Computers in biology and medicine最新文献

{"title":"Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations","authors":"Carla Martins ,&nbsp;Henrique Barros ,&nbsp;André Moreira","doi":"10.1016/j.compbiomed.2024.109341","DOIUrl":"10.1016/j.compbiomed.2024.109341","url":null,"abstract":"<div><h3>Problem</h3><div>Current spirometers face challenges in evaluating acceptability criteria, often requiring manual visual inspection by trained specialists. Automating this process could improve diagnostic workflows and reduce variability in test assessments.</div></div><div><h3>Aim</h3><div>This study aimed to apply transfer learning to convolutional neural networks (CNNs) to automate the classification of spirometry flow-volume curves based on acceptability criteria.</div></div><div><h3>Methods</h3><div>A total of 5287 spirometry flow-volume curves were divided into three categories: (A) all criteria met, (B) early termination, and (C) non-acceptable results. Six CNN models (VGG16, InceptionV3, Xception, ResNet152V2, InceptionResNetV2, DenseNet121) were trained using a balanced dataset after data augmentation. The models' performance was evaluated on part of the original unbalanced dataset with accuracy, precision, recall, and F1-score metrics.</div></div><div><h3>Results</h3><div>VGG16 achieved the highest accuracy at 93.9 %, while ResNet152V2 had the lowest at 83.0 %. Non-acceptable curves (Group C) were the easiest to classify, with precision reaching at least 87.7 %. Early termination curves (Group B) were the most challenging, with precision ranging from 75.0 % to 90.3 %.</div></div><div><h3>Conclusion</h3><div>CNN models, particularly VGG16, show promise in automating spirometry quality control, potentially reducing the need for manual inspection by specialized technicians. This approach can streamline spirometry assessments, offering consistent, high-quality diagnostics even in non-specialized or low-resource environments.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109341"},"PeriodicalIF":7.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615857","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":"Evolutionary bioinformatics with veiled biological database for health care operations","authors":"Hariprasath Manoharan ,&nbsp;S.A. Edalatpanah","doi":"10.1016/j.compbiomed.2024.109418","DOIUrl":"10.1016/j.compbiomed.2024.109418","url":null,"abstract":"<div><div>The tremendous growth of biological data processing systems in the realm of health care applications has made real-time information accessible to everyone with no processing lags. Bioinformatics is even integrated into most wireless technology applications to account for all physical characteristics. The planned model focuses on evolutionary bioinformatics for medical sensor applications in health care. The optimization scenario is executed by combining genetic and ant colony optimization methods (GACO). In the proposed technique, the design concerns are implemented with appropriate transmitting and receiving modules, and individual bits are framed for extra bioinformatics data processing components. a design that completely minimizes all errors in the big data processing stage. Such a design completely lowers the overall error in the huge data processing state since all channels can be accessed in accordance with the framed bits. Furthermore, the quality of service is maximized because all channels carrying bioinformatics data are kept at high quality bits, increasing utility rates. The experiments were conducted using five scenarios to evaluate the effectiveness of the proposed design. The findings indicate that the proposed technique can handle bioinformatics data for healthcare in real time with a service quality of 95 %.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109418"},"PeriodicalIF":7.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616403","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":"SPE-YOLO: A deep learning model focusing on small pulmonary embolism detection","authors":"Houde Wu ,&nbsp;Qifei Xu ,&nbsp;Xinliu He ,&nbsp;Haijun Xu ,&nbsp;Yun Wang ,&nbsp;Li Guo","doi":"10.1016/j.compbiomed.2024.109402","DOIUrl":"10.1016/j.compbiomed.2024.109402","url":null,"abstract":"<div><h3>Objectives</h3><div>By developing the deep learning model SPE-YOLO, the detection of small pulmonary embolism has been improved, leading to more accurate identification of this condition. This advancement aims to better serve medical diagnosis and treatment.</div></div><div><h3>Methods</h3><div>This retrospective study analyzed images of 142 patients from Tianjin Medical University General Hospital using YOLOv8 as the foundational framework. Firstly, a small detection head P2 was added to better capture and identify small targets. Secondly, the SEAttention mechanism was integrated into the model to enhance focus on crucial features and optimize detection accuracy. Lastly, the feature extraction process was refined by introducing ODConv convolution to capture more comprehensive contextual information, thereby enhancing the detection performance of small pulmonary embolisms. The model's practical application ability was evaluated using 2000 cases from the RSNA dataset as an external test set.</div></div><div><h3>Results</h3><div>In the Tianjin test set, our model achieved a precision of 84.20 % and an accuracy of 81.50 %. This represents an improvement of approximately 5 % and 4 % respectively compared to the original YOLOv8. F1 scores, recall rates and average accuracy have also increased by 4 %, 5 %, 6 %, respectively. In data from the external validation set of RSNA, SPE-YOLO exhibited its effectiveness with a sensitivity of 90.70 % and an accuracy of 86.45 %.</div></div><div><h3>Conclusion</h3><div>The SPE-YOLO algorithm demonstrates strong capability in identifying small pulmonary embolisms, offering clinicians a more accurate and efficient diagnostic tool. This advancement is expected to enhance the quality of pulmonary embolism diagnosis and support the progress of medical services.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109402"},"PeriodicalIF":7.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615722","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":"Computational-driven discovery of AI-2 quorum sensing inhibitor targeting the 5′- methylthioadenosine/S-adenosylhomocysteine nucleosidase (MTAN) to combat drug-resistant Helicobacter pylori","authors":"Manish Kumar ,&nbsp;Avinash Karkada Ashok ,&nbsp;Thejaswi Bhat ,&nbsp;Krishnakumar Ballamoole ,&nbsp;Pavan Gollapalli","doi":"10.1016/j.compbiomed.2024.109409","DOIUrl":"10.1016/j.compbiomed.2024.109409","url":null,"abstract":"<div><div>MTAN is an attainable therapeutic target for <em>H. pylori</em> because it may minimize virulence production, limit resistance, and impair quorum sensing without affecting gut flora. Here, 457 compounds with anti-<em>H. pylori</em> activity were methodically analyzed, revealing a diverse array of chemical classes and unique compounds. Molecular docking studies identified three potential compounds with high binding affinities, Dehydrocostus lactone, keramamine B, and ZINC00013531409, each having binding affinity of −7.9, −9.2, and −8.3 kcal/mol, respectively. Molecular dynamics simulations of the ZINC00013531409-MTAN interactions in comparison with Apo-MTAN demonstrated stability and interactions of 300 ns, with key residues involved in protein-ligand binding illuminated. Analysis of hydrogen bonds (Ile52, Met174, and Arg194) and secondary structure variations further elucidated the binding interactions and conformational changes within the complex. Binding free energy calculations shed light on the energetics and interactions governing the complex formation of the ZINC00013531409-MTAN complex. PCA elucidated the dominant modes of motion, along with FEL revealed the energetically favorable states and then DCCM shed light on the correlated motions between residues. Overall, this study offers a detailed computational evaluation of ZINC00013531409 with anti-<em>H. pylori</em> activity, highlighting toxicity profile, conformational stability, and binding interactions, providing a foundation for further drug development efforts toward bacterial resistance.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109409"},"PeriodicalIF":7.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616397","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":"Feature-centric registration of large deformed images using transformers and correlation distance","authors":"Heeyeon Kim ,&nbsp;Minkyung Lee ,&nbsp;Bohyoung Kim ,&nbsp;Yeong-Gil Shin ,&nbsp;Minyoung Chung","doi":"10.1016/j.compbiomed.2024.109356","DOIUrl":"10.1016/j.compbiomed.2024.109356","url":null,"abstract":"<div><div>In deformable medical image registration, both a robust backbone registration network and a suitable similarity metric are essential. This paper introduces a robust registration network combined with a feature-based loss function, specifically designed to handle large deformations and address the challenge of the absence of ground truth data. Tackling large deformations typically requires either expanding the receptive field or breaking down extensive deformations into smaller, more manageable ones. We address this challenge through two key network components: the coarse-to-fine estimation of the target displacement vector field (DVF) and the integration of the Transformer’s feature attention mechanism. To further enhance registration performance, we propose a novel feature correlation-based distance metric that leverages the symmetric properties of the correlation matrix to efficiently exploit feature correlations. Additionally, by utilizing the features extracted directly from the registration network, we eliminate the need for additional feature extraction networks. Experimental results demonstrate that our feature correlation-based loss function is particularly effective in achieving accurate registration in the absence of ground truth data. Our method has proven successful in both mono-modality abdomen CT registration and brain MRI atlas registration, leading to improvements in Dice similarity coefficient and other evaluation metrics.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109356"},"PeriodicalIF":7.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616412","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":"Exploring natural products potential: A similarity-based target prediction tool for natural products","authors":"Abeer Abdulhakeem Mansour Alhasbary ,&nbsp;Nurul Hashimah Ahamed Hassain Malim ,&nbsp;Siti Zuraidah Mohamad Zobir","doi":"10.1016/j.compbiomed.2024.109351","DOIUrl":"10.1016/j.compbiomed.2024.109351","url":null,"abstract":"<div><div>Natural products are invaluable resources in drug discovery due to their substantial structural diversity. However, predicting their interactions with druggable protein targets remains a challenge, primarily due to the limited availability of bioactivity data. This study introduces CTAPred (Compound-Target Activity Prediction), an open-source command-line tool designed to predict potential protein targets for natural products. CTAPred employs a two-stage approach, combining fingerprinting and similarity-based search techniques to identify likely drug targets for these bioactive compounds. Despite its simplicity, the tool's performance is comparable to that of more complex methods, demonstrating proficiency in target retrieval for natural product compounds. Furthermore, this study explores the optimal number of reference compounds most similar to the query compound, aiming to refine target prediction accuracy. The findings demonstrated the superior performance of considering only the most similar reference compounds for target prediction. CTAPred is freely available at <span><span>https://github.com/Alhasbary/CTAPred</span><svg><path></path></svg></span>, offering a valuable resource for deciphering natural product-target associations and advancing drug discovery.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109351"},"PeriodicalIF":7.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142616408","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":"Using advanced machine learning algorithms to predict academic major completion: A cross-sectional study","authors":"Alireza Kordbagheri ,&nbsp;Mohammadreza Kordbagheri ,&nbsp;Natalie Tayim ,&nbsp;Abdulnaser Fakhrou ,&nbsp;Mohammadreza Davoudi","doi":"10.1016/j.compbiomed.2024.109372","DOIUrl":"10.1016/j.compbiomed.2024.109372","url":null,"abstract":"<div><h3>Background</h3><div>Existing prediction methods for academic majors based on personality traits have notable gaps, including limited model complexity and generalizability.The current study aimed to utilize advanced Machine Learning (ML) algorithms with smoothing functions to predict academic majors completed based on personality subscales.</div></div><div><h3>Methods</h3><div>We used reports from 59,413 individuals to perform the current study. All advanced algorithms implemented in this article were based on R software (version 4.1.3, R Core Team, 2021). All model parameters were optimized based on resampling and cross-validation (CV). In addition, pseudo-R² as a robust metric has been used to compare the performance of models, which, unlike most studies, considers the quality of model-predicted probabilities.</div></div><div><h3>Result</h3><div>The results indicated that advanced ML models' performance on training and test data was superior to logistic regression. Pseudo-R² and AUC results showed that advanced models such as kNN, GBE, and RF had the highest scores based on test data compared to other models. The pseudo-R² values for the models used in this study varied across the test dataset; the lowest value belonged to the logistic regression algorithm at .022, and the highest value was recorded for the kNN algorithm at .099. The agreeableness subscale is the most influential component in predicting the completion of university education, followed by conscientiousness and emotional stability.</div></div><div><h3>Conclusion</h3><div>The potential of advanced methods to enhance the accuracy and validity of predictions is a promising development in our field. Their performance, particularly in handling large data sets with complex patterns, is a reason for optimism about the future of research in this area.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109372"},"PeriodicalIF":7.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615876","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":"MCRANet: MTSL-based connectivity region attention network for PD-L1 status segmentation in H&E stained images","authors":"Xixiang Deng ,&nbsp;Jiayang Luo ,&nbsp;Pan Huang ,&nbsp;Peng He ,&nbsp;Jiahao Li ,&nbsp;Yanan Liu ,&nbsp;Hualiang Xiao ,&nbsp;Peng Feng","doi":"10.1016/j.compbiomed.2024.109357","DOIUrl":"10.1016/j.compbiomed.2024.109357","url":null,"abstract":"<div><div>The quantitative analysis of Programmed death-ligand 1 (PD-L1) via Immunohistochemical (IHC) plays a crucial role in guiding immunotherapy. However, IHC faces challenges, including high costs, time consumption and result variability. Conversely, Hematoxylin-Eosin (H&amp;E) staining offers cost-effectiveness, speed, and stable results. Nonetheless, H&amp;E staining, which solely visualizes cellular morphological features, lacks clinical applicability in detecting biomarker expressions like PD-L1. Substituting H&amp;E staining for IHC in determining PD-L1 status is a clinically significant and challenging task. Motivated by above observations, we propose a Multi-Task supervised learning (MTSL)-based connectivity region attention network (MCRANet) for PD-L1 status segmentation in H&amp;E stained images. To reduce interference from non-tumor areas, the MTSL-based region attention is proposed to enhances the network's capability to distinguish between tumor and non-tumor regions. Consequently, this augmentation further improves the network's segmentation efficacy for PD-L1 positive and negative regions. Furthermore, the PD-L1 expression regions demonstrate interconnection throughout the tissue section. Leveraging this topological prior knowledge, we integrate a connectivity modeling module (CM module) within the MTSL-based region attention module (MRA module) to enhance the precision of MTSL-based region attention localization. This integration further improves the structural similarity between the segmentation results and the ground truth. Extensive visual and quantitative results demonstrate that our supervised-learning-guided MRA module produces more interpretable attention and the introduced CM module provides accurate positional attention to the MRA module. Compared to other state-of-the-art networks, MCRANet exhibits superior segmentation performance with a dice similarity coefficient (DSC) of 79.6 % on the lung squamous cell carcinoma (LUSC) PD-L1 status dataset.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109357"},"PeriodicalIF":7.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615232","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":"The impact of PTEN status on glioblastoma multiforme: A glial cell type-specific study identifies unique prognostic markers","authors":"Sai Krishna A.V.S. ,&nbsp;Swati Sinha ,&nbsp;Manchanahalli R. Satyanarayana Rao ,&nbsp;Sainitin Donakonda","doi":"10.1016/j.compbiomed.2024.109395","DOIUrl":"10.1016/j.compbiomed.2024.109395","url":null,"abstract":"<div><div>Glioblastoma multiforme (GBM) is the most invasive form of brain tumor, accounting for 5 % of the cases per 100,000 people in various countries. The phosphatase and tensin homolog deleted from chromosome 10 (PTEN) is a well-known tumor suppressor, and its alteration leads to a deleterious effect on GBM progression. The molecular mechanism of tumorigenesis in glial cell types, driven by PTEN status, is yet to be elucidated. In this study, we analyzed publicly available single-cell transcriptome profiles of PTEN wild-type (WT) and NULL GBM patients. We compared them with normal brain data to uncover many unique gene sets influenced by PTEN status. The co-expression network analysis of differentially expressed genes (DEGs) between normal brain and PTEN (WT and NULL) identified highly interconnected genes. The weighted gene co-expression network analysis (WGCNA), based on the DESeq2 algorithm, identified glial cell-type-specific modules in PTEN status-dependent bulk RNA expression profiles. We overlapped network module gene sets from single-cell and bulk transcriptome profiles, and shared genes were considered for further analysis. The hallmark pathway enrichment analysis of the genes unique to PTEN-WT and NULL revealed various tumor growth-related pathways across the glial cell types. Further characterization of PTEN-WT and PTEN-NULL networks belonging to the single-cell and bulk RNA datasets revealed that PTEN status influences the network modules in astrocytes, microglia, and oligodendrocyte precursor cells. An integrated influence value algorithm identified hub genes for each glial cell type. The prognostic analysis identified clinically relevant hub genes specific to the cell type in PTEN-WT: <em>GLIPR2</em> (astrocytes), <em>CFH</em>, <em>IL32</em>, <em>MXRA5</em> (microglia), and PTEN-NULL: <em>ID1</em> (astrocytes) and <em>LAT2</em> (microglia). Our glial cell type-level transcriptome analysis unearthed unique molecular pathways and prognostic markers in PTEN status-dependent GBM patients.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109395"},"PeriodicalIF":7.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615780","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":"SARS-CoV-2 main protease (M-pro) mutational profiling: An insight into mutation coldspots","authors":"Pol Garcia-Segura,&nbsp;Ariadna Llop-Peiró,&nbsp;Nil Novau-Ferré,&nbsp;Júlia Mestres-Truyol,&nbsp;Bryan Saldivar-Espinoza,&nbsp;Gerard Pujadas,&nbsp;Santiago Garcia-Vallvé","doi":"10.1016/j.compbiomed.2024.109344","DOIUrl":"10.1016/j.compbiomed.2024.109344","url":null,"abstract":"<div><div>SARS-CoV-2 and the COVID-19 pandemic have marked a milestone in the history of scientific research worldwide. To ensure that treatments are successful in the mid-long term, it is crucial to characterize SARS-CoV-2 mutations, as they might lead to viral resistance. Data from &gt;5,700,000 SARS-CoV-2 genomes available at GISAID was used to report SARS-CoV-2 mutations. Given the pivotal role of its main protease (M-pro) in virus replication, a detailed analysis of SARS-CoV-2 M-pro mutations was conducted, with particular attention to mutation-resistant residues or mutation coldspots, defined as those residues that have mutated in five or fewer genomes. 32 mutation coldspots were identified, most of which mediate interprotomer interactions or funneling interaction networks from the substrate-binding site towards the dimerization surface and <em>vice versa</em>. Besides, mutation coldspots were virtually conserved in all main proteases from other CoVs. Our results provide valuable information about key residues to M-pro structure that could be useful in rational target-directed drug design and establish a solid groundwork based on mutation analyses for the inhibition of M-pro dimerization, with a potential applicability to future coronavirus outbreaks.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"184 ","pages":"Article 109344"},"PeriodicalIF":7.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615502","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}