BioMedical Engineering OnLine最新文献

{"title":"Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.","authors":"Haixiang Zhang, Yuhong Tang, Peipei Li, Weijian Fan, Xiangzi Chen","doi":"10.1186/s12938-026-01593-8","DOIUrl":"10.1186/s12938-026-01593-8","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer ranks among the most lethal malignancies globally, and its traditional diagnosis suffers from strong subjectivity, high misdiagnosis rates and uneven medical resources. To overcome the poor feature alignment caused by simply concatenating CT images and clinical text, this paper proposes a lung cancer multimodal auxiliary diagnosis model based on entropy weight decision fusion.</p><p><strong>Methods: </strong>This retrospective cohort study enrolled 5847 participants from 2020 to 2025, comprising 1823 lung cancer cases, 2253 normal controls and 1771 pulmonary nodule controls. All CT images and corresponding reports were analyzed, and three datasets were established via random sampling from the original dataset. The study incorporated Vision Transformer (ViT) and Bidirectional Encoder Representations from Transformers (BERT) as feature extractors for images and text, respectively, to extract high-dimensional semantic features from lung CT images and CT Imaging Report. Secondly, independent classifiers based on Multi-Layer Perceptron (MLP) were established to convert the embedding vectors of different modalities into predicted probability distributions (Logits). Finally, the entropy weight method was employed to adaptively fuse the decision results of images and text. The model performance evaluation indicators include area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1-score.</p><p><strong>Results: </strong>The proposed method in this study can fully leverage the complementary information from CT images and imaging text multimodality. On the clinical dataset, it achieved an accuracy of 0.9375, a precision of 0.9324, a recall of 0.9322, and an F1-score of 0.9322, significantly improving diagnostic performance.</p><p><strong>Conclusions: </strong>This study validates that, on a real-world lung cancer dataset, multimodal data decision fusion outperforms unimodal models and common fusion methods in terms of diagnostic accuracy, precision and recall. It provides a potential reference for the early auxiliary diagnosis of pulmonary nodules and lung cancer, and lays a foundation for subsequent clinical applications.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148270169","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":"Potentials of BMSCs for regulating osteogenic-vascular-neural-lymphatic coupling in bone regeneration.","authors":"Nan Zhang, Yuwen Luo, Peng Luo, Jiarui Cao, Yuning Cheng, Mengyao Kang, Jianping Mao, Jing-Jun Nie, Da-Fu Chen","doi":"10.1186/s12938-026-01594-7","DOIUrl":"10.1186/s12938-026-01594-7","url":null,"abstract":"<p><strong>Background: </strong>Bone regeneration assisted by synthetic bone substitutes largely depends on the integration of the vascular, neural, and lymphatic systems in the bone. Bone marrow mesenchymal stem cells (BMSCs) are the key cells for this process. However, their role in regulating the integration has not been fully characterized.</p><p><strong>Methods: </strong>Human BMSCs (hBMSCs) were treated with osteogenic induction and collected from 0 to 504 h for bulk RNA sequencing (RNA-Seq). Differentially expressed genes (DEGs) were identified and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Time-Series Transcriptomic Trend Analysis were used to comprehensively analyze the possible pathways and functions associated with these DEGs. Weighted Gene Co-expression Network Analysis (WGCNA) was constructed to identify the modules and hub genes of the process. Quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) were performed to validate the expression of key genes identified by RNA-Seq.</p><p><strong>Results: </strong>Time-series analysis of the hBMSCs transcriptome suggested a dynamic expression trajectory during osteogenic differentiation, which was characterized by four functional patterns: the initial adaptation stage (1-24 h), the proliferation activation stage (24-72 h), the differentiation regulation stage (72-336 h) and the remodeling stability stage (336-504 h). Moreover, 72 h was suggested as a potential key time point in the osteogenic-vascular-neural-lymphatic coupling process based on transcriptomic analysis, with typical activation of BMP, vascular endothelial growth factor (VEGF) and PPAR signaling pathways. Four modules and closely related hub genes such as growth differentiation factor 5 (GDF5), matrix Gla protein (MGP) and pregnancy-associated plasma protein A2 (PAPPA2), whose expressions were validated by qRT-PCR and ELISA were also identified and highlighted.</p><p><strong>Conclusions: </strong>Our study revealed the temporal trends of angiogenesis, lymphangiogenesis, and neurogenesis during BMSCs osteogenic differentiation, which not only supplemented the transcriptional regulation in bone regeneration, but also provided a theoretical basis for the design of synthetic bone substitutes.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148257218","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":"Protein adsorption at material interface: mechanistic design framework for engineering ceramic scaffolds for bone repair applications.","authors":"Gayathri S S, Sunita Nayak","doi":"10.1186/s12938-026-01591-w","DOIUrl":"10.1186/s12938-026-01591-w","url":null,"abstract":"<p><p>Ceramic materials are widely used in bone tissue engineering applications due to their chemical similarity to human bone, along with their inherent biocompatibility. Bone scaffolds fabricated with ceramics thus serve as a framework for supporting new bone formation through cell attachment, proliferation, and differentiation, and subsequently integrate with the host tissue. However, the cells are unable to attach directly to the implanted ceramic surfaces. Instead, they interact with the dynamic layer of proteins adsorbed on the material surface, which mediates cell adhesion and proliferation via specific integrin-ligand signalling. Therefore, rather than a passive process, protein adsorption can be utilized as a design criterion to develop advanced ceramic scaffolds. Despite extensive efforts in surface modification, the mechanistic relationship between ceramic surface properties, protein adsorption behaviour, and subsequent osteogenic signalling remains fragmented across the literature. This review emphasizes the central and often underexplored role of protein adsorption in mediating the initial cell-ceramic interactions critical for bone tissue regeneration. In contrast to previous pieces of literature, this paper critically examines the studies in the past decade, with special focus on the last five years, on how the protein-ceramic interaction can be manipulated to improve its biocompatibility, which adds to the novelty of this review. By critically evaluating the in vitro and in vivo studies, we propose that the protein adsorption on ceramic scaffolds can be treated as a controllable bio-instructive design parameter for next-generation osteoinductive ceramics. This review highlights the multifaceted nature of protein adsorption and its pivotal role in developing more biocompatible ceramic materials for bone tissue engineering.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13488242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238702","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":"Machine learning models of segmentation in acute ischemic stroke: a systematic review and meta-analysis.","authors":"Sadaf Salehi, Sahar Birzhandi, Zeinab Torbati Aghdam, Raha Amirvala, Hediyeh Dasoomi, Zayd M Yaghi, Mohammad Saeed Soleimani Meigoli, Zahra Moghari Hesari, Kiana Taghipoor, Kiana Saffarian, Mohsen Shahba, Alireza Ghaedamini, Sasan Ghazanafar Ahari, Melika Arab Bafrani, Matin Akhbari, Ali Babapour, Amirhossein Rigi, Samira Peiravi, Mahsa Asadi Anar","doi":"10.1186/s12938-026-01590-x","DOIUrl":"10.1186/s12938-026-01590-x","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Accurate segmentation of acute ischemic stroke (AIS) lesions on neuroimaging is essential for diagnosis, treatment decision-making, and prognostication. Manual methods are limited by time and variability. Machine learning (ML), especially deep learning, has emerged as a powerful tool for automated lesion segmentation, yet a systematic synthesis of model performance, methodological rigor, and clinical applicability remains lacking.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To systematically review and quantitatively evaluate the performance of ML-based segmentation models for AIS using a meta-analytic approach, and to identify factors associated with model accuracy and robustness across imaging modalities, architectures, and datasets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a systematic review and meta-analysis in accordance with PRISMA 2020 guidelines. Comprehensive searches were performed in PubMed, Scopus, and Web of Science databases through March 2025. Eligible studies included those reporting on machine learning (ML)-based segmentation of acute ischemic stroke (AIS) lesions on CT or MRI and providing quantitative performance metrics (e.g., Dice, sensitivity, specificity, AUC). Data were systematically extracted on study design, ML architecture, imaging modality, dataset size and composition, and segmentation performance. Random-effects meta-analyses were conducted using inverse-variance weighting, with logit transformation applied to bounded metrics to stabilize variance. Between-study heterogeneity was assessed using the I&lt;sup&gt;2&lt;/sup&gt; statistic and Cochran's Q-test. Meta-regression analyses explored the influence of covariates such as lesion volume, sample size, and stroke severity (mRS), while subgroup analyses examined performance variations by imaging modality and model type. Visualizations included forest plots, funnel plots, bubble plots, and correlation matrices, generated in Python using standardized meta-analysis and statistical libraries.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Out of 4755 screened records, 101 studies met the inclusion criteria. Deep learning approaches, especially U-Net variants, dominated the field (78%). The pooled Dice coefficient was 0.84 (I&lt;sup&gt;2&lt;/sup&gt; = 0%), indicating high and consistent segmentation accuracy. Additional pooled estimates were AUC 0.91, accuracy 0.89, sensitivity 0.85, and specificity 0.93. Models using multimodal MRI inputs outperformed single-modality CT models. Meta-regressions revealed no significant association between lesion volume or sample size and Dice scores, though segmentation performance trended higher with increasing clinical severity (mRS). Specificity showed weak correlation with recall and Dice, indicating potential trade-offs in model optimization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;ML-based segmentation models for AIS demonstrate high accuracy, especially when using multimodal MRI and deep learning architectures. Performance is robust across varying d","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148148177","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":"The influence of successful septal myectomy on myocardial stress distributions in the left ventricle: a computational analysis.","authors":"Heng Zuo, Long Deng, An Li, Dalin Tang, Xueying Huang","doi":"10.1186/s12938-026-01592-9","DOIUrl":"10.1186/s12938-026-01592-9","url":null,"abstract":"<p><strong>Background: </strong>Mechanical forces significantly influence the initiation, progression, and remodeling of cardiovascular disease. In this study, CT-based patient-specific computational models were constructed to investigate the effects of the successful septal myectomy on myocardial stress distribution in the left ventricle.</p><p><strong>Methods: </strong>CT imaging data from five patients, collected both before and after successful septal myectomy, were used to create 10 patient-specific finite element models. Myocardial stress and strain of all integral nodes in the myocardium of the left ventricle were extracted from the simulation results. All the nodes in the left ventricular wall were divided into apex, midventricular, and basal region groups according to their position in the left ventricle.</p><p><strong>Results: </strong>It was found that the mean stress decreased by 27.7% (p < 0.001) and the mean strain decreased by 26.3% (p < 0.001) after the surgery. The reduction in myocardial stress was more pronounced in the midventricular (50.3% decrease) and apex (54.0% decrease) regions compared to the basal region (23.5% decrease).</p><p><strong>Conclusions: </strong>These findings suggest that successful septal myectomy significantly reduces myocardial stress within the left ventricle, particularly in the midventricular and apex regions. Beyond alleviating left ventricular outflow tract obstruction, this surgery may also lower the risk of myocardial ischemia, acute myocardial injury, and subsequent myocardial fibrosis by reducing myocardial stress.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13439876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148148182","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":"Resting-state brain network alterations in adolescent idiopathic scoliosis using functional near-infrared spectroscopy.","authors":"Wei Wang, Fanyuan Meng, Kerong Li, Chu Wang, Fengqi Hu, Oluwarotimi Williams Samuel, Lijuan Ao, Guanglin Li","doi":"10.1186/s12938-026-01584-9","DOIUrl":"10.1186/s12938-026-01584-9","url":null,"abstract":"<p><strong>Background: </strong>Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity arising during adolescence, a critical period for brain development. Although most AIS analyses predominantly focus on spinal or behavioral aspects, it remains unclear what brain functional alteration is associated with AIS. This gap in knowledge impedes a holistic understanding of the condition's pathophysiology. In this study, functional near-infrared spectroscopy (fNIRS) was used to explore the resting-state brain network in AIS.</p><p><strong>Methods: </strong>The study recruited 25 AIS patients and 25 age-matched healthy controls and measured their brain activities during resting-state lying using fNIRS. Brain functional connectivity and network topology were evaluated for the two groups and their correlations with demographic and pathological variables were examined.</p><p><strong>Results: </strong>The functional connectivity in the patients, particularly single-curve patients, decreased and was sparser in the parietal and prefrontal regions in comparison to the healthy controls. The regional nodal metrics in the patients were significantly altered, with smaller nodal degrees and efficiency observed in certain nodes. Among the affected regions, the dorsolateral prefrontal cortex emerged repeatedly as a hub showing altered connectivity in patients, particularly in relation to parietal and sensorimotor regions. In addition, different correlations between brain network metrics and demographic as well as pathological parameters were identified within patients. For instance, a larger primary Cobb angle was associated with poorer small-world properties, suggesting greater structural deformity may be linked to less efficient functional network organization. Furthermore, a support vector machine-based classification model using functional connectivity achieved an average accuracy of 81.0%, demonstrating that fNIRS-based network features are sensitive to group-level neurofunctional differences.</p><p><strong>Discussion: </strong>These findings provide insights into how scoliosis, including curvature, physical parameters, and pathological conditions, affects AIS patients in shaping abnormal brain network organization. Such network-based approaches may facilitate future studies aimed at assessing neurofunctional involvement associated with AIS rehabilitation interventions.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13430834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148100944","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":"A survey of deep learning techniques in detecting neurological disorders using MRI.","authors":"Deepak Mane, Ranjeet Bidwe, Rivan Shetty, Atharva Shinde, Gaurav Shende, Vishal Sharma","doi":"10.1186/s12938-026-01587-6","DOIUrl":"10.1186/s12938-026-01587-6","url":null,"abstract":"<p><p>Magnetic resonance imaging (MRI) is widely regarded as the most reliable non-invasive imaging modality for detecting neurological disorders. However, manual interpretation of MRI scans is often time-consuming and prone to inter-observer variability, which can lead to inconsistencies in diagnosis. The global burden of neurological disorders-including Alzheimer's disease, brain tumors, Parkinson's disease, multiple sclerosis, and schizophrenia-continues to increase, creating an urgent demand for accurate, scalable, and automated diagnostic solutions. In recent years, deep learning (DL) has emerged as a powerful paradigm for medical image analysis, enabling automated feature extraction and improved diagnostic performance in neuroimaging applications. This survey provides a comprehensive analysis of deep learning approaches for MRI-based detection of neurological disorders. A systematic review of 47 research articles published between 2019 and 2025 is conducted, covering over 40 deep learning architectures evaluated on 34 publicly available and clinical datasets. The study categorizes and critically examines convolutional neural networks (CNNs), Vision Transformers (ViTs), hybrid CNN-Transformer models, and other specialized deep learning frameworks developed for neurological disease detection and classification. Comparative analyses are presented across different neurological conditions, highlighting model performance, dataset characteristics, evaluation protocols, and computational requirements. Furthermore, the survey identifies emerging architectural trends and evaluates the relative strengths and limitations of existing approaches with respect to generalization, interpretability, and clinical applicability. Key research gaps are highlighted, including limited cross-institutional validation, dataset heterogeneity, insufficient explainability, and challenges in real-world clinical deployment. Finally, the paper outlines promising research directions such as multimodal learning, self-supervised representation learning, and robust cross-domain generalization to enhance the reliability and clinical translation of MRI-based deep learning systems.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13425867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148052281","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":"Plant-mediated green nanoparticles: combining nanometal and biometabolite potential for Alzheimer's treatment.","authors":"Farahnaz Behzad, Foad Rahmanpour Leili, MohammadJavad Ebrahimi, Maral Moafi, Pourya Bagherian Kenari, Ali Alizadeh, Soheila Adeli, Masood Khosravani, Mohammad Amin Jadidi Kouhbanani, Javad Fahanik-Babaei","doi":"10.1186/s12938-026-01572-z","DOIUrl":"10.1186/s12938-026-01572-z","url":null,"abstract":"<p><p>Alzheimer's disease (AD) can cause cognitive and memory dysfunction due to insufficient acetylcholine (ACh). In principle, acetylcholinesterase (AChE) hydrolyzes ACh into acetic acid and choline, rendering the latter inactive. Acetylcholinesterase inhibitors (AChEI) are currently the main treatment strategy used to increase ACh availability and decrease the effects of cholinergic loss. The FDA has currently approved donepezil, rivastigmine, and galantamine (GAL) as AchEI drugs to treat AD. Among these drugs, only the alkaloid galantamine is found naturally among the AChEIs. The synergistic effect of plant metabolites and metal nanoparticles (MNPs) presents a promising avenue for AD treatment. Green synthesis approach leverages the unique properties of MNPs combined with the therapeutic potential of plant-derived compounds. In the present review, recent developments in nanotechnology, including the green synthesis of MNPs, have been discussed in relation to the treatment of AD. Given the recent advancements, we hope that the combination of nanotechnology and medicinal plants will eventually result in the development of highly successful strategies for the treatment of AD.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13491619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148052274","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":"Dosimetric comparison of three different radiotherapy regimens for bilateral breast cancer.","authors":"Fanyang Kong, Linyan Xie, Bingyuan Wang, Haiyang Wang, Yawei Li, Meilian Dong, Shujing Shen, Yi Yu","doi":"10.1186/s12938-026-01574-x","DOIUrl":"10.1186/s12938-026-01574-x","url":null,"abstract":"<p><strong>Objective: </strong>To compare the dosimetric characteristics of three radiotherapy techniques-VMAT, HT-5, and HT-2.5-in patients with bilateral breast cancer, and to serve as a reference for clinical plan optimization.</p><p><strong>Materials and methods: </strong>For 30 patients with bilateral breast cancer, radiotherapy plans were generated using Monaco-based volumetric modulated arc therapy (VMAT) and Tomo-based helical tomotherapy with 5 cm field width (HT-5) and 2.5 cm field width (HT-2.5). The prescription dose was 50 Gy in 25 fractions. The conformity index (CI) and homogeneity index (HI) of the planning target volume (PTV) were evaluated. For organs at risk, the V₅, V₁₀, V₂₀, V₃₀, and mean dose (Dmean) of the lungs and heart, as well as the Dmean of the left anterior descending coronary artery (LAD), were analyzed. Beam-on time was recorded for each technique. Statistical analysis and data visualization were performed using R (version 4.3.2).</p><p><strong>Results: </strong>All three techniques met clinical requirements for target coverage. VMAT achieved the highest CI (P < 0.05), with no significant difference in HI (P > 0.05) among the three techniques. HT-2.5 had a slightly lower Whole Lung-Dmean (10.59 ± 1.09 Gy) than VMAT (10.65 ± 0.69 Gy), but the difference was not statistically significant (P > 0.05). Whole Lung-V5% was significantly lower with HT-5 (52.61 ± 4.90%) (P < 0.05) and HT-2.5 (50.23 ± 4.43%) (P < 0.05) compared to VMAT (57.24 ± 5.37%), while VMAT had significantly lower V₁₀ (P < 0.05)and V₂₀(P < 0.05). For the heart, HT-5 and HT-2.5 provided lower Dmean (5.71 ± 1.07 Gy and 5.40 ± 0.94 Gy) (P < 0.05,P < 0.05) and V₅ (24.25 ± 8.25% and 21.88 ± 7.37%) (P < 0.05,P < 0.05) than VMAT (6.37 ± 0.95 Gy and 49.52 ± 13.33%). However, heart V₃₀ was significantly lower with VMAT compared to HT-5 (P < 0.05) and HT-2.5 (P < 0.05). LAD Dmean was significantly lower with VMAT compared to HT-5 (P < 0.05) and HT-2.5(P < 0.05). Beam-on time was shortest with VMAT (4.5 ± 0.5 min), followed by HT-5 (8.1 ± 0.3 min) and HT-2.5 (14.8 ± 0.6 min).</p><p><strong>Conclusion: </strong>All three techniques offer acceptable target coverage for bilateral breast cancer. VMAT provides superior target conformity, LAD sparing, and the shortest treatment time. HT-5 and HT-2.5 demonstrate advantages in cardiac protection, with lower heart Dmean and V₅, while HT-2.5 offers the best reduction in low-dose lung exposure. Considering the combined factors of lung and heart sparing as well as overall treatment time, the Complete block type of 5 cm HT plan can also be recommended for synchronous bilateral breast cancer.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13386559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148013292","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":"Asymmetric tripolar nerve cuff electrode designs for optimizing anodal block of myelinated fibers.","authors":"Daniel Tovbis, Ryan G L Koh, Rania Jeong, Paul B Yoo","doi":"10.1186/s12938-026-01579-6","DOIUrl":"10.1186/s12938-026-01579-6","url":null,"abstract":"<p><strong>Purpose: </strong>Anodal block is a common mechanism used to generate unidirectional activation of myelinated nerve fibers. However, nerve block is often inconsistent and difficult to achieve. In this study, we used a computational model to investigate electrode cuff designs that can be exploited to enhance anodal block.</p><p><strong>Methods: </strong>Asymmetric bipolar and tripolar nerve cuff electrodes were initially tested and validated in a rodent sciatic nerve model. A finite element model was implemented, with physical dimensions approximating a rat sciatic nerve implanted with a flat-interface nerve electrode. The McIntyre-Richardson-Grill model was used to predict the activation, block, and reactivation thresholds of myelinated fibers (diameter = 5.7 to 16 μm) as the stimulation amplitude was increased. In vivo experiments were performed using identical nerve cuff electrodes. The asymmetric electrode designs were subsequently investigated using a human-sized model of a nerve implanted with a cylindrical cuff electrode. Key design parameters included the electrode edge length (EEL), anodic contact width, inter-electrode distance, and current division between primary and secondary anodes.</p><p><strong>Results: </strong>The rodent model reproduced blocking properties in both computational and in vivo studies, where maximum anodic block reached 88% and 75% of activated fibers, respectively. Simulations involving the human nerve model identified two critical design parameters that can markedly enhance anodal block. An optimal distal EEL (≤ 1 mm) enhanced the performance of bipolar stimulation (anodic block = 91%), whereas, optimizing the current ratio between the primary:secondary anodes (e.g., 6:4) enhanced nerve block in asymmetric tripolar electrodes (anodic block = 90%).</p><p><strong>Conclusion: </strong>This study presents a set of instructions for optimizing the anodal blocking properties of nerve cuff electrodes, where an asymmetric tripolar design with variable anodic current offers a novel method that can adjust for variability at the nerve electrode interface.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13374333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148004404","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}