Biomedical Signal Processing and Control最新文献

{"title":"ECM-TransUNet: Edge-enhanced multi-scale attention and convolutional Mamba for medical image segmentation","authors":"Chunjie Lv ,&nbsp;Biyuan Li ,&nbsp;Xiuwei Wang ,&nbsp;Pengfei Cai ,&nbsp;Bo Yang ,&nbsp;Gaowei Sun ,&nbsp;Jun Yan","doi":"10.1016/j.bspc.2025.107845","DOIUrl":"10.1016/j.bspc.2025.107845","url":null,"abstract":"<div><div>The segmentation of CT and MRI images faces challenges such as detail loss and the inability to ensure consistency in physiological tissue representation. To address these issues, we propose a Edge-enhanced multi-scale attention and Convolutional Mamba Transformer UNet (ECM-TransUNet). ECM-TransUNet integrates the ECM-Block into the skip connections, incorporating the Edge-Enhanced Multi-Scale Transposed Attention (E-MTA) and the Multi-Scale Convolutional State-Space Module (MS-CSM) to improve feature extraction and spatial consistency modeling. Specifically, E-MTA enhances sensitivity to subtle grayscale variations, enabling accurate modeling of both local and global structural details in complex regions. Unlike traditional attention mechanisms, E-MTA integrates multi-scale depthwise convolutions to strengthen local feature representation, while the Sobel edge detection module further refines the extraction of critical edges and local detail features. MS-CSM combines state-space modeling with multi-scale feature extraction to improve the accuracy of local detail representation and global feature integration, while significantly reducing computational complexity. Compared to traditional convolution-based methods and earlier state-space models, it demonstrates superior performance and efficiency. Additionally, to achieve end-to-end feature balance within skip connections, we introduce the Cross-Region Multi-Scale Attention (CR-MSA) mechanism into the Transformer-based encoder architecture. CR-MSA effectively harmonizes multi-scale and spatial feature fusion, establishes cross-regional feature relationships, and enhances the model’s ability to capture both local and global information, thereby further improving segmentation accuracy and stability. Our method effectively addresses the limitations of existing medical image segmentation techniques. Experimental results on large-scale annotated CT and MRI datasets demonstrate that our approach achieves an optimal balance between segmentation accuracy and computational efficiency. Specifically, on the Synapse dataset, ECM-TransUNet achieved a DSC of 84.68 %, with a computational cost of 50.68G FLOPs and a parameter count of 66.47 M. These findings underscore the reliability and efficiency of our method, offering a robust solution for complex medical image segmentation tasks. is available at: https://github.com/lvchunjie/ECM-TransUNet.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107845"},"PeriodicalIF":4.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the pneumonia from the CT lung images by using the multiband google NET CNN","authors":"J. Senthil Kumar ,&nbsp;R. Pradeepa ,&nbsp;Dr. Arulkarthick ,&nbsp;S. Chandragandhi","doi":"10.1016/j.bspc.2025.107738","DOIUrl":"10.1016/j.bspc.2025.107738","url":null,"abstract":"<div><div>Pneumonia is a severe infectious illness which has affected significant bereavement worldwide. This has been prevalent with people who have weak immune systems. The most efficient and sought out method to identify this via imaging is Computed Tomography Scans (CT scans). A disease like Pneumonia can be cured only when treated at the right time. This research involves a simple, innovative and effective methodology to detect pneumonia in individuals with the support of Deep Learning methodologies. With the use of image segmentation, 3D modelling and annotation, we aim at identifying this disease in human lungs. The data used here is obtained from RIDER Lung CT collection. This image data is put under sectioning and pre-processing. The mentioned techniques are done via Laplacian Partial Differential Equation-Based Histogram Equalization and a Weighted Iterative Median Filter. The required features are extracted through recursive isomapping and non-linear component analysis. By using uplift-weighted fuzzy method, the abnormal areas are segmented later. These segmented areas are converted into 3D models for better visualization using the Canny Inductive Frustum model. Finally, abnormalities are classified using the Multiband Google NET CNN classifier. This proposed method shows improved results and offers a generalized approach that can be applied to other similar datasets as well.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107738"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weighted sparsity regularization for solving the inverse EEG problem: A case study","authors":"Ole Løseth Elvetun,&nbsp;Niranjana Sudheer","doi":"10.1016/j.bspc.2025.107673","DOIUrl":"10.1016/j.bspc.2025.107673","url":null,"abstract":"<div><div>We study the potential of detecting brain activity in terms of dipoles using weighted sparsity regularization. The work is based on theoretical results that we have proved in previous papers, but it requires modifications to fit into the classical EEG framework. In particular, to represent any dipole at a given position, we only need three basis dipoles with independent directions, but we will demonstrate that it might be beneficial to use more than three dipoles, i.e., a redundant basis/frame. This approach will, in fact, be more in line with the theoretical assumptions needed to guarantee the recovery of a single dipole. We demonstrate through several different experiments that the method does not suffer from the so-called depth bias, and we use standard measures to judge the ability of the method to recover one or two dipole sources. The results show that we do indeed find sparse solutions with relatively small dipole localization errors.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107673"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical study on the application of a high-sensitivity electronic nose on thin-film gas sensor array technology combined with deep learning algorithm for early non-invasive diagnosis of chronic atrophic gastritis","authors":"Mengting Zhang ,&nbsp;Long Zhu ,&nbsp;Jiezhou He ,&nbsp;Yufei Liu ,&nbsp;Shanshan Ding ,&nbsp;Xuejuan Lin","doi":"10.1016/j.bspc.2025.107851","DOIUrl":"10.1016/j.bspc.2025.107851","url":null,"abstract":"<div><div>Chronic atrophic gastritis (CAG) is a common digestive disorder often diagnosed late due to its nonspecific symptoms. Our team developed a high-sensitivity electronic nose (HSe-nose) using thin-film gas sensor array technology for early, non-invasive CAG diagnosis by detecting breath odor changes. It directly analyzes original breath samples, unlike traditional ones. With ppb level sensitivity, it generates odor fingerprints, enhancing classification. It’s user-friendly, non-invasive, and can replace gastroscopy and biopsy, with up to 0.1 ppm sensitivity.</div><div>The research involved 596 participants from two hospitals, and after applying exclusion criteria, 522 samples were analyzed. Machine learning and pattern recognition methods were used, with the Random Forest algorithm and SMOTE showing the highest classification accuracy, distinguishing CAG patients from healthy controls with an accuracy of 0.9682.</div><div>Further analysis with deep learning algorithms revealed significant differences in exhaled odor profiles between CAG and chronic non-atrophic gastritis (CNAG) patients, and between CAG and CAG with intestinal metaplasia (CAG-IM) patients, with accuracies of 85.57 % and 93.75 % respectively. Specific volatile organic compounds (VOCs) such as H₂S, triethylamine, methane, and formic acid were identified as potential CAG markers, while benzene, toluene, xylene, ethylacetate, and isopropanol were found in CAG-IM cases.</div><div>The study concludes that the electronic nose is a promising tool for the early and non-invasive diagnosis of CAG, providing a cost-effective, rapid method. The identified VOCs could shed light on the pathophysiology of CAG and its progression to gastric cancer.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107851"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MUCM-FLLs: Multimodal ultrasound-based classification model for focal liver lesions","authors":"Tong Li ,&nbsp;Jiali Guo ,&nbsp;Wenjing Tao ,&nbsp;Rui Bu ,&nbsp;Tao Feng","doi":"10.1016/j.bspc.2025.107864","DOIUrl":"10.1016/j.bspc.2025.107864","url":null,"abstract":"<div><div>Timely detection and accurate classification of focal liver lesions (FLLs) are crucial for improving patient survival rates and providing optimal treatment strategies. This study proposes a multimodal ultrasound-based classification model (MUCM-FLLs) to assist clinicians in efficiently leveraging multimodal ultrasound data for FLL diagnosis. We utilized data from 359 patients with histopathologically confirmed FLLs to develop a model that integrates lesion B-mode ultrasound images, background liver ultrasound images, color Doppler flow imaging, and clinical data. Incremental modality experiments were conducted, demonstrating average classification accuracies of 55.0%, 54.2%, 61.8%, and 83.7% for single-mode to four-mode configurations. These results highlight the effectiveness of combining multiple modalities and reveal differing sensitivities of various diseases to specific modalities. Cross-validation further validated the model’s robustness and generalizability, confirming the advantages of multimodal diagnosis. During training, we introduced a gradient adjustment strategy with a learning score metric to address learning rate disparities among modalities under multimodal data training. This strategy effectively mitigated imbalances in modality optimization, ensuring that each modality received adequate training. Additionally, we quantitatively analyzed the contributions of different modalities to the diagnosis of various diseases and calculated inter-modality weights, significantly improving the model’s predictive accuracy. Supported by these strategies, MUCM-FLLs achieved an overall accuracy of 92.2%. This study highlights the potential of multimodal fusion and optimization strategies to enhance the diagnostic performance of FLLs and provides significant technical support for clinical diagnosis.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107864"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DEO-Fusion: Differential evolution optimization for fusion of CNN models in eye disease detection","authors":"Sohaib Asif","doi":"10.1016/j.bspc.2025.107853","DOIUrl":"10.1016/j.bspc.2025.107853","url":null,"abstract":"<div><div>Eye diseases pose a significant health concern globally, emphasizing the need for accurate and efficient diagnostic methods. The manual recognition of eye disorders is both time-consuming and challenging. Deep learning (DL) techniques have demonstrated their effectiveness in the analysis of medical images, underscoring their capability to improve the identification and categorization of eye-related conditions. This study introduces DEO-Fusion, a pioneering approach aimed at enhancing the accuracy of eye disease detection through a Weighted Averaging Ensemble (WEAE) technique. In contrast to previous research focusing on individual models, our work delves into the largely unexplored potential of ensemble learning. Initially, Transfer Learning (TL) is employed with four base models, bolstering their image representation capabilities via additional layers. The WEAE scheme combines their outputs, and novel weight allocation is achieved through an Evolutionary Algorithm-based Differential Evolution Optimization (DEO) approach. In contrast to the commonly employed experimental weight assignments in the literature, DEO optimally allocates weights to each model, leading to a substantial improvement in performance. The comparison with other optimization algorithms was also conducted to evaluate the performance and effectiveness of the DEO algorithm in weight optimization for ensemble model, providing a comprehensive assessment of its capabilities in the context of eye disease detection. The proposed approach underwent evaluation using two publicly available datasets—one comprising digital camera images with cataract and normal classes, and the other containing fundus images with four classes (cataract, glaucoma, diabetic retinopathy, and normal). The method attained impressive accuracy rates of 98.34 % and 94.92 % on the digital camera images dataset and retinal fundus images datasets, respectively. These results underscore the superior performance of DEO-Fusion compared to existing methods and widely employed ensemble techniques. Grad-CAM analyses were conducted to elucidate infected areas in the eye, providing clinicians with valuable insights for prompt and accurate diagnoses of eye diseases.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107853"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposition of high-density sEMG signals: Extracting multiple spikes from single time windows","authors":"Xi Jiang ,&nbsp;Weiyu Guo ,&nbsp;Ziwei Cui ,&nbsp;Chuang Lin ,&nbsp;Jingyong Su","doi":"10.1016/j.bspc.2025.107771","DOIUrl":"10.1016/j.bspc.2025.107771","url":null,"abstract":"<div><div>Decomposing high-density surface electromyography (HD-sEMG) signals has become a powerful tool in various applications, including prosthetic control and human–machine interaction (HMI). Using deep learning methods to decompose HD-sEMG signals can eliminate preprocessing such as whitening of sEMG, thereby reducing its latency in HMI applications. However, current deep learning methods for blind separation mainly use window-based classification methods, which cannot accurately decompose spike-dense areas. In this paper, we rethink from the perspective of sequence to sequence (seq2seq), define the surface electromyography signal decomposition problem as a regression problem, and propose an HD-sEMG signal decoding method CKC-TCN. This is the first time that the problem of extracting multiple spikes from single time windows has been solved. Rather than class labels, we treat the innervation pulse trains (IPTs) of each motor unit (MU) that are derived from the convolution kernel compensation (CKC) algorithm as continuous time series. We train the temporal convolutional network (TCN) to extract sample-level accuracy IPTs of each MU from the unprocessed HD-sEMG signals, and then extract MU firing time sequences. To evaluate the effectiveness of the proposed method, we conduct experiments on both simulated and real data of HD-sEMG. The results show that, CKC-TCN reduces inference time by 99% compared to traditional methods and improves separation accuracy by over 10% compared to the state-of-the-art deep learning solutions, achieving a significant performance enhancement. This makes it more suitable for real-time applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107771"},"PeriodicalIF":4.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phonocardiography for robust fetal heart rate estimation in real clinical conditions","authors":"Mariel Reséndiz Rojas ,&nbsp;Bertrand Rivet ,&nbsp;Nefeli M.P. Kavouni ,&nbsp;Julie Fontecave-Jallon","doi":"10.1016/j.bspc.2025.107807","DOIUrl":"10.1016/j.bspc.2025.107807","url":null,"abstract":"<div><div>Fetal heart rate (FHR) analysis is the standard technique for monitoring fetal well-being. Since the non-invasive clinical reference technique for measuring FHR, known as cardiotocography (CTG), suffers from several drawbacks, alternative technologies are now attracting interest. Among them, phonocardiography (PCG) has shown interesting potential. In this study, an original solution is proposed, using a single abdominal PCG (aPCG) to address both the feasibility in clinical routine and the challenge of the difficult detection of temporal events in signals recorded in real clinical conditions. Based on the combination of Non-negative Matrix Factorization (NMF) and Hidden Markov Model (HMM) approaches, the proposed solution exploits the semi-periodicity of the fetal PCG and the temporal continuity a priori of cardiac rhythm to estimate the FHR. Including a pre-processing step of Template subtraction (TS) on aPCG to attenuate maternal component, the proposed algorithm TS+NMF+HMM is evaluated on a database of 38 recordings of pregnant women (total duration of nearly 20 h) and compared to several methods from literature. This new methodological proposition allows a good agreement between estimated FHR and the reference values from CTG (median value of ratio of good agreement at nearly 80% for the whole database) and underlines the interest of using abdominal PCG for fetal well-being monitoring.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107807"},"PeriodicalIF":4.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing multiple sclerosis diagnosis and prognosis through a dual Patch-Wise CNN architecture","authors":"HM Rehan Afzal ,&nbsp;Siyao Li ,&nbsp;Yanru Feng ,&nbsp;Muhammad Kamran Afzal ,&nbsp;Pengfei Yang","doi":"10.1016/j.bspc.2025.107835","DOIUrl":"10.1016/j.bspc.2025.107835","url":null,"abstract":"<div><div>Multiple sclerosis (MS) is a complex neurological disorder that requires accurate early prediction and prognosis for timely intervention, which can be facilitated through the detection and tracking of lesion development from medical images. In medical image processing, traditional manual segmentation methods can be very time-consuming. To overcome these limitations, the present study introduces a novel dual 2D patch-wise parallel convolutional neural network (CNN) model designed to improve both lesion segmentation accuracy and the prediction of MS. In detail, the first CNN is dedicated to accurately segmenting lesions from MRI scans, while the second CNN reduces false positives, thereby increasing overall efficiency. Moreover, by integrating T1-w, T2-w, and FLAIR MRI sequences, the model achieves enhanced accuracy, adapting to variations across different MRI scanners. Following lesion identification, another CNN model to predict MS disease is developed, specifically tailored to MRI, and achieves an overall accuracy of 91% in the prediction of MS disease. The high accuracy, along with a precision of 87%, recall of 77%, and an F1 score of 80.5%, demonstrates the effectiveness of the proposed model as a robust tool for early diagnosis and prognosis in MS. The present dual CNN approach not only improves lesion segmentation but also provides clinicians with valuable insights into disease trajectory, offering a new dimension of predictive analysis for MS management.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107835"},"PeriodicalIF":4.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic detection of Alzheimer’s disease from EEG signals using hybrid PSO-GWO algorithm","authors":"Ruofan Wang ,&nbsp;Haojie Xu ,&nbsp;Deri Yi ,&nbsp;Changzhi Song ,&nbsp;Yanqiu Che","doi":"10.1016/j.bspc.2025.107798","DOIUrl":"10.1016/j.bspc.2025.107798","url":null,"abstract":"<div><div>Early diagnosis of Alzheimer’s disease (AD) is vital. EEG is effective; however, its multi-channel property leads to redundancy and affects classification performance. Current studies frequently neglect the synergy of multi-feature extraction and Intelligent optimisation algorithm for overall performance in EEG channel screening.</div><div>This study innovatively combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to develop a PSO-GWO hybrid model that aims to overcome the limitations of PSO, in particular its tendency to converge to local optima. The model significantly improves the performance of multi-channel EEG signal screening for AD. First, geometric features are extracted from AD EEG signals using a second order difference plot (SODP), resulting in two sets of uncorrelated features. Key features SAV, SCC and CTM are selected using XGBoost feature importance ranking and statistical analysis. The Relief algorithm then merges these key features into a fused vector for each channelNext, the PSO-GWO method is used to determine the optimal channel combination (Fp1, T3, T5, P3, and O2), which is input into the XGBoost classifier. 5-fold Cross-validation and LOSO validation accuracy of 96.35% and 91.08%, respectively, are achieved between patients and the normal control group. Finally, SHAP analysis highlights the positive contributions of the selected channels, confirming the effectiveness of the framework in accelerating channel selection and improving AD detection efficiency.</div><div>This study fills the void of collaborative optimisation of multi-features and intelligent algorithms in EEG channel screening, provides an efficient framework for AD detection, and enhances the understanding of neurological disease mechanisms.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107798"},"PeriodicalIF":4.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}