Computer Methods in Biomechanics and Biomedical Engineering最新文献

An aneurysm is a disease condition, which is due to the pathological weakening of an arterial wall. These aneurysms are often found in various branch points and bifurcations of an artery in the cerebral circulation. Most aneurysms come to medical attention, either due to brain hemorrhages caused by rupture or found unruptured. To consider surgically invasive treatment modalities, clinicians need scientific methods such as, hemodynamic analysis to assess rupture risk. The arterial wall loses its structural integrity when wall shear stress (WSS) and other hemodynamic parameters exceed a certain threshold. In the present study, numerical simulations are carried out for unruptured middle cerebral artery (MCA) aneurysms. Three distinct representative sizes are chosen from a larger patient pool of 26 MCA aneurysms. Logically, these aneurysms represent three growth stages of any patient with similar anatomical structure. Simulations are performed to compare the three growth phases (with different aspect ratios) of an aneurysm and correlate their hemodynamic parameters. Simulations with patient specific boundary conditions reveal that, aneurysms with a higher aspect ratio (AR) correspond to an attendant decrease in both time-averaged wall shear stress (TAWSS) and spatial wall shear stress gradients (WSSG). Smaller MCAs were observed to have higher positive wall shear stress divergence (WSSD), exemplifying the tensile nature of arterial wall stretching. Present study identifies positive wall shear stress divergence (PWSSD) to be a potential biomarker for evaluating the growth of an aneurysm.

The mortality rates due to cardiovascular diseases are on a rise globally. One of the major cardiovascular diseases is stroke which occurs due to atherosclerotic plaques build-up in the carotid artery. The common carotid artery (CCA) bifurcates into the internal carotid artery (ICA) and external carotid artery (ECA). Sinus present at ICA is an ellipsoidal-shaped dilated region acting as a pressure receptor and blood flow regulator. Dimensions of the sinus vary from person to person, affecting the hemodynamics of the carotid artery. The current numerical study manifests a 3D flow analysis by varying the sinus length to investigate its local and global effects on the hemodynamics of the carotid artery using various biomechanical risk analysis parameters of atherosclerosis. User-defined function (UDF) dictates the pulsatile flow velocity profile imposed at the inlet. Near the outer wall (OW) of the sinus, the blood flow velocities are lower and recirculation zones are more. Though the recirculation zones for shorter sinus will be close to the inner wall (IW), interestingly, with an increase in the sinus length, the recirculation zones shift toward the OW with higher strength. These significantly decrease the x-wall shear stress (x-WSS) and time-averaged wall shear stress (TAWSS) values on the OW of the longer sinus. The other risk analysis parameters, like oscillatory shear index (OSI) and relative residence time (RRT), support the described consequences. These results reveal that sinus of increased length is more prone to developing atherosclerotic plaque.

ADHD is a prevalent childhood behavioral problem. Early ADHD identification is essential towards addressing the disorder and minimizing its negative impact on school, career, relationships, as well as general well-being. The present ADHD diagnosis relies primarily on an emotional assessment which can be readily influenced by clinical expertise and lacks a basis of objective markers. In this paper, an innovative IoT based ADHD detection is proposed using an EEG signal. To the input EEG signal, the min-max normalization technique is processed. Features are extracted as the subsequent step, where improved fuzzy feature, in which the entropy is estimated to increase the effectiveness of recognizing the vector along with, fractal dimension, wavelet transform and non-linear features are extracted. Also, proposes the new hybrid PUDMO algorithm to select the optimal features from the extracted feature set. Subsequently, the selected features are fed to the proposed hybrid detection system that including IDBN and LSTM classifier to detect whether it is ADHD or not. Further, the weights of both classifiers are tuned optimally as per the hybrid PUDMO algorithm to enhance the detection performance. The PUDMO achieved an accuracy of 0.9649 in the best statistical metric, compared to the SLO's 0.8266, SOA's 0.8201, SMA's 0.8060, BRO's 0.8563, DE's 0.8083, POA's 0.8537, and DMOA's 0.8647, respectively. Thus, the assessments and detection help the clinicians to take appropriate decision.

The increasing prevalence of adult spinal deformity requires long spino-pelvic instrumentation, but pelvic fixation faces challenges due to distal forces and reduced bone quality. Bi-planar multi-energy X-rays (BMEX) were used to develop a patient-specific finite element model (FEM) for evaluating pelvic fixation. Calibration involved 10 patients, and an 81-year-old female test case was used for FEM customization and pullout simulation validation. Calibration yielded a root mean square error of 74.7 mg/cm³ for HU. The simulation accurately replicated the experimental pullout test with a force of 565 N, highlighting the method's potential for optimizing biomechanical performance for pelvic fixation.

Aortic valves with bicuspids have two rather than three leaflets, which is a congenital heart condition. About 0.5-2% of people have a bicuspid aortic valve. Blood flow through the aorta is commonly believed to be laminar, although aortic valve disorders can cause turbulent transitions. Understanding the impact of turbulence is crucial for foreseeing how the disease will progress. The study's objective was use large eddy simulation to provide a thorough analysis of the turbulence in bicuspid aortic valve dysfunction. Using a large eddy simulation, the blood flow patterns of the bicuspid and tricuspid aortic valves were compared, and significant discrepancies were found. The velocity field in flow in bicuspid configurations was asymmetrically distributed toward the ascending aorta. In tricuspid aortic valve (TAV) the flow, on the other hand, was symmetrical within the same aortic segment. Moreover, we looked into standard deviation, Q-criterion, viscosity ratio and wall shear stresses for each cases to understand transition to turbulence. Our findings indicate that in the bicuspid aortic valve (BAV) case, the fluid-dynamic abnormalities increase. The global turbulent kinetic energy and time-averaged wall shear stress for the TAV and BAV scenarios were also examined. We discovered that the global turbulent kinetic energy was higher in the BAV case compared to TAV, in addition to the increased wall shear stress induced by the BAV in the ascending aorta.

This paper presents a novel computational framework for neural-driven finite element muscle models, with an application to amyotrophic lateral sclerosis (ALS). The multiscale neuromusculoskeletal (NMS) model incorporates physiologically accurate motor neurons, 3D muscle geometry, and muscle fiber recruitment. It successfully predicts healthy muscle force and tendon elongation and demonstrates a progressive decline in muscle force due to ALS, dropping from 203 N (healthy) to 155 N (120 days after ALS onset). This approach represents a preliminary step towards developing integrated neural and musculoskeletal simulations to enhance our understanding of neurodegenerative and neurodevelopmental conditions through predictive NMS models.

Wearable sensors allow mobility assessment required for better locomotion, neurological and musculoskeletal disorders, current limitations include unknown reliability and accuracy in real-life settings. This work determined the concurrent validity and repeatability of the proposed foot-worn gait evaluation system using objective gait features and recurrence quantification analysis from 52 participants. Its agreement with the commercially available OpenGo^® system in the unrestricted outdoor environment is determined. Reported measures showed no significant differences (p > 0.05) between systems. Test-retest reliability showed that the mean of the second-third trial (T2-T3) is the most significant. Thus, an affordable system provides accurate measurement of gait ensuring its suitability even in small clinical-settings.

This study aimed to explore the roles of radiotherapy-sensitive and ferroptosis genes in breast cancer (BRCA). Genes differentially expressed pre- and post-radiotherapy from the GSE59733 dataset were intersected with ferroptosis-related genes. Through a protein-protein interaction network, 10 hub genes were identified. BRCA patients were categorized into three clusters, with cluster 1 and cluster 2 showing the most significant survival difference. Cluster 1 demonstrated higher immune infiltration levels but poorer response to immune therapy compared to cluster 2. Moreover, cluster 1 and cluster 2 exhibited sensitivity to different drugs. These 10 hub genes can effectively classify patients and suggest potential drugs.

A physical model of soft tissue that provides realistic and real-time haptic and visual feedback is crucial for neurosurgical procedures. This paper investigates the interaction between surgical instruments and soft brain tissue, proposing a soft tissue deformation simulation method based on the principle of energy minimization and constrained energy function. The model includes a permanent deformation energy function induced by friction and a volume preservation energy function to more accurately depict tissue response during procedures such as resection of convex meningiomas and evacuation of intracerebral hematomas. Experimental results show that the proposed method meets the requirements of neurosurgical simulation.

Prostate cancer is a prevalent malignant disease among middle-aged and elderly men. Its prevention and detection are significant public health issues. We aimed to construct an interpretable model for predicting death risk in prostate cancer patients. We performed model development using the Cancer Genome Atlas and the Genotype-Tissue Expression databases. In comparison among models, the SVM model has the highest prediction performance among the eight models. The SHAP method, sorted by importance, reveals the top eight predictors of prostate cancer disease. This effective computer-aided approach can facilitate frontline clinicians in the diagnosis and management of patients with prostate cancer.