Computer Methods in Biomechanics and Biomedical Engineering最新文献

Early cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We introduce a multiple instance learning method based on convolutional neural networks and self-attention (MICA). First, MICA preprocesses TCR-seq using word vectors and then extracts features using convolutional neural networks. Second, MICA uses an enhanced self-attention mechanism to extract relational features of instances. Finally, MICA can extract the crucial TCR-seq. After cross-validation, MICA achieves an area under the curve (AUC) of 0.911 and 0.946 on the lung and thyroid cancer datasets, which are 7.1% and 2.1% higher than other methods, respectively.

A rapid, sensitive, and low-damage method for isolating circulating tumor cells (CTCs) is crucial for cancer research. This study, based on dielectrophoresis (DEP) and finite element modeling, investigates multitarget cell separation from blood on microfluidic chips. The effects of electrode shape, dielectric conductivity, and flow rate on cell movement and separation efficiency were analyzed. The results showed optimal separation with a 90° electrode angle, 1.5 V applied voltage, and a 1:3 inlet flow rate ratio. This study provides valuable insights for optimizing DEP-based microfluidic devices to improve multitarget cell separation efficiency and purity.

Uterine fibroids are common benign gynecological tumors that are observed in up to 80% of premenopausal women. It is understood that as the fibroid size increases, the surrounding tissues will be subject to greater loads. However, the effect of fibroid region on the uterine structure is not as clear. To better understand the mechanical loading of the endometrium due to the presence of a uterine fibroid, we developed a finite element model of the uterus to examine the effect of both fibroid depth and region in relation to the endometrium. The finite element model of the uterus, endometrium, and a uterine fibroid were created from a 3D segmentation of a patient's magnetic resonance images. This model was then loaded into ANSYS Mechanical 2023 R1, and then deformation, stress, and strain of the endometrium was measured for 24 fibroid positions (8 regions × 3 depths). The highest endometrial loads (deformation, stress, strain) were observed when the fibroid region was superior to the uterus and the depth was deep. Superior regions generated 10-20% higher loads on the endometrium in comparison to other regions, while deep locations had 5-10% higher endometrium loads when compared to superficial depths across almost all regions. A simple uterus model was used to show the effect of fibroid position on loads acting on the endometrium. This can provide insight into mechanisms of abnormal uterine bleeding and infertility and better inform clinical decision making.

Objective: To investigate the impact of tension and laxity in the sacroiliac interosseous ligament on lumbar spine displacement and force response in vibration environments.

Methods: A finite element model of the lumbar-pelvis, previously crafted and rigorously validated, was used to simulate ligament tension and laxity by adjusting the elastic modulus of the SIL under a sinusoidal vertical load of ±40 N at 5 Hz. Comparisons of lumbar spine horizontal and axial displacements as well as annulus fibrous stress, nucleus pulposus pressure, and facet joint force were performed, respectively.

Results: With the elastic modulus of the SIL varying by +50, -50, and -90%, the maximum vibration amplitude changed by +20.00, -175.00, and -627.27% for lumbar horizontal displacement, +30.00, -157.14, and -627.22% for lumbar axial displacements, +5.88, -19.35, and -245.16% for annulus fibrous stress, +10.00, -25.00, and -157.14% for nucleus pulposus pressure, as well as +6.54, -20.13, and -255.37% for facet joint force, respectively.

Conclusion: In contrast to static environments, large laxity of the SILs not only diminishes lumbar spine stability in vibrational settings but also significantly amplifies dynamic loads, thereby heightening the risk of lumbar spine vibratory injuries and low back pain disorders.

Skiing accidents may lead to severe head and neck injuries. This study uses the THUMS model to evaluate the initial conditions and related injury mechanisms of head impacts with the snow surface during skiing falls. Initial speed was found to be the main factor affecting head injury criterion (HIC). Impact position is the key factor affecting cervical curvature. Rear impacts cause excessive cervical spine extension, while frontal impacts cause excessive cervical spine flexion. Rear impacts are more likely to cause neck injuries. As the angle and speed increase, the degree of cervical spine injury also increases.

In this study, polyamine metabolism related genes (PMRGs) were used to establish a breast cancer (BC) prognostic model. Using PMRGs, TCGA BC samples were divided into cluster1 and cluster2. A 13-gene BC prognostic model was constructed by screening differential genes. High-risk BC patients exhibited heightened immunoinfiltration levels, potentially impeding immunotherapy responses. Drug response predicted that BC patients in the low-risk group might benefit more from chemotherapy and targeted therapy. In conclusion, a novel 13-gene BC prognostic risk model based on PMRGs was established to effectively predict prognosis, immune microenvironment, and drug therapy response in patients with BC.

The early stage of the Epileptic Seizure Anticipation (ESA) model plays a significant part in supplying accurate medical care. In this research work, a novel Multi Serial Cascaded Network with Feature Specific model is developed. The scalogram images are given as input to a developed model. Here, the Target Feature Selection is performed optimally using the Improved Fitness Value Index-Archimedes Optimization (IFVI-AO) Algorithm. Finally, the selections of accurate features are subjected to 'Bi-directional Long Short-Term Memory (Bi-LSTM)'. The implemented model is validated and provides timely results to detect epileptic seizure disorder.

In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased human stress level measurement technique using electroencephalogram (EEG), and pulse rate. In this research, EEG signals and pulse rate of healthy subjects are recorded while they solve four different question sets of increasing complexity. It is assumed that the subjects undergo through four different stress levels, i.e., 'no stress', 'low stress', 'medium stress', and 'high stress', while solving these question sets. An attention mechanism-based CNN-TLSTM (convolutional neural network-tanh long short-term memory) model is proposed to detect the mental stress level of a person. An attention layer is incorporated into the designed TLSTM network to increase the classification accuracy of the CNN-TLSTM model. The CNN network is used for the automated extraction of intricate features from the EEG signals and pulse rate. Then TLSTM is used to classify the stress level of a person into four different categories using the CNNextracted features. The obtained average accuracy of the proposed CNN-TLSTM model is 97.86%. Experimentally, it is found that the designed stress level measurement technique is highly effective and outperforms most existing state-of-the-art techniques. In the future, functional Near-Infrared Spectroscopy (fNIRS), ECG, and Galvanic Skin Response (GSR) can be employed with EEG and pulse rate to increase the effectiveness of the designed stress level measurement technique.

We aimed to explore the best orthodontic step distance of the right upper central incisor with mild, moderate, and severe pathological displacement achieved via a clear aligner. Three-dimensional models of maxilla-tooth-periodontal ligament clear aligner of the right upper central incisors with five different steps of 0.1, 0.125, 0.15, 0.165, 0.25 mm and three different alveolar bone heights were established via finite element analysis. We analysed the changing trends in initial displacement, the periodontal ligament, the alveolar bone, and apical stress of right upper central incisor. In the process of retraction, the right upper central incisor a movement trend of the crown deviating from the distal root to the mesial, and with the decrease of the height of the alveolar bone and the increase of the displacement, the crown would appear distal labial torsion with a deepening trend of vertical overlay.The maximum stress distribution of the periodontal ligament and alveolar bone showed a positive correlation. The overall stress distribution of the periodontal ligament and apical stress increased with decrease of alveolar bone height and the increase of alveolar bone displacement. In patients with mild, moderate, and severe pathological displacement of the right upper central incisor, the best step distance of anterior tooth retraction is 0.165, 0.15, and 0.125 mm, respectively.

The article aims to design and develop a topology-optimized endosseous cuspid tooth implant of the maxilla region. The manuscript presents a numerical analysis of the resulting von Mises stresses and effective strain resulting in the topology-optimized implant with occlusal loading of 110 N. Solid Isotropic Material with Penalization (SIMP) method is employed for topology optimization and four different models, namely model-1, model-2, model-3, and model-4, are developed based on volume reduction rates of 8%, 16%, 24%, and 32%, respectively. FEA results highlight that the maximum stress and strain in the screw increases with volume reduction rates. The comparative analyses of the resulting stresses in the compact and cancellous bone along with the strain in the screw led to the conclusion that model-1, model-2, and model-3 resulted in moderate stresses on compact and cancellous bone compared to the original model of the implant. However, the screw and bones are subjected to maximum stress and strain in the model-4. The study concludes that model-2, with 16% reduced volume and 14.2% reduced mass as compared to the original implant, may be considered as the optimized design of the model. The resulting model offers a significant reduction in the weight and volume with a minor increase in effective stress and strain without negatively impacting the functionality and bio-mechanical performance of the implant. The optimized dental implant prototype is also fabricated as a proof of concept by the Fused Deposition Modelling process.