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Simulation study on the mechanical mechanism of humeral fracture during throwing.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-13 DOI: 10.1080/10255842.2025.2476175
Yutao Men, Peng Liu, Yeming Wang, Pujun Hao, Yucheng Ren
{"title":"Simulation study on the mechanical mechanism of humeral fracture during throwing.","authors":"Yutao Men, Peng Liu, Yeming Wang, Pujun Hao, Yucheng Ren","doi":"10.1080/10255842.2025.2476175","DOIUrl":"https://doi.org/10.1080/10255842.2025.2476175","url":null,"abstract":"<p><p>The purpose of this study was to understand the mechanical mechanisms behind throwing fractures and their relation to bone degeneration. Finite models were built based on three bone degeneration evaluation indexes. All models showed that the crack initiated at the morphological transition junction of the distal 1/3 of the humeral shaft. The initial crack line was lateral to the humerus and extended spirally at 45° with the humerus axis. The fracture torque of healthy humerus was about 63 N·m, with a fracture torque degree of about 12°, both decreasing with increasing bone degeneration.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-9"},"PeriodicalIF":1.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626751","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
DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-13 DOI: 10.1080/10255842.2025.2476184
Ming Meng, Guanzhen Chen, Siqi Chen, Yuliang Ma, Yunyuan Gao, Zhizeng Luo
{"title":"DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification.","authors":"Ming Meng, Guanzhen Chen, Siqi Chen, Yuliang Ma, Yunyuan Gao, Zhizeng Luo","doi":"10.1080/10255842.2025.2476184","DOIUrl":"https://doi.org/10.1080/10255842.2025.2476184","url":null,"abstract":"<p><p>Manifold learning with Symmetric Positive Definite (SPD) matrices has demonstrated potential for classifying Electroencephalography (EEG) in Brain-Computer Interface (BCI) applications. However, SPD matrices may lead to crucial information loss of EEG signals. This paper proposes a dimensionality reduction method based on discriminative geometric perception on the Riemannian manifold to enhance SPD matrix discriminability. Experiments on BCI Competition IV Dataset 1 and Dataset 2a show the proposed method improves accuracy by 5.0% and 19.38% respectively, demonstrating that applying discriminative geometric perception can effectively maintain robust performance associated with the dimensionality-reduced SPD matrix.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-11"},"PeriodicalIF":1.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626748","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
Transcriptome-based insights into the role of cancer-associated fibroblasts in lung adenocarcinoma prognosis and therapy.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-12 DOI: 10.1080/10255842.2025.2476186
Yinhe Feng, Jianming Zeng, Xiaoli Zhong, Chunfang Zeng
{"title":"Transcriptome-based insights into the role of cancer-associated fibroblasts in lung adenocarcinoma prognosis and therapy.","authors":"Yinhe Feng, Jianming Zeng, Xiaoli Zhong, Chunfang Zeng","doi":"10.1080/10255842.2025.2476186","DOIUrl":"https://doi.org/10.1080/10255842.2025.2476186","url":null,"abstract":"<p><p>Cancer-associated fibroblasts (CAFs) are related to drug resistance and prognosis of tumor patients. This study aimed to investigate the relationship between prognosis and drug treatment response in patients with CAF and lung adenocarcinoma (LUAD). The data pertaining to LUAD patients were obtained from The Cancer Genome Atlas-LUAD and GSE68465 datasets. Four different algorithms were used to quantify CAF infiltration and stromal scores. Weighted gene network co-expression analysis was used to identify CAF-related modules and hub genes. Univariate Cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis were used to construct CAF signatures, whose ability to predict prognosis was verified by individual CAF scores. The CAF-related signature of eight genes was constructed, and the CAF score was calculated. The prognosis of LUAD patients with high CAF scores was significantly worse than that of patients with low CAF scores. CAF score was an independent risk factor for LUAD prognosis. Patients with high CAF scores were sensitive to some chemotherapy drugs, and in most cases, they were non-responsive to immunotherapy. Eight-gene CAF signature may predict LUAD patient prognosis and evaluate clinical responses to chemotherapy and immunotherapy, enabling individualized treatment for the patients.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607066","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 design and analysis of customized fixation plate for treating middle one-third clavicle fracture.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-11 DOI: 10.1080/10255842.2025.2477207
Abderrazak Kedadria, Abdelkader Benaouali, Lionel Gilson, Yacine Benabid, Abdelghani May, Luc Rabet
{"title":"Numerical design and analysis of customized fixation plate for treating middle one-third clavicle fracture.","authors":"Abderrazak Kedadria, Abdelkader Benaouali, Lionel Gilson, Yacine Benabid, Abdelghani May, Luc Rabet","doi":"10.1080/10255842.2025.2477207","DOIUrl":"https://doi.org/10.1080/10255842.2025.2477207","url":null,"abstract":"<p><p>Plate fixation is the primary treatment for clavicle fractures, but standard plates often fail, requiring reoperation due to irritation, bending, or fracture. These issues are linked to poor geometric fit suboptimal plate thickness, and material performance. This study proposes a personalized clavicle plate design methodology for middle one-third fractures. Using a reverse engineering approach, a 3D model of a 15-B1.2 oblique fractured clavicle bone is created from CT scan data. Customized plates of varying thicknesses are designed and simulated using titanium, stainless steel, and cobalt-chromium-molybdenum alloys. This research proposes a methodology based on finite element modeling (FEM) to assess structural stability, safety, and fatigue life by analyzing stresses and displacements under diverse loading conditions. This investigation aims to improve clinical decisions and patient outcomes through personalized treatments and enhanced fracture stability.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-11"},"PeriodicalIF":1.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607063","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
Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of prognostic value of T cell-related genes in cervical cancer.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-10 DOI: 10.1080/10255842.2025.2475483
Yao Fu, Xiubing Zhang, Lili Yu, Guiping Zhang, Xinyu Liu, Wei Ren
{"title":"Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of prognostic value of T cell-related genes in cervical cancer.","authors":"Yao Fu, Xiubing Zhang, Lili Yu, Guiping Zhang, Xinyu Liu, Wei Ren","doi":"10.1080/10255842.2025.2475483","DOIUrl":"https://doi.org/10.1080/10255842.2025.2475483","url":null,"abstract":"<p><p>The relationship between cervical cancer (CESC) and T cells is mainly seen in the anti-tumor functions of T cells. This study aims to identify prognostic genes associated with CESC and T cells, providing a foundation for developing immunotherapy strategies. This study used data from public databases to identify T cell-related prognostic genes for CESC patients through differential expression analysis and single-cell clustering. A prognostic risk model and nomogram were constructed and validated based on these genes. Pseudotime analysis clarified T cell differentiation processes in CESC. Ultimately, Mendelian randomization (MR) was applied to determine the causal relationship between the prognostic genes and CESC. In this study, CXCL2, ANKRD22, SPP1, and C1QB were identified as prognostic genes for CESC. Survival analysis indicated that the survival rate of the high-risk cohort (HRC) was significantly lower compared to that of the low-risk cohort (LRC). A nomogram also demonstrated strong predictive capability. Notably, higher expression levels of prognostic genes were observed during the early stages of T cell differentiation. MR analyses revealed that SPP1 was a risk factor for CESC (OR = 1.165; 95% CI: 1.028-1.320; <i>p</i> = .017), while C1Q8 acted as a protective factor (OR = 0.820; 95% CI: 0.685-0.983; <i>p</i> = .032). CXCL2, ANKRD22, SPP1, and C1QB showed strong prognostic characteristics in CESC and significant predictive capabilities for patient outcomes. The study also emphasized the critical role of T cells in CESC progression.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-21"},"PeriodicalIF":1.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587883","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
Investigating the impact of feature extraction methods on prediction accuracy of neurological recovery levels in comatose patients post-cardiac arrest.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-10 DOI: 10.1080/10255842.2025.2475466
Sabri Can Çelik, Semiha Sude Özgüzel, İsmail Cantürk
{"title":"Investigating the impact of feature extraction methods on prediction accuracy of neurological recovery levels in comatose patients post-cardiac arrest.","authors":"Sabri Can Çelik, Semiha Sude Özgüzel, İsmail Cantürk","doi":"10.1080/10255842.2025.2475466","DOIUrl":"https://doi.org/10.1080/10255842.2025.2475466","url":null,"abstract":"<p><p>Cardiac arrest can cause irreversible Post-Cardiac Arrest Brain Injury (PCABI), but predicting PCABI with certainty remains challenging. This study aims to improve prognostication by predicting neurological recovery using EEG data from the 'I-CARE: International Cardiac Arrest Research Consortium Database.' Data were preprocessed with an FIR Equiripple Bandpass Filter, and three feature extraction methods were applied. Decision Tree, KNN, SVM, and Ensemble Learning algorithms were evaluated using F1-Score, Accuracy, and ROC-AUC. The highest accuracy, 0.89, was achieved with Hamming-windowed streamline feature extraction and Decision Tree after feature selection.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587843","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
Enhancing cardiovascular risk prediction: the role of wall viscoelasticity in machine learning models.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-10 DOI: 10.1080/10255842.2025.2475479
Duc-Manh Dinh, Belilla Yonas Berfirdu, Kyehan Rhee
{"title":"Enhancing cardiovascular risk prediction: the role of wall viscoelasticity in machine learning models.","authors":"Duc-Manh Dinh, Belilla Yonas Berfirdu, Kyehan Rhee","doi":"10.1080/10255842.2025.2475479","DOIUrl":"https://doi.org/10.1080/10255842.2025.2475479","url":null,"abstract":"<p><p>This study aims to evaluate the significance of wall viscoelasticity in enhancing cardiovascular disease (CVD) risk prediction. We collected data on ten patient features, categorized into demographics (age, gender, blood pressure, smoking history), blood lab data (HDL, LDL, blood glucose levels), and wall mechanics (Peterson's modulus, stiffness parameter, energy dissipation ratio). Outcome variables were classified as low or high CVD risk based on total plaque area computed from carotid ultrasound images. We employed eight machine learning classifiers and conducted a comparative analysis of feature importance. Incorporating mechanical attributes significantly improved predictive accuracies for most machine learning models. The Random Forest Bagging Method (RFBM) showed the best performance, achieving an accuracy of 93.0% and an AUC of 0.98 with all 10 features. Including either elastic or viscous features alongside the conventional features enhanced prediction for most models. For the tree-based bagging models (DTBM and RFBM), including the viscous feature (energy dissipation ratio) alongside conventional features resulted in greater accuracy improvements compared to the elastic features. This study underscores the significant impact of integrating wall viscosity on CVD prediction and highlights the potential for combining both elastic and viscous wall characteristics to achieve more accurate risk assessment.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143587880","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
Deep learning‑based prediction of in‑hospital mortality for acute kidney injury.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-07 DOI: 10.1080/10255842.2025.2470809
Li Yong, Dou Ruiyin, Wang Xia, Shi Zhao
{"title":"Deep learning‑based prediction of in‑hospital mortality for acute kidney injury.","authors":"Li Yong, Dou Ruiyin, Wang Xia, Shi Zhao","doi":"10.1080/10255842.2025.2470809","DOIUrl":"10.1080/10255842.2025.2470809","url":null,"abstract":"<p><p>Acute kidney injury (AKI) is a prevalent clinical syndrome that causes over one-fifth of hospitalized patients worldwide to suffer from AKI. We proposed the GCAT, which aims to identify high-risk AKI patients in the hospital settings using the MIMIC-III dataset. Firstly, it fully explores the similarity of attribute features among a large number of patients and calculates the attribute similarity values between patients to generate a node similarity matrix. Then, it selects nodes with high similarity to construct a patient feature similarity network (PFSN). Experiments demonstrate that the GCAT achieves an accuracy of 88.57%, its effectiveness is superior to state-of-the-art methods.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574375","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
Biomechanical analysis of triply periodic minimal surfaces-based porous dental implants versus solid implants: impact of peri-implant bone density on micromotion.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-03 DOI: 10.1080/10255842.2025.2472018
Deepak Sharma, Varun Sharma
{"title":"Biomechanical analysis of triply periodic minimal surfaces-based porous dental implants versus solid implants: impact of peri-implant bone density on micromotion.","authors":"Deepak Sharma, Varun Sharma","doi":"10.1080/10255842.2025.2472018","DOIUrl":"https://doi.org/10.1080/10255842.2025.2472018","url":null,"abstract":"<p><p>Dental implants restore facial appearance and improve chewing and speaking abilities in edentulous patients. However, solid implants often cause stress shielding, peri-implantitis, and poor bone integration due to low osteointegration, leading to failure. To address this, three porous implants-Gyroid, Schwarz Diamond (DI), and Schwarz Primitive-were designed and evaluated using Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD). FEA confirmed mechanical stability, with DI reducing bone stress but increasing micromotion in lower-density bone. Experimental and computational testing showed FEA slightly overpredicted stress, while CFD confirmed DI's permeability closely matches cancellous bone.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-21"},"PeriodicalIF":1.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544525","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 Adaptive Dendritic Neural Model for Lung Cancer Prediction.
IF 1.7 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-03 DOI: 10.1080/10255842.2025.2472013
Umair Arif, Chunxia Zhang, Muhammad Waqas Chaudhary, Sajid Hussain
{"title":"An Adaptive Dendritic Neural Model for Lung Cancer Prediction.","authors":"Umair Arif, Chunxia Zhang, Muhammad Waqas Chaudhary, Sajid Hussain","doi":"10.1080/10255842.2025.2472013","DOIUrl":"https://doi.org/10.1080/10255842.2025.2472013","url":null,"abstract":"<p><p>Lung cancer is a leading cause of cancer-related deaths, often diagnosed late due to its aggressive nature. This study presents a novel Adaptive Dendritic Neural Model (ADNM) to enhance diagnostic accuracy in high-dimensional healthcare data. Utilizing hyperparameter optimization and activation mechanisms, ADNM improves scalability and feature selection for multi-class lung cancer prediction. Using a Kaggle dataset, Particle Swarm Optimization (PSO) selected features, while bootstrap assessed performance. ADNM achieved 98.39% accuracy, 99% AUC, and a Cohen's kappa of 96.95%, with rapid convergence via the Adam optimizer, demonstrating its potential for improving early diagnosis and personalized treatment in oncology.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544501","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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