发布求助
文献互助 智能选刊 最新文献

Computer Methods in Biomechanics and Biomedical Engineering最新文献

筛选
英文 中文
A novel approach for enhanced early breast cancer detection. 一种增强早期乳腺癌检测的新方法。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-02 DOI: 10.1080/10255842.2025.2553347
Şeyma Aymaz, Samet Aymaz
{"title":"A novel approach for enhanced early breast cancer detection.","authors":"Şeyma Aymaz, Samet Aymaz","doi":"10.1080/10255842.2025.2553347","DOIUrl":"https://doi.org/10.1080/10255842.2025.2553347","url":null,"abstract":"<p><p>Breast cancer is a leading cause of women's mortality globally, with early diagnosis crucial for survival. This study addresses diagnostic challenges including imbalanced, noisy datasets and irrelevant features using Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Breast Cancer Database (WBCD) datasets. The proposed approach integrates Custom Adaptive Teaching-Learning-Based Optimization (TLBO) for optimal feature selection and a novel Focal Long Short-Term Memory (Focal LSTM) network to handle imbalanced data effectively. Performance evaluation using accuracy, precision, sensitivity, specificity, F-score, and AUC metrics demonstrates significant improvements. This innovative machine learning approach successfully addresses dataset limitations, contributing robust and accessible diagnostic solutions for healthcare applications.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-25"},"PeriodicalIF":1.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977275","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
Identification of lysosome-related molecular subtypes and diagnostic biomarkers in diabetic nephropathy. 糖尿病肾病中溶酶体相关分子亚型和诊断生物标志物的鉴定。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-02 DOI: 10.1080/10255842.2025.2554262
Jing Qi, Shanshan Liu, Yu Zhang, Caili Du
{"title":"Identification of lysosome-related molecular subtypes and diagnostic biomarkers in diabetic nephropathy.","authors":"Jing Qi, Shanshan Liu, Yu Zhang, Caili Du","doi":"10.1080/10255842.2025.2554262","DOIUrl":"https://doi.org/10.1080/10255842.2025.2554262","url":null,"abstract":"<p><strong>Objective: </strong>Lysosomes hold a pivotal role in the initiation and advancement of diverse diseases. Nevertheless, the specific biological functions of lysosomes in diabetic nephropathy (DN) remain undisclosed. This study seeks to uncover relevant lysosome-related molecular subtypes and biomarkers for DN through bioinformatics analysis.</p><p><strong>Methods: </strong>Four DN-related mRNA expression profiles (GSE1009, GSE30528, GSE96804, and GSE30122) were downloaded from GEO database, with GSE30122 as validation set. Meanwhile, lysosome-related genes (LRGs) were extracted from hLGDB and MSigDB. Limma and Venn analyses were utilized to screen differential expressed LRGs within DN vs. control, followed by functional enrichment analysis. Lysosomes-associated subtypes were identified by consensus clustering, and differences in immune cells between subtypes were compared. Further, WGCNA and machine learning algorithms were applied to screen key biomarkers. Diagnostic performance and expression levels of these biomarkers were evaluated in validation set. Finally, correlation between diagnostic genes and immune cells were analyzed.</p><p><strong>Result: </strong>A total of 37 LRGs were identified in DN, that were mainly involved in lysosome signaling pathways. Three lysosomes-associated subtypes with significant different immune patterns were obtained. Three machine learning algorithms identified seven overlapping genes as potential biomarkers. Further validation analyses ultimately revealed three genes showing high diagnostic value (AUC > 7), including AP3M2, CTSC, and MAN2B1. Moreover, there was a meaningful correlation between three diagnostic genes and immune cell infiltration.</p><p><strong>Conclusions: </strong>The findings of this study provide new insights for understanding the molecular mechanisms of DN and developing of accurate therapeutic targets.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977272","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
Assessment of pre- and post-operative biomechanical changes at adjacent levels induced by muscle damage following spondylolisthesis fusion: musculoskeletal modeling of open vs. minimally invasive surgery. 评估滑脱融合术后肌肉损伤引起的相邻节段术前和术后生物力学变化：开放手术与微创手术的肌肉骨骼模型
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-01 DOI: 10.1080/10255842.2025.2552432
Sajad Azizi, Mohammad Nikkhoo, Mostafa Rostami, Chi-Chien Niu, Chih-Hsiu Cheng
{"title":"Assessment of pre- and post-operative biomechanical changes at adjacent levels induced by muscle damage following spondylolisthesis fusion: musculoskeletal modeling of open <i>vs.</i> minimally invasive surgery.","authors":"Sajad Azizi, Mohammad Nikkhoo, Mostafa Rostami, Chi-Chien Niu, Chih-Hsiu Cheng","doi":"10.1080/10255842.2025.2552432","DOIUrl":"https://doi.org/10.1080/10255842.2025.2552432","url":null,"abstract":"<p><p>This study compared biomechanical impacts of conventional open surgery (COS) and minimally invasive surgery (MIS) for spondylolisthesis using a musculoskeletal model with in-vivo data from 31 patients undergoing L4-L5 fusion. Pre- and post-operative conditions with simulated muscle injury were analyzed, focusing on kinematics, muscle forces, and adjacent segment loading. Both methods altered lumbosacral parameters, but COS caused greater lumbar-pelvic rhythm reduction (60% vs. 14%), largely due to decreased trunk flexion. MIS showed increased multifidus contribution and reduced adjacent loading, though it raised upper segment passive moment and compression. Findings suggest MIS better preserves stability and favorable biomechanics than COS.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977333","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
Finite element modeling of pedicle screw fixation considering patient-specific bone density. 考虑患者特定骨密度的椎弓根螺钉固定的有限元建模。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-01 DOI: 10.1080/10255842.2025.2552437
Daniela Nguyen, Marie-Hélène Beauséjour, Carolina Solorzano Barrera, Ningxin Qiao, Isabelle Villemure, Carl-Éric Aubin
{"title":"Finite element modeling of pedicle screw fixation considering patient-specific bone density.","authors":"Daniela Nguyen, Marie-Hélène Beauséjour, Carolina Solorzano Barrera, Ningxin Qiao, Isabelle Villemure, Carl-Éric Aubin","doi":"10.1080/10255842.2025.2552437","DOIUrl":"https://doi.org/10.1080/10255842.2025.2552437","url":null,"abstract":"<p><p>Surgical instrumentation and fusion are necessary in severe cases of spinal deformity. In patients with reduced bone quality, pedicle screw fixation remains challenging due to possible loosening or pullout. The objective was to develop and validate a comprehensive finite element model of pedicle screw fixation considering patient-specific bone density. A bi-planar multi-energy X-ray derived algorithm personalized vertebral bone mechanical properties. It was tested against a reference FEM without patient-specific density (trabecular bone modeled as homogeneous), to assess biomechanical performance. Screw dimensional specifications and trajectory were parametrically modeled, and fixation performance was tested under axial pull-out loads.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-11"},"PeriodicalIF":1.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977304","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
Integrating multiple machine learning approaches with Mendelian randomization to unveil the effects of BMI-mediated matrix metalloproteinase 3 levels on hearing. 结合孟德尔随机化的多种机器学习方法揭示bmi介导的基质金属蛋白酶3水平对听力的影响。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-09-01 DOI: 10.1080/10255842.2025.2553181
Jiahui Wu, Juan Zhao, Chengyu Li, Aitong Xie, Chuyu Liang, Shuo Li, Xiao Zhu
{"title":"Integrating multiple machine learning approaches with Mendelian randomization to unveil the effects of BMI-mediated matrix metalloproteinase 3 levels on hearing.","authors":"Jiahui Wu, Juan Zhao, Chengyu Li, Aitong Xie, Chuyu Liang, Shuo Li, Xiao Zhu","doi":"10.1080/10255842.2025.2553181","DOIUrl":"10.1080/10255842.2025.2553181","url":null,"abstract":"<p><p>This study aimed to investigate the genetic mechanism of the levels of matrix metalloproteinase 3 (MMP-3)-induced hearing impairment and whether BMI mediates their causal relationship by using Mendelian randomization (MR) and machine learning. This research employed aggregated GWAS data from the IEU (Integrative Epidemiology Unit) database for MR and Bayesian Weighted Mendelian Randomization (BWMR) analysis, to uncover the causal association between MMP-3 levels and hearing, along with a potential mediating factor BMI. Inverse variance weighted (IVW), MR Egger, Weighted median, simple mode, and Weighted mode approaches were utilized, and multi-effect testing and heterogeneity testing were conducted. Finally, unsupervised and supervised machine learning were used to verify the robustness of MR Results, as long as with statistical power. In our study, it was revealed that MMP-3 levels exerted an inhibitory effect on normal hearing, without demonstrating pleiotropy or heterogeneity in the IVW analysis (<i>p</i> = 0.019, OR with 95%CI = 0.995 [0.992-0.999). Mediating analysis indicated that BMI served as the mediating factor, suggesting that the MMP-3 level might lead to hearing issues <i>via</i> BMI, and the BWMR proved to be dependable. Machine learning and statistical power results verify the robustness of Mendelian randomization. Our discoveries imply that the levels of MMP-3 exert a perilous impact on hearing <i>via</i> BMI, offering a potential route for prevention and intercession in populations at risk. This finding emphasizes the significance of regulating MMP-3 levels and BMI as tactics to diminish the likelihood of incurring hearing impairments.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977316","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
Predictive factors affecting hepatitis patients survival results via application of the machine learning methods. 应用机器学习方法研究影响肝炎患者生存结果的预测因素。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-08-31 DOI: 10.1080/10255842.2025.2546917
Xiaohua Li, Minghong Yang
{"title":"Predictive factors affecting hepatitis patients survival results via application of the machine learning methods.","authors":"Xiaohua Li, Minghong Yang","doi":"10.1080/10255842.2025.2546917","DOIUrl":"10.1080/10255842.2025.2546917","url":null,"abstract":"<p><p>Hepatitis, caused by viruses A-E, can silently progress to liver damage, cirrhosis, or cancer. Chronic B and C increase failure risk. Machine learning models help predict hepatitis risks using patient data, symptoms, and history. This study used Decision Tree Classification (DTC) and Extreme Gradient Boosting Classification (XGBC) with three optimizers Rhizotomy Optimization Algorithm (ROA), Gold Rush Optimizer (GRO), and Motion-Encoded Electric Charged Particles Optimization Algorithm (MEPO) to enhance accuracy. Among hybrids, DTRO achieved the highest accuracy (0.991), outperforming DTC. XGRO followed with 0.991, and DTME with 0.954. DTRO emerged as the most reliable model for predicting hepatitis survival.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.6,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977301","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 ACL length, strain and tensile force in high impact and daily activities through machine learning. 通过机器学习研究高冲击和日常活动中ACL长度、应变和拉伸力。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-08-30 DOI: 10.1080/10255842.2025.2551846
Elisa Roldán Ciudad, Neil D Reeves, Glen Cooper, Kirstie Andrews
{"title":"Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning.","authors":"Elisa Roldán Ciudad, Neil D Reeves, Glen Cooper, Kirstie Andrews","doi":"10.1080/10255842.2025.2551846","DOIUrl":"https://doi.org/10.1080/10255842.2025.2551846","url":null,"abstract":"<p><p>Anterior cruciate ligament (ACL) reconstruction rates are rising, particularly among female athletes, though causes remain unclear. This study: (i) identify accurate machine learning models to predict ACL length, strain, and force during six high-impact and daily activities; (ii) assess the significance of kinematic and constitutional parameters; and (iii) analyse gender-based injury risk patterns. Using 9,375 observations per variable, 42 models were trained. Cubist, Generalized Boosted Models (GBM), and Random Forest (RF) achieved the best <i>R</i><sup>2</sup>, RMSE, and MAE. Knee flexion and external rotation strongly predicted ACL strain and force. Female athletes showed higher rotation during cuts, elevating ACL strain and risk.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.6,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977251","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 and optimization of gradient porous TPMS-based cervical fusion cage: a finite element study. 基于梯度多孔tpms的颈椎融合器生物力学分析及优化：有限元研究。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-08-28 DOI: 10.1080/10255842.2025.2551844
Tianxiang Dong, Wei Jiang, Fulin Zhao
{"title":"Biomechanical analysis and optimization of gradient porous TPMS-based cervical fusion cage: a finite element study.","authors":"Tianxiang Dong, Wei Jiang, Fulin Zhao","doi":"10.1080/10255842.2025.2551844","DOIUrl":"https://doi.org/10.1080/10255842.2025.2551844","url":null,"abstract":"<p><p>Anterior Cervical Discectomy and Fusion (ACDF) often suffers from cage subsidence, compromising alignment and causing postoperative pain. To address this, we propose a gradient porous titanium alloy fusion cage based on Triply Periodic Minimal Surface (TPMS) structures and variable-density topology optimization. Using a Gyroid microstructure, the design was evaluated in an ACDF C4-C5 model. Compared with solid cages, the optimized design reduced maximum contact stress on the C5 superior surface by 25.6% and improved normal strain by up to 19.2%, demonstrating reduced subsidence risk and preserved biomechanical stability.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977330","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 three-dimensional finite element analysis of removable partial denture restoration: a comparison study of the combined impression versus the traditional open impression techniques. 可摘局部义齿修复的三维有限元分析：组合式印模与传统开放式印模的比较研究。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-08-28 DOI: 10.1080/10255842.2025.2551015
Xiangyu Cheng, Shanchun Zhang, Jingtong Wu, Liping Ren
{"title":"A three-dimensional finite element analysis of removable partial denture restoration: a comparison study of the combined impression <i>versus</i> the traditional open impression techniques.","authors":"Xiangyu Cheng, Shanchun Zhang, Jingtong Wu, Liping Ren","doi":"10.1080/10255842.2025.2551015","DOIUrl":"https://doi.org/10.1080/10255842.2025.2551015","url":null,"abstract":"<p><p>This study aimed to evaluate the mechanical behavior of a removable partial denture (RPD) using a new technique of impression-the combined impression technique (CIT)-via three-dimensional finite element analysis (FEA). A patient was selected to have removable partial dentures fabricated using CIT and the open-mouth impression technique (OIT), and the results were compared through FEA. Compared to OIT, CIT resulted in lower von Mises stresses on abutment teeth and alveolar ridges, with no significant stress concentrations and more balanced mucosal displacement in edentulous areas. CIT proved promising as it presented more favorable results than OIT in this simulation.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977344","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 novel fuzzy deep learning network for electroencephalogram classification of major depressive disorder. 一种新的模糊深度学习网络用于重度抑郁症脑电图分类。
IF 1.6 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-08-25 DOI: 10.1080/10255842.2025.2484568
Rong Hu, Tangsen Huang, Xiangdong Yin, Ensong Jiang
{"title":"A novel fuzzy deep learning network for electroencephalogram classification of major depressive disorder.","authors":"Rong Hu, Tangsen Huang, Xiangdong Yin, Ensong Jiang","doi":"10.1080/10255842.2025.2484568","DOIUrl":"https://doi.org/10.1080/10255842.2025.2484568","url":null,"abstract":"<p><p>This study introduces the EEG-FDL model, a novel optimized fuzzy deep learning approach for classifying Major Depressive Disorder (MDD) using EEG data. Integrating deep learning with fuzzy learning via the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), EEG-FDL optimizes fuzzy membership functions and backpropagation. The model handles noise and data uncertainty, achieving a remarkable 99.72% accuracy in distinguishing MDD from healthy EEG signals using 5-fold cross-validation on a large dataset. External validation further confirms its efficacy. EEG-FDL outperforms traditional classifiers due to its effective handling of uncertainties and optimized parameter tuning.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977284","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信