Computer Methods in Biomechanics and Biomedical Engineering最新文献_第8页

Biomechanical characteristics of arteries during pelvic fracture reduction and dynamic simulation analysis. 骨盆骨折还原过程中动脉的生物力学特征及动态模拟分析。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-01 Epub Date: 2024-03-04 DOI: 10.1080/10255842.2024.2324880

Dongwei Lei, Jingtao Lei, Haifei Xu

{"title":"Biomechanical characteristics of arteries during pelvic fracture reduction and dynamic simulation analysis.","authors":"Dongwei Lei, Jingtao Lei, Haifei Xu","doi":"10.1080/10255842.2024.2324880","DOIUrl":"10.1080/10255842.2024.2324880","url":null,"abstract":"During robot-assisted reduction of pelvic fracture, blood vessels are susceptible to tensile and shear forces, making them prone to injury. Considering the impact of pelvic reduction on the risk of arterial injury, the biomechanical characteristics of arteries during the pelvic fracture reduction process are studied, and a refined coupled composite model of the damaged pelvic structure is established. Dynamic simulations of pelvic fracture reduction are conducted based on the planned reduction path. The simulation results show that during the reduction process, when the affected side is rotated, the stress and strain of the artery are maximum, particularly at the locations of the iliac common artery, internal iliac artery, and the superior gluteal artery arch endure significant stress and strain. After reduction, the maximum stress is observed in the right superior gluteal artery, and the maximum strain occurs at the intersection of the right iliac common artery. The stretch ratio of both the left and right iliac common arteries is considerable. Therefore, it can be concluded that the superior gluteal artery and the internal iliac artery are prone to injury, particularly the segment from the origin of the superior gluteal artery to its passage around the greater sciatic notch. After reduction, substantial traction on the iliac common artery, which makes it more susceptible to deformation, carries a risk of arterial rupture and aneurysm formation. This study provides a reference for planning the safe reduction path of pelvic fracture surgery and improving safety.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1319-1332"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140029434","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

A multi-branch convolutional neural network for snoring detection based on audio. 基于音频的打鼾检测多分支卷积神经网络。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-01 Epub Date: 2024-02-19 DOI: 10.1080/10255842.2024.2317438

Hao Dong, Haitao Wu, Guan Yang, Junming Zhang, Keqin Wan

{"title":"A multi-branch convolutional neural network for snoring detection based on audio.","authors":"Hao Dong, Haitao Wu, Guan Yang, Junming Zhang, Keqin Wan","doi":"10.1080/10255842.2024.2317438","DOIUrl":"10.1080/10255842.2024.2317438","url":null,"abstract":"Obstructive sleep apnea (OSA) is associated with various health complications, and snoring is a prominent characteristic of this disorder. Therefore, the exploration of a concise and effective method for detecting snoring has consistently been a crucial aspect of sleep medicine. As the easily accessible data, the identification of snoring through sound analysis offers a more convenient and straightforward method. The objective of this study was to develop a convolutional neural network (CNN) for classifying snoring and non-snoring events based on audio. This study utilized Mel-frequency cepstral coefficients (MFCCs) as a method for extracting features during the preprocessing of raw data. In order to extract multi-scale features from the frequency domain of sound sources, this study proposes the utilization of a multi-branch convolutional neural network (MBCNN) for the purpose of classification. The network utilized asymmetric convolutional kernels to acquire additional information, while the adoption of one-hot encoding labels aimed to mitigate the impact of labels. The experiment tested the network's performance by utilizing a publicly available dataset consisting of 1,000 sound samples. The test results indicate that the MBCNN achieved a snoring detection accuracy of 99.5%. The integration of multi-scale features and the implementation of MBCNN, based on audio data, have demonstrated a substantial improvement in the performance of snoring classification.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1243-1254"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139900739","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

Computer model of non-Newtonian canalicular fluid flow in lacunar-canalicular system of bone tissue. 骨组织腔隙-腔隙系统中非牛顿流体流动的计算机模型。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-01 Epub Date: 2024-02-19 DOI: 10.1080/10255842.2024.2317442

Rakesh Kumar

{"title":"Computer model of non-Newtonian canalicular fluid flow in lacunar-canalicular system of bone tissue.","authors":"Rakesh Kumar","doi":"10.1080/10255842.2024.2317442","DOIUrl":"10.1080/10255842.2024.2317442","url":null,"abstract":"Brittle bone diseases are a global healthcare problem for orthopaedic clinicians, that reduces bone strength and promotes bone fracture risk. In vivo studies reported that loading-induced fluid flow through the lacunar-canalicular channel (LCS) of bone tissue inhibit such bone loss and encourages osteogenesis i.e. new bone formation. Canalicular fluid flow converts mechanical signals into biological signals and regulates bone reconstruction by releasing signalling molecules responsible for mechanotransduction. In-silico model mostly considers canalicular fluid is Newtonian, however, physiological canalicular fluid may be non-Newtonian in nature as it contains nutrients and supplements. Accordingly, this study attempts to develop a two-dimensional in-silico model to compute loading-induced non-Newtonian canalicular fluid flow in a complex LCS of bone tissue. Moreover, canalicular fluid is considered as a Jeffery fluid, that can easily be reduced to Newtonian fluid as a special case. The results show that physiological loading modulates the canalicular fluid flow, wall shear stress (WSS) and streamline in bone LCS. Fluid velocity and WSS increases with increase in non-dimensional frequency and non-Newtonian parameter (Jeffery fluid parameters) and reduce with change in permeability. The outcomes of this study may provide new insights in the role of mechanical loading-induced non-Newtonian canalicular fluid flow dynamics in bone LCS. The key findings of this study can be used to improve the understanding of osteocyte mechanobiology involved inside the bone tissue.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1255-1269"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139900767","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

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations. 基于脑电表征的深度学习模型预测重度抑郁症脑刺激结果。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-28 DOI: 10.1080/10255842.2025.2511222

Mohsen Sadat Shahabi, Ahmad Shalbaf, Reza Rostami, Reza Kazemi

{"title":"Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.","authors":"Mohsen Sadat Shahabi, Ahmad Shalbaf, Reza Rostami, Reza Kazemi","doi":"10.1080/10255842.2025.2511222","DOIUrl":"https://doi.org/10.1080/10255842.2025.2511222","url":null,"abstract":"Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial and error between various treatments. An individualized treatment selection is crucial for saving time and financial resources and preventing possible side effects. Because of the complex nature of this problem, a Deep Learning (DL) approach, as a promising method for the precision medicine, was utilized to identify the responders to the treatment using pre-treatment EEG signals. Eighty-three patients with MDD participated in this study to receive treatment using Repetitive Transcranial Magnetic Stimulation (rTMS). A deep hybrid neural network was developed based on three pre-trained convolutional neural networks named DenseNet121, EfficientNetB0, and Xception. The training of each model was performed by feeding three types of EEG representations as the inputs into the models including the Wavelet Transform (WT) images, the connectivity matrix between electrode pairs, and the raw EEG signals. The performance of the proposed models were assessed for the three different input types and achieved the highest accuracy of 94.7% in classifying patients as responders or non-responders when utilizing a sequence of raw EEG images. For the WT and connectivity inputs the best accuracy of model was 94.38% and 94.25% respectively. Therefore, the proposed model can be a step forward towards the clinical implementation of an end-to-end treatment selection framework using raw EEG signals.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144163477","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

Exploring the genetic characteristics of overweight-related osteoarthritis using machine learning. 利用机器学习探索超重相关骨关节炎的遗传特征。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-28 DOI: 10.1080/10255842.2025.2510366

Zhaohui Jiang, Chunlei Xu, Wei Shi, Zhou Lin, Hui Li, Huafeng Zhang, Zhijun Li

{"title":"Exploring the genetic characteristics of overweight-related osteoarthritis using machine learning.","authors":"Zhaohui Jiang, Chunlei Xu, Wei Shi, Zhou Lin, Hui Li, Huafeng Zhang, Zhijun Li","doi":"10.1080/10255842.2025.2510366","DOIUrl":"https://doi.org/10.1080/10255842.2025.2510366","url":null,"abstract":"This investigation employed a synergistic approach integrating bioinformatics and machine learning methodologies to scrutinize overweight-related osteoarthritis characteristic genes (OROCGs). The research team procured gene expression profiles from osteoarthritis (OA) patients' cartilage and meniscus, derived from GEO database datasets GSE98918 and GSE117999. These profiles underwent meticulous examination through differential gene expression (DEG) identification, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), support vector machine - recursive feature elimination (SVM-RFE), and single-sample gene set enrichment analysis (ssGSEA), culminating in the identification of six OROCGs. Furthermore, the study unveiled an augmented presence of myeloid-derived suppressor cells (MDSCs) and B cells in overweight-associated OA. The investigators formulated a diagnostic model encompassing pivotal genes related to DNA replication, chronic inflammation, and epigenetics, including CHTH18, CYSLTR2, HSF4, KDM6B, NR4A2, and UCKL1. The model's diagnostic precision was corroborated through receiver operating characteristic (ROC) curves and a nomogram applied to the test set and validation set GSE129147. This model efficaciously delineates the expression alterations and immune infiltration linked to overweight-related OA, thereby nominating these genes as prospective candidates for immunomodulatory therapeutic interventions.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175763","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 electromyography-based multi-muscle fatigue model to investigate operational task performance. 基于肌电图的多肌肉疲劳模型研究操作任务表现。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-27 DOI: 10.1080/10255842.2025.2510369

Leonardo H Wei, Suman K Chowdhury

引用次数: 0

A novel fractional computational neural framework for analyzing cancer model under chemotherapy drug. 一种用于化疗药物作用下肿瘤模型分析的新型分数计算神经框架。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-26 DOI: 10.1080/10255842.2025.2508227

Muhammad Farhan, Zhi Ling, Jie Ding, Zahir Shah, Robert Daniel Dobrotă

{"title":"A novel fractional computational neural framework for analyzing cancer model under chemotherapy drug.","authors":"Muhammad Farhan, Zhi Ling, Jie Ding, Zahir Shah, Robert Daniel Dobrotă","doi":"10.1080/10255842.2025.2508227","DOIUrl":"10.1080/10255842.2025.2508227","url":null,"abstract":"In this study, a novel Caputo fractional-order model is proposed to represent the complex interactions among stem cells, effector cells, and tumor cells, considering both scenarios of chemotherapy. Furthermore, the proposed model, which incorporates treatment with effective chemotherapy, is thoroughly examined. The necessary properties, including the positivity and equilibrium points, as well as the local asymptotic stability analysis, are investigated. Additionally, the existence and uniqueness of solutions for the proposed model are thoroughly analyzed. We perform a thorough assessment of the solutions produced by the deep neural network by comparing them against established benchmarks and carefully analyzing them through testing, validation, training, error distribution analysis, and regression analysis. The temporal concentration pattern of stem, effector and tumor cells as well as chemotherapy drugs are examined. It is noted that chemotherapy leads to a decrease in tumor cell density over time, which extends the period required to achieve equilibrium. The decay rates of stem cells and tumor cells are recognized as essential elements affecting cancer dynamics. Furthermore, the integration of fractional orders is found to be important for precisely depicting the concentrations of cancer cells.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-19"},"PeriodicalIF":1.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152798","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

Network pharmacology, docking, and molecular dynamics analysis of Sanhuang decoction in diabetic foot ulcers. 三黄汤治疗糖尿病足溃疡的网络药理学、对接及分子动力学分析。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-23 DOI: 10.1080/10255842.2025.2506798

Bo Wu, Xiaohong Lan, Yang Yang, Yuekun Wang

{"title":"Network pharmacology, docking, and molecular dynamics analysis of Sanhuang decoction in diabetic foot ulcers.","authors":"Bo Wu, Xiaohong Lan, Yang Yang, Yuekun Wang","doi":"10.1080/10255842.2025.2506798","DOIUrl":"https://doi.org/10.1080/10255842.2025.2506798","url":null,"abstract":"This study aims to systematically and comprehensively elucidate the mechanism of action of Sanhuang decoction (SHD) in the treatment of diabetic foot ulcers (DFUs). The active ingredients and potential targets of SHD, as well as the human targets associated with DFUs, were obtained from various databases, including TCMSP, GeneCards, OMIM, PharmGKB, DrugBank, and others. STRING, Cytoscape 3.10.1, and Metascape were utilized for a series of network constructions and module analyses of common targets. Subsequently, the pivotal targets were chosen for molecular docking with the principal active constituents. A comprehensive screening of 59 active compounds and 447 targets associated with SHD was conducted, resulting in the identification of 182 intersection targets. Notably, key targets involved in this study included IL10, CCL2, IL6, IFNG, IL1B, CXCL8, IL2, CXCL10, IL1A, and TNF. It found that SHD was rich in various active ingredients that regulate the targets of DFUs. Molecular dynamics simulations indicate that β-sitosterol, stigmasterol, and 5,8,2'-trihydroxy-7-methoxyflavone bind tightly to IL2. Our study provides preliminary insights into the modulatory effects of SHD on DFUs and identifies SHD as an effective strategy for treating DFUs through multi-component and multi-target modulation of the inflammatory response. Subsequent trials will be conducted to confirm that the above findings are expected to help improve personalized treatment for patients with DFUs.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128735","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 analysis of oscillating microbubbles coated with a lipid monolayer near a flexible tissue. 柔性组织附近涂有脂质单层的振荡微泡的数值分析。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-21 DOI: 10.1080/10255842.2025.2505651

Ramyar Doustikhah, Saeed Dinarvand, Pedram Tehrani, Mohammad Eftekhari Yazdi, Gholamreza Salehi

引用次数: 0

Modified quantum dilated convolutional neural network for cancer prediction using gene expression data. 利用基因表达数据进行癌症预测的改进量子扩展卷积神经网络。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-05-20 DOI: 10.1080/10255842.2025.2502816

Magendiran N, Karthik R, Dhanalakshmi V, Sangeetha S

引用次数: 0