Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"Drug usage classification based on personality and demographic features using a combination of sampling and machine learning algorithms.","authors":"Shuoxu Zhang","doi":"10.1080/10255842.2025.2530648","DOIUrl":"https://doi.org/10.1080/10255842.2025.2530648","url":null,"abstract":"<p><p>Drug use stems from biopsychosocial factors. This study classified 18 drug types using personality and demographics. After preprocessing, three sampling techniques Random Oversampling, Synthetic Minority Over-sampling Technique using Euclidean Norm (SMOTEN), Synthetic Minority Over-sampling Technique using Euclidean Norm - Edited Nearest Neighbors (SMOTEENN) and seven machine learning (ML) models Random Forest (RF), Extreme Gradient Boosting (XGBoost), Decision tree (DT), Extra Tree, Support Vector Classification (SVC), Linear SVC and Logistic Regression (LR) were applied to build a robust, accurate prediction model for drug classification. Random Over Sampler and Extra Trees improved F1 scores in unbalanced data, as shown in a case study.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-22"},"PeriodicalIF":1.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-phase fluid structure interaction model of mucociliary clearance driven by cilium.","authors":"Kavin Vishnu, Karupppasamy Subburaj, Monika Colombo","doi":"10.1080/10255842.2025.2535014","DOIUrl":"https://doi.org/10.1080/10255842.2025.2535014","url":null,"abstract":"<p><p>This study addresses the critical role of mucociliary clearance in respiratory health by developing a novel two-phase fluid-structure interaction model. The aim is to simulate realistic mucus transport driven by ciliary motion. Using direct cilia modeling and Carreau non-Newtonian rheology for the mucus layer, the model incorporates a new method for prescribing cilia beat patterns. Two-phase fluid-structure interaction simulations reveal how cilia dynamics and mucus properties interact to influence clearance efficiency. These findings highlight the importance of fluid-structure coupling and mucus rheology in replicating physiological transport, offering insights for understanding airway diseases and designing therapeutic interventions.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable one-dimensional convolutional neural network with modified Gabor wavelet transform for the identification of exons.","authors":"K Jayasree, Malaya Kumar Hota","doi":"10.1080/10255842.2025.2535003","DOIUrl":"https://doi.org/10.1080/10255842.2025.2535003","url":null,"abstract":"<p><p>In this paper, we propose an effective one-dimensional CNN (1D-CNN) model for the identification of exons by considering the features extracted from the DNA sequences using DSP approaches (short-time discrete Fourier transform and the modified Gabor wavelet transform), along with various numerical mapping methods. To preserve the feature information without any information loss, a novel CNN model is proposed by excluding the pooling layer. The experimental outcomes reveal that the 1D-CNN model with the Voss-MGWT feature extraction method outperforms other discussed methods in improving the identification accuracy by using the HMR195 dataset.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-10"},"PeriodicalIF":1.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Lax-Friedrichs method in one-dimensional hemodynamics and its simplifying effect on boundary and coupling conditions.","authors":"Anika Beckers, Niklas Kolbe","doi":"10.1080/10255842.2025.2532027","DOIUrl":"https://doi.org/10.1080/10255842.2025.2532027","url":null,"abstract":"<p><p>The discretization of reduced one-dimensional hyperbolic models of blood flow using the Lax-Friedrichs method is discussed. Deriving the well-established scheme from a relaxation approach leads to new simplified boundary and coupling conditions in vascular networks accounting e.g. for vascular occlusions and bifurcations. In particular, blood flow modeling in networks of vessels can be realized with minimal information on the eigenstructure of the coupled models. The scheme, a MUSCL-type extension and the coupling conditions are obtained evaluating a discrete relaxation limit. Numerical experiments in uncoupled and coupled cases verify the consistency and convergence of the approach.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scaled generic and fetus-specific musculoskeletal modeling for simulating posterior arm delivery in the shoulder dystocia management.","authors":"Morgane Devismes-Ferrandini, Tien-Tuan Dao","doi":"10.1080/10255842.2025.2532814","DOIUrl":"https://doi.org/10.1080/10255842.2025.2532814","url":null,"abstract":"<p><p>Current childbirth simulations lack realism due to oversimplified fetal models. This study introduces a foetus-specific musculoskeletal model to simulate posterior arm delivery in shoulder dystocia and compares it to a scaled adult-to-foetus model. Using medical imaging and synthetic kinematic data, the fetal model showed notable differences in joint and muscle behaviors, with up to 39 mm deviation in elbow movement. This is the first use of a foetus-specific model for simulating delivery complications, highlighting its clinical relevance for improving accuracy in childbirth simulations and identifying potential risk factors in complex deliveries.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Myeloid cell differentiation-related gene signature predicts the prognosis and immunotherapy response in bladder cancer.","authors":"Lewei Guan, Fuchun Zheng, Sheng Li, Yuyang Yuan, Situ Xiong, Xiaoqiang Liu, Bin Fu","doi":"10.1080/10255842.2025.2532034","DOIUrl":"https://doi.org/10.1080/10255842.2025.2532034","url":null,"abstract":"<p><p>This study aims to identify prognostic and therapy-response biomarkers in bladder cancer (BC) by developing a predictive gene signature based on myeloid cell differentiation-related genes (MCDGs) to enhance patient management. BC patient data from TCGA and GEO were analyzed using non-negative matrix factorization (NMF) to classify subgroups. Survival differences and pathway variations were assessed. A prognostic MCDG model was constructed using univariate Cox regression and LASSO analyses, validated through Kaplan-Meier survival and ROC curves. Clinical relevance, tumor microenvironment (TME), drug response, and immunotherapy potential were evaluated. ACTN1 was verified via qRT-PCR and functional assays, including transwell migration, wound healing, colony formation, and EDU assays. NMF identified two BC subgroups (CA and CB), with CB showing better survival. Six key MCDGs linked to prognosis were identified. High-risk gene profiles correlated with poorer outcomes. Significant differences in immune infiltration, checkpoint expression, TME, and treatment response were observed. Notably, ACTN1 silencing suppressed BC cell proliferation. The MCDG signature predicts BC prognosis and may guide immunotherapy selection. ACTN1 is crucial in BC proliferation, highlighting its potential as a therapeutic target.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical simulation of blood flow in 3D CT-based healthy and atherosclerosis carotid artery bifurcation models to compare the hemodynamics and biomechanics using FSI method under realistic boundary conditions.","authors":"Hiwa Aryan, Melika Rasi, Sasan Asiaei","doi":"10.1080/10255842.2025.2530654","DOIUrl":"https://doi.org/10.1080/10255842.2025.2530654","url":null,"abstract":"<p><p>Atherosclerosis, a primary cause of cardiovascular diseases, arises from intricate interactions between hemodynamic factors and vascular biology. This condition is characterized by a reduction in luminal cross-sectional area, which consequently impairs blood supply. In this study, patient-specific models of a stenosis carotid artery and its digitally-created healthy counterpart were reconstructed from CT scans. Employing the finite element method and a two-way fluid-structure interaction (FSI) coupling approach, non-Newtonian simulations of pulsatile and laminar blood flow were performed. The arterial wall was modeled as a linear, elastic, isotropic, and homogeneous material. The presence of plaque led to an approximately two-fold increase in peak velocity within the stenotic region, rising from approximately 0.3 m/s in the healthy model to 0.7 m/s, directly attributable to the reduced luminal area. The maximum shear stress of the wall at the location of the plaque reached 40 Pa. Furthermore, the maximum wall displacement increased from 1.5 mm in the healthy artery to 1.7 mm in the stenosis artery. While pressure results indicated minor localized increases and decreases before and after the plaque site, respectively, these changes did not significantly affect the total arterial pressure. Examination of blood flow streamlines revealed flow recirculation regions in the carotid sinus bulb of both arteries. In the stenosis artery, an additional and more pronounced flow recirculation region formed distal to the plaque, owing to the post-stenotic expansion. This phenomenon led to a substantial increase of approximately 240% in the oscillatory shear index (OSI) within the internal carotid artery branch. The relative residence time (RRT) remained relatively constant in the common carotid artery and the bifurcation region. However, RRT decreased by approximately 40% in the carotid branches, predominantly in the external carotid artery. Comparison of hemodynamic parameters and biological indices between healthy and stenosed arteries suggests that atherosclerotic plaques significantly alter local hemodynamics, potentially creating novel regions susceptible to atherosclerosis that are absent in healthy artery. In the healthy artery, about 8.3% of the vessel area was at risk for disease (TAWSS < 0.4 Pa), but this increased to 20% in the stenosed artery due to plaque accumulation, a 2.4-fold expansion. Regarding RRT, an increase was observed; areas with RRT > 10 expanded by approximately 1.6 times in the stenosed artery (from 3.1% in healthy to 5% in diseased).</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-28"},"PeriodicalIF":1.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144644071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive approach to proactive performance assessment in safety-critical industries through EEG monitoring and advanced analysis.","authors":"Gunda Yugaraju, Mohd Maneeb Masood, Suprakash Gupta","doi":"10.1080/10255842.2025.2527385","DOIUrl":"https://doi.org/10.1080/10255842.2025.2527385","url":null,"abstract":"<p><p>Enhancing human performance is crucial in various industries for improved operational efficiency and safety, as even minor fluctuations can lead to severe consequences. The integration of electroencephalography (EEG) and advanced analysis methods have become tailor-made for understanding and optimizing cognitive processes to mitigate such errors and accidents. This article delves into the realm of cognitive assessment and its implications for the optimization of human performance to forge a tool for predicting cognitive capacities. The methodology relies on the collection of EEG data, with a specific focus on the activity in the prefrontal cortex, which serves as an index for attention and working memory status. Ten healthy adults participated in these experiments, undergoing EEG measurements, and standardized cognitive tests in controlled environments over 15 d. The data analysis involved preprocessing EEG signals, feature extraction, and modeling using machine learning techniques including k-nearest neighbor (KNN), decision trees, support vector machines, and artificial neural network (ANN) models. The findings unequivocally single out the decision tree model as the leading performer among the machine learning techniques scrutinized. It impressively attained a sensitivity of 94.25%, underscoring its precision in identifying individuals with robust attentional performance. The model's precision soaring at 84.97% and accuracy at 83.47% reinforce its ability to differentiate true positive cases with a minimal margin of false positives. However, the ANN model stands out as the best performer among memory models with an impressive accuracy of 83.90%. These findings add on the potential of EEG signals and machine learning for practical applications, emphasizing the value of eye blink patterns and neurophysiological data in predicting cognitive performance.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A permutation importance and ensemble learning based feature selection approach for muscular intent decoding.","authors":"Anil Sharma, Ila Sharma","doi":"10.1080/10255842.2025.2526017","DOIUrl":"https://doi.org/10.1080/10255842.2025.2526017","url":null,"abstract":"<p><p>Muscle signals are indeterministic and contain huge inter-subject variations. The work proposes a subject-specific feature selection approach employing permutation importance-based weight calculation to identify different hand movements correctly. The performance of the proposed method is evaluated in terms of accuracy, F1 score, and computational time. The study finds that merely 25% of the features are enough to predict the movements using the ensemble-based classifier. The accuracy and F1 score increment are almost 3-5% with only 25% features. The feature reduction significantly reduces the training and validation time by almost 40% compared to the time taken for the whole feature group.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the effect of lumbar lordosis angle on vertebral load distribution during walking.","authors":"Jie Chen, Patria A Hume, Hannah Wyatt, Ted Yeung, Julie Choisne","doi":"10.1080/10255842.2025.2530658","DOIUrl":"https://doi.org/10.1080/10255842.2025.2530658","url":null,"abstract":"<p><p>Atypical sagittal spinopelvic alignment is correlated with exacerbating lower back pain (LBP). This study investigated the effects of simulated sagittal spinopelvic alignment <i>via</i> altered lumbar lordosis (LL) on lumbar vertebral contact forces during walking. A full-body OpenSim model with custom lumbar joints was developed to estimate lumbar vertebral loads for self-selected speed walking gaits of 18 healthy participants. Limited LL during walking augmented the resultant vertebral compressive and shear forces, and vertebral body compression. Excessive LL increased resultant vertebral shear forces, compression at facet joints and L5/S1 vertebral body, potentially progressing to different types of LBP.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.7,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}