Computer Methods in Biomechanics and Biomedical Engineering最新文献

Low-intensity pulsed ultrasound (LIPUS) is a potential effective means for the prevention and treatment of disuse osteoporosis. In this paper, the effect of LIPUS exposure on the mechanical properties distribution of the osteocyte system (osteocyte body contains nucleus, osteocyte process, and primary cilia) is simulated. The results demonstrate that the mechanical micro-environment of the osteocyte is significantly improved by ultrasound exposure, and the mean von Mises stress of the osteocyte system increases linearly with the excitation sound pressure amplitude. The mechanical effect of LIPUS on osteocytes is enhanced by the stress amplification mechanism of the primary cilia and osteocyte processes.

High-throughput technologies and machine learning (ML), when applied to a huge pool of medical data such as omics data, result in efficient analysis. Recent research aims to apply and develop ML models to predict a disease well in time using available omics datasets. The present work proposed a framework, 'OmicPredict', deploying a hybrid feature selection method and deep neural network (DNN) model to predict multiple diseases using omics data. The hybrid feature selection method is developed using the Analysis of Variance (ANOVA) technique and firefly algorithm. The OmicPredict framework is applied to three case studies, Alzheimer's disease, Breast cancer, and Coronavirus disease 2019 (COVID-19). In the case study of Alzheimer's disease, the framework predicts patients using GSE33000 and GSE44770 dataset. In the case study of Breast cancer, the framework predicts human epidermal growth factor receptor 2 (HER2) subtype status using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. In the case study of COVID-19, the framework performs patients' classification using GSE157103 dataset. The experimental results show that DNN model achieved an Area Under Curve (AUC) score of 0.949 for the Alzheimer's (GSE33000 and GSE44770) dataset. Furthermore, it achieved an AUC score of 0.987 and 0.989 for breast cancer (METABRIC) and COVID-19 (GSE157103) datasets, respectively, outperforming Random Forest, Naïve Bayes models, and the existing research.

Childbirth is a complex physiological process in which a foetal neuromusculoskeletal model is of great importance to develop realistic delivery simulations and associated complication analyses. However, the estimation of hip joint centre (HJC) in foetuses remains a challenging issue. Thus, this paper aims to propose and evaluate a new approach to locate the HJC in foetuses. Hip CT-scans from 25 children (F = 11, age = 5.5 ± 2.6 years, height = 117 ± 21 cm, mass = 26 kg ± 9.5 kg) were used to propose and evaluate the novel acetabulum sphere fitting process to locate the HJC. This new approach using the acetabulum surface was applied to a population of 57 post-mortem foetal CT scans to locate the HJC as well as to determine associated regression equations using multiple linear regression. As results, the average distance between the HJC located using acetabulum sphere fitting and femoral head sphere fitting in children was 1.5 ± 0.7 mm. The average prediction error using our developed foetal HJC regression equations was 3.0 ± 1.5 mm, even though the equation for the x coordinate had a poor value of R² (R² for the x coordinate = 0.488). The present study suggests that the use of the acetabulum sphere fitting approach is a valid and accurate method to locate the HJC in children, and then can be extrapolated to get an estimation of the HJC in foetuses with incomplete bone ossification. Therefore, the present paper can be used as a guideline for foetus specific neuromusculoskeletal modelling.

In this article, a cytokine-enhanced viral infection model with cytotoxic T lymphocytes (CTLs) immune response and antibody immune response is proposed and analyzed. The model contains six compartments: uninfected CD4⁺T cells, infected CD4⁺T cells, inflammatory cytokines, viruses, CTLs and antibodies. Different from the previous works, this model not only considers virus-to-cell transmission and cell-to-cell transmission, but also includes a new infection mode, namely cytokine-enhanced viral infection. The incidence rates of the healthy CD4⁺T cells with viruses, infected cells and inflammatory cytokines are given by general functions. Moreover, the production/proliferation and removal/death rates of all compartments are represented by general functions. Firstly, we prove that all the solutions of the model are nonnegative and uniformly bounded. Then, five key parameters with strong biological significance, namely the virus basic reproduction number R₀, CTLs immune response reproduction number R₁, antibody immune response reproductive number R₂, CTLs immune competitive reproductive number R₃ and antibody immune competitive reproductive number R₄ are derived. Then, by using Lyapunov's method and LaSalle's invariance principle, we have shown the global stability of each equilibrium. In addition, the numerical simulation results also show that the theoretical results are correct. Finally, we formulate an optimal control problem and solve it using Pontryagins Maximum Principle and an efficient iterative numerical methods. The results of our numerical simulation show that it is very important to control the infection between viruses and cells and between cells and inflammatory cytokines for controlling HIV.

The objective of this study was to analyze the effects of annulus fibrosus incision and foraminoplasty on lumbar biomechanics during posterior lateral approach translaminar percutaneous endoscopic lumbar discectomy (PELD) using a lumbar 4/5 segment model and three-dimensional finite element analysis (FEA). We created a model of the L4 to L5 segment and performed simulated foraminoplasty, annulus fibrosus incision, and a combined operation. The models were tested under six working conditions, and we recorded the deformation and equivalent strain/stress of each group. Results showed that foraminoplasty can affect the stability and rotation axis of the segment during rotation without significantly impacting discal stress. Conversely, annulus fibrosus incision significantly increases discal stress except for when the patient is doing a forward flexion movement. We recommend that surgical maneuvers minimize the removal and destruction of the annulus fibrosus and that rotation movements are avoided during the short-term recovery period following PELD surgery.

Predicting late adverse events in aortic dissections is challenging. One commonly observed risk factor is partial thrombosis of the false lumen. In this study we investigated false lumen thrombus progression over 7 days in four mice with angiotensin II-induced aortic dissection. We performed computational fluid dynamic simulations with subject-specific boundary conditions from velocity and pressure measurements. We investigated endothelial cell activation potential, mean velocity, thrombus formation potential, and other hemodynamic factors. Our findings support the hypothesis that flow stagnation is the predominant hemodynamic factor driving a large thrombus ratio in false lumina, particularly those with a single fenestration.

Metatarsalgia occurring in individuals with pes cavus is typically associated with abnormal loading patterns in the forefoot resulting from structural alterations. Simultaneously, the frequent overstress of the plantar fascia (PF) caused by the persistence of this foot deformity may further exacerbate the chronic pain induced by metatarsal overload. We aimed to investigate and quantify the effects of PF stiffness on the internal biomechanics of pes cavus using a computational modelling approach. A patient-specific finite element model of the foot-ankle complex using the actual three-dimensional geometry of idiopathic pes cavus bones and soft tissues was reconstructed. A sensitivity study was conducted to evaluate the effects of varying elastic modulus (0-700 MPa) of the PF on the metatarsal stress distribution, and force transmission through the metatarsophalangeal (MTP) and tarsometatarsal (TMT) joints in the pes cavus. The results indicated that variations in PF stiffness led to stress redistribution in the metatarsal region. Peak stress gradually reduced with decreasing stiffness until the PF was released, eventually resulting in a reduction of 22.39% compared to the reference value of 350 MPa. Furthermore, adjusting the PF stiffness to twice the reference value (700 MPa) increased the contact forces through the TMT and MTP joints by up to 23% and 116%, respectively. The reduction of PF stiffness alleviated focal metatarsal loading, and therefore, surgical fascia release can be considered to alleviate metatarsalgia in patients with pes cavus.

Myocardial infarction (MI), referred to as a heart attack, is a life-threatening condition that happens due to blood clots, typically, blood flow to a portion of the heart muscle is blocked. The cardiac muscle may become permanently damaged if there is insufficient oxygen and blood flow to the affected area. It's crucial to treat MI as soon as possible because even a small delay might have serious effects. The primary diagnostic tool to track and identify the signs of MI is the electrocardiogram (ECG). The complexity of MI signals combined with noise makes it difficult for clinicians to make a precise and prompt diagnosis. It might be laborious and time-consuming to manually analyse an enormous quantity of ECG data. Therefore, techniques for autonomously diagnosing from the ECG data are required. There have been numerous research on the topic of MI espial, but the majority of the algorithms are cognitively intensive when working with empirical data. The current study suggests a unique method for the efficient and reliable identification of MI. We employed circulant singular spectrum analysis (CSSA) for baseline wander removal, a 4-stage Savitzky-Golay (SG) filter to expunge powerline interference from the ECG signal and segmented in the preprocessing stage. Thus segmented ECG has been decomposed using CSSA, entropy based features are extracted. The best features are selected by using binary Harris hawk optimization (BHHO) and to machine learning (ML) classifiers like Naive Bayes, Decision tree, K-nearest neighbor (KNN), Support vector machine (SVM), and Ensemble subspace KNN. Our suggested method has been examined from both class as well as subject oriented perspectives. While the subject-oriented technique uses data from one patient for testing while using data from the other subjects for training, the class-wise strategy divides data as test data as well as training data regardless of subjects. We succeeded in achieving accuracy ($A_c\%$) of 99.8, sensitivity ($S_e\%$) of 99, and 100 specificity (S_p%) under the class-oriented approach. Similarly, for the subject wise strategy we achieved a mean $A_c\%,$ S_e%, and S_p% of 85.2, 83.1, and 84.5, respectively.

Blood Pressure (BP) is considered an essential factor that provides information regarding cardiovascular function. Regular monitoring of the BP is required for proper healthcare maintenance that avoids the high risk of life due to high and low BP. Several methods were devised for the estimation of BP, but the estimation accuracy is still a challenging task. Hence this research introduces an efficient BP estimation technique using the Fact Finding Instructor (FFI) based clustering method by considering the speech signal of the patients. An efficient BP extraction technique is introduced using the FFI Optimization algorithm an integration of the mannerism of the fact finder that identifies the suspect who commits the criminal offense and, with the instructor with good knowledge, these make the trainee more efficient. The detection and suspect's arrest contain two phases, the fact-finding phase and the chasing phase. Initially, the speech signal is collected from the database and pre-processed for removing noise and artifacts. Then feature extraction is used for the minimization of the computation overhead that generates a feature vector. The clustering of BP is employed with the k-means clustering algorithm and the proposed FFI optimization algorithm. The FFI Optimization algorithm provides a fast convergence rate due to the fact-finding phase and provides accurate detection of the suspect's location along with that the clustering of classes of patients' BP by considering the feature of the speech signal. The clusters formed using the FFI optimization algorithm are combined with the K-means clustering, by multiplying the clusters the BP estimation is implemented on three criteria Low BP, Normal, and, High BP. Finally, the output generated by both the clustering operations is multiplied together for the estimation of the BP. The performance of the proposed method is evaluated using the metrics like Davies Bouldin score, Homogeneity score, Completeness score, Jacquard Similarity score, Silhouette score, and Dunn's Index which acquired the improvement rate of 0.98, 0.96, 0.96, 0.98, 0.95, and 0.98 for training percentage 90, respectively to the existing Teaching Learning Based Optimization(TLBO) clustering technique.

As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.