Genomics, proteomics & bioinformatics最新文献

Calling for Diversity: Improving Transfusion Safety Through High-Throughput Blood Group Microarray Genotyping. 呼唤多样性：通过高通量血型微阵列基因分型提高输血安全性。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-05-04 DOI: 10.1093/gpbjnl/qzag035

Michael Wittig, Tim Alexander Steiert, Hesham ElAbd, Frauke Degenhardt, Luca Valenti, Daniele Prati, Luisa Ronzoni, Luis Bujanda, Jesus M Banales, Natalia Blay, Pietro Invernizzi, Maria Buti, Javier Fernández, Nicoletta Sacchi, Antonio Julià, Anna Latiano, Rafael de Cid, Mauro D'Amato, Rosanna Asselta, Matthias Laudes, Wolfgang Lieb, David Juhl, Christoph Gassner, Andre Franke

{"title":"Calling for Diversity: Improving Transfusion Safety Through High-Throughput Blood Group Microarray Genotyping.","authors":"Michael Wittig, Tim Alexander Steiert, Hesham ElAbd, Frauke Degenhardt, Luca Valenti, Daniele Prati, Luisa Ronzoni, Luis Bujanda, Jesus M Banales, Natalia Blay, Pietro Invernizzi, Maria Buti, Javier Fernández, Nicoletta Sacchi, Antonio Julià, Anna Latiano, Rafael de Cid, Mauro D'Amato, Rosanna Asselta, Matthias Laudes, Wolfgang Lieb, David Juhl, Christoph Gassner, Andre Franke","doi":"10.1093/gpbjnl/qzag035","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag035","url":null,"abstract":"Blood transfusions, conducted between donors compatible in their red blood cell (RBC) antigens, play a life-saving role in transfusion medicine. Genetic differences at blood group loci between ethnicities result in diversity and altered frequency of RBC antigens that need to be considered in blood transfusions. Consequently, comprehensive and accurate blood group antigen typing is especially relevant for interethnic blood transfusions and for minorities underrepresented in the donor population. Blood group microarray genotyping is a cost-efficient and scalable method for comprehensive blood group typing. Previously, however, microarray typing was challenging for the clinically important blood group systems Rh and MNS, as these feature highly paralogous genomic loci, which lead to mixed signals. Here, we present an approach for accurately typing blood group systems, including Rh and MNS variations. We benchmarked this approach in an ethnically diverse cohort. We tested its performance using gold-standard, diagnostic-grade matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) data from 1052 samples, including 334 Centre d'Etude du Polymorphisme Humain (CEPH)-diversity samples. Overall, we obtained a 99.95% benchmarking concordance and a 99.65% call rate. In summary, we provide a highly accurate and cost-efficient high-throughput genotyping method for comprehensive blood group analysis that is also suitable for ethnically diverse sample sets (https://github.com/ikmb/BloodTypingArray).","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147847780","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

mTM-align2: A Server for Real-time Protein Structure Database Search and Alignment. mTM-align2：一个实时蛋白质结构数据库搜索和比对服务器。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-05-04 DOI: 10.1093/gpbjnl/qzag036

Qiuyi Lyu, Hong Wei, Shuaishuai Chen, Zhenling Peng, Jianyi Yang

引用次数: 0

K6 Atypical Ubiquitin Chain Regulates Intracellular Serine Homeostasis Through Tor1-Npr1-Par32-Gnp1 Signaling Axis. K6非典型泛素链通过Tor1-Npr1-Par32-Gnp1信号轴调控细胞内丝氨酸稳态。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-28 DOI: 10.1093/gpbjnl/qzag034

Yanchang Li, Yihao Wang, Yonghong Wang, Xinyu Cheng, Qiuyan Lan, Hui Lu, Hui Liu, Zhen Sun, Lei Chang, Yue Gao, Fuchu He, Ping Xu

{"title":"K6 Atypical Ubiquitin Chain Regulates Intracellular Serine Homeostasis Through Tor1-Npr1-Par32-Gnp1 Signaling Axis.","authors":"Yanchang Li, Yihao Wang, Yonghong Wang, Xinyu Cheng, Qiuyan Lan, Hui Lu, Hui Liu, Zhen Sun, Lei Chang, Yue Gao, Fuchu He, Ping Xu","doi":"10.1093/gpbjnl/qzag034","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag034","url":null,"abstract":"Maintaining proper intracellular serine levels is essential for various biological processes, including one-carbon cycle, amino acid synthesis and nucleotide production. However, the precise mechanisms governing serine uptake, biosynthesis and catalytic processes are not fully understood. In this study, quantitative proteomics demonstrated that the absence of K6 ubiquitin chain disrupts serine metabolism by reducing serine biosynthesis and increasing catalytic processes. Phenotype assays revealed that the ubiquitin K6R mutant showed heightened sensitivity to glycerol as a carbon source, suggesting mitochondrial damage, and increased tolerance to myriocin without a clear explanation. Importantly, these phenotypes were predominantly dependent on exogenous serine in the medium, indicating abnormal serine uptake in the K6R mutant. Further investigation identified Gnp1 as a key transporter responsible for excessive serine uptake, elevated sphingolipid synthesis and abnormal myriocin tolerance. Our findings demonstrated that the K6 ubiquitin chains modified on Par32 served as dual regulatory roles in this process. The K6 ubiquitin chain at K265 site on Par32 could affect the stability of Gnp1. The absence of K6 ubiquitin chain on Par32 decreased its interaction with the upstream kinase Npr1, reduced phosphorylation, thereby promoting its interaction with Gnp1 and inhibiting its endocytosis. Additionally, K6 chain could modulate the stability of Par32 and lack of it could increase the amount of Par32, enhancing the inhibitory effect. Our findings highlight the K6 atypical ubiquitin chain as a crucial regulatory factor involved in the Tor1-Npr1-Par32-Gnp1 signaling axis for nutrient sensing, providing insights into the homeostasis of amino acid transporters.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793007","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

OBC: Optimized Batch Correction with Dual-level Quality Control for Scalable Proteomics and Metabolomics. OBC：优化批量校正与双水平质量控制可扩展的蛋白质组学和代谢组学。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-25 DOI: 10.1093/gpbjnl/qzag033

Helong Zheng, Zengqi Tan, Peng Xue, Feng Guan, Yue Xuan, Yue Zhou

{"title":"OBC: Optimized Batch Correction with Dual-level Quality Control for Scalable Proteomics and Metabolomics.","authors":"Helong Zheng, Zengqi Tan, Peng Xue, Feng Guan, Yue Xuan, Yue Zhou","doi":"10.1093/gpbjnl/qzag033","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag033","url":null,"abstract":"The technological advancements in mass spectrometry-based proteomics and metabolomics have enabled large-scale studies, in which hundreds to thousands of samples are analyzed in batches across multiple instruments over extended periods. Consequently, there are inevitable technical variations introduced as batch effects in the results. To address these issues, we developed a pipeline named \"omics batch correct (OBC)\" that integrates optimized data preprocessing steps, including normalization, handling of missing values, and batch correction, with a two-tier quality control (QC) system designed for both proteomic and metabolomic data. The first-tier QC incorporates methods such as principal component analysis, t-distributed stochastic neighbor embedding, uniform manifold approximation and projection, relative standard deviation analysis, Pearson correlation, and principal variance component analysis. The second-tier QC focuses on comparing differentially expressed molecules, particularly those with known regulatory roles, before and after batch correction. We validated the OBC through comprehensive cross-validation using clinical proteomic and metabolomic datasets, demonstrating its superior performance in mitigating batch effects while preserving biologically significant variations. The OBC pipeline is accessible via a user-friendly web interface at https://zhljude.shinyapps.io/OBC-app.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147792996","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

PROBind: A Web Server for Prediction, Analysis, and Visualization of Protein-Protein and Protein-Nucleic Acid Binding Residues. PROBind：用于预测、分析和可视化蛋白质-蛋白质和蛋白质-核酸结合残基的Web服务器。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-24 DOI: 10.1093/gpbjnl/qzag032

Chaojin Wu, Fuhao Zhang, Pengzhen Jia, Jiuxiang Zhu, Min Zeng, Gang Hu, Kui Wang, Lukasz Kurgan, Min Li

{"title":"PROBind: A Web Server for Prediction, Analysis, and Visualization of Protein-Protein and Protein-Nucleic Acid Binding Residues.","authors":"Chaojin Wu, Fuhao Zhang, Pengzhen Jia, Jiuxiang Zhu, Min Zeng, Gang Hu, Kui Wang, Lukasz Kurgan, Min Li","doi":"10.1093/gpbjnl/qzag032","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag032","url":null,"abstract":"Protein-protein and protein-nucleic acid interactions are fundamental to numerous cellular functions, yet only a small fraction have been experimentally characterized. Although modern computational methods have been developed for predicting interacting residues in proteins, they are challenging to use due to individual installation and execution requirements, lack of a standardized input or output format, inability to cover multiple target biomolecules and absence of support for result analysis. Moreover, the performance of many methods varies across different proteins. For instance, algorithms trained on complexes or intrinsically disordered regions may not perform well on other types. To overcome these challenges, we have developed PROBind, a web server for predicting, analyzing and interactively visualizing protein, DNA and RNA binding residues. PROBind integrates 12 predictors trained on structural or disordered proteins. It supports protein sequences and structures as input for predicting binding residues and allows for the integration of prediction results from external predictors. By normalizing and averaging predictions from multiple predictors targeting the same ligand, PROBind generates meta-predictions that balance discrepancies among different methods. Furthermore, it provides interactive graphical tools for result analysis and contextualization. Overall, PROBind accommodates diverse ligand types and supports predictions and analysis based on both structure and sequence data, overcoming the limitations of existing tools. PROBind is freely accessible at https://www.csuligroup.com/PROBind.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793001","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

Urinary Proteomics: Biological Foundations, Analytical Frameworks, and Clinical Translation Across Human Diseases. 尿蛋白质组学：人类疾病的生物学基础、分析框架和临床翻译。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-22 DOI: 10.1093/gpbjnl/qzag028

Fanjie Meng, Yuxuan Xiao, Xincheng Li, Jiawen Chen, Bin Hu, Jun Wang, Kezhong Chen

{"title":"Urinary Proteomics: Biological Foundations, Analytical Frameworks, and Clinical Translation Across Human Diseases.","authors":"Fanjie Meng, Yuxuan Xiao, Xincheng Li, Jiawen Chen, Bin Hu, Jun Wang, Kezhong Chen","doi":"10.1093/gpbjnl/qzag028","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag028","url":null,"abstract":"Urinary proteomics has swiftly emerged as a formidable tool for the identification of non-invasive biomarkers and the surveillance of diseases. The progression in high-resolution mass spectrometry and data-independent acquisition techniques has facilitated the urinary proteome in providing detailed insights into intricate pathophysiological processes impacting multiple organ systems. This review synthesizes the recent advancements in urinary proteomics, detailing the analytical methodologies utilized, the challenges associated with standardization, and the normalization strategies crucial to the discipline. We undertake a comparative analysis of data-dependent and data-independent acquisition methodologies and examine their complementary roles in clinical workflows for biomarker discovery and translation. Additionally, we highlight both common and disease-specific proteomic signatures across a spectrum of disorders, including oncological, renal, cardiovascular, metabolic, and neurodegenerative diseases. We also investigate the role of artificial intelligence and multi-omics integration in supporting predictive modeling. Lastly, we discuss the ongoing developments in regulatory and implementation frameworks, such as data privacy regulations and clinical validation standards, that are positioning urinary proteomics as a key component of preventive and precision medicine.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793005","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

Metabolomics Identified Caloric Restriction-associated Glycerophospholipid Alterations in ApoE-/- Mice. 代谢组学鉴定ApoE-/-小鼠中与热量限制相关的甘油磷脂改变。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-21 DOI: 10.1093/gpbjnl/qzag030

Zi-Yu Wei, He-Ping Wang, Song Tang, Bo Yang, Shuang-Jie Lv, Hui-Yu Wang, Yu-Fei Zhang, Ming Li, Wenjie Zheng, Xiaoman Wang, Xiaoming Shi, De-Pei Liu, Hou-Zao Chen

{"title":"Metabolomics Identified Caloric Restriction-associated Glycerophospholipid Alterations in ApoE-/- Mice.","authors":"Zi-Yu Wei, He-Ping Wang, Song Tang, Bo Yang, Shuang-Jie Lv, Hui-Yu Wang, Yu-Fei Zhang, Ming Li, Wenjie Zheng, Xiaoman Wang, Xiaoming Shi, De-Pei Liu, Hou-Zao Chen","doi":"10.1093/gpbjnl/qzag030","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag030","url":null,"abstract":"Caloric restriction (CR) improves metabolic health and reduces the risk of aging-related vascular diseases. However, the systematic metabolic reprogramming associated with CR remains unclear. To address this, we performed multi-tissue metabolomic profiling (liver, heart, and serum) in apolipoprotein E-deficient (ApoE-/-) mice subjected to CR. Metabolomic analyses of the multiple tissues revealed that glycerophospholipid metabolism pathway was consistently modulated by CR. To explore its relevance in vascular diseases, we performed serum metabolomic profiling in an abdominal aortic aneurysm (AAA) model induced by angiotensin Ⅱ (AngⅡ) infusion in ApoE-/- mice. The level of lysophosphatidylethanolamine (LPE) (16:0/0:0), a metabolite in the glycerophospholipid metabolism pathway, was elevated during AAA progression and significantly reduced by CR intervention, suggesting its potential as a vascular disease risk factor. Notably, glycerophospholipid metabolism and LPE (16:0) were significantly associated with vascular diseases and aging-related indicators in human multi-omics data, including public transcriptomic and lipidomic, and our serum multi-omics profiling of 76 healthy aged individuals. Collectively, our findings establish glycerophospholipid metabolism and LPE (16:0) as systemic signatures of CR with diagnostic potential. They highlight a crucial link between systemic metabolism and vascular remodeling and remodeling-associated vascular diseases, while also functioning as indicators of systemic aging.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147793044","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

Variant Calling in the Dark Genome: Benchmarking SNV Calls in the Flanks of Structural Variants. 暗基因组中的变异召唤：对结构变异侧翼的SNV召唤进行基准测试。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-20 DOI: 10.1093/gpbjnl/qzag031

Ningxin Dang, Peng Jia, Jiadong Lin, Yutong Xie, Yongyong Kang, Zixuan Li, Kai Ye, Stephen J Bush

{"title":"Variant Calling in the Dark Genome: Benchmarking SNV Calls in the Flanks of Structural Variants.","authors":"Ningxin Dang, Peng Jia, Jiadong Lin, Yutong Xie, Yongyong Kang, Zixuan Li, Kai Ye, Stephen J Bush","doi":"10.1093/gpbjnl/qzag031","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag031","url":null,"abstract":"Single nucleotide variant calling protocols routinely discard calls in the regions immediately flanking structural variants (often 50 bp either side) because it is technically challenging to call them accurately. Although there are undoubtedly true variants of interest in these regions, many remain hidden as they are considered too difficult to distinguish from false positive calls. Nevertheless, with advances in both long-read sequencing and deep-learning algorithms, it is increasingly possible to resolve structural variants and their context more accurately. To provide guidance on SNV calling in the regions flanking structural variants (SVs), and to facilitate ongoing method development, we refined data from the Chinese Quartet project to construct a benchmarking set of 1000 SVs (more precisely, 299 deletions and 701 insertions), each on a completely assembled chromosome arm, supported by multiple sequencing technologies, and manually curated. We then used this real data, alongside corroboratory simulated data, to evaluate the performance of 35 short-read and 19 long-read variant calling pipelines at calling SNVs in their vicinity, representing combinations of up to 9 read aligners with up to 10 variant callers. Our datasets extend the scope of human benchmarking resources into these specific regions of the 'dark genome' and our results highlight practical strategies for variant calling within them.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147731109","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

StarFunc: Fusing Template-based and Deep Learning Approaches for Accurate Protein Function Prediction. StarFunc：融合基于模板和深度学习的精确蛋白质功能预测方法。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-06 DOI: 10.1093/gpbjnl/qzag018

Chengxin Zhang, Quancheng Liu, Lydia Freddolino

引用次数: 0

MetaDIA: A DDA-free Database Reduction Strategy for DIA Human Gut Metaproteomics. 媒体：DIA人类肠道宏蛋白质组学的无dda数据库缩减策略。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-04-04 DOI: 10.1093/gpbjnl/qzag029

Haonan Duan, Zhibin Ning, Zhongzhi Sun, Tiannan Guo, Yingying Sun, Daniel Figeys

{"title":"MetaDIA: A DDA-free Database Reduction Strategy for DIA Human Gut Metaproteomics.","authors":"Haonan Duan, Zhibin Ning, Zhongzhi Sun, Tiannan Guo, Yingying Sun, Daniel Figeys","doi":"10.1093/gpbjnl/qzag029","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag029","url":null,"abstract":"Microbiomes, especially within the gut, are complex and may comprise hundreds of species. The identification of peptides in metaproteomics presents a substantial challenge, as it involves matching peptides to mass spectra within an enormous search space for complex and unknown samples. This poses difficulties for both the accuracy and the speed of identification. Specifically, analysis of data-independent acquisition (DIA) datasets has relied on libraries constructed from prior data-dependent acquisition (DDA) results. However, this method is resource-intensive, consumes samples, and limits identification to peptides previously identified. These limitations restrict the application of DIA in metaproteomics research. We introduced a novel strategy to reduce the search space by utilizing species abundance and functional abundance information from the microbiome to score each peptide and prioritize those most likely to be detected. Using this strategy, we have developed and optimized a workflow called MetaDIA for the analysis of microbiome data generated by DIA, which operates independently of DDA assistance. Our approach successfully created a smaller, yet sufficient database for DIA data search in metaproteomics. The results demonstrated strong consistency with the traditional DDA-based library approach at both protein and functional levels. MetaDIA is readily accessible as an open-source project hosted on GitHub (https://github.com/northomics/MetaDIA).","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625236","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