Genomics, proteomics & bioinformatics最新文献

rMATS-cloud: Large-scale Alternative Splicing Analysis in the Cloud. rMATS-cloud：云中的大规模可选拼接分析。

Genomics, proteomics & bioinformatics Pub Date : 2025-07-11 DOI: 10.1093/gpbjnl/qzaf036

Jenea I Adams, Eric Kutschera, Qiang Hu, Chun-Jie Liu, Qian Liu, Kathryn Kadash-Edmondson, Song Liu, Yi Xing

{"title":"rMATS-cloud: Large-scale Alternative Splicing Analysis in the Cloud.","authors":"Jenea I Adams, Eric Kutschera, Qiang Hu, Chun-Jie Liu, Qian Liu, Kathryn Kadash-Edmondson, Song Liu, Yi Xing","doi":"10.1093/gpbjnl/qzaf036","DOIUrl":"10.1093/gpbjnl/qzaf036","url":null,"abstract":"Although gene expression analysis pipelines are often a standard part of bioinformatics analysis, with many publicly available cloud workflows, cloud-based alternative splicing analysis tools remain limited. Our lab released rMATS in 2014 and has continuously maintained it, providing a fast and versatile solution for quantifying alternative splicing from RNA sequencing (RNA-seq) data. Here, we present rMATS-cloud, a portable version of the rMATS workflow that can be run in virtually any cloud environment suited for biomedical research. We compared the time and cost of running rMATS-cloud with two RNA-seq datasets on three different platforms (Cavatica, Terra, and Seqera). Our findings demonstrate that rMATS-cloud handles RNA-seq datasets with thousands of samples, and therefore is ideally suited for the storage capacities of many cloud data repositories. rMATS-cloud is available at https://dockstore.org/workflows/github.com/Xinglab/rmats-turbo/rmats-turbo-cwl, https://dockstore.org/workflows/github.com/Xinglab/rmats-turbo/rmats-turbo-wdl, and https://dockstore.org/workflows/github.com/Xinglab/rmats-turbo/rmats-turbo-nextflow.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12248417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data. 通过整合scRNA-seq和scATAC-seq数据推断单细胞调节网络。

Genomics, proteomics & bioinformatics Pub Date : 2025-07-01 DOI: 10.1093/gpbjnl/qzaf060

Xueli Xu, Yanran Liang, Miaoxiu Tang, Jiongliang Wang, Xi Wang, Yixue Li, Jie Wang

{"title":"ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.","authors":"Xueli Xu, Yanran Liang, Miaoxiu Tang, Jiongliang Wang, Xi Wang, Yixue Li, Jie Wang","doi":"10.1093/gpbjnl/qzaf060","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf060","url":null,"abstract":"Each cell possesses a unique gene regulatory network. However, limited methods exist for inferring cell-specific regulatory networks, particularly through the integration of single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data. Herein, we develop a novel algorithm, named single-cell regulatory network inference (ScReNI), for inferring gene regulatory networks at the single-cell level. In ScReNI, the nearest neighbors algorithm is utilized to establish the neighboring cells for each cell, where nonlinear regulatory relationships between gene expression and chromatin accessibility are inferred through a modified random forest. ScReNI is designed to analyze both paired and unpaired datasets for scRNA-seq and scATAC-seq. ScReNI demonstrates more accurate regulatory relationships and outperforms existing cell-specific network inference methods in network-based cell clustering. ScReNI also shows superior performance in inferring cell type-specific regulatory networks through integrating gene expression and chromatin accessibility. Importantly, ScReNI offers the unique function of identifying cell-enriched regulators based on each cell-specific network. Overall, ScReNI facilitates the inferences of cell-specific regulatory networks and cell-enriched regulators, providing insights into single-cell regulatory mechanisms of diverse biological processes. ScReNI is available at https://github.com/Xuxl2020/ScReNI.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data. LEGEND：在多模态转录组测序数据中识别共表达基因。

Genomics, proteomics & bioinformatics Pub Date : 2025-07-01 DOI: 10.1093/gpbjnl/qzaf056

Tao Deng, Mengqian Huang, Kaichen Xu, Yan Lu, Yucheng Xu, Siyu Chen, Nina Xie, Qiuyue Tao, Hao Wu, Xiaobo Sun

{"title":"LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data.","authors":"Tao Deng, Mengqian Huang, Kaichen Xu, Yan Lu, Yucheng Xu, Siyu Chen, Nina Xie, Qiuyue Tao, Hao Wu, Xiaobo Sun","doi":"10.1093/gpbjnl/qzaf056","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf056","url":null,"abstract":"Identifying co-expressed genes across tissue domains and cell types is essential for revealing co-functional genes involved in biological or pathological processes. While both single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomic (SRT) data offer insights into gene co-expression patterns, current methods typically utilize either data type alone, potentially diluting the co-functionality signals within co-expressed gene groups. To bridge this gap, we introduce muLtimodal co-Expressed GENes finDer (LEGEND), a novel computational method that integrates scRNA-seq and SRT data for identifying groups of co-expressed genes at both cell type and tissue domain levels. LEGEND employs an innovative hierarchical clustering algorithm designed to maximize intra-cluster redundancy and inter-cluster complementarity, effectively capturing more nuanced patterns of gene co-expression and spatial coherence. Enrichment and co-function analyses further showcase the biological relevance of these gene clusters, and their utilities in exploring context-specific novel gene functions. Notably, LEGEND can reveal shifts in gene-gene interactions under different conditions, furnishing insights for disease-associated gene crosstalk. Moreover, LEGEND can be utilized to enhance the annotation accuracy of both spatial spots in SRT and single cells in scRNA-seq, and pioneers in identifying genes with designated spatial expression patterns. LEGEND is available at https://github.com/ToryDeng/LEGEND.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lineage-associated Human Divergently-paired Genes Exhibit Structural and Regulatory Characteristics. 谱系相关的人类差异配对基因表现出结构和调控特征。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-26 DOI: 10.1093/gpbjnl/qzaf058

Guangya Duan, Sisi Zhang, Bixia Tang, Jingfa Xiao, Zhang Zhang, Peng Cui, Jun Yu, Wenming Zhao

{"title":"Lineage-associated Human Divergently-paired Genes Exhibit Structural and Regulatory Characteristics.","authors":"Guangya Duan, Sisi Zhang, Bixia Tang, Jingfa Xiao, Zhang Zhang, Peng Cui, Jun Yu, Wenming Zhao","doi":"10.1093/gpbjnl/qzaf058","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf058","url":null,"abstract":"Divergently-paired genes (DPGs) are minimal co-transcriptional units of clustered genes, representing over 10% of human genes. Our previous studies have shown that vertebrate DPGs are highly conserved compared to those from invertebrates. Three critical questions remain: (1) which DPGs are conserved across vertebrates, especially among mammals and primates? (2) to what extent and precision do these paired promotors share their sequences mechanistically and stringently? and (3) how are human DPGs distributed over selected primate lineages, and what are their possible biological functional consequences? There are 1399 human DPGs (approximately 12% of all human protein-coding genes), of which 1136, 1118, 925, and 830 human DPGs show conservation when compared to selected primates, mammals, avians, and fishs, respectively. DPGs are not only functionally enriched toward direct protein-DNA interactions and cell cycle synchronization, but also exhibit lineage association, narrow in principle toward synchronization of certain core molecular mechanisms and cellular processes. Second, the inter-transcription start sites (inter-TSS) distances affect both co-expression strength and disparity between the two genes of a DPG. Finally, among primates, human-associated DPGs exhibit diversification in their co-expression patterns and gene duplication events, and are obviously involved in neural development. Comparing high-quality human reference genomes from European (T2T-CHM13) and Chinese (T2T-YAO) populations, we identified 55 and 357 DPGs unique to the former and the latter, respectively. Our findings offer novel insights into the regulatory characteristics between neighboring genes and their structure-function selection among functionally conserved gene clusters.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foundation Model: A New Era for Plant Single-cell Genomics. 基础模型：植物单细胞基因组学的新时代。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-25 DOI: 10.1093/gpbjnl/qzaf059

Yuansong Zeng, Yuedong Yang

引用次数: 0

clusIBD: Robust Detection of Identity-by-descent Segments Using Unphased Genetic Data from Poor-quality Samples. clusIBD：使用来自低质量样本的非相位遗传数据进行血统识别片段的鲁棒检测。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-20 DOI: 10.1093/gpbjnl/qzaf055

Ran Li, Yu Zang, Zhentang Liu, Jingyi Yang, Nana Wang, Jiajun Liu, Enlin Wu, Riga Wu, Hongyu Sun

引用次数: 0

EPSD 2.0: An Updated Database of Protein Phosphorylation Sites Across Eukaryotic Species. EPSD 2.0：一个更新的真核生物物种蛋白质磷酸化位点数据库。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-20 DOI: 10.1093/gpbjnl/qzaf057

Miaomiao Chen, Yujie Gou, Ming Lei, Leming Xiao, Miaoying Zhao, Xinhe Huang, Dan Liu, Zihao Feng, Di Peng, Yu Xue

{"title":"EPSD 2.0: An Updated Database of Protein Phosphorylation Sites Across Eukaryotic Species.","authors":"Miaomiao Chen, Yujie Gou, Ming Lei, Leming Xiao, Miaoying Zhao, Xinhe Huang, Dan Liu, Zihao Feng, Di Peng, Yu Xue","doi":"10.1093/gpbjnl/qzaf057","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf057","url":null,"abstract":"As one of the most crucial post-translational modifications (PTMs), protein phosphorylation regulates a broad range of biological processes in eukaryotes. Biocuration, integration, and annotation of reported phosphorylation events will deliver a valuable resource for the community. Here, we present an updated database, the eukaryotic phosphorylation site database 2.0 (EPSD 2.0), which includes 2,769,163 experimentally identified phosphorylation sites (p-sites) in 362,707 phosphoproteins from 223 eukaryotes. From the literature, 873,718 new p-sites identified through high-throughput phosphoproteomic research were first collected, and 1,078,888 original phosphopeptides together with primary references were reserved. Then, this dataset was merged into EPSD 1.0, comprising 1,616,804 p-sites within 209,326 proteins across 68 eukaryotic organisms [1]. We also integrated 362,190 additional known p-sites from 10 public databases. After redundancy clearance, we manually re-checked each p-site and annotated 88,074 functional events for 32,762 p-sites, covering 58 types of downstream effects on phosphoproteins, and regulatory impacts on 107 biological processes. In addition, phosphoproteins and p-sites in 8 model organisms were meticulously annotated utilizing information supplied by 100 external platforms encompassing 15 areas. These areas included kinase/phosphatase, transcription regulators, three-dimensional structures, physicochemical characteristics, genomic variations, functional descriptions, protein domains, molecular interactions, drug-target associations, disease-related data, orthologs, transcript expression levels, proteomics, subcellular localization, and regulatory pathways. We expect that EPSD 2.0 will become a useful database supporting comprehensive studies on phosphorylation in eukaryotes. The EPSD 2.0 database is freely accessible online at https://epsd.biocuckoo.cn/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Integrative Polygenic and Epigenetic Risk Score for Overweight-related Hypertension in Chinese Population. 中国人群超重相关高血压的综合多基因和表观遗传风险评分。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-16 DOI: 10.1093/gpbjnl/qzaf048

Yaning Zhang 张亚宁, Qiwen Zheng 郑启文, Qili Qian 钱其溧, Na Yuan 苑娜, Tianzi Liu 刘天资, Xingjian Gao 高行健, Xiu Fan 凡秀, Youkun Bi 毕友坤, Guangju Ji 姬广聚, Peilin Jia 贾佩林, Sijia Wang 汪思佳, Fan Liu 刘凡, Changqing Zeng 曾长青

{"title":"An Integrative Polygenic and Epigenetic Risk Score for Overweight-related Hypertension in Chinese Population.","authors":"Yaning Zhang 张亚宁, Qiwen Zheng 郑启文, Qili Qian 钱其溧, Na Yuan 苑娜, Tianzi Liu 刘天资, Xingjian Gao 高行健, Xiu Fan 凡秀, Youkun Bi 毕友坤, Guangju Ji 姬广聚, Peilin Jia 贾佩林, Sijia Wang 汪思佳, Fan Liu 刘凡, Changqing Zeng 曾长青","doi":"10.1093/gpbjnl/qzaf048","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf048","url":null,"abstract":"Overweight-related hypertension (OrH), defined by the coexistence of excess body weight and hypertension (HTN), is an increasing health concern elevating cardiovascular disease risks. This study evaluated the prediction performance of polygenic risk scores (PRS) and methylation risk scores (MRS) for OrH in 7605 Chinese participants from two cohorts: the Chinese Academy of Sciences (CAS) and the National Survey of Physical Traits (NSPT). In CAS cohort, which predominantly consists of academics, males showed significantly higher prevalence of obesity, HTN, and OrH, along with worse metabolic syndrome indicators, compared to females. This disparity was less pronounced in NSPT cohort and in broader Chinese studies. Among ten PRS methods, PRS-CSx was the most effective, enhancing prediction accuracy for obesity [area under the curve (AUC) = 0.75], HTN (AUC = 0.74), and OrH (AUC = 0.75), compared to baseline models using only age and sex (AUC = 0.55-0.71). Similarly, least absolute shrinkage and selection operator (LASSO)-based MRS models improved prediction accuracies for obesity (AUC = 0.70), HTN (AUC = 0.73), and OrH (AUC = 0.78). Combining PRS and MRS further boosted prediction accuracy to the AUC of 0.77, 0.76, and 0.80, respectively. These models stratified individuals into high (> 0.6) or low (< 0.1) risk categories, covering 59.95% for obesity, 31.75% for HTN, and 43.89% for OrH, respectively. Our findings highlight a higher OrH risk among male academics, emphasize the influence of metabolic and lifestyle factors on MRS predictions, and highlight the value of multi-omics approaches in enhancing risk stratification.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deciphering Haploid Chromosome Conformation Alternation in Down Syndrome by Multiple Haploid Omics Analysis. 通过多重单倍体组学分析解读唐氏综合征的单倍体染色体构象变化。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-12 DOI: 10.1093/gpbjnl/qzaf054

Chengchao Wu, Tianshu Zhou, Wenfu Ke, Wei Xiong, Zhihui Zhang, Siheng Zhang, Jinyue Wang, Lulu Deng, Keji Yan, Man Wang, Shenglong He, Qi Gong, Chao Ma, Xiaping Chen, Yan Li, He Long, Chong Guo, Gang Cao, Zhijun Zhang

{"title":"Deciphering Haploid Chromosome Conformation Alternation in Down Syndrome by Multiple Haploid Omics Analysis.","authors":"Chengchao Wu, Tianshu Zhou, Wenfu Ke, Wei Xiong, Zhihui Zhang, Siheng Zhang, Jinyue Wang, Lulu Deng, Keji Yan, Man Wang, Shenglong He, Qi Gong, Chao Ma, Xiaping Chen, Yan Li, He Long, Chong Guo, Gang Cao, Zhijun Zhang","doi":"10.1093/gpbjnl/qzaf054","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf054","url":null,"abstract":"For chromosome abnormalities (CA), such as Down syndrome (DS), the influence of genomic variations to chromosome conformation and gene transcription remains elusive. Based on the complete genomic sequence from the parents of the DS trisomy patient, we systematically delineated an atlas of parental-specific haploid single nucleotide polymorphism (SNP), copy number variation (CNV), three-dimensional (3D) genome, and RNA expression profiles of the diencephalon in the DS patient. The integrated haploid multi-omics analysis demonstrated that one-dimensional genomic variations including SNPs and CNVs in the DS patient are highly correlated with the alterations of the 3D genome and the subsequent gene transcription. The correlation relationship remains valid in haploid-levels. Moreover, we revealed the 3D genome alteration associated mis-regulation of DS-related genes, which facilitates to understanding the pathogenesis of CA. Together, our study contributes to decipher the coding from one-dimensional genomic variations to 3D genomic architecture and the subsequent gene transcription in healthy and diseases.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144277053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SRPS: Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery. SRPS：多中心蛋白质组学亚型和生物标志物发现的生存强化迁移学习。

Genomics, proteomics & bioinformatics Pub Date : 2025-06-10 DOI: 10.1093/gpbjnl/qzaf052

Linhai Xie, Pei Jiang, Cheng Chang

{"title":"SRPS: Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery.","authors":"Linhai Xie, Pei Jiang, Cheng Chang","doi":"10.1093/gpbjnl/qzaf052","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf052","url":null,"abstract":"Omics-based molecular subtyping in large-scale and multicentric cohort studies is a prerequisite for proteomics-driven precision medicine (PDPM). However, keeping the subtypes with robust molecular features and significant associations with prognosis across different cohorts is challenging due to the biological heterogeneity and technical inconsistency. Herein, we propose a subtyping algorithm, named Survival Reinforced Patient Stratification (SRPS), to adapt the known subtypes from a discovery cohort to another by simultaneously preserving the distinct prognosis and molecular characteristics of each subtype. SRPS has been benchmarked on simulated and real-world datasets, where it shows a 12% increase in classification accuracy and possesses the best prognostic discrimination. Moreover, based on the calculated subtype significance score, an \"unpopular\" protein, Peptidylprolyl Isomerase C (PPIC), was identified as the top-1 remarkable protein for subtyping the hepatocellular carcinoma (HCC) patients with the worst prognosis. Eventually, PPIC was experimentally proved to be a pro-cancer protein in HCC, confirming our work as a demonstration of interpretable machine learning guided biological discovery in PDPM research. SRPS is publicly available at https://github.com/PHOENIXcenter/SRPS and https://ngdc.cncb.ac.cn/biocode/tool/BT007770.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0