Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"A computational model of system dynamics of calcium and nitric oxide in pancreatic beta-cell.","authors":"Neeru Adlakha","doi":"10.1080/10255842.2025.2548571","DOIUrl":"https://doi.org/10.1080/10255842.2025.2548571","url":null,"abstract":"<p><p>Calcium (<math><mi>C</mi><mrow><msup><mrow><mi>a</mi></mrow><mrow><mn>2</mn><mo>+</mo></mrow></msup></mrow></math>) and nitric oxide (<i>NO</i>) play a crucial role in chemical signaling, as regulators of various cellular functions, and as cytotoxic agents under different physiological and pathological settings. These two signaling systems have been investigated in the past as individual systems in pancreatic <math><mrow><mi>β</mi></mrow></math>-cells without considering their spatio-temporal relationships. These studies have generated limited insights, and thus, their role in regulatory and cytotoxic functions of pancreatic <math><mrow><mi>β</mi></mrow></math>-cells is poorly understood. Therefore, an effort has been put forth to create a mathematical model to explore spatio-temporal relationships of cytosolic calcium and <i>NO</i> in a <math><mrow><mi>β</mi></mrow></math>-cell based on the experimental and theoretical data. The model has been framed in terms of reaction-diffusion equation involving <i>ER</i> leak, <math><mrow><mtext>SERCA</mtext></mrow></math> pump, <math><mrow><mtext>PMCA</mtext></mrow></math> pump, <math><mrow><mtext>VGCC</mtext></mrow><mtext>,</mtext></math><math><mi>I</mi><mrow><msub><mrow><mi>P</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></math> receptor, <math><mrow><mtext>EGTA</mtext></mrow></math> buffer, etc. The finite element and Crank-Nicolson methods have been used for numerical simulation. The impacts of various parameters involved in the regulation of <math><mi>C</mi><mrow><msup><mrow><mi>a</mi></mrow><mrow><mn>2</mn><mo>+</mo></mrow></msup></mrow></math>- <i>NO</i> dynamics for space and time have been identified from the numerical results. The regulatory and cytotoxic conditions for the <math><mrow><mi>β</mi></mrow></math>-cell have been assessed with the help of various parameters involved in the calcium and <i>NO</i> dynamics. The proposed model provides novel insights of the impacts of changes in various calcium signaling mechanism on <i>NO</i> dynamics in <math><mrow><mi>β</mi></mrow></math>-cell. The insights into spatio-temporal relationships of these two signaling systems can be helpful for developing various clinical applications.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977257","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":"Convolutional slime mold deep learning model for diagnosis of PD.","authors":"Sk Wasim Akram, A P Siva Kumar","doi":"10.1080/10255842.2025.2542942","DOIUrl":"10.1080/10255842.2025.2542942","url":null,"abstract":"<p><p>The proposed study aims to develop an efficient PD detection scheme using a novel optimized deep learning mechanism. Initially, the input multiple human voice recordings are pre-processed to lessen the unwanted noises. Then, the relevant features are selected to reduce the complexity problems in the feature selection stage using chi-square feature statistical model. Finally, an Enhanced Convolutional Slime Mold Attention (ECSMA) model is proposed for categorizing the input voice recordings. The simulation results portray that the proposed PD detection model attains higher performance than other existing methods and mitigate the costs of healthcare in identifying upcoming disease stages.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977287","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 the effects of subcutaneous injection in conjunction with periodic compression on tissue pressure and drug diffusion.","authors":"Zhendong Shang, Pengyang Meng, Zhengfeng Han","doi":"10.1080/10255842.2025.2541194","DOIUrl":"10.1080/10255842.2025.2541194","url":null,"abstract":"<p><p>This paper investigates the effects of periodic compression on tissue pressure, drug diffusion, and pain sensation during subcutaneous injection. A tissue porosity model was developed based on fluid dynamics principles and simulated on the Fluent platform with dynamic mesh techniques. The results demonstrated that periodic compression reduced tissue pressure and improved drug diffusion compared to the control group. The combination of small amplitude and low-frequency periodic compression enhanced drug absorption, minimized local drug accumulation, and alleviated pain during injection. These findings suggest that periodic compression improves the efficiency of subcutaneous drug delivery.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-19"},"PeriodicalIF":1.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144876632","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 diffusion-driven phase-field model for simulation of glioma growth.","authors":"Meisam Soleimani, Harold F Hounchonou, Joachim K Krauss, Matthias Simon, Philipp Junker, Majid Esmaeilzadeh","doi":"10.1080/10255842.2025.2544792","DOIUrl":"https://doi.org/10.1080/10255842.2025.2544792","url":null,"abstract":"<p><p>Modelling glioma remains a critical area of research due to its poor prognosis. Phase-field modelling is an effective computational approach to simulate the dynamics of biological systems, including tumor growth like glioma. Here, the growth of a tumor is studied in the proximity of a blood artery that nourishes the tumor. The mathematical model reflects a diffusion-driven growth using a phase-field approach coupled with mechanical deformation. The numerical implementation of the mathematical model is realized in an FEM framework. Several numerical examples are provided to show the applicability of the model especially in clinical practices.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812653","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":"Enhancing single-lead ECG arrhythmia classification via multi-teacher decomposed feature distillation.","authors":"Majid Sepahvand, Maytham N Meqdad, Fardin Abdali-Mohammadi","doi":"10.1080/10255842.2025.2542947","DOIUrl":"https://doi.org/10.1080/10255842.2025.2542947","url":null,"abstract":"<p><p>When an arrhythmia occurs in the heart, all electrocardiogram (ECG) leads show evidence of it, but it is more prominent in some leads. This medical fact serves as the foundation for the knowledge distillation (KD) model proposed in this paper, which aims to enhance weak leads by leveraging information from stronger ones. The model employs single-lead signals for the student network and twelve-lead signals for the teacher network. Tucker decomposition is used in this KD model to decompose the teacher's feature maps. According to evaluations, the student model achieves an accuracy of 96.48% on the Chapman ECG dataset classification task.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805230","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":"Investigation and optimization of tumor-treating fields technique for breast cancer treatment: a simulation study.","authors":"Shimaa Mahdy, Haitham S Mohammed, Omnia Hamdy","doi":"10.1080/10255842.2025.2542939","DOIUrl":"https://doi.org/10.1080/10255842.2025.2542939","url":null,"abstract":"<p><p>Tumor-treating fields (TTFields) use alternating electric fields (1-3 V/cm, 100-300 kHz) to disrupt tumor cell division. This study explored TTFields for breast cancer using a COMSOL-based model of a breast with irregular tumors in various glandular positions and volumes. Multiple electrode configurations were analyzed. Skin temperature over time was evaluated using the bioheat equation. Results showed that applying ±20 V with a 12-electrode setup delivered effective tumor targeting while maintaining safe skin temperatures. This finite-element analysis highlights TTFields' potential for breast cancer therapy and provides a foundation for optimizing treatment parameters in future clinical applications.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144800858","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 analysis of the acoustic pressure inside blood vessel with exposure to high-intensity focused ultrasound.","authors":"Jonghyok Ri, Na Pang, Lisheng Xu, Ning Ji, Xiangji Yue, Insong Kim, Li Shen, Dingchang Zheng","doi":"10.1080/10255842.2025.2541896","DOIUrl":"https://doi.org/10.1080/10255842.2025.2541896","url":null,"abstract":"<p><p>This study investigates acoustic pressure distribution in blood vessels under high-intensity focused ultrasound (HIFU) for sonothrombolysis (STL). A tissue-mimicking phantom (skin, fat, muscle, blood) was modeled, and pressure was calculated using the Westervelt equation. Results show peak pressure increases with frequency (0.5-2 MHz), while attenuation intensifies with higher power, frequency, and vessel depth (10-30 mm). Frequencies above 1.1 MHz caused greater attenuation, influenced by skin (1-5 mm) and fat (2-7 mm) thicknesses. Below 1.1 MHz, consistent HIFU power yields similar clinical outcomes across patients, aiding STL treatment optimization.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.6,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144785869","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 investigation of blood rheology in ventricular assist devices: effects on performance and shear stress.","authors":"Mohamed Bounouib, Reda Lakraimi, Mourad Taha-Janan","doi":"10.1080/10255842.2025.2512876","DOIUrl":"10.1080/10255842.2025.2512876","url":null,"abstract":"<p><p>This study evaluates whether Newtonian models can replace non-Newtonian models in ventricular assist device (VAD) simulations. Five rheological models were compared in an axial-flow VAD using ANSYS CFX. High-shear conditions (92% > 300 s^-1 rendered non-Newtonian effects negligible, with errors <1% for pressure rise, efficiency, and torque. Wall shear stress variations were minimal (±5 Pa) and below hemolysis thresholds. Newtonian models suffice for performance predictions in high-shear regions, reducing computational costs by 30-50%. However, localized non-Newtonian effects in stagnation zones may need analysis for thrombogenicity. These findings streamline VAD design without compromising accuracy.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1836-1846"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200663","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":"Chronic ankle instability affects the association between knee joint angle and loading: musculoskeletal simulation study.","authors":"Hoon Kim, Kristof Kipp","doi":"10.1080/10255842.2024.2327632","DOIUrl":"10.1080/10255842.2024.2327632","url":null,"abstract":"<p><p>The purpose of this study was to calculate and compare (1) knee loads, (2) muscle-specific contributions to knee loads, and (3) effects of knee flexion angle on knee loads and muscle-specific load contributions during a forward jump-landing task in people with and without chronic ankle instability (CAI). Eight CAI patients and seven healthy controls performed a forward jump-landing task. We collected 3D kinematics, ground reaction force, and muscle activation and used musculoskeletal modeling. The results showed that only healthy controls exhibited an association between knee flexion angle and knee compressive impulse (<i>r</i> = 0.854, <i>p</i> = .014). The lack of association in CAI group may lead to knee instability and increase knee injury risk in people with CAI.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1555-1564"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140102841","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":"TriKSV-LG: a robust approach to disease prediction in healthcare systems using AI and Levy Gazelle optimization.","authors":"Kavitha Dhanushkodi, Prema Vinayagasundaram, Vidhya Anbalagan, Surendran Subbaraj, Ravikumar Sethuraman","doi":"10.1080/10255842.2024.2339479","DOIUrl":"10.1080/10255842.2024.2339479","url":null,"abstract":"<p><p>A seamless connection between the Internet and people is provided by the Internet of Things (IoT). Furthermore, lives are enhanced using the integration of the cloud layer. In the healthcare domain, a reactive healthcare strategy is turned into a proactive one using predictive analysis. The challenges faced by existing techniques are inaccurate prediction and a time-consuming process. This paper introduces an Artificial Intelligence (AI) and IoT-based disease prediction method, the TriKernel Support Vector-based Levy Gazelle (TriKSV-LG) Algorithm, which aims to improve accuracy, and reduce the time of predicting diseases (kidney and heart) in healthcare systems. The IoT sensors collect information about patients' health conditions, and the AI employs the information in disease prediction. TriKSV utilizes multiple kernel functions, including linear, polynomial, and radial basis functions, to classify features more effectively. By learning from different representations of the data, TriKSV better handles variations and complexities within the dataset, leading to more robust disease prediction models. The Levy Flight strategy with Gazelle optimization algorithm tunes the hyperparameters and balances the exploration and exploitation for optimal hyperparameter configurations in predicting chronic kidney disease (CKD) and heart disease (HD). Furthermore, TriKSV's incorporation of multiple kernel functions, combined with the Gazelle optimization strategy, helps mitigate overfitting by providing a more comprehensive search space for optimal hyperparameter selection. The proposed TriKSV-LG method is applied to two different datasets, namely the CKD dataset and the HD dataset, and evaluated using performance measures such as AUC-ROC, specificity, F1-score, recall, precision, and accuracy. The results demonstrate that the proposed TriKSV-LG method achieved an accuracy of 98.56% in predicting kidney disease using the CKD dataset and 98.11% accuracy in predicting HD using the HD dataset.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1783-1799"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140868111","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}