Briefings in Functional Genomics最新文献_第2页

An overview of key online resources for human genomics: a powerful and open toolbox for in silico research. 人类基因组学主要在线资源概览：用于硅学研究的强大而开放的工具箱。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-12-06 DOI: 10.1093/bfgp/elae029

Diego A Forero, Diego A Bonilla, Yeimy González-Giraldo, George P Patrinos

引用次数: 0

A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data. 针对单细胞和空间转录组学数据的降维和聚类方法综合调查。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-12-06 DOI: 10.1093/bfgp/elae023

Yidi Sun, Lingling Kong, Jiayi Huang, Hongyan Deng, Xinling Bian, Xingfeng Li, Feifei Cui, Lijun Dou, Chen Cao, Quan Zou, Zilong Zhang

{"title":"A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data.","authors":"Yidi Sun, Lingling Kong, Jiayi Huang, Hongyan Deng, Xinling Bian, Xingfeng Li, Feifei Cui, Lijun Dou, Chen Cao, Quan Zou, Zilong Zhang","doi":"10.1093/bfgp/elae023","DOIUrl":"10.1093/bfgp/elae023","url":null,"abstract":"In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"733-744"},"PeriodicalIF":2.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Characterization of double-stranded RNA and its silencing efficiency for insects using hybrid deep-learning framework. 利用混合深度学习框架鉴定双链 RNA 及其对昆虫的沉默效率。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-12-06 DOI: 10.1093/bfgp/elae027

Han Cheng, Liping Xu, Cangzhi Jia

{"title":"Characterization of double-stranded RNA and its silencing efficiency for insects using hybrid deep-learning framework.","authors":"Han Cheng, Liping Xu, Cangzhi Jia","doi":"10.1093/bfgp/elae027","DOIUrl":"10.1093/bfgp/elae027","url":null,"abstract":"RNA interference (RNAi) technology is widely used in the biological prevention and control of terrestrial insects. One of the main factors with the application of RNAi in insects is the difference in RNAi efficiency, which may vary not only in different insects, but also in different genes of the same insect, and even in different double-stranded RNAs (dsRNAs) of the same gene. This work focuses on the last question and establishes a bioinformatics software that can help researchers screen for the most efficient dsRNA targeting target genes. Among insects, the red flour beetle (Tribolium castaneum) is known to be one of the most sensitive to RNAi. From iBeetle-Base, we extracted 12 027 efficient dsRNA sequences with a lethality rate of ≥20% or with experimentation-induced phenotypic changes and processed these data to correspond to specific silence efficiency. Based on the first complied novel benchmark dataset, we specifically designed a deep neural network to identify and characterize efficient dsRNA for RNAi in insects. The dna2vec word embedding model was trained to extract distributed feature representations, and three powerful modules, namely convolutional neural network, bidirectional long short-term memory network, and self-attention mechanism, were integrated to form our predictor model to characterize the extracted dsRNAs and their silencing efficiencies for T. castaneum. Our model dsRNAPredictor showed reliable performance in multiple independent tests based on different species, including both T. castaneum and Aedes aegypti. This indicates that dsRNAPredictor can facilitate prescreening for designing high-efficiency dsRNA targeting target genes of insects in advance.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"858-865"},"PeriodicalIF":2.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sesame Genomic Web Resource (SesameGWR): a well-annotated data resource for transcriptomic signatures of abiotic and biotic stress responses in sesame (Sesamum indicum L.). 芝麻基因组网络资源（SesameGWR）：芝麻（Sesamum indicum L.）非生物和生物胁迫反应转录组特征的完善注释数据资源。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-12-06 DOI: 10.1093/bfgp/elae022

Himanshu Avashthi, Ulavappa Basavanneppa Angadi, Divya Chauhan, Anuj Kumar, Dwijesh Chandra Mishra, Parimalan Rangan, Rashmi Yadav, Dinesh Kumar

{"title":"Sesame Genomic Web Resource (SesameGWR): a well-annotated data resource for transcriptomic signatures of abiotic and biotic stress responses in sesame (Sesamum indicum L.).","authors":"Himanshu Avashthi, Ulavappa Basavanneppa Angadi, Divya Chauhan, Anuj Kumar, Dwijesh Chandra Mishra, Parimalan Rangan, Rashmi Yadav, Dinesh Kumar","doi":"10.1093/bfgp/elae022","DOIUrl":"10.1093/bfgp/elae022","url":null,"abstract":"Sesame (Sesamum indicum L.) is a globally cultivated oilseed crop renowned for its historical significance and widespread growth in tropical and subtropical regions. With notable nutritional and medicinal attributes, sesame has shown promising effects in combating malnutrition cancer, diabetes, and other diseases like cardiovascular problems. However, sesame production faces significant challenges from environmental threats such as charcoal rot, drought, salinity, and waterlogging stress, resulting in economic losses for farmers. The scarcity of information on stress-resistance genes and pathways exacerbates these challenges. Despite its immense importance, there is currently no platform available to provide comprehensive information on sesame, which significantly hinders the mining of various stress-associated genes and the molecular breeding of sesame. To address this gap, here a free, web-accessible, and user-friendly genomic web resource (SesameGWR, http://backlin.cabgrid.res.in/sesameGWR/) has been developed This platform provides key insights into differentially expressed genes, transcription factors, miRNAs, and molecular markers like simple sequence repeats, single nucleotide polymorphisms, and insertions and deletions associated with both biotic and abiotic stresses.. The functional genomics information and annotations embedded in this web resource were predicted through RNA-seq data analysis. Considering the impact of climate change and the nutritional and medicinal importance of sesame, this study is of utmost importance in understanding stress responses. SesameGWR will serve as a valuable tool for developing climate-resilient sesame varieties, thereby enhancing the productivity of this ancient oilseed crop.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"828-842"},"PeriodicalIF":2.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis. 精准医疗的前沿：单细胞多组学在植入前遗传学诊断中的应用。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-12-06 DOI: 10.1093/bfgp/elae041

Jinglei Zhang, Nan Zhang, Qingyun Mai, Canquan Zhou

{"title":"The frontier of precision medicine: application of single-cell multi-omics in preimplantation genetic diagnosis.","authors":"Jinglei Zhang, Nan Zhang, Qingyun Mai, Canquan Zhou","doi":"10.1093/bfgp/elae041","DOIUrl":"10.1093/bfgp/elae041","url":null,"abstract":"The advent of single-cell multi-omics technologies has revolutionized the landscape of preimplantation genetic diagnosis (PGD), offering unprecedented insights into the genetic, transcriptomic, and proteomic profiles of individual cells in early-stage embryos. This breakthrough holds the promise of enhancing the accuracy, efficiency, and scope of PGD, thereby significantly improving outcomes in assisted reproductive technologies (ARTs) and genetic disease prevention. This review provides a comprehensive overview of the importance of PGD in the context of precision medicine and elucidates how single-cell multi-omics technologies have transformed this field. We begin with a brief history of PGD, highlighting its evolution and application in detecting genetic disorders and facilitating ART. Subsequently, we delve into the principles, methodologies, and applications of single-cell genomics, transcriptomics, and proteomics in PGD, emphasizing their role in improving diagnostic precision and efficiency. Furthermore, we review significant recent advances within this domain, including key experimental designs, findings, and their implications for PGD practices. The advantages and limitations of these studies are analyzed to assess their potential impact on the future development of PGD technologies. Looking forward, we discuss the emerging research directions and challenges, focusing on technological advancements, new application areas, and strategies to overcome existing limitations. In conclusion, this review underscores the pivotal role of single-cell multi-omics in PGD, highlighting its potential to drive the progress of precision medicine and personalized treatment strategies, thereby marking a new era in reproductive genetics and healthcare.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"726-732"},"PeriodicalIF":2.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review: simulation tools for genome-wide interaction studies. 综述：全基因组相互作用研究的模拟工具。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-12-06 DOI: 10.1093/bfgp/elae034

Junliang Shang, Anqi Xu, Mingyuan Bi, Yuanyuan Zhang, Feng Li, Jin-Xing Liu

{"title":"A review: simulation tools for genome-wide interaction studies.","authors":"Junliang Shang, Anqi Xu, Mingyuan Bi, Yuanyuan Zhang, Feng Li, Jin-Xing Liu","doi":"10.1093/bfgp/elae034","DOIUrl":"10.1093/bfgp/elae034","url":null,"abstract":"Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"745-753"},"PeriodicalIF":2.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-omics integration analysis reveals the role of N6-methyladenosine in lncRNA translation during glioma stem cell differentiation. 多组学整合分析揭示 N6-甲基腺苷在胶质瘤干细胞分化过程中 lncRNA 翻译中的作用

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-12-06 DOI: 10.1093/bfgp/elae037

Meng Zhang, Runqiu Cai, Jingjing Liu, Yulan Wang, Shan He, Quan Wang, Xiaofeng Song, Jing Wu, Jian Zhao

{"title":"Multi-omics integration analysis reveals the role of N6-methyladenosine in lncRNA translation during glioma stem cell differentiation.","authors":"Meng Zhang, Runqiu Cai, Jingjing Liu, Yulan Wang, Shan He, Quan Wang, Xiaofeng Song, Jing Wu, Jian Zhao","doi":"10.1093/bfgp/elae037","DOIUrl":"10.1093/bfgp/elae037","url":null,"abstract":"Glioblastoma is one of the most lethal brain diseases in humans. Although recent studies have shown reciprocal interactions between N6-methyladenosine (m6A) modifications and long noncoding RNAs (lncRNAs) in gliomagenesis and malignant progression, the mechanism of m6A-mediated lncRNA translational regulation in glioblastoma remains unclear. Herein, we profiled the transcriptomes, translatomes, and epitranscriptomics of glioma stem cells and differentiated glioma cells to investigate the role of m6A in lncRNA translation comprehensively. We found that lncRNAs with numerous m6A peaks exhibit reduced translation efficiency. Transcript-level expression analysis demonstrates an enrichment of m6A around short open reading frames (sORFs) of translatable lncRNA transcripts. Further comparison analysis of m6A modifications in different RNA regions indicates that m6A peaks downstream of sORFs inhibit lncRNA translation more than those upstream. Observations in glioma-associated lncRNAs H19, LINC00467, and GAS5 further confirm the negative effect of m6A methylation on lncRNA translation. Overall, these findings elucidate the dynamic profiles of the m6A methylome and enhance the understanding of the complexity of lncRNA translational regulation.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"806-815"},"PeriodicalIF":2.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Less is more: relative rank is more informative than absolute abundance for compositional NGS data. 少即是多：对于成分 NGS 数据而言，相对等级比绝对丰度更有参考价值。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-11-20 DOI: 10.1093/bfgp/elae045

Xubin Zheng, Nana Jin, Qiong Wu, Ning Zhang, Haonan Wu, Yuanhao Wang, Rui Luo, Tao Liu, Wanfu Ding, Qingshan Geng, Lixin Cheng

{"title":"Less is more: relative rank is more informative than absolute abundance for compositional NGS data.","authors":"Xubin Zheng, Nana Jin, Qiong Wu, Ning Zhang, Haonan Wu, Yuanhao Wang, Rui Luo, Tao Liu, Wanfu Ding, Qingshan Geng, Lixin Cheng","doi":"10.1093/bfgp/elae045","DOIUrl":"https://doi.org/10.1093/bfgp/elae045","url":null,"abstract":"High-throughput gene expression data have been extensively generated and utilized in biological mechanism investigations, biomarker detection, disease diagnosis and prognosis. These applications encompass not only bulk transcriptome, but also single cell RNA-seq data. However, extracting reliable biological information from transcriptome data remains challenging due to the constrains of Compositional Data Analysis. Current data preprocessing methods, including dataset normalization and batch effect correction, are insufficient to address these issues and improve data quality for downstream analysis. Alternatively, qualification methods focusing on the relative order of gene expression (ROGER) are more informative than the quantification methods that rely on gene expression abundance. The Pairwise Analysis of Gene expression method is an enhancement of ROGER, designed for data integration in either sample space or feature space. In this review, we summarize the methods applied to transcriptome data analysis and discuss their potentials in predicting clinical outcomes.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features. DeepMEns：基于多种特征预测 sgRNA 靶向活性的集合模型。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-11-11 DOI: 10.1093/bfgp/elae043

Shumei Ding, Jia Zheng, Cangzhi Jia

{"title":"DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features.","authors":"Shumei Ding, Jia Zheng, Cangzhi Jia","doi":"10.1093/bfgp/elae043","DOIUrl":"https://doi.org/10.1093/bfgp/elae043","url":null,"abstract":"The CRISPR/Cas9 system developed from Streptococcus pyogenes (SpCas9) has high potential in gene editing. However, its successful application is hindered by the considerable variability in target efficiencies across different single guide RNAs (sgRNAs). Although several deep learning models have been created to predict sgRNA on-target activity, the intrinsic mechanisms of these models are difficult to explain, and there is still scope for improvement in prediction performance. To overcome these issues, we propose an ensemble interpretable model termed DeepMEns based on deep learning to predict sgRNA on-target activity. By using five different training and validation datasets, we constructed five sub-regressors, each comprising three parts. The first part uses one-hot encoding, wherein 0-1 representation of the secondary structure is used as the input to the convolutional neural network (CNN) with Transformer encoder. The second part uses the DNA shape feature matrix as the input to the CNN with Transformer encoder. The third part uses positional encoding feature matrices as the proposed input into a long short-term memory network with an attention mechanism. These three parts are concatenated through the flattened layer, and the final prediction result is the average of the five sub-regressors. Extensive benchmarking experiments indicated that DeepMEns achieved the highest Spearman correlation coefficient for 6 of 10 independent test datasets as compared to previous predictors, this finding confirmed that DeepMEns can accomplish state-of-the-art performance. Moreover, the ablation analysis also indicated that the ensemble strategy may improve the performance of the prediction model.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases. 单细胞 RNA-seq 和 ATAC-seq 计算算法在神经退行性疾病中的应用。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-11-05 DOI: 10.1093/bfgp/elae044

Hwisoo Choi, Hyeonkyu Kim, Hoebin Chung, Dong-Sung Lee, Junil Kim

{"title":"Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases.","authors":"Hwisoo Choi, Hyeonkyu Kim, Hoebin Chung, Dong-Sung Lee, Junil Kim","doi":"10.1093/bfgp/elae044","DOIUrl":"https://doi.org/10.1093/bfgp/elae044","url":null,"abstract":"Recent advancements in single-cell technologies, including single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), have greatly improved our insight into the epigenomic landscapes across various biological contexts and diseases. This paper reviews key computational tools and machine learning approaches that integrate scRNA-seq and scATAC-seq data to facilitate the alignment of transcriptomic data with chromatin accessibility profiles. Applying these integrated single-cell technologies in neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, reveals how changes in chromatin accessibility and gene expression can illuminate pathogenic mechanisms and identify potential therapeutic targets. Despite facing challenges like data sparsity and computational demands, ongoing enhancements in scATAC-seq and scRNA-seq technologies, along with better analytical methods, continue to expand their applications. These advancements promise to revolutionize our approach to medical research and clinical diagnostics, offering a comprehensive view of cellular function and disease pathology.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0