Medical & Biological Engineering & Computing最新文献_第5页

Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning. 基于深度学习语义分割集成模型的心胸比自动测量。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2024-12-21 DOI: 10.1007/s11517-024-03263-0

Jiajun Feng, Yuqian Huang, Zhenbin Hu, Junjie Guo

{"title":"Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning.","authors":"Jiajun Feng, Yuqian Huang, Zhenbin Hu, Junjie Guo","doi":"10.1007/s11517-024-03263-0","DOIUrl":"10.1007/s11517-024-03263-0","url":null,"abstract":"The objective of this study is to investigate the efficacy of the semantic segmentation model in predicting cardiothoracic ratio (CTR) and heart enlargement and compare its consistency with the reference standard. A total of 650 consecutive chest radiographs from our center and 756 public datasets were retrospectively included to develop a segmentation model. Three semantic segmentation models were used to segment the heart and lungs. A soft voting integration method was used to improve the segmentation accuracy and measure CTR automatically. Bland-Altman and Pearson's correlation analyses were used to compare the consistency and correlation between CTR automated measurements and reference standards. CTR automated measurements were compared with reference standard using the Wilcoxon signed-rank test. The diagnostic efficacy of the model for heart enlargement was evaluated using the AUC. The soft voting integration model was strongly correlated (r = 0.98, P < 0.001) and consistent (average standard deviation of 0.0048 cm/s) with the reference standard. No statistical difference between CTR automated measurement and reference standard in healthy subjects, pneumothorax, pleural effusion, and lung mass patients (P > 0.05). In the external test data, the accuracy, sensitivity, specificity, and AUC in determining heart enlargement were 96.0%, 79.5%, 99.1%, and 0.988, respectively. The deep learning method was calculated faster per chest radiograph than the average time manually calculated by the radiologist (about 2 s vs 25.75 ± 4.35 s, respectively, P < 0.001). This study provides a semantic segmentation integration model of chest radiographs to measure CTR and determine heart enlargement with chest structure changes due to different chest diseases effectively, faster, and accurately. The development of the automated segmentation integration model is helpful in improving the consistency of CTR measurement, reducing the workload of radiologists, and improving their work efficiency.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1343-1353"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872615","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

Toward efficient slide-level grading of liver biopsy via explainable deep learning framework. 通过可解释的深度学习框架实现肝脏活检的高效分级。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-13 DOI: 10.1007/s11517-024-03266-x

Bingchen Li, Qiming He, Jing Chang, Bo Yang, Xi Tang, Yonghong He, Tian Guan, Guangde Zhou

{"title":"Toward efficient slide-level grading of liver biopsy via explainable deep learning framework.","authors":"Bingchen Li, Qiming He, Jing Chang, Bo Yang, Xi Tang, Yonghong He, Tian Guan, Guangde Zhou","doi":"10.1007/s11517-024-03266-x","DOIUrl":"10.1007/s11517-024-03266-x","url":null,"abstract":"In the context of chronic liver diseases, where variability in progression necessitates early and precise diagnosis, this study addresses the limitations of traditional histological analysis and the shortcomings of existing deep learning approaches. A novel patch-level classification model employing multi-scale feature extraction and fusion was developed to enhance the grading accuracy and interpretability of liver biopsies, analyzing 1322 cases across various staining methods. The study also introduces a slide-level aggregation framework, comparing different diagnostic models, to efficiently integrate local histological information. Results from extensive validation show that the slide-level model consistently achieved high F1 scores, notably 0.9 for inflammatory activity and steatosis, and demonstrated rapid diagnostic capabilities with less than one minute per slide on average. The patch-level model also performed well, with an F1 score of 0.64 for ballooning and 0.99 for other indicators, and proved transferable to public datasets. The conclusion drawn is that the proposed analytical framework offers a reliable basis for the diagnosis and treatment of chronic liver diseases, with the added benefit of robust interpretability, suggesting its practical utility in clinical settings.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1435-1449"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980407","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

Automatic positioning of cutting planes for bone tumor resection surgery. 骨肿瘤切除手术切面的自动定位。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-17 DOI: 10.1007/s11517-024-03281-y

Alessio Romanelli, Michaela Servi, Francesco Buonamici, Yary Volpe

{"title":"Automatic positioning of cutting planes for bone tumor resection surgery.","authors":"Alessio Romanelli, Michaela Servi, Francesco Buonamici, Yary Volpe","doi":"10.1007/s11517-024-03281-y","DOIUrl":"10.1007/s11517-024-03281-y","url":null,"abstract":"In bone tumor resection surgery, patient-specific cutting guides aid the surgeon in the resection of a precise part of the bone. Despite the use of automation methodologies in surgical guide modeling, to date, the placement of cutting planes is a manual task. This work presents an algorithm for the automatic positioning of cutting planes to reduce healthy bone resected and thus improve post-operative outcomes. The algorithm uses particle swarm optimization to search for the optimal positioning of points defining a cutting surface composed of planes parallel to a surgical approach direction. The quality of a cutting surface is evaluated by an objective function that considers two key variables: the volumes of healthy bone resected and tumor removed. The algorithm was tested on three tumor cases in long bone epiphyses (two tibial, one humeral) with varying plane numbers. Optimal optimization parameters were determined, with varying parameters through iterations providing lower mean and standard deviation of the objective function. Initializing particle swarm optimization with a plausible cutting surface configuration further improved stability and minimized healthy bone resection. Future work is required to reach 3D optimization of the planes positioning, further improving the solution.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1521-1534"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control. MVMD-MSI算法在基于ssvep的脑机接口频率识别中的性能研究及其在机械臂控制中的应用

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2024-12-27 DOI: 10.1007/s11517-024-03236-3

Rongrong Fu, Shaoxiong Niu, Xiaolei Feng, Ye Shi, Chengcheng Jia, Jing Zhao, Guilin Wen

{"title":"Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.","authors":"Rongrong Fu, Shaoxiong Niu, Xiaolei Feng, Ye Shi, Chengcheng Jia, Jing Zhao, Guilin Wen","doi":"10.1007/s11517-024-03236-3","DOIUrl":"10.1007/s11517-024-03236-3","url":null,"abstract":"This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1367-1381"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899973","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

RGVPSeg: multimodal information fusion network for retinogeniculate visual pathway segmentation. RGVPSeg：基于多模态信息融合网络的视网膜回状视觉通路分割。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-02 DOI: 10.1007/s11517-024-03248-z

Qingrun Zeng, Lin Yang, Yongqiang Li, Lei Xie, Yuanjing Feng

{"title":"RGVPSeg: multimodal information fusion network for retinogeniculate visual pathway segmentation.","authors":"Qingrun Zeng, Lin Yang, Yongqiang Li, Lei Xie, Yuanjing Feng","doi":"10.1007/s11517-024-03248-z","DOIUrl":"10.1007/s11517-024-03248-z","url":null,"abstract":"The segmentation of the retinogeniculate visual pathway (RGVP) enables quantitative analysis of its anatomical structure. Multimodal learning has exhibited considerable potential in segmenting the RGVP based on structural MRI (sMRI) and diffusion MRI (dMRI). However, the intricate nature of the skull base environment and the slender morphology of the RGVP pose challenges for existing methodologies to adequately leverage the complementary information from each modality. In this study, we propose a multimodal information fusion network designed to optimize and select the complementary information across multiple modalities: the T1-weighted (T1w) images, the fractional anisotropy (FA) images, and the fiber orientation distribution function (fODF) peaks, and the modalities can supervise each other during the process. Specifically, we add a supervised master-assistant cross-modal learning framework between the encoder layers of different modalities so that the characteristics of different modalities can be more fully utilized to achieve a more accurate segmentation result. We apply RGVPSeg to an MRI dataset with 102 subjects from the Human Connectome Project (HCP) and 10 subjects from Multi-shell Diffusion MRI (MDM), the experimental results show promising results, which demonstrate that the proposed framework is feasible and outperforms the methods mentioned in this paper. Our code is freely available at https://github.com/yanglin9911/RGVPSeg .","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1397-1411"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142915995","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

Interpretable machine learning models for COPD ease of breathing estimation. COPD呼吸便利度评估的可解释机器学习模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-14 DOI: 10.1007/s11517-025-03285-2

Thomas T Kok, John Morales, Dirk Deschrijver, Dolores Blanco-Almazán, Willemijn Groenendaal, David Ruttens, Christophe Smeets, Vojkan Mihajlović, Femke Ongenae, Sofie Van Hoecke

{"title":"Interpretable machine learning models for COPD ease of breathing estimation.","authors":"Thomas T Kok, John Morales, Dirk Deschrijver, Dolores Blanco-Almazán, Willemijn Groenendaal, David Ruttens, Christophe Smeets, Vojkan Mihajlović, Femke Ongenae, Sofie Van Hoecke","doi":"10.1007/s11517-025-03285-2","DOIUrl":"10.1007/s11517-025-03285-2","url":null,"abstract":"Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of research. We introduce a novel method for estimating changes in ease of breathing for COPD patients, using obstructed breathing data collected via wearables. Physiological signals were recorded, including respiratory airflow, acceleration, audio, and bio-impedance. By comparing patient-specific measurements, this approach enables non-intrusive remote monitoring. We analyze the influence of signal selection, window parameters, feature engineering, and classification models on predictive performance, finding that acceleration signals are most effective, complemented by audio signals. The best model achieves an F1-score of 0.83. To facilitate clinical adoption, we incorporate interpretability by designing novel saliency map methods, highlighting important aspects of the signals. We adapt local explainability techniques to time series and introduce a novel imputation method for periodic signals, improving faithfulness to the data and interpretability.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1481-1495"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980490","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

Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic imaging. 系统回顾心电图成像中的计算技术、数据集利用和特征提取。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-09 DOI: 10.1007/s11517-024-03264-z

Dagoberto Mayorca-Torres, Alejandro J León-Salas, Diego H Peluffo-Ordoñez

引用次数: 0

Foot tissue stress in chronic ankle instability during the stance phase of cutting. 脚部组织应力在慢性踝关节不稳定的立场阶段切割。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-15 DOI: 10.1007/s11517-024-03276-9

Peimin Yu, Xuanzhen Cen, Liangliang Xiang, Alan Wang, Yaodong Gu, Justin Fernandez

{"title":"Foot tissue stress in chronic ankle instability during the stance phase of cutting.","authors":"Peimin Yu, Xuanzhen Cen, Liangliang Xiang, Alan Wang, Yaodong Gu, Justin Fernandez","doi":"10.1007/s11517-024-03276-9","DOIUrl":"10.1007/s11517-024-03276-9","url":null,"abstract":"Lower limb biomechanics of chronic ankle instability (CAI) individuals has been widely investigated, but few have evaluated the internal foot mechanics in CAI. This study evaluated bone and soft tissue stress in CAI contrasted with copers and non-injured participants during a cutting task. Integrating scanned 3D foot shapes and free-form deformation, sixty-six personalized finite element foot models were developed. Computed Achilles tendon forces and measured regional plantar pressure were applied as boundary loading conditions for simulation. It was observed that the primary group differences in foot stress occurred during midstance and heel-off phases of the cutting task. Specifically, healthy individuals had significantly higher stress in the talus and soft tissue around the talus compared to CAI participants. In contrast, CAI participants had significantly higher stress in the cuneiforms and lateral forefoot bones during mid-stance and push-off phases. CAI participants appeared to adopt a protective strategy by transferring greater force to the lateral forefoot at the heel-off phase while lowering stress around the talus, which may be associated with pain relief near the ankle. These findings suggest further attention should be placed on internal stress in CAI at the push-off phase with implications for long-term foot adaptation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1507-1519"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment. 改进的深度典型相关融合方法用于早期轻度认知障碍的检测。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-14 DOI: 10.1007/s11517-024-03282-x

Sreelakshmi Shaji, Rohini Palanisamy, Ramakrishnan Swaminathan

{"title":"Improved deep canonical correlation fusion approach for detection of early mild cognitive impairment.","authors":"Sreelakshmi Shaji, Rohini Palanisamy, Ramakrishnan Swaminathan","doi":"10.1007/s11517-024-03282-x","DOIUrl":"10.1007/s11517-024-03282-x","url":null,"abstract":"Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition. For this, obtained structural MR images of healthy controls and EMCI subjects are preprocessed. Lateral ventricles and corpus callosum structures are segmented from these images and features are extracted. Extracted features from different brain structures are fused using non-linear orthogonal iteration-based deep CCA. Fused features are employed to differentiate healthy controls and EMCI condition using extreme learning machine classifier. Results indicate that fused callosal and ventricular features are able to detect EMCI. Improved deep CCA algorithm with tuned hyperparameters achieves the highest classifier performance with an F-score of 82.15%. The proposed framework is compared with state-of-the-art CCA approaches, and the results demonstrate its improved performance in EMCI detection. This highlights the potential of the proposed framework in the automated diagnosis of preclinical MCI conditions.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1451-1461"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980487","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 in 3D cardiac reconstruction: a systematic review of methodologies and dataset. 三维心脏重建中的深度学习：方法和数据集的系统回顾。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-05-01 Epub Date: 2025-01-04 DOI: 10.1007/s11517-024-03273-y

Rajendra Kumar Pandey, Yogesh Kumar Rathore

{"title":"Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.","authors":"Rajendra Kumar Pandey, Yogesh Kumar Rathore","doi":"10.1007/s11517-024-03273-y","DOIUrl":"10.1007/s11517-024-03273-y","url":null,"abstract":"This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs. The statistical shape models were utilized to capture anatomical variations through principal component analysis (PCA), while GCNs refined the meshes derived from segmented slices. Synthetic data generated by progressive GANs enabled augmentation, particularly useful for congenital heart conditions. Evaluation of the reconstruction accuracy was performed using metrics such as Dice similarity coefficient (DSC), Chamfer distance, and Hausdorff distance, with the proposed framework demonstrating superior anatomical precision and functional relevance compared to traditional methods. This approach highlights the potential for automated, high-resolution 3D heart reconstruction applicable in both clinical and research settings. The results emphasize the critical role of deep learning in enhancing anatomical accuracy, particularly for rare and complex cardiac conditions. This paper is particularly important for researchers wanting to utilize deep learning in cardiac imaging and 3D heart reconstruction, bringing insights into the integration of modern computational methods.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"1271-1287"},"PeriodicalIF":2.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928351","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