necessary to improve component designs and surgical techniques to achieve better outcomes. We investigated the biomechanical effects of ligament model complexity and ligament wrapping during laxity tests using a virtual joint motion simulator. There was little difference in kinematics due to ligament complexity or ligament wrapping.