Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"An interaction model for predicting brace migration and validation through walking experiment.","authors":"Yuzhou Yan, Mengzhao Cao, Bing Han, Hui Li, Geng Liu","doi":"10.1080/10255842.2024.2307918","DOIUrl":"10.1080/10255842.2024.2307918","url":null,"abstract":"<p><p>Brace migration undermines therapeutic efficacy, which is traditionally evaluated through walking experiments. This study developed an interaction model that considered the instantaneous center of rotation (ICR) misalignment to predict migration. The model was validated by walking experiment. Results show a strong positive correlation for four-linkage (FL) (<i>r</i> = 0.952, <i>p</i> < 0.01, root mean squared error (RMSE) = 0.53 mm) and spur gear (SG) (<i>r</i> = 0.898, <i>p</i> < 0.01, RMSE = 1.35 mm) mechanisms. The FL exhibits lower migration than SG (<i>p</i> < 0.05). In conclusion, the interaction model accurately predicts migration, emphasizing the influence of mechanism on migration.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"962-971"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693468","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}

{"title":"Unveiling fetal heart health: harnessing auto-metric graph neural networks and Hazelnut tree search for ECG-based arrhythmia detection.","authors":"M Suganthy, B Sarala, G Sumathy, W T Chembian","doi":"10.1080/10255842.2025.2481232","DOIUrl":"https://doi.org/10.1080/10255842.2025.2481232","url":null,"abstract":"<p><p>Fetal electrocardiogram (ECG) provides a non-invasive means to assess fetal heart health, but isolating the fetal signal from the dominant maternal ECG remains challenging. This study introduces the FHH-AMGNN-HTSOA-ECG-AD method for enhanced fetal arrhythmia detection. It employs Dual Tree Complex Wavelet Transform for denoising and utilizes an Auto-Metric Graph Neural Network (AMGNN) optimized by the Hazelnut Tree Search Algorithm (HTSOA). This integration enables accurate classification of normal and abnormal fetal heart signals. Experimental results demonstrate that the proposed approach significantly outperforms existing methods in terms of accuracy, precision, and specificity.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044115","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 immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies.","authors":"Xiao Jin, Xiang Zhao","doi":"10.1080/10255842.2025.2495250","DOIUrl":"https://doi.org/10.1080/10255842.2025.2495250","url":null,"abstract":"<p><p><b>Background:</b> Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. <b>Methods:</b> We explored SAH-IA links using machine learning and bioinformatics, identifying 66 IA-related SAH genes. KEGG analysis highlighted pathways like NF-κB, TNF, and COVID-19. <b>Results:</b> Two immune-related genes (ZNF281, LRRN3) were identified, and a ceRNA network was constructed. Ten potential SAH-IA drugs were screened via CMAP. <b>Conclusion:</b> ZNF281 and LRRN3 may regulate immune pathways (T cells, NK cells, macrophages), influencing IA-related SAH development, and could serve as therapeutic targets.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065181","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 biomechanical injury calculator: a postprocessor software for a finite element human body model.","authors":"Srihari Menon, Quenton Hua, Nancy J Currie-Gregg","doi":"10.1080/10255842.2024.2448554","DOIUrl":"https://doi.org/10.1080/10255842.2024.2448554","url":null,"abstract":"<p><p>An injury risk assessment postprocessor for the Global Human Body Model Consortium (GHBMC) model is presented. The Biomechanical Injury Calculator (BIC) calculates injury probabilities for the head, neck, spine, and pelvis post-simulation, along with a total injury probability for the entire complex. It also generates an injury heatmap. Developed for the GHBMC M50-OS v2.3 +DeformSpine, BIC was validated by comparing 103 airmen's seat ejection injuries to BIC-predicted injury probabilities in 30 vertical seat load simulations. Observed injury rates correlated strongly with BIC predictions (Spearman=0.943, Pearson=0.982) within 5.16% margin. The total injury probability of 58.48% closely matched the 56.3% observed rate.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044113","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":"Epileptic seizure detection in EEG signals using deep learning: LSTM and bidirectional LSTM.","authors":"Ghezala Chekhmane, Radhwane Benali","doi":"10.1080/10255842.2025.2490136","DOIUrl":"https://doi.org/10.1080/10255842.2025.2490136","url":null,"abstract":"<p><p>This paper established a new automatic method to detect epileptic seizures in EEG signals based on discret wavelet transform (DWT) and Deep Learning (DL). DWT is used to decompose EEG into different sub-bands. Moreover, the proposed model combines Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) networks with one layer of each network consecutive. The experimental results yield higher accuracies of 100% which it is demonstrated that the obtained results achieve better performance by using the new hybrid LSTM-BiLSTM network than other works. Finally, this hybrid LSTM-BiLSTM model confirmed their effectiveness for the classification of epileptic EEG signals.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-24"},"PeriodicalIF":1.7,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057586","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}