Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"Modeling the tendon-bone dynamic interaction while wrapping and unwrapping using bond graph.","authors":"Arvind Kumar Pathak, Anand Vaz","doi":"10.1080/10255842.2025.2512880","DOIUrl":"https://doi.org/10.1080/10255842.2025.2512880","url":null,"abstract":"<p><p>The dynamic wrapping and unwrapping of tendons or ligaments around bones critically influence tendon stretch, bone-periosteum contact forces, and joint motion. Accurately modeling this interaction over irregular bone surfaces is complex. This study introduces a novel framework using 3D point cloud bone geometry enveloped by a soft periosteum layer, modeled via nonlinear stiffness and damping fields. Tendons/ligaments are represented as elastic strings with distributed point masses linked by nonlinear stiffness and damping. Multibond graph submodels capture the coupled dynamics between tendon, periosteum, and bone. Simulations in planar and 3D cases demonstrate the model's robustness, offering new insights into biomechanical motion and joint mechanics.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-24"},"PeriodicalIF":1.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762179","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":"Inverse finite element identification of murine aortic material properties: <i>in vivo</i> and <i>ex vivo</i> comparisons.","authors":"Hadi Wiputra, Sydney Q Clark, Craig J Goergen, Victor H Barocas, Matthew R Bersi","doi":"10.1080/10255842.2025.2537332","DOIUrl":"https://doi.org/10.1080/10255842.2025.2537332","url":null,"abstract":"<p><p>Inverse finite element (FE) models can non-invasively estimate aortic mechanical properties from <i>in vivo</i> imaging. However, few studies have compared model predictions with direct mechanical characterization in the same samples. To address this, we used a mouse model of thoracic aneurysm to develop (from <i>in vivo</i> ultrasound imaging) and validate (from <i>ex vivo</i> biomechanical testing) an inverse FE approach to estimate material properties of the ascending thoracic aorta. The proposed inverse FE model was constructed based on a combination of image-based tracking of tissue deformation in 4D ultrasound images (4DUS; volumetric images over time) and non-invasive hemodynamic measures (pulsed wave Doppler velocity and tail-cuff blood pressure). Following an optimization scheme to estimate the biaxial pre-stretches that best represent the <i>in vivo</i> radii obtained from 4DUS images, material properties were identified, and aortic stiffness was calculated for each mouse included in the study (<i>n</i> = 8 total). Inverse FE predictions were compared with paired <i>ex vivo</i> material characterization results for each ascending aortic sample. Multiple assumptions related to boundary conditions and unloaded tissue geometry were required to constrain the inverse identification procedure; sensitivity analysis was performed for each simplifying assumption and uncertainty in the estimated axial pre-stretch was identified as a primary contributor to the observed discrepancies between <i>in vivo</i> and <i>ex vivo</i> material property estimates. Findings from two material models (neo-Hookean and four-fiber family) were compared and all data has been provided as a benchmark for future inverse FE studies.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-19"},"PeriodicalIF":1.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762176","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":"Machine learning-driven prognostic and diagnostic models for lung adenocarcinoma using intratumor heterogeneity and multi-omics data.","authors":"Xiaohua Li, Xiaohong Nie, Shiwei Gan, Yuntao Wang, Xuefeng Zeng, Hua Guo","doi":"10.1080/10255842.2025.2535010","DOIUrl":"https://doi.org/10.1080/10255842.2025.2535010","url":null,"abstract":"<p><p>Intratumor heterogeneity (ITH) significantly impacts cancer prognosis and treatment response. Focusing on lung adenocarcinoma (LUAD), this study investigates the relationship between ITH and clinical outcomes, and constructs machine learning-based prognostic and diagnostic models. ITH scores were calculated using the DEPTH2 package, and weighted gene co-expression network analysis (WGCNA) was applied to identify ITH-associated core genes. A 19-gene prognostic model was developed using Elastic Net (Enet), and a 7-gene diagnostic model was built through a combination of LASSO and Random Forest (RF). The prognostic model was validated across six independent datasets, while the diagnostic model was tested in three. ITH was found to correlate significantly with clinical characteristics such as gender, M stage, and overall survival. WGCNA revealed the black and lightgreen modules as key to ITH, contributing 126 core genes. Both models demonstrated strong predictive performance and generalizability, accurately stratifying LUAD patients and distinguishing them from healthy controls. These findings underscore the clinical value of incorporating ITH and multi-omics data into model construction to enhance precision in LUAD diagnosis and prognosis.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762178","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 of lumbar injury in out-of-position occupants via seat back angles under AES.","authors":"Sen Xiao, Xikai Zhang, Yu Liu, Huida Zhang, Libin Zang","doi":"10.1080/10255842.2025.2525976","DOIUrl":"https://doi.org/10.1080/10255842.2025.2525976","url":null,"abstract":"<p><p>The effects of seatback recline angles and occupant displacement during emergency steering maneuvers on lumbar injury trends were investigated. A collision simulation environment was created using the AC-HUM model combined with a partial vehicle model. Under varying seatback recline configurations, two pre-collision scenarios were simulated: with and without autonomous emergency steering (AES) intervention. Kinematic and dynamic response analyses revealed that increased seatback recline angles and lateral out-of-position dynamics significantly increased lumbar injury propensity while compromising the protective efficacy of seatbelt and airbag systems in nonstandard postures. This research provides critical insights into injury mechanisms associated with complex occupant displacement postures during pre-impact phases.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762177","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":"Effects of chondrocyte shape and orientation on continuous ultrasound propagation in microscale articular cartilage.","authors":"Sattik Basu, Sarma L Rani","doi":"10.1080/10255842.2025.2536677","DOIUrl":"10.1080/10255842.2025.2536677","url":null,"abstract":"<p><p>We apply the finite element method (FEM) to model the propagation of ultrasound waves in the extracelluar matrix (ECM) of the cartilage tissue, and quantify the effects of chondrocyte geometry and orientation, material properties, and ultrasound frequency on the deformations in the ECM. The computational domain consists of a 2-D ECM layer with embedded chondrons. The interactions of elliptical chondrons with the ultrasound waves are quantified for frequencies in the 0.5 MHz to 4 MHz range. Three orientations are considered for the elliptical chondrocytes-horizontal, vertical, or at a 45º angle. Chondron orientation significantly influences the attenuation of ultrasound amplitudes, with the horizontal chondrons being the most effective. The screening effects of chondrons are also a function of depth in the ECM layer. In the superficial zone, chondrons at 45º angle are more effective in screening ultrasound waves at all frequencies, while in the deep zone, these chondrons show a frequency-dependent behavior.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-29"},"PeriodicalIF":1.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144745816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic model based on KCNK family genes for predicting prognosis and immunotherapy response in lung adenocarcinoma patients.","authors":"Li Xu, Danting Zheng, Jisong Zhang, Huihui Hu, Liangliang Dong, Enguo Chen","doi":"10.1080/10255842.2025.2532809","DOIUrl":"https://doi.org/10.1080/10255842.2025.2532809","url":null,"abstract":"<p><p>The tow-pore domain (KCNK) potassium channel family is associated with tumor progression, but its prognostic value in lung adenocarcinoma (LUAD) remains unclear. In this study, we integrated data from the TCGA and GEO databases to identify 9 KCNK-related differentially expressed genes, and based on this, we classified two molecular subtypes with significantly different prognoses. A 11-gene prognostic model with independent prognostic value was constructed through regression analysis. Immuno-analysis revealed that the low-risk group had stronger immune infiltration and might be more suitable for immunotherapy. These findings reveal the prognostic significance of the KCNK genes and provide a reference for immunotherapy.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735006","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":"Multi-cascaded heart disease prediction using hybrid deep learning and optimization techniques.","authors":"K Lakshmanan, P Gomathi","doi":"10.1080/10255842.2025.2525981","DOIUrl":"https://doi.org/10.1080/10255842.2025.2525981","url":null,"abstract":"<p><p>A novel deep learning based heart disease prediction model is proposed. Initially, the collected data is fed into the preprocessing phase using the NaN fill method. Then, the preprocessed data is given to the data transformation phase using data normalization approach. Further, the transformed data are fed into the optimal weighted feature selection process, which is selected by using the developed Mutated Iteration-based Fire Hawk with Coyote Optimization (MI-FHCO). Subsequently, heart disease is predicted by Multi-Cascaded Deep Learning Network (MDLNet). The best accuracy rate of the proposed approach is attained as 96.65% for dataset 4 to demonstrate its superior performance.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-36"},"PeriodicalIF":1.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709728","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":"Patient-specific hemodynamic analysis of the anterior communicating artery: comparative evaluation of unilateral and bilateral vascular models using MRI-based computational simulations.","authors":"Zhen-Ye Chen, Te-Chang Wu, Tzu-Ching Shih","doi":"10.1080/10255842.2025.2532807","DOIUrl":"https://doi.org/10.1080/10255842.2025.2532807","url":null,"abstract":"<p><p>The anterior communicating artery (AComA) is a key collateral pathway in the Circle of Willis. This study investigated the effects of bilateral inflows and vascular geometry on hemodynamics using computational fluid dynamics (CFD) simulations derived from magnetic resonance angiography (MRA) data in four subjects. Flow rates were measured using phase-contrast MRA (1.5T Siemens) at major anterior circulation segments. CFD models incorporated rigid walls, pulsatile flow, and MRA-derived inlet waveform curves under laminar, incompressible assumptions. Simulated internal carotid artery velocities correlated strongly (>90%) with measurements. While inlet phase lags had limited impact on time-averaged wll shear stress (TAWSS), significant TAWSS differences (>90%) were observed in a subject with marked anterior cerebral artery (ACA) asymmetry (92% diameter ratio). Bilateral ACA inflows are critical for accurate TAWSS estimation, especially in symmetric anatomies. These findings support incorporting patient-specific bilateral inflows in CFD models for improved AComA hemodynamic evaluation and treatment planning.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700234","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":"Using continuous reinforcement learning to obtain optimal dose of the drug in patients with melanoma during initial stage.","authors":"Elnaz Kalhor, Amin Noori, Sara Saboori Rad","doi":"10.1080/10255842.2025.2519418","DOIUrl":"https://doi.org/10.1080/10255842.2025.2519418","url":null,"abstract":"<p><p>The most important issue, which is met in this paper is quick treatment of melanoma. Medically, melanoma is known as one of the most malignant types of cancers. This disease can put the patients in the risk of death, if no quick action is taken. Mostly, medical experts tolerate serious challenges to determine the optimal dose. Intelligent methods can pave this way and efficiently assist them to reliably provide the best suitable dose for quick treatment. The RL approach seems to be one of the best candidates. But, the conventional RL lacks of high accuracy and speed, due to discrete states and actions and may result in increased control effort. These drawbacks have directed us to adopt the continuous RL, a combination of NNs and the RL approach. This has increased the accuracy and optimality of the dose in a continuous state space to control and annihilate the population of cancer cells, while the complexity of the approach is significantly low. According to physicians, treatment of melanoma in its initial stage takes two months. After this period, cancer cells will be completely eliminated in the patient's body. Accordingly, a mathematical model of a patient with melanoma in initial stage is employed. The proposed method is analyzed using the Eligibility Traces algorithm, Q-learning algorithm and constant-dose injection method. The simulation results have indicated that when the combination of RL approach and NNs is adopted, after 50 days, the cancer cells will completely vanish. Besides, other parameters of the considered model will be within their normal range. However, when the Eligibility Traces and Q-learning algorithm is employed, after 50 days, cancer cells will be still present in the patient's body. When the proposed hybrid method is used, the injected dose is significantly lower than that of other methods. As a consequence, the side effects of the drug will be reduced. Finally, in this result, the effectiveness of the proposed approach is evaluated in 5 melanoma patients, under the presence of uncertainty and noise. The obtained results have confirmed the promising capability of the adopted approach to control the population of cancer cells and reach a desired level.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-26"},"PeriodicalIF":1.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700235","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":"Human motion measurement methods under the background of molecular chain conformation changes.","authors":"Meizhi Wang","doi":"10.1080/10255842.2025.2532031","DOIUrl":"https://doi.org/10.1080/10255842.2025.2532031","url":null,"abstract":"<p><p>This study proposes a human motion measurement model combining molecular chain conformation with a silicone rubber strain sensor embedded with carbon nanotubes to enhance signal response stability. An improved least mean square algorithm is used to optimize signal processing. Experimental results show the model achieves 95.12% measurement accuracy, 92.45% F1 score, 35.14 dB SNR, and 60.45 ms latency. Across different age groups and motion states such as gait, running, and jumping, the average detection error remains below 3%, and physiological monitoring errors for heart rate and oxygen saturation are as low as 0.42. The model operates stably in dynamic conditions.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692307","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}