Biomedical Signal Processing and Control最新文献

{"title":"A comparative study of time–frequency features based spatio-temporal analysis with varying multiscale kernels for emotion recognition from EEG","authors":"Md Raihan Khan,&nbsp;Airin Akter Tania,&nbsp;Mohiuddin Ahmad","doi":"10.1016/j.bspc.2025.107826","DOIUrl":"10.1016/j.bspc.2025.107826","url":null,"abstract":"<div><div>EEG-based emotion recognition has become recognized as a crucial field of study, utilizing brainwave patterns for understanding human emotional states. To identify emotions from EEG data, this study examines several time–frequency features utilizing spatiotemporal analysis using the DEAP dataset. Down sampling, bandpass filtering, segmentation, trimming, labeling, and common reference averaging are all part of the data pre-processing pipeline. Continuous Wavelet Transform (CWT) with Morlet wavelets was used to extract features, followed by the computation of differential entropy, wavelet energy, cross-correlation, and phase locking value (PLV). The distribution of features, participant-specific changes, and associations were examined through an exploratory feature analysis. Subsequently, final representations functioning in a spatiotemporal manner were constructed. For classification, a 3D Convolutional Neural Network with different kernel sizes (3×3×3, 5×5×5, and 7×7×7) was employed. Training accuracies reached up to 98.86% for arousal and 98.97% for valence, demonstrating robust generalization of particular feature-kernel combinations. The results also show that the 5×5×5 kernel size achieved the highest test accuracy for arousal (96.13% for differential entropy) and valence (96.19% for wavelet energy). In order to show the model’s universality, it was also validated using the popular SEED dataset. With PLV (kernel 7×7×7), the arousal validation accuracy was 97.42%, and with WE (kernel 3×3×3), the valence validation accuracy was 97.50%. This work sheds light on the choice of features and kernels for emotion identification models based on EEG.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107826"},"PeriodicalIF":4.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695920","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":"Brain tumor detection with bi-directional cascade Gaussian kernel feature-generative adversarial networks","authors":"S. Anjana ,&nbsp;P.M. Siva Raja ,&nbsp;K. Rejini ,&nbsp;Moses Garuba ,&nbsp;A. Ananth","doi":"10.1016/j.bspc.2025.107838","DOIUrl":"10.1016/j.bspc.2025.107838","url":null,"abstract":"<div><div>The detection of brain tumors is critical in neurology and oncology. Advanced medical imaging does not mitigate challenges such as tumor variability, the diversity of imaging data, and the demand for high computational efficiency. Methods available so far face issues in accuracy and processing speed. The solution presented in this research will address all the problems mentioned above using the Bi-directional Cascade Gaussian Kernel Feature-Generative Adversarial Networks approach. Pre-training convolutional neural networks are then used for padding, resizing, normalization, and augmentation in advance preprocessing. Afterward, segmentation of the image by the Asymmetric Compound Boundary Guidance Branch Transformer (ABGBT) promotes boundary refinement, thus reducing the uncertainty. Integration of bi-directional cascade Gaussian kernels and generative adversarial networks in BCK-GAN helps in effectively extracting features as well as the detection process. In addition, the ATTAO further optimizes the network by applying an adaptive sigmoid attenuation function to optimize hyperparameters, thereby improving overall performance. Extensive experiments were performed using Python, which yielded impressive Dice scores of 96.04% on BraTS2018, 95.53% on BraTS2019, 96.13% on BraTS2020, and 95.79% on BraTS2021 Task 1. The detection speeds are 2.8 secs, 4.63 secs, 3.62 secs, and 3.2 secs, respectively, which significantly enhances brain tumor detection accuracy and efficiency.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107838"},"PeriodicalIF":4.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704455","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":"Glaucoma diagnosis using Gabor and entropy coded Sine Cosine integration in adaptive partial swarm optimization-based FAWT","authors":"Rajneesh Kumar Patel,&nbsp;Nancy Kumari,&nbsp;Siddharth Singh Chouhan","doi":"10.1016/j.bspc.2025.107832","DOIUrl":"10.1016/j.bspc.2025.107832","url":null,"abstract":"<div><h3>Purpose</h3><div>Projections estimate that 112 million people could be influenced by glaucoma by 2040, making it a substantial public health concern and a prominent source of blindness due to optic nerve damage from elevated intraocular pressure. Diagnosis and treatment rely on manual or medical imaging techniques requiring expert supervision. However, early detection through computerized analysis of eye fundus images could help delay total blindness.</div></div><div><h3>Design &amp; Method</h3><div>This work proposes a modified Flexible Analytical Wavelet Transform based on Adaptive Partial Swarm Optimization for Optimal Parameter Selection (APSO-FAWT). It will help to solve an inequality constraint problem and decompose images into sub-bands. The RGB fundus images are split into three channels at the initial stage. Then, the blue channel is selected for APSO-FAWT-based decomposition because it highlights defects in the retinal nerve fiber layers, aiding glaucoma detection and enhancing nerve fiber visibility. In the second stage, Gabor-based features are extracted from Blue Sub-band images, and the entropy-coded Sine Cosine algorithm is deployed to minimize the dimensions of the extracted features. Then, highlighted features are ranked using the t-value technique, and these features are applied to the LS-SVM to categorize the Glaucoma or Normal images. Additionally, ablation studies were performed to assess the effectiveness of each component within the model.</div></div><div><h3>Outcomes</h3><div>The model was evaluated using tenfold cross-validation, achieving an Accuracy of 97.42%, Specificity of 98.01%, and Sensitivity of 96.83%. The projected Glaucoma diagnosis model shows improved performance compared to existing methods, offering a promising tool for automated glaucoma detection.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107832"},"PeriodicalIF":4.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684347","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":"EC-HDLNet: Extended coati-based hybrid deep dilated convolutional learning network for brain tumor classification","authors":"Madona B Sahaai ,&nbsp;K Karthika ,&nbsp;Aaron Kevin Cameron Theoderaj","doi":"10.1016/j.bspc.2025.107865","DOIUrl":"10.1016/j.bspc.2025.107865","url":null,"abstract":"<div><div>Brain tumors are one of the most aggressive and dangerous forms of brain cancer, making their accurate and rapid detection critical for effective treatment. In this study, an innovative optimization driven hybrid deep learning model EC-HDLNet is proposed for classifying brain tumors in medical images. The model addresses limitations found in existing methods by minimizing pre-processing steps and optimizing deep learning models for better performance. The input images are pre-processed using Gaussian bilateral filtering (GBF), which effectively reduces noise while preserving edges. The Decouple SegNet module is then employed to segment the regions of interest, and deep features are extracted using the InceptionV3 model. For classification, the deep residual dilated convolution network (DResdiL) is introduced to enhance tumor classification accuracy. The proposed hybrid model presents a significant step forward in brain tumor classification, offering a more efficient, accurate, and practical solution for medical imaging applications. The experimental results show that EC-HDLNet outperforms existing state-of-the-art methods with an impressive accuracy of 99.78 %, precision of 99.65 %, recall of 99.72 %, and F1-score of 99.69 %. This method not only improves classification results but also reduces computational complexity and processing time by optimizing the model’s hyper parameters and integrating multiple advanced techniques.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107865"},"PeriodicalIF":4.9,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696039","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":"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}