Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"A framework for studying oxygen and nitric oxide transport in unstable flow through a patient-based abdominal aortic aneurysm model.","authors":"Robert A Peattie, Sudharsan Madhavan, Brian Fix, Robert J Fisher, Simone Melchionna, Erica Cherry Kemmerling","doi":"10.1080/10255842.2025.2510363","DOIUrl":"10.1080/10255842.2025.2510363","url":null,"abstract":"<p><p>Abdominal Aortic Aneurysm (AAA) is a potentially life-threatening permanent, localized dilation in the abdominal aorta wall. Previous studies have suggested that the presence of a layer of intraluminal thrombus (ILT), which is found adhering to the wall inner surface in 80-90% of all AAAs, is associated with a significant decrease in the oxygen (O₂) level within the wall. However, although turbulence normally has a major influence on solute transport, its effect on this decrease has not yet been investigated. In the present study, a computational technique for evaluating wall O₂ and NO concentration distributions in a patient-based model with separate lumen, thrombus, and wall layers is developed. Flow in this model was evaluated by Direct Numerical Simulation, using pathophysiologically realistic flow and transport conditions accounting for instability and turbulence development. Concentration distributions were determined by solution of advection-diffusion-reaction equations appropriate to each layer. Normalized O₂ concentration at the wall inner surface decreased as ILT thickness increased up to 0.4 cm but then plateaued at ∼0.7 (normalized). Contrary to expectations, turbulence had minimal impact on transport, which was consistent with calculation of an effective Damkohler number for the AAA, indicating that solute levels were governed by reaction-limited rather than transport-limited dynamics. Since NO production was driven by shear stress at the lumen-wall interface, NO was absent in ILT-covered regions, creating spatial disparities in wall NO concentration between thrombus-covered and clear regions of the wall surface. The results suggest that ILT induces wall hypoxia and impairs NO-mediated vascular homeostasis.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1500-1519"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200660","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":"Optimization enabled ensemble based deep learning model for elderly falling risk prediction.","authors":"Li Chen, Wei Chen","doi":"10.1080/10255842.2025.2514802","DOIUrl":"10.1080/10255842.2025.2514802","url":null,"abstract":"<p><p>Predicting fall risk in the elderly is crucial for enhancing safety and well-being. Aging and chronic diseases often impair balance, increasing fall risk. This study aims to develop an advanced fall risk prediction model using an optimized deep learning approach. Data undergoes pre-processing and augmentation to increase size, then is fed into an ensemble learning model,like Extreme Gradient Boosting (XGBoost), One Dimensional Convolutional Neural Network, and Deep Belief Network. The model is trained with a novel Double Exponential Lyrebird Optimization algorithm, combining double exponential smoothing and Lyrebird Optimization . Experimental results show that DELOA-based ensemble learning model achieved better results.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1520-1537"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144276527","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 framework for the analysis of skin sores disease using evolutionary intelligent computing approach.","authors":"Muhammad Shoaib, Rafia Tabassum, Kottakkaran Sooppy Nisar, Muhammad Asif Zahoor Raja","doi":"10.1080/10255842.2024.2326888","DOIUrl":"10.1080/10255842.2024.2326888","url":null,"abstract":"<p><p>The most common and contagious bacterial skin disease i.e. skin sores (impetigo) mostly affects newborns and young children. On the face, particularly around the mouth and nose area, as well as on the hands and feet, it typically manifests as reddish sores. In this study, a neuro-evolutionary global algorithm is introduced to solve the dynamics of nonlinear skin sores disease model (SSDM) with the help of an artificial neural network. The global genetic algorithm is integrated with local sequential quadratic programming (GA-LSQP) to obtain the optimal solution for the proposed model. The designed differential model of skin sores disease is comprised of susceptible (<i>S</i>), infected (<i>I</i>), and recovered (<i>R</i>) categories. An activation function based neural network modeling is exploited for skin sores system through mean square error to achieve best trained weights. The integrated approach is validated and verified through the comparison of results of reference Adam strategy with absolute error analysis. The absolute error results give accuracy of around <math><mrow><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>11</mn></mrow></msup></mrow><mi> </mi><mtext>to</mtext><mi> </mi><mrow><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>5</mn></mrow></msup></mrow><mo>,</mo></math> demonstrating the worthiness and efficacy of proposed algorithm. Additionally, statistical investigations in form of mean absolute deviation, root mean square error, and Theil's inequality coefficient are exhibited to prove the consistency, stability, and convergence criteria of the integrated technique. The accuracy of the proposed solver has been examined from the smaller values of minimum, median, maximum, mean, semi-interquartile range, and standard deviation, which lie around <math><mrow><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>12</mn></mrow></msup></mrow><mi> </mi><mtext>to</mtext><mi> </mi><mrow><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>2</mn></mrow></msup></mrow><mo>.</mo></math></p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1462-1476"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140102840","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":"Automated posture adjustment system for immobilized patients using EEG signals.","authors":"Nikhil Kushwaha, Nitin Mishra, Rajveer Singh Lalawat, Prabin Kumar Padhy, Vijay Kumar Gupta","doi":"10.1080/10255842.2025.2523322","DOIUrl":"https://doi.org/10.1080/10255842.2025.2523322","url":null,"abstract":"<p><p>This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-processing involved filtering, followed by feature extraction using a Convolutional Recurrent Denoising Autoencoder (CRDAE) model. After that Classification is performed using artificial intelligence (AI) models, including Gated Recurrent Unit (GRU) with Attention, Temporal Transformer (TT), Bidirectional Long Short-Term Memory with attention mechanisms (Bi-LSTM with AM), and proposed Graph Transformer All Attention (GTAA). The GTAA model demonstrates superior performance, achieving the highest classification accuracy among the evaluated models. Additionally, the proposed system validated against the BCI Competition IV 2a datasets and ten-fold subject cross-validation, demonstrating its reliability and efficiency for real-time BCI applications. This study underscores the potential of integrating advanced AI techniques with EEG signal measurement and instrumentation for practical implementations.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144545871","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":"Advancing COVID-19 stochastic modeling: a comprehensive examination integrating vaccination classes through higher-order spectral scheme analysis.","authors":"Laiquan Wang, Sami Ullah Khan, Farman U Khan, Salman A AlQahtani, Atif M Alamri","doi":"10.1080/10255842.2024.2319276","DOIUrl":"10.1080/10255842.2024.2319276","url":null,"abstract":"<p><p>This research article presents a comprehensive analysis aimed at enhancing the stochastic modeling of COVID-19 dynamics by incorporating vaccination classes through a higher-order spectral scheme. The ongoing COVID-19 pandemic has underscored the critical need for accurate and adaptable modeling techniques to inform public health interventions. In this study, we introduce a novel approach that integrates various vaccination classes into a stochastic model to provide a more nuanced understanding of disease transmission dynamics. We employ a higher-order spectral scheme to capture complex interactions between different population groups, vaccination statuses, and disease parameters. Our analysis not only enhances the predictive accuracy of COVID-19 modeling but also facilitates the exploration of various vaccination strategies and their impact on disease control. The findings of this study hold significant implications for optimizing vaccination campaigns and guiding policy decisions in the ongoing battle against the COVID-19 pandemic.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1409-1423"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139941122","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":"Design and performance analysis of a new inferior vena cava filter.","authors":"Xianying Wang, Huajie Wang, Hongliang Qian, Hongdong Su, Deda Lou, Lijian Tian, Deshen Chen, Huafang Ding, Feng Fan","doi":"10.1080/10255842.2024.2326084","DOIUrl":"10.1080/10255842.2024.2326084","url":null,"abstract":"<p><p>This study proposes a novel inferior vena cava filter (IVCF) design, \"Lotus,\" aiming to enhance release stability and endothelialization. A catheter-filter-vessel model was established for IVCF property analysis, validated by comparing numerical simulations and in vitro tests. Lotus's mechanical properties were analyzed, and optimization suggestions are provided. Compared to existing clinical filters, Lotus demonstrates improved release stability and thrombus capture ability. This work suggests Lotus as a potential technical reference for improved IVCF treatment.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1437-1449"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140094994","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":"Prognostic prediction of laryngeal cancer patients based on nitrogen metabolism-related genes.","authors":"Yifang Wang, Tianping Shen, Yan Wang","doi":"10.1080/10255842.2025.2523309","DOIUrl":"https://doi.org/10.1080/10255842.2025.2523309","url":null,"abstract":"<p><strong>Background: </strong>Nitrogen metabolism plays a crucial role in cancer progression. This study aimed to construct a prognostic model using nitrogen metabolism-related genes (NMRGs) for laryngeal cancer (LC).</p><p><strong>Methods: </strong>NMRGs for LC were identified from public databases and literature. A prognostic model was constructed through regression analysis, and differential and enrichment analyses were performed to explore differentially expressed genes (DEGs) and their functional implications. Immune cell differences between risk groups were assessed, and gene mutations were analyzed using TCGA data. Drug sensitivity predictions for different risk groups were also conducted.</p><p><strong>Results: </strong>A total of 203 NMRGs were identified, leading to eight genes used in a risk-scoring model. Enrichment analysis showed that DEGs in the high-risk group (993 genes) were linked to processes like neuroactive ligand-receptor interaction and calcium signaling. Immune analysis revealed high infiltration of activated NK cells and CD4+ T cells in the low-risk group, while CD8+ T cells and macrophages were prominent in the high-risk group. Drug sensitivity analysis identified KIN001-135, Phenformin, and Gemcitabine as potential treatments.</p><p><strong>Conclusion: </strong>Nitrogen metabolism is closely related to LC prognosis, and the NMRG-based model effectively distinguishes risk groups with distinct immune landscapes and drug sensitivities.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144545872","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":"Seizure prediction based on improved vision transformer model for EEG channel optimization.","authors":"Nan Qi, Yan Piao, Hao Zhang, Qi Wang, Yue Wang","doi":"10.1080/10255842.2024.2326097","DOIUrl":"10.1080/10255842.2024.2326097","url":null,"abstract":"<p><p>Epileptic seizures are unpredictable events caused by abnormal discharges of a patient's brain cells. Extensive research has been conducted to develop seizure prediction algorithms based on long-term continuous electroencephalogram (EEG) signals. This paper describes a patient-specific seizure prediction method that can serve as a basis for the design of lightweight, wearable and effective seizure-prediction devices. We aim to achieve two objectives using this method. The first aim is to extract robust feature representations from multichannel EEG signals, and the second aim is to reduce the number of channels used for prediction by selecting an optimal set of channels from multichannel EEG signals while ensuring good prediction performance. We design a seizure-prediction algorithm based on a vision transformer (ViT) model. The algorithm selects channels that play a key role in seizure prediction from 22 channels of EEG signals. First, we perform a time-frequency analysis of processed time-series signals to obtain EEG spectrograms. We then segment the spectrograms of multiple channels into many non-overlapping patches of the same size, which are input into the channel selection layer of the proposed model, named Sel-JPM-ViT, enabling it to select channels. Application of the Sel-JPM-ViT model to the Boston Children's Hospital-Massachusetts Institute of Technology scalp EEG dataset yields results using only three to six channels of EEG signals that are slightly better that the results obtained using 22 channels of EEG signals. Overall, the Sel-JPM-ViT model exhibits an average classification accuracy of 93.65%, an average sensitivity of 94.70% and an average specificity of 92.78%.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1450-1461"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140050926","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":"CFD simulation of cannabidiol delivery through microneedle patches.","authors":"Liqun Shi, Jianfeng Xu, Lihua Zhang, Weiping Zuo, Binbin Ni, Mingqiang Lai, Maoqi Fu","doi":"10.1080/10255842.2024.2324881","DOIUrl":"10.1080/10255842.2024.2324881","url":null,"abstract":"<p><p>This study investigates the efficiency and influence of microneedle parameters, specifically Needle Point Angle (a) and Needle Height (h), on the diffusion of Cannabidiol (CBD) across varying skin depths. Utilizing the Latin Hypercube Sampling method, twelve distinct cases were analyzed. Observations reveal a consistent high concentration of CBD delivered <i>via</i> the microneedle patch, with a notable decrease in concentration as the depth increases, displaying a non-linear trend. Multivariate polynomial regression offers a quantitative relationship between the variables, with the third-order bivariate fitting providing the most accurate representation. Compared to other CBD delivery mechanisms, microneedle patches present enhanced CBD concentrations, circumventing challenges faced by other methods such as dosage inaccuracy, systemic absorption issues, and CBD degradation. The results highlight the potential of microneedle patches as a promising avenue for optimized transdermal drug delivery.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1424-1436"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140094993","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":"Sensitivity analysis and optimization of a cardiovascular lumped parameter model for patient-specific modelling.","authors":"Siti Munirah Muhammad Ali, Wahbi El-Bouri, Wan Naimah Wan Ab Naim, Mohd Jamil Mohamed Mokhtarudin","doi":"10.1080/10255842.2025.2525980","DOIUrl":"https://doi.org/10.1080/10255842.2025.2525980","url":null,"abstract":"<p><p>Parameter estimation poses a significant challenge in developing patient-specific cardiovascular models. This study presents a framework that enhances parameter estimation in lumped parameter cardiovascular models by combining sensitivity analysis for parameter selection with multi-objective genetic algorithm optimization. Four key parameters were identified as the most influential and subsequently optimized. Model outputs, specifically mean arterial pressure (MAP), were validated against clinical values from a public database. The optimized model's MAP demonstrated a strong correlation with clinical MAP (<i>r</i> = 0.99997, <i>p</i> < 0.001), and a t-test analysis (<i>p</i> = 0.752) confirmed statistical equivalence with clinical data. This approach highlights the potential of sensitivity analysis and genetic algorithms to improve accuracy in patient-specific cardiovascular modelling.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144545873","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}