Cell Reports Methods最新文献_第4页

Genetic analyses of eight complex diseases using predicted continuous representations of disease. 使用疾病预测连续表示的八种复杂疾病的遗传分析。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-07-25 DOI: 10.1016/j.crmeth.2025.101115

Robert Chen, Ghislain Rocheleau, Ben Omega Petrazzini, Iain S Forrest, Joshua K Park, Áine Duffy, Ha My T Vy, Daniel Jordan, Ron Do

{"title":"Genetic analyses of eight complex diseases using predicted continuous representations of disease.","authors":"Robert Chen, Ghislain Rocheleau, Ben Omega Petrazzini, Iain S Forrest, Joshua K Park, Áine Duffy, Ha My T Vy, Daniel Jordan, Ron Do","doi":"10.1016/j.crmeth.2025.101115","DOIUrl":"10.1016/j.crmeth.2025.101115","url":null,"abstract":"We evaluated whether predicted continuous disease representations could enhance genetic discovery beyond case-control genome-wide association study (GWAS) phenotypes across eight complex diseases in up to 485,448 UK Biobank participants. Predicted phenotypes had high genetic correlations with case-control phenotypes (median rg = 0.66) but identified more independent associations (median 306 versus 125). While some predicted phenotype associations were spurious, multi-trait analysis of GWAS-boosted case-control phenotypes identified a median of 46 additional variants per disease, of which a median of 73% replicated in FinnGen, 37% reached genome-wide significance in a UK Biobank/FinnGen meta-analysis, and 45% had supporting evidence. Predicted phenotypes also identified 14 genes targeted by phase I-IV drugs not identified by case-control phenotypes, and combined polygenic risk scores (PRSs) using both phenotypes improved prediction performance, with a median 37% increase in Nagelkerke's R2. Predicted phenotypes represent composite biomarkers complementing case-control approaches in genetic discovery, drug target prioritization, and risk prediction, though efficacy varies across diseases.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101115"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144718788","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

Optimized pipeline and designer cells for synthetic-biology-based high-throughput screening of viral protease inhibitors. 基于合成生物学的病毒蛋白酶抑制剂高通量筛选的优化管道和设计细胞。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-08-07 DOI: 10.1016/j.crmeth.2025.101139

Shlomi Edri, Shayma El-Atawneh, Tehila Ernst, Maayan Elnekave, Chaja Katzman, Tali Lanton, Ido Aldar, Omri Wolk, Noa Stern, Amiram Goldblum, Lior Nissim

{"title":"Optimized pipeline and designer cells for synthetic-biology-based high-throughput screening of viral protease inhibitors.","authors":"Shlomi Edri, Shayma El-Atawneh, Tehila Ernst, Maayan Elnekave, Chaja Katzman, Tali Lanton, Ido Aldar, Omri Wolk, Noa Stern, Amiram Goldblum, Lior Nissim","doi":"10.1016/j.crmeth.2025.101139","DOIUrl":"10.1016/j.crmeth.2025.101139","url":null,"abstract":"A reliable, efficient, high-throughput pipeline to evaluate viral protease inhibitors would enhance antiviral drug discovery. Methods such as crystallography and phenotypic screening are often constrained by complex assay conditions, limited physiological relevance, or live virus handling safety concerns. Proof-of-concept studies previously demonstrated synthetic gene circuits that produce a quantitative reporter upon protease inhibition, enabling functional virus-independent evaluation of viral protease inhibitors in live cells. Using the SARS-CoV-2 3-chymotrypsin-like protease (3CLpro) as a model, we advanced this approach into a high-throughput first-pass qualitative assay (\"hit/no-hit\") to rapidly identify promising drug candidates. Our optimized circuit design was used to produce stable HEK293T and HeLa designer cells that generate two distinct fluorescence outputs, simultaneously reporting protease inhibition and cytotoxicity. The screening pipeline is designed to minimize labor, costs, and false-positive observations, thus enabling versatile, safe, and efficient functional drug screening suitable for any conventional biological laboratory.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101139"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805008","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

Building simplified cancer subtyping and prediction models with glycan gene signatures. 利用聚糖基因特征建立简化的癌症亚型和预测模型。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-08-11 DOI: 10.1016/j.crmeth.2025.101140

Jing Kai, Luyao Yang, Ayman F AbuElela, Alyaa M Abdel-Haleem, Asma S AlAmoodi, Abdulghani A Bin Nafisah, Alfadel Alshaibani, Ali S Alzahrani, Vincenzo Lagani, David Gomez-Cabrero, Xin Gao, Jasmeen S Merzaban

{"title":"Building simplified cancer subtyping and prediction models with glycan gene signatures.","authors":"Jing Kai, Luyao Yang, Ayman F AbuElela, Alyaa M Abdel-Haleem, Asma S AlAmoodi, Abdulghani A Bin Nafisah, Alfadel Alshaibani, Ali S Alzahrani, Vincenzo Lagani, David Gomez-Cabrero, Xin Gao, Jasmeen S Merzaban","doi":"10.1016/j.crmeth.2025.101140","DOIUrl":"10.1016/j.crmeth.2025.101140","url":null,"abstract":"We identified a gene panel comprising 71 glycosyltransferases (GTs) that alter glycan patterns on cancer cells as they become more virulent. When these cancer-pattern GTs (CPGTs) were run through an algorithm trained on The Cancer Genome Atlas, they differentiated tumors from healthy tissue with 97% accuracy and clustered 27 cancers with 94% accuracy in external validation, revealing each variety's \"biometric glycan ID.\" Using machine learning, we built four models for cancer classification, including two for detecting the molecular subtypes of breast cancer and glioma using even smaller CPGT sets. Our results reveal the power of using glyco-genes for diagnostics: Our breast cancer classifier was almost twice as effective in independent testing as the widely used prediction analysis of microarray 50 (PAM50) subtyping kit at differentiating between luminal A, luminal B, HER2-enriched, and basal-like breast cancers based on a comparable number of genes. Only four GT genes were needed to build a prognostic model for glioma survival.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101140"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838030","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

AAV vectors for specific and efficient gene expression in microglia. 小胶质细胞特异性高效基因表达的AAV载体。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-07-30 DOI: 10.1016/j.crmeth.2025.101116

Ryo Aoki, Ayumu Konno, Nobutake Hosoi, Hayato Kawabata, Hirokazu Hirai

{"title":"AAV vectors for specific and efficient gene expression in microglia.","authors":"Ryo Aoki, Ayumu Konno, Nobutake Hosoi, Hayato Kawabata, Hirokazu Hirai","doi":"10.1016/j.crmeth.2025.101116","DOIUrl":"10.1016/j.crmeth.2025.101116","url":null,"abstract":"Microglia are crucial targets for therapeutic interventions in diseases like Alzheimer's and stroke, but efficient gene delivery to these immune cells is challenging. We developed an adeno-associated virus (AAV) vector that achieves specific and efficient gene delivery to microglia. This vector incorporates the mIba1 promoter, GFP, miRNA target sequences (miR.Ts), WPRE, and poly(A) signal. Positioning miR.Ts on both sides of WPRE significantly suppressed non-microglial expression, achieving over 90% specificity and more than 60% efficiency in microglia-specific gene expression 3 weeks post-administration. Additionally, this vector enabled GCaMP expression, facilitating real-time calcium dynamics monitoring in microglial processes. Using a blood-brain barrier-penetrant AAV-9P31 capsid variant, intravenous administration resulted in broad and selective microglial GFP expression across the brain. These results establish our AAV vector as a versatile tool for long-term, highly specific, and efficient gene expression in microglia, advancing microglial research and potential therapeutic applications.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101116"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144761590","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

Combining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing. 结合基于面板和基于全转录组的基因融合检测的长读测序。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-07-21 DOI: 10.1016/j.crmeth.2025.101111

Karleena Rybacki, Feng Xu, Hannah M Deutsch, Mian Umair Ahsan, Joe Chan, Zizhuo Liang, Yuanquan Song, Marilyn Li, Kai Wang

{"title":"Combining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing.","authors":"Karleena Rybacki, Feng Xu, Hannah M Deutsch, Mian Umair Ahsan, Joe Chan, Zizhuo Liang, Yuanquan Song, Marilyn Li, Kai Wang","doi":"10.1016/j.crmeth.2025.101111","DOIUrl":"10.1016/j.crmeth.2025.101111","url":null,"abstract":"We present a comprehensive gene fusion (GF) detection and analysis workflow that combines targeted panel-based and whole-transcriptome long-read sequencing. We first adapted libraries from the short-read CHOP Cancer Fusion Panel, which targets 119 oncogenes commonly implicated in cancer fusions, for use on Oxford Nanopore Technologies' long-read sequencing platform. Long-read sequencing successfully detected known GFs in panel-positive samples, confirming compatibility, and enabled reduced turnaround times. To expand GF discovery in clinically challenging cases, we analyzed 24 glioma samples with negative short-read fusion panel results using whole-transcriptome long-read sequencing. This identified 20 candidate GFs in panel-negative samples that were absent from current fusion databases, all of which were experimentally validated. In summary, we introduce a computational workflow that combines panel-based and whole-transcriptome long-read sequencing with tailored analysis pipelines to enable fast and comprehensive GF detection in cancer.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101111"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144691720","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

3D-printed plugs enhance cell usage efficiency for single-cell migration and neuron axon guidance assays. 3d打印插头提高单细胞迁移和神经元轴突引导分析的细胞使用效率。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-07-28 DOI: 10.1016/j.crmeth.2025.101117

Jinxiong Cheng, Edwin C Rock, Mishal Rao, Hsiao-Chun Chen, Yushu Ma, Kun-Che Chang, Yu-Chih Chen

引用次数: 0

All-in-one medical image-to-image translation. 一体化医学图像到图像的翻译。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-08-11 DOI: 10.1016/j.crmeth.2025.101138

Luyi Han, Tao Tan, Yunzhi Huang, Haoran Dou, Tianyu Zhang, Yuan Gao, Xin Wang, Chunyao Lu, Xinglong Liang, Yue Sun, Jonas Teuwen, S Kevin Zhou, Ritse Mann

{"title":"All-in-one medical image-to-image translation.","authors":"Luyi Han, Tao Tan, Yunzhi Huang, Haoran Dou, Tianyu Zhang, Yuan Gao, Xin Wang, Chunyao Lu, Xinglong Liang, Yue Sun, Jonas Teuwen, S Kevin Zhou, Ritse Mann","doi":"10.1016/j.crmeth.2025.101138","DOIUrl":"10.1016/j.crmeth.2025.101138","url":null,"abstract":"The growing availability of public multi-domain medical image datasets enables training omnipotent image-to-image (I2I) translation models. However, integrating diverse protocols poses challenges in domain encoding and scalability. Therefore, we propose the \"every domain all at once\" I2I (EVA-I2I) translation model using DICOM-tag-informed contrastive language-image pre-training (DCLIP). DCLIP maps natural language scan descriptions into a common latent space, offering richer representations than traditional one-hot encoding. We develop the model using seven public datasets with 27,950 scans (3D volumes) for the brain, breast, abdomen, and pelvis. Experimental results show that our EVA-I2I can synthesize every seen domain at once with a single training session and achieve excellent image quality on different I2I translation tasks. Results for downstream applications (e.g., registration, classification, and segmentation) demonstrate that EVA-I2I can be directly applied to domain adaptation on external datasets without fine-tuning and that it also enables the potential for zero-shot domain adaptation for never-before-seen domains.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101138"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838029","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

Volumetric imaging and computation to explore contractile function in zebrafish hearts. 斑马鱼心脏收缩功能的体积成像与计算研究。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-07-23 DOI: 10.1016/j.crmeth.2025.101113

Alireza Saberigarakani, Riya P Patel, Milad Almasian, Xinyuan Zhang, Jonathan Brewer, Sohail S Hassan, Jichen Chai, Juhyun Lee, Baowei Fei, Jie Yuan, Kelli Carroll, Yichen Ding

{"title":"Volumetric imaging and computation to explore contractile function in zebrafish hearts.","authors":"Alireza Saberigarakani, Riya P Patel, Milad Almasian, Xinyuan Zhang, Jonathan Brewer, Sohail S Hassan, Jichen Chai, Juhyun Lee, Baowei Fei, Jie Yuan, Kelli Carroll, Yichen Ding","doi":"10.1016/j.crmeth.2025.101113","DOIUrl":"10.1016/j.crmeth.2025.101113","url":null,"abstract":"Novel insights into cardiac contractile dysfunction at the cellular level could deepen understanding of arrhythmia and heart injury, which are leading causes of morbidity and mortality worldwide. We present a comprehensive experimental and computational framework combining light-field microscopy and single-cell tracking to investigate real-time volumetric data in live zebrafish hearts, which share structural and electrical similarities to the human heart. Our system acquires 200 vol/s with lateral resolution of up to 5.02 ± 0.54 μm and axial resolution of 9.02 ± 1.11 μm across the whole depth using an expectation-maximization-smoothed deconvolution algorithm. We apply a deep-learning approach to quantify cell displacement and velocity in blood flow and myocardial motion and to perform real-time volumetric tracking from end-systole to end-diastole within a virtual reality environment. This capability delivers high-speed and high-resolution imaging of cardiac contractility at single-cell resolution over multiple cycles, supporting in-depth investigation of intercellular interactions in health and disease.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101113"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709203","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

CUT&Tag and DiBioCUT&Tag enable investigation of the AT-rich epigenome of Plasmodium falciparum from low-input samples. CUT&Tag和DiBioCUT&Tag可以从低输入样本中研究富含at的恶性疟原虫表观基因组。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-07-16 DOI: 10.1016/j.crmeth.2025.101110

Jonas Gockel, Gala Ramón-Zamorano, Jessica Kimmel, Tobias Spielmann, Richárd Bártfai

{"title":"CUT&Tag and DiBioCUT&Tag enable investigation of the AT-rich epigenome of Plasmodium falciparum from low-input samples.","authors":"Jonas Gockel, Gala Ramón-Zamorano, Jessica Kimmel, Tobias Spielmann, Richárd Bártfai","doi":"10.1016/j.crmeth.2025.101110","DOIUrl":"10.1016/j.crmeth.2025.101110","url":null,"abstract":"Phenotypic variation between malaria parasites is a major contributor to the pathogen's success, facilitated by heritable yet dynamic changes in (hetero)chromatin structure. Currently, the chromatin landscape is mostly profiled by chromatin immunoprecipitation sequencing (ChIP-seq), which has several drawbacks: (1) GC-content-related artifacts, (2) substantial material requirement, and (3) a labor-intensive protocol. To overcome these limitations, we adapted cleavage under targets and tagmentation (CUT&Tag) to Plasmodium falciparum. Despite the AT richness of the genome, CUT&Tag results in reproducible heterochromatin profiles concordant with ChIP-seq data while using as little as 10,000 nuclei or crude parasite isolates. We also developed DiBioCUT&Tag, a method utilizing dimerization-induced recruitment of biotin ligase for proximity labeling of core chromatin components during the binding of regulatory proteins followed by anti-biotin CUT&Tag. These methods hence provide substantially improved means for genome-wide profiling of chromatin-associated proteins from low-input samples in the malaria parasite and potentially beyond.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101110"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660454","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

Prediction of the hypothalamus-pituitary organoid formation using machine learning. 使用机器学习预测下丘脑-垂体类器官的形成。

IF 4.5

Cell Reports Methods Pub Date : 2025-08-18 Epub Date: 2025-08-04 DOI: 10.1016/j.crmeth.2025.101119

Ryusaku Matsumoto, Hidetaka Suga, Yutaka Takahashi, Takashi Aoi, Takuya Yamamoto

{"title":"Prediction of the hypothalamus-pituitary organoid formation using machine learning.","authors":"Ryusaku Matsumoto, Hidetaka Suga, Yutaka Takahashi, Takashi Aoi, Takuya Yamamoto","doi":"10.1016/j.crmeth.2025.101119","DOIUrl":"10.1016/j.crmeth.2025.101119","url":null,"abstract":"Multi-cellular organoids are self-assembly aggregates that mimic biological functions and developmental processes of many tissue types in vitro. They are widely employed for disease modeling and functional studies. Hypothalamus-pituitary organoids can be generated through differentiation induction from pluripotent stem cells. However, their maturation is time consuming and labor intensive, and the quality of the resulting organoids can vary. Here, we developed a machine learning model capable of accurately predicting the successful generation of high-quality hypothalamus-pituitary organoids based solely on phase-contrast images captured during the early stage of differentiation. The model achieved an accuracy of 79% using images from organoids on day 9 to predict pituitary cell differentiation at day 40. Moreover, the computational approach identified the shape of the organoid surface as a critical determining factor that significantly affected the prediction. This model can help to enhance the efficiency of organoid induction experiments and illuminate the molecular mechanisms involved in hypothalamus-pituitary differentiation.","PeriodicalId":29773,"journal":{"name":"Cell Reports Methods","volume":" ","pages":"101119"},"PeriodicalIF":4.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790140","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