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Rational microfluidic design for dielectrophoresis-based multitarget separation of blood cells and circulating tumor cells. 基于介电泳的血液细胞和循环肿瘤细胞多靶点分离的合理微流体设计。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2024-12-06 DOI: 10.1080/10255842.2024.2436913
Wu Ye, Huancheng Zhu, Ming Liu, Wenjie Wu
{"title":"Rational microfluidic design for dielectrophoresis-based multitarget separation of blood cells and circulating tumor cells.","authors":"Wu Ye, Huancheng Zhu, Ming Liu, Wenjie Wu","doi":"10.1080/10255842.2024.2436913","DOIUrl":"10.1080/10255842.2024.2436913","url":null,"abstract":"<p><p>A rapid, sensitive, and low-damage method for isolating circulating tumor cells (CTCs) is crucial for cancer research. This study, based on dielectrophoresis (DEP) and finite element modeling, investigates multitarget cell separation from blood on microfluidic chips. The effects of electrode shape, dielectric conductivity, and flow rate on cell movement and separation efficiency were analyzed. The results showed optimal separation with a 90° electrode angle, 1.5 V applied voltage, and a 1:3 inlet flow rate ratio. This study provides valuable insights for optimizing DEP-based microfluidic devices to improve multitarget cell separation efficiency and purity.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1358-1370"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787600","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}
引用次数: 0
An efficient deep learning approach for automatic speech recognition using EEG signals. 基于脑电信号的语音自动识别的高效深度学习方法。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2025-02-16 DOI: 10.1080/10255842.2025.2456982
Babu Chinta, Madhuri Pampana, Moorthi M
{"title":"An efficient deep learning approach for automatic speech recognition using EEG signals.","authors":"Babu Chinta, Madhuri Pampana, Moorthi M","doi":"10.1080/10255842.2025.2456982","DOIUrl":"10.1080/10255842.2025.2456982","url":null,"abstract":"<p><p>Electroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2% accuracy, outperforming baseline methods. This framework advances EEG based speech recognition aiding brain-computer interfaces and assistive technologies for individuals with speech disorders.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1652-1672"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434313","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}
引用次数: 0
Identification of pregnancy in women based on fingertip pulse using a multi-feature fusion neural network model. 基于指尖脉搏的多特征融合神经网络模型识别女性妊娠。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2024-12-18 DOI: 10.1080/10255842.2024.2433082
Zhuya Huang, Junsheng Yu, Ying Shan
{"title":"Identification of pregnancy in women based on fingertip pulse using a multi-feature fusion neural network model.","authors":"Zhuya Huang, Junsheng Yu, Ying Shan","doi":"10.1080/10255842.2024.2433082","DOIUrl":"10.1080/10255842.2024.2433082","url":null,"abstract":"<p><p>This study proposes a rapid method for determining pregnancy status based on fingertip pulse signals. A finger pulse sensor collects data, which is processed into unified multimodal signals. The Bamboo-Net model, combining ResNet, LSTM, and 1D-CNN, extracts key features from time, frequency, and time-frequency domains. Tested on 346 training and 138 testing samples, the model achieves 91% accuracy with 6 s input, outperforming mainstream methods. Recognition rates for mid and late pregnancy are higher than for early pregnancy, highlighting its potential for practical applications.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1298-1311"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856537","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}
引用次数: 0
The effect of uterine fibroid region and depth on endometrial stress and strain: a finite element approach. 子宫肌瘤区域及深度对子宫内膜应力应变的影响：有限元方法。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2024-12-06 DOI: 10.1080/10255842.2024.2431653
Blake S Miller, Sukhbir S Singh, Teresa E Flaxman
{"title":"The effect of uterine fibroid region and depth on endometrial stress and strain: a finite element approach.","authors":"Blake S Miller, Sukhbir S Singh, Teresa E Flaxman","doi":"10.1080/10255842.2024.2431653","DOIUrl":"10.1080/10255842.2024.2431653","url":null,"abstract":"<p><p>Uterine fibroids are common benign gynecological tumors that are observed in up to 80% of premenopausal women. It is understood that as the fibroid size increases, the surrounding tissues will be subject to greater loads. However, the effect of fibroid region on the uterine structure is not as clear. To better understand the mechanical loading of the endometrium due to the presence of a uterine fibroid, we developed a finite element model of the uterus to examine the effect of both fibroid depth and region in relation to the endometrium. The finite element model of the uterus, endometrium, and a uterine fibroid were created from a 3D segmentation of a patient's magnetic resonance images. This model was then loaded into ANSYS Mechanical 2023 R1, and then deformation, stress, and strain of the endometrium was measured for 24 fibroid positions (8 regions × 3 depths). The highest endometrial loads (deformation, stress, strain) were observed when the fibroid region was superior to the uterus and the depth was deep. Superior regions generated 10-20% higher loads on the endometrium in comparison to other regions, while deep locations had 5-10% higher endometrium loads when compared to superficial depths across almost all regions. A simple uterus model was used to show the effect of fibroid position on loads acting on the endometrium. This can provide insight into mechanisms of abnormal uterine bleeding and infertility and better inform clinical decision making.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1249-1258"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787605","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}
引用次数: 0
Numerical modeling of hydrogel scaffold anisotropy during extrusion-based 3D printing for tissue engineering. 组织工程挤压3D打印中水凝胶支架各向异性的数值模拟。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2025-01-02 DOI: 10.1080/10255842.2024.2448557
Van Than Mai, Robin Chatelin, Edwin-Joffrey Courtial, Caroline Boulocher, Romain Rieger
{"title":"Numerical modeling of hydrogel scaffold anisotropy during extrusion-based 3D printing for tissue engineering.","authors":"Van Than Mai, Robin Chatelin, Edwin-Joffrey Courtial, Caroline Boulocher, Romain Rieger","doi":"10.1080/10255842.2024.2448557","DOIUrl":"10.1080/10255842.2024.2448557","url":null,"abstract":"<p><p>Extrusion-based 3D printing is a widely utilized tool in tissue engineering, offering precise 3D control of bioinks to construct organ-sized biomaterial objects with hierarchically organized cellularized scaffolds. Topological properties in flowing polymers are determined by macromolecule conformation, namely orientation and stretch degree. We utilized the micro-macro approach to describe hydrogel macromolecule orientation during extrusion, offering a two-scale fluid behavior description. Results show that shear rate significantly drives alignment, while the interaction coefficient (<math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>i</mi></mrow></msub></mrow></math>)captures particle interactions. This approach provides an initial but robust framework to model scaffold anisotropy, enabling optimization of the extrusion process while maintaining computational feasibility.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1458-1477"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916162","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}
引用次数: 0
Fractional-order modeling of human behavior in infections: analysis using real data from Liberia. 人类感染行为的分数阶模型：使用来自利比里亚的真实数据进行分析。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2025-01-06 DOI: 10.1080/10255842.2024.2448559
Parisa Shekari, Amin Jajarmi, Leila Torkzadeh, Kazem Nouri
{"title":"Fractional-order modeling of human behavior in infections: analysis using real data from Liberia.","authors":"Parisa Shekari, Amin Jajarmi, Leila Torkzadeh, Kazem Nouri","doi":"10.1080/10255842.2024.2448559","DOIUrl":"10.1080/10255842.2024.2448559","url":null,"abstract":"<p><p>This paper presents a fractional-order model using the Caputo differential operator to study Ebola Virus Disease (EVD) dynamics, calibrated with Liberian data. The model demonstrates improved accuracy over integer-order counterparts, particularly in capturing behavioral changes during outbreaks. Stability analysis, Lyapunov functions, and a validated numerical method strengthen its mathematical foundation. Simulations highlight its utility in accurately describing EVD evolution and guiding outbreak management. The study underscores the role of behavioral interventions in epidemic control, offering valuable insights for public health and policymaking. This research advances infectious disease models and enhances strategies for mitigating EVD outbreaks.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1492-1506"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933426","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}
引用次数: 0
Numerical simulation on the effect of impeller radial gap on hemodynamics and hemocompatibility of a centrifugal blood pump. 叶轮径向间隙对离心血泵血流动力学和血液相容性影响的数值模拟。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2025-01-11 DOI: 10.1080/10255842.2024.2448299
Shen Lv, Zhi-Peng He, Guang-Mao Liu, Sheng-Shou Hu
{"title":"Numerical simulation on the effect of impeller radial gap on hemodynamics and hemocompatibility of a centrifugal blood pump.","authors":"Shen Lv, Zhi-Peng He, Guang-Mao Liu, Sheng-Shou Hu","doi":"10.1080/10255842.2024.2448299","DOIUrl":"10.1080/10255842.2024.2448299","url":null,"abstract":"<p><p>Impeller radial gap is one of important parts within a blood pump, which may affect the hemodynamics and hemocompatibility. In this study, computational fluid dynamics method was performed to evaluate the impact of radial gap sizes. The volume of scalar shear stress decreased with radial gap sizes increasing. On the contrary, the residence time increased with radial gap sizes increasing, especially in the bottom gap. The hemolysis index and platelet activation status at three flow rates decreased with the increase of radial gap sizes. Compared with the hemolysis index when the radial gap size was 0.6 mm, the hemolysis index for the radial gap of 1.0 mm decreased by 27.6%, 25.4% and 21.1% from low flow rate to high flow rate, respectively. Similarly, the platelet activation status for the radial gap of 1.0 mm decreased by 13.0%, 11.5% and 9.1%, respectively. As a novelty, this study revealed that radial gap sizes can significantly influence the blood pump hemocompatibility, especially at low flow rate. In addition, the hemolysis performance can be more affected by radial gaps than that on thrombosis risk.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1407-1418"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967413","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}
引用次数: 0
Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection. 基于卷积神经网络和自关注的多实例学习方法用于早期癌症检测。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2024-12-07 DOI: 10.1080/10255842.2024.2436909
Junjiang Liu, Shusen Zhou, Mujun Zang, Chanjuan Liu, Tong Liu, Qingjun Wang
{"title":"Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection.","authors":"Junjiang Liu, Shusen Zhou, Mujun Zang, Chanjuan Liu, Tong Liu, Qingjun Wang","doi":"10.1080/10255842.2024.2436909","DOIUrl":"10.1080/10255842.2024.2436909","url":null,"abstract":"<p><p>Early cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We introduce a multiple instance learning method based on convolutional neural networks and self-attention (MICA). First, MICA preprocesses TCR-seq using word vectors and then extracts features using convolutional neural networks. Second, MICA uses an enhanced self-attention mechanism to extract relational features of instances. Finally, MICA can extract the crucial TCR-seq. After cross-validation, MICA achieves an area under the curve (AUC) of 0.911 and 0.946 on the lung and thyroid cancer datasets, which are 7.1% and 2.1% higher than other methods, respectively.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1342-1357"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792691","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}
引用次数: 0
Identification of polyamine metabolism-related prognostic biomarkers for predicting breast cancer prognosis, immune microenvironment, and candidate drugs. 多胺代谢相关预后生物标志物的鉴定预测乳腺癌预后、免疫微环境和候选药物
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2024-12-02 DOI: 10.1080/10255842.2024.2433112
Dejie Zhang, Pengfei Li, Xinjie Du, Ming Zhang, Qi Li, Qicai Wang, Xingfeng Tu, Guoliang Lin
{"title":"Identification of polyamine metabolism-related prognostic biomarkers for predicting breast cancer prognosis, immune microenvironment, and candidate drugs.","authors":"Dejie Zhang, Pengfei Li, Xinjie Du, Ming Zhang, Qi Li, Qicai Wang, Xingfeng Tu, Guoliang Lin","doi":"10.1080/10255842.2024.2433112","DOIUrl":"10.1080/10255842.2024.2433112","url":null,"abstract":"<p><p>In this study, polyamine metabolism related genes (PMRGs) were used to establish a breast cancer (BC) prognostic model. Using PMRGs, TCGA BC samples were divided into cluster1 and cluster2. A 13-gene BC prognostic model was constructed by screening differential genes. High-risk BC patients exhibited heightened immunoinfiltration levels, potentially impeding immunotherapy responses. Drug response predicted that BC patients in the low-risk group might benefit more from chemotherapy and targeted therapy. In conclusion, a novel 13-gene BC prognostic risk model based on PMRGs was established to effectively predict prognosis, immune microenvironment, and drug therapy response in patients with BC.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1312-1325"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774413","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}
引用次数: 0
Identification of ferroptosis-related LncRNAs as potential targets for improving immunotherapy in glioblastoma. 鉴定嗜铁相关lncrna作为改善胶质母细胞瘤免疫治疗的潜在靶点。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-05-01 Epub Date: 2025-01-01 DOI: 10.1080/10255842.2024.2448556
Zhaochen Wang, Xiao Jin, Xiaoli Yong
{"title":"Identification of ferroptosis-related LncRNAs as potential targets for improving immunotherapy in glioblastoma.","authors":"Zhaochen Wang, Xiao Jin, Xiaoli Yong","doi":"10.1080/10255842.2024.2448556","DOIUrl":"10.1080/10255842.2024.2448556","url":null,"abstract":"<p><p>The effect of ferroptosis-related long non-coding RNAs (lncRNAs) in predicting immunotherapy response to glioblastoma (GBM) remains obscure. This study established a 11-lncRNAs prognostic signature. Differential gene expression analysis, univariate and multivariate Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to identify prognostic ferroptosis-related genes and establish a nomogram model of risk score. Kaplan-Meier survival plots and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic accuracy of the model in the TCGA-GBM cohort. To verify the expression of these signatures, we analyzed the expression levels of three lncRNAs (AGAP2-AS1, OSMR-AS1, UNC5B-AS1) in LN229 and U87 cells. The ROC analysis showed that the area under curve (AUC) of this signature is 0.814, suggesting that it has a promising performance on GBM prognostic prediction. Kaplan-Meier analysis showed that the survival rate of GBM patients in high-risk group was significantly lower than low-risk group, and the performance of this signature on GBM prognostic prediction was superior to conventional clinicopathological factors. Further qRT-PCR experiment also confirmed our prediction of lncRNA signatures. These ferroptosis-related lncRNAs might be therapeutic targets for glioblastoma, and targeting these lncRNAs can also improve the efficacy of immunotherapy, especially immune checkpoint inhibitors. Mechanistically, these findings might attribute to N6-methyladenosine (m6A) mRNA modification on lncRNAs.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1445-1457"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916157","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}
引用次数: 0
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