Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"Research on hoisting skill level of hydro turbine rotor based on human brain cognitive behavior.","authors":"Fuwang Wang, Qi Guo, Xiaolei Zhang","doi":"10.1080/10255842.2026.2656798","DOIUrl":"https://doi.org/10.1080/10255842.2026.2656798","url":null,"abstract":"<p><p>The primary purpose of the study is to distinguish the differences in operating levels of hydro turbine rotor hoisting between novice and experienced operators from the viewpoint of human cognitive behavior. This study utilized the characteristics of electroencephalogram (EEG) signals and employed the Multi-scale Residual Shrinkage Network (MS-RSN) method to investigate the differences in operational proficiency among personnel with different levels of operational experience. The research results show that EEG signals can effectively reflect the proficiency of the operator. The MS-RSN method shows high accuracy in identifying the operator's hoisting proficiency, with an accuracy rate of 98.66%.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.6,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788122","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":"Genetic algorithm-guided design of garlic-derived ligands targeting the TEN domain of telomerase reverse transcriptase with machine learning-based activity classification.","authors":"Hassen Elmir, Abdelkader Ghazli, Larbi Boubchir, Abdelaziz Daoudi","doi":"10.1080/10255842.2026.2658116","DOIUrl":"https://doi.org/10.1080/10255842.2026.2658116","url":null,"abstract":"<p><p>Human telomerase reverse transcriptase (hTERT) plays a key role in cancer cell immortalization and represents an important therapeutic target for anticancer drug discovery. In this study, a computational pipeline combining genetic algorithms (GAs) and machine learning (ML) was developed to design and screen garlic-derived bioactive compounds as potential telomerase inhibitors. Garlic phytochemicals were used as the initial chemical space, which was iteratively evolved through mutation and fragment expansion to generate novel ligand candidates. A multi-parameter fitness function incorporating Lipinski, Veber, and Ghose drug-likeness rules, quantitative estimate of drug-likeness (QED), hydrogen bond donor/acceptor balance, and aromatic ring constraints was used to guide optimization. In addition, a RandomForest-based classifier was applied to pre-screen compounds for predicted telomerase activity prior to molecular docking. The shortlisted ligands were evaluated using CB-Dock2, and further assessed for pharmacokinetic and toxicity properties using SwissADME, including solubility, lipophilicity, and bioavailability. Out of 125 generated ligands, 14 met both drug-likeness and predicted activity criteria and progressed through the full pipeline. The highest binding affinity observed was -10.5 kcal/mol; however, this top-scoring compound was excluded due to ADMET rule violations. The remaining candidates exhibited favorable physicochemical properties, acceptable solubility, balanced lipophilicity, and good predicted oral bioavailability. Overall, the results demonstrate that genetic algorithms can efficiently generate structurally diverse and pharmacologically relevant scaffolds, and that integrating an activity-based machine learning filter prior to docking improves screening efficiency by prioritizing biologically meaningful candidates. Compared with traditional GA-based docking workflows, this integrated strategy provides a more selective and cost-effective approach for early-stage ligand discovery.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-22"},"PeriodicalIF":1.6,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147788072","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":"Distributed loads alter internal load predictions in the hands and forearm.","authors":"Ryan Chhiba, Daanish M Mulla, Peter J Keir","doi":"10.1080/10255842.2026.2658129","DOIUrl":"https://doi.org/10.1080/10255842.2026.2658129","url":null,"abstract":"<p><p>This study examined the influence of distributed external hand forces joint moments and muscle activation. Kinematics and forces were collected during multiple finger tasks while surface pressure was measured to create 3 force input models in OpenSim: (1) segment centre of mass (CM), (2) centre of pressure (CP), and (3) single point force on each digit (SP). CM and CP moment and activations were found equivalent. Distributed loading (CM and CP) produced lower moments and activation than SP. We found that internal loads are sensitive to external force distribution, supporting improved biofidelity via complex input forces in modelling the hand.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.6,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730482","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":"Development and validation of simplified head and thorax finite element models for ballistic impact assessment of non-lethal projectiles.","authors":"Younes Kebbab, Amar Oukara, Mohamed Abderaouf Louar, Djalel Eddine Tria, Nestor Ndompetelo Nsiampa","doi":"10.1080/10255842.2026.2658117","DOIUrl":"https://doi.org/10.1080/10255842.2026.2658117","url":null,"abstract":"<p><p>Despite their classification as \"non-lethal,\" Kinetic Energy Non-Lethal Projectiles (KENLPs) still causing fatal injuries, necessitating rigorous biomechanical assessment. Ethical and technical limits of Post Mortem Human Subjects (PMHS) and experimental testing have elevated Finite Element Human Body Models (HBMs) for blunt trauma research. This study develops a two-stage numerical framework for cranial and thoracic injury prediction using LS-DYNA. Firstly, Hybrid III (H3) sub-models are validated against the Ballistics Load Sensing Headform (BLSH) envelope and NATO STANREC 4744 (AEP-99) thorax guidelines using validated KENLPs. Building upon these validations, the second stage proposes the Simplified Head (SH-FEM), featuring a dual-layer scalp and skull architecture, and the Simplified Thorax (STh-FEM)-a three-layer construct comprising muscle, a lung slab, and a central skeletal structure preserving dominant load paths while reducing computational cost. Simulation results indicate peak forces scale nonlinearly from 0.82 to 16.03 kN across 20-80 m⋅s⁻¹, with neck coupling reducing peaks by 20-30%. A velocity inflection at 40 m⋅s⁻¹ marks sub-concussive-to-injurious transitions: <33 m⋅s⁻¹ yields <2.1 kN (insignificant risk), while >55 m⋅s⁻¹ exceeds 7.5 kN (fracture/coma). For thoracic impacts across Cases A-E, VC_max-based AIS ≥ 2 risks vary by model and projectile. In Case E, STh-FEM predicted 46% risk versus H3 at 91%; in Case C, values were 10% and 52%, respectively. Furthermore, STh-FEM overpredicted rigid PVC projectile forces (98% in Case C) but matched the deformable SIR-X projectile (∼8 kN peaks). These simplified models demonstrate controlled, reproducible responses, confirming their feasibility as performant alternatives for rapid KENLPs design screening and safety assessment.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718877","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":"Stability analysis and optimal control of the fractional-order SEIQR epidemic model with logistic input.","authors":"Ruisheng Yu, Shenglong Chen, Zhiming Li","doi":"10.1080/10255842.2026.2645172","DOIUrl":"https://doi.org/10.1080/10255842.2026.2645172","url":null,"abstract":"<p><p>This article mainly investigates a fractional-order SEIQR model with a logistic input and a saturated incidence rate, and formulates a fractional optimal control problem (FOCP). The nonnegativity of solutions is analyzed, and the existence of disease-free and endemic equilibria is demonstrated by deriving a threshold <math><mrow><mrow><msub><mrow><mi>R</mi></mrow><mn>0</mn></msub></mrow></mrow><mtext>.</mtext></math> Sufficient conditions are established to guarantee local asymptotic stability of these equilibria. Considering infectious disease prevention and control strategies, we prove the existence of FOCP solutions and analyze the effects of the optimal control strategy. Furthermore, the effectiveness of the theoretical results is confirmed. Finally, the proposed model is applied to COVID-19 data.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.6,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700608","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":"The gender blind spot in personalized knee joint modeling: rethinking biomechanical models.","authors":"Ge Shen, Nan Zhang, Jifeng Yao","doi":"10.1080/10255842.2026.2656284","DOIUrl":"https://doi.org/10.1080/10255842.2026.2656284","url":null,"abstract":"<p><p>Sexual dimorphism in knee anatomy, tissue properties, and biomechanics is well documented, but most musculoskeletal and finite element knee models remain sex-neutral. This review shows that few models account for sex-specific differences in geometry, material properties, or functional parameters, which may bias predictions of joint loading, stability, and tissue stress. Such limitations reduce reliability in surgical planning and injury-risk assessment. Feasible strategies include sex-aware scaling of muscle force, ligament properties, bone density-modulus relationships, and soft-tissue representation. Incorporating sex-specific factors can improve physiological realism and support more individualized clinical and biomechanical applications.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.6,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700562","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":"Sensitivity of various material properties to the intervertebral disc biomechanics.","authors":"Qiaoqiao Zhu, Xiaofeng Jia, Bing Qin, Ling Tao, Zhiyu Qian","doi":"10.1080/10255842.2026.2649538","DOIUrl":"https://doi.org/10.1080/10255842.2026.2649538","url":null,"abstract":"<p><p>This study aims to conduct a parameter sensitivity analysis to identify the most influential material properties governing intervertebral disc creep behavior. The 12 key material properties include water contents (<math><mrow><msup><mrow><mi>ϕ</mi></mrow><mrow><mi>w</mi></mrow></msup></mrow></math>), fixed charge densities (<math><mrow><msup><mrow><mi>c</mi></mrow><mrow><mi>f</mi></mrow></msup></mrow></math>), and mechanical properties (<i>μ, λ, k₁</i>)) across the nucleus pulposus, annulus fibrosus, and cartilaginous endplate. Finite element simulations of creep tests were validated against experimental bounds. Of 600 simulations, 58 fell within experimental bounds (R² > 0.8). Water content was the most influential parameter across all creep response variables, followed by fiber reinforcement and fixed charge density. Our findings are important for understanding disc biomechanics.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-11"},"PeriodicalIF":1.6,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693358","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":"Thorough analysis of tumor microenvironment, prognostic model, and immunotherapy prediction in hepatocellular carcinoma based on vascular normalization-related genes.","authors":"Yuting Hu, Mingwei Zeng, Yushan Zhang, Shijie Zhu, Yibo Hu, Shu Zhou, Fang Xia","doi":"10.1080/10255842.2026.2656292","DOIUrl":"https://doi.org/10.1080/10255842.2026.2656292","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) ranks sixth in incidence and third in mortality worldwide. Immunotherapy for advanced HCC is limited by drug resistance. Vascular normalization (VN) restores tumor neovasculature, alleviates immunosuppression, and enhances immunotherapy. However, roles of VN-related genes (VNRGs) in HCC remain understudied. In this study, bioinformatics methods explored roles and constructed a prognostic model. The VNRGs-high expression group had higher immune infiltration, lower tumor purity and worse outcomes. An eight-VNRG prognostic model was constructed. High-risk patients had poor outcomes but better immunotherapy response. This study highlights VNRGs' significance and shows the risk score function as a prognostic and immune response indicator.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700606","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":"Sensitivity of finite element models to relationship between <i>T₂</i> relaxation and modulus in articular cartilage.","authors":"Alexander A Donabedian, Deva D Chan","doi":"10.1080/10255842.2026.2658122","DOIUrl":"https://doi.org/10.1080/10255842.2026.2658122","url":null,"abstract":"<p><p>Correlating articular cartilage material properties to quantitative magnetic resonance imaging biomarkers is a powerful approach to biofidelic finite element models. However, subject-specific relationships between imaging biomarkers such as <i>T₂</i> and material properties like dynamic modulus are uncertain. To evaluate the sensitivity of finite element models to this uncertainty, we shifted the slope and intercept of a linear <i>T₂</i>-dynamic modulus relationship used to define cartilage properties. Modulus shifts led to notable percent changes in the top 1% of calculated stress and strain, while modulating slope had a negligible impact, together supporting the use of physiologically relevant moduli ranges in subject-specific models.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693340","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":"Integrated machine learning algorithms for prediction of prognosis in ovarian cancer patients based on mitochondrial-related genes.","authors":"Ke Xu, Ning Zhang, Haoyu He, Hua Zhang, Yuzhou Gao, Run Miao, Zhihao Xu, Yuchen Zhang, Dongmei Ji","doi":"10.1080/10255842.2026.2658118","DOIUrl":"https://doi.org/10.1080/10255842.2026.2658118","url":null,"abstract":"<p><p>Mitochondrial dysfunction drives ovarian cancer (OC) progression. This study constructed a robust prognostic model (MITO-OC) based on mitochondria-related genes using ten machine-learning algorithms on TCGA, ICGC, and GEO data. We identified 241 differentially expressed genes and built the optimal MITO-OC model using StepCox[forward] and RSF algorithms (C-index=0.73). The model accurately predicts patient overall survival and strongly correlates with tumor immune infiltration. Furthermore, single-cell and pan-cancer analyses highlighted CHCHD2 as a critical component in OC and other tumors. MITO-OC provides a highly effective, personalized prognostic tool and reveals underlying metabolic mechanisms for OC clinical management.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693395","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}