Computer Methods in Biomechanics and Biomedical Engineering最新文献_第9页

DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification. 基于黎曼流形SPD矩阵的脑电分类判别几何感知降维。

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

引用次数: 0

Transcriptome-based insights into the role of cancer-associated fibroblasts in lung adenocarcinoma prognosis and therapy. 基于转录组的癌症相关成纤维细胞在肺腺癌预后和治疗中的作用。

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":"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.","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. 定制固定钢板治疗中三分之一锁骨骨折的数值设计与分析。

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

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. 单细胞和大体积组织转录组的综合免疫学分析揭示了T细胞相关基因在宫颈癌预后中的作用。

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":"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; p = .017), while C1Q8 acted as a protective factor (OR = 0.820; 95% CI: 0.685-0.983; p = .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.","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. 探讨特征提取方法对心脏骤停后昏迷患者神经恢复水平预测准确性的影响。

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

引用次数: 0

Enhancing cardiovascular risk prediction: the role of wall viscoelasticity in machine learning models. 增强心血管风险预测：壁粘弹性在机器学习模型中的作用。

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":"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.","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. 基于深度学习的急性肾损伤住院死亡率预测

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

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. 三周期最小表面多孔牙种植体与固体牙种植体的生物力学分析：种植体周围骨密度对微动的影响。

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

引用次数: 0

An Adaptive Dendritic Neural Model for Lung Cancer Prediction. 肺癌预测的自适应树突神经模型。

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

引用次数: 0

Treatment prediction with machine learning in prostate cancer patients. 利用机器学习对前列腺癌患者进行治疗预测。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-03-01 Epub Date: 2023-12-26 DOI: 10.1080/10255842.2023.2298364

Emre Alataş, Handan Tanyıldızı Kökkülünk, Hilal Tanyıldızı, Goksel Alcın

{"title":"Treatment prediction with machine learning in prostate cancer patients.","authors":"Emre Alataş, Handan Tanyıldızı Kökkülünk, Hilal Tanyıldızı, Goksel Alcın","doi":"10.1080/10255842.2023.2298364","DOIUrl":"10.1080/10255842.2023.2298364","url":null,"abstract":"There are various treatment modalities for prostate cancer, which has a high incidence. In this study, it is aimed to make predictions with machine learning in order to determine the optimal treatment option for prostate cancer patients. The study included 88 male patients diagnosed with prostate cancer. Independent variables were determined as Gleason scores, biopsy, PSA, SUVmax, and age. Prostate cancer treatments, which are dependent variables, were determined as hormone therapy(n = 30), radiotherapy(n = 28) and radiotherapy + hormone therapy(n = 30). Machine learning was carried out in the Python with SVM, RF, DT, ETC and XGBoost. Metrics such as accuracy, ROC curve, and AUC were used to evaluate the performance of multi-class predictions. The model with the highest number of successful predictions was the XGBoost. False negative rates for hormone therapy, radiotherapy, and radiotherapy + hormone therapy treatments were, respectively, 12.5, 33.3, and 0%. The accuracy values were computed as 0.61, 0.83, 0.83, 0.72 and 0.89 for SVM, RF, DT, ETC and XGBoost, respectively. The three features that had the greatest influence on the treatment model prediction for prostate cancer with XGBoost were biopsy, Gleason score (3 + 3), and PSA level, respectively. According to the AUC, ROC and accuracy, it was determined that the XGBoost was the model that made the best estimation of prostate cancer treatment. Among the variables biopsy, Gleason score, and PSA level are identified as key variables in prediction of treatment.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"572-580"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139040856","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