Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"The influence of different implantation depths on the hemodynamics in the treatment of stenosis in the starting segment of vertebral arteries.","authors":"Yilin Zheng, Haiquan Feng, Qingsong Han, Yonggang Wang, Juan Su","doi":"10.1080/10255842.2025.2480269","DOIUrl":"10.1080/10255842.2025.2480269","url":null,"abstract":"<p><p>Stent implantation depth significantly influences the hemodynamics of the vertebral and subclavian arteries in treating vertebral artery stenosis. This study utilized computational fluid dynamics (CFD) to analyze key hemodynamic parameters in a vertebral artery model with a stent implanted at different depths. Results showed that excessive stent extension into the subclavian artery alters local blood flow, increasing the risk of thrombosis and plaque formation. An optimal implantation depth of 1-2 mm minimizes these risks. These findings provide a theoretical basis for optimizing stent placement, improving the efficacy and safety of interventional treatments for vertebral artery stenosis.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"841-854"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702010","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":"Estimating tongue deformation during laryngoscopy using a hybrid FEM-multibody model and intraoperative tracking - a cadaver study.","authors":"Xiaotian Wu, C Antonio Sánchez, John E Lloyd, Heather Borgard, Sidney Fels, Joseph A Paydarfar, Ryan J Halter","doi":"10.1080/10255842.2023.2301672","DOIUrl":"10.1080/10255842.2023.2301672","url":null,"abstract":"<p><p>Throat tumour margin control remains difficult due to the tight, enclosed space of the oral and throat regions and the tissue deformation resulting from placement of retractors and scopes during surgery. Intraoperative imaging can help with better localization but is hindered by non-image-compatible surgical instruments, cost, and unavailability. We propose a novel method of using instrument tracking and FEM-multibody modelling to simulate soft tissue deformation in the intraoperative setting, without requiring intraoperative imaging, to improve surgical guidance accuracy. We report our first empirical study, based on four trials of a cadaveric head specimen with full neck anatomy, yields a mean TLE of 10.8 ± 5.5 mm, demonstrating methodological feasibility.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"739-749"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11231054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification method of traditional Chinese medicine compound decoction duration based on multi-dimensional feature weighted fusion.","authors":"Zhibiao Li, Huayong Zhao, Genhua Zhu, Jianqiang Du, Zhenfeng Wu, Zhicheng Jiang, Yiwen Li","doi":"10.1080/10255842.2024.2302225","DOIUrl":"10.1080/10255842.2024.2302225","url":null,"abstract":"<p><p>This paper extends a text classification method utilizing natural language processing (NLP) into the field of traditional Chinese medicine (TCM) compound decoction to effectively and scientifically extend the TCM compound decoction duration. Specifically, a TCM compound decoction duration classification named TCM-TextCNN is proposed to fuse multi-dimensional herb features and improve TextCNN. Indeed, first, we utilize word vector technology to construct feature vectors of herb names and medicinal parts, aiming to describe the herb characteristics comprehensively. Second, considering the impact of different herb features on the decoction duration, we use an improved Term Frequency-Inverse Word Frequency (TF-IWF) algorithm to weigh the feature vectors of herb names and medicinal parts. These weighted feature vectors are then concatenated to obtain a multi-dimensional herb feature vector, allowing for a more comprehensive representation. Finally, the feature vector is input into the improved TextCNN, which uses k-max pooling to reduce information loss rather than max pooling. Three fully connected layers are added to generate higher-level feature representations, followed by softmax to obtain the final results. Experimental results on a dataset of TCM compound decoction duration demonstrate that TCM-TextCNN improves accuracy, recall, and F1 score by 5.31%, 5.63%, and 5.22%, respectively, compared to methods solely rely on herb name features, thereby confirming our method's effectiveness in classifying TCM compound decoction duration.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"867-881"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139405108","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":"Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier.","authors":"Ganesh Chandrasekaran, S Dhanasekaran, C Moorthy, A Arul Oli","doi":"10.1080/10255842.2024.2313066","DOIUrl":"10.1080/10255842.2024.2313066","url":null,"abstract":"<p><p>Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the Hybrid LXGB (Long short-term memory Extreme Gradient Boosting) Model, a novel approach for multimodal sentiment analysis that merges the strengths of long short-term memory (LSTM) and XGBoost classifiers. The primary objective is to address the intricate task of understanding emotions across diverse data sources, such as textual data, images, and audio cues. By leveraging the capabilities of deep learning and gradient boosting, the Hybrid LXGB Model achieves an exceptional accuracy of 97.18% on the CMU-MOSEI dataset, surpassing alternative classifiers, including LSTM, CNN, DNN, and XGBoost. This study not only introduces an innovative model but also contributes to the field by showcasing its effectiveness and balance in capturing the nuanced spectrum of sentiments within multimodal datasets. The comparison with equivalent studies highlights the model's remarkable success, emphasizing its potential for practical applications in real-world scenarios. The Hybrid LXGB Model offers a unique and promising perspective in the realm of multimodal sentiment analysis, demonstrating the significance of integrating LSTM and XGBoost for enhanced performance.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"777-799"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139716600","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 assessment of implant shape-dependent failure mechanisms in primary total hip arthroplasty using finite element analysis.","authors":"Siavash Kazemirad, Mohammad Ali Yazdi","doi":"10.1080/10255842.2023.2301676","DOIUrl":"10.1080/10255842.2023.2301676","url":null,"abstract":"<p><p>The three mechanisms known to be responsible for the failure of uncemented femoral stems in primary total hip arthroplasty (THA) are the stress shielding, excessive bone-implant interface stress, and excessive initial micromotion. Since implant designers usually have to sacrifice two mechanisms to improve the other one, the aim of this study was to assess which of them plays a more important role in the failure of uncemented stems. Two hip implant stems which are widely used in the primary THA and their mid-term clinical outcomes are available, were selected. Then, the amount of the three failure mechanisms created by each stem during the normal walking gait cycle was determined for a 70 kg female patient using the finite element method. The results indicated that the stem with better clinical outcome induced an average of 36.6% less stress shielding in the proximal regions of femur bone compared with the other stem. However, the maximum bone-implant interface stress and maximum initial micromotion were, respectively, 30 and 155% higher for the stem with better clinical outcomes. It was therefore concluded that the stress shielding has a more significant impact on the mid-term life of uncemented stems. However, care must be taken to ensure that the other two failure mechanisms do not exceed a certain threshold. It was also observed that the thinner and shorter stem created a smaller amount of stress shielding in the femur bone. The outcomes of this study can be used to design new hip implant stems that can potentially last longer.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"750-763"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139514104","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":"Comparative study of microneedle insertion across different skin models.","authors":"Xuan He, Xue Sui, Yongchao Liu, Rui Zhou, Shouying Du","doi":"10.1080/10255842.2025.2495300","DOIUrl":"https://doi.org/10.1080/10255842.2025.2495300","url":null,"abstract":"<p><p>Microneedles (MNs) enhance drug delivery by painlessly penetrating the stratum corneum, with hardness being crucial for safe insertion. While human/animal skins and synthetic materials are used to test MNs, their variability affects results. Finite element methods (FEM) simulate insertion, but skin model complexity and software limitations cause inconsistencies. This study employs FEM to compare MN performance across skun models under uniform conditions. Results reveal small volume change differences during insertion and rank penetration ease for human/animal models, aiding skin model selection and species-specific MN design optimization.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057238","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":"Unveiling the strain uniformity challenge: design and evaluation of a PDMS membrane for precise mechanobiology studies.","authors":"Nilüfer Düz, Yasin Gülsüm, Waleed Odeibat, Ismail Uyanık, Samet Akar, Pervin Dinçer","doi":"10.1080/10255842.2025.2495254","DOIUrl":"https://doi.org/10.1080/10255842.2025.2495254","url":null,"abstract":"<p><p>Mechanotransduction and mechanosensing enable cells to respond to mechanical stimuli, essential in various physiological functions. Specialized cell stretching devices use stretchable, transparent, and biocompatible elastomeric membranes to study these responses. However, achieving strain uniformity is a key challenge, affecting data accuracy and reliability. This study designed a polydimethylsiloxane (PDMS) membrane with optimized uniformity for electromechanical cell stretching. Finite element analysis optimized membrane size and shape, achieving a 90% strain uniformity index-a 233% improvement over commercial membranes. By tailoring material properties like cross-linker ratio and curing time, membrane failure issues were resolved, enhancing applications in tissue engineering and mechanobiology research.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052239","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":"Biomechanical effects of upper articular process resection proportion on lumbar after transforaminal endoscopic spine system surgery.","authors":"Hanqiang Liu, Aobo Zhang, Hao Chen, Yang Liu, Bin Sun, Hao Liu, Qing Han, Jincheng Wang, Peng Xia","doi":"10.1080/10255842.2025.2495252","DOIUrl":"https://doi.org/10.1080/10255842.2025.2495252","url":null,"abstract":"<p><p>Transforaminal endoscopic spine system resection of upper articular process with different proportions often has different effects on lumbar spine biomechanics. Therefore, finite element method was used to explore this problem in this study. The Finite Element (FE) model constructed and validated in this study was a 3D heterogeneous model of the L1-S1 lumbar spine. Nine groups of models with different resect proportions were constructed and compared in six physiological movements. Following the experiment, we reached the following main results. The peak Von Mises stress of the left upper articular process of L5 increased most significantly when the resection proportion increased from 40% to 50% under flexion-extension and bending conditions, with an increase value of 6.04-11.47 MPa. When the resection proportion increased from 70% to 80% under rotation conditions, the peak Von Mises stress of the left articular process of L5 increased most significantly, with an increase value of 26.89 and 37.43 MPa. When the resection proportion increased from 70% to 80% under rotation conditions, the peak Von Mises stress of L4-L5 disc increased by 0.69 and 1.84 MPa. The stress concentration area was mainly located in the junction area of articular process and articular cartilage. Based on the above results, we draw the following conclusions. The resection of 50% of the upper articular process is the critical value of lumbar spine biomechanical change. Especially when the proportion of upper articular process resection exceeds 80%, lumbar instability and postoperative pain may occur during rotation. The postoperative pain is related to the stress concentration stimulation of the articular process and articular cartilage junction.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.7,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055629","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":"Identification of cancer-associated fibroblast characteristics for predicting outcome and response to immunotherapy in renal cell carcinoma.","authors":"Jie-Bo Zhou, Chun-Xiang Li, Lei Qian, Jian-Hua Chu","doi":"10.1080/10255842.2025.2495299","DOIUrl":"https://doi.org/10.1080/10255842.2025.2495299","url":null,"abstract":"<p><strong>Objectives: </strong>To elucidate the prognostic effect of cancer-associated fibroblasts (CAFs) on renal cell carcinoma.</p><p><strong>Methods: </strong>CAFs and stromal scores were calculated using various algorithms including Estimating the Proportions of Immune and Cancer cells (EPIC), Microenvironment Cell Populations-counter (MCP counter), Tumor immune dysfunction and exclusion (TIDE) and xCell. Weighted gene co-expression network analysis (WGCNA) was conducted to determine the CAF-associated modules and key genes. The functional pathways of key genes in important CAF modules were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. CAF-associated signatures were established through univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Kaplan-Meier and receiver operating characteristic (ROC) analyses were carried out to assess the predictive value of CAF signatures.</p><p><strong>Results: </strong>WGCNA analysis distinguished several CAF-associated modules for renal clear cell carcinoma (KIRC), renal chromophobe cell carcinoma (KICH) and renal papillary cell carcinoma (KIRP) respectively. CAF signatures were established containing two and four genes for KIRC and KIRP, respectively. In KIRC and KIRP, patients with high-risk scores had unfavorable outcome than those with low-risk scores. Additionally, in both KIRC and KIRP, the ratio of patients responding to immunotherapy was obviously higher in low-risk group than in high-risk group. Finally, the mutation frequency of some genes differed significantly between two groups.</p><p><strong>Conclusion: </strong>Our study provided valuable CAF signatures for predicting the outcome of KIRC and KIRP patients. These CAF signatures were also used to predict immunotherapy response, providing strategies for individualized therapy of patients.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144004163","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":"HybridGWOSPEA2ABC: a novel feature selection algorithm for gene expression data analysis and cancer classification.","authors":"Ashimjyoti Nath, Chandan Jyoti Kumar, Sanjib Kr Kalita, Thipendra Pal Singh, Renu Dhir","doi":"10.1080/10255842.2025.2495248","DOIUrl":"https://doi.org/10.1080/10255842.2025.2495248","url":null,"abstract":"<p><strong>Background and objective: </strong>DNA micro-array technology has a remarkable impact on biological research, particularly in categorizing and diagnosing cancer and studying gene features and functions. With the availability of extensive collections of cancer-related data, there has been an increased focus on developing optimized Machine Learning (ML) techniques for cancer classification through gene pattern analysis and the identification of specific genes for cancer type categorization. The relevant gene selection for diagnosing and treating cancer poses a significant challenge, which requires efficient feature selection methods.</p><p><strong>Methods: </strong>This study introduces a novel hybrid algorithm, for gene selection, integrating the Grey Wolf Optimizer (GWO), Strength Pareto Evolutionary Algorithm 2 (SPEA2), and Artificial Bee Colony (ABC). This combination uses intelligence and evolutionary computation to enhance solution diversity, convergence efficiency, and exploration and exploitation capabilities in high-dimensional gene expression data. The algorithm was compared with five bio-inspired algorithms using five different classifiers on various cancer datasets to validate its effectiveness in feature selection.</p><p><strong>Results: </strong>The HybridGWOSPEA2ABC algorithm demonstrated superior performance in identifying relevant cancer biomarkers compared to the conventional bio-inspired algorithms. Comparison with the benchmark algorithms has shown the hybrid approach's enhanced capability in addressing the challenges of high-dimensional data and advancing the gene selection problem for cancer classification.</p><p><strong>Conclusion: </strong>The novel hybridization algorithm enhances performance by maintaining solution diversity, efficiently converging to optimal solutions, and improving the exploration and exploitation of the search space. This study provides a better understanding of relevant genes for cancer classification and promotes effective methodologies for disease detection and classification.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-22"},"PeriodicalIF":1.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057589","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}