Computational and structural biotechnology journal最新文献

{"title":"BCMA: An integrative and versatile database for multi-scale and multi-omics molecular atlas of breast cancer.","authors":"Wenbo Guo, Zikang Yin, Qinglin Mei, Lianshuo Li, Yonghui Gong, Xinqi Li, Wei Zhang, Wenjie Lei, Bingqiang Liu, Lin Hou, Mei Yang, Jin Gu","doi":"10.1016/j.csbj.2025.06.031","DOIUrl":"10.1016/j.csbj.2025.06.031","url":null,"abstract":"<p><p>Breast cancer (BC) is one of the most common cancer types among women worldwide. Understanding the complex molecular and cellular characteristics of BC is crucial for advancing precision treatment. To enable more reliable and reproducible biological discoveries, it is critical to collect molecular data from diverse BC cohorts and establish an integrative, versatile analysis platform. Here, we present BCMA (Breast Cancer Molecular Atlas, http://lifeome.net/database/bcma/), a multi-scale, multi-omics BC database that encompasses 6 bulk multi-omics datasets and 9 single-cell transcriptomics datasets, collectively covering 5424 cases and 236,363 cells. The BCMA systemically characterizes the molecular features of BC, including gene mutations, copy number alterations, RNA expression, miRNA expression, DNA methylation, as well as clinical phenotypes and cell heterogeneity. Meanwhile, a user-friendly interface for gene-centered search is provided, achieving the clinical information statistics, genomic events analysis, differential multi-omics feature identification, functional enrichment analysis, survival analysis, co-expression analysis, as well as single-cell gene expression profiling and cell type annotation. This platform holds great potential to enhance the understanding of molecular characteristics underlying BC and to facilitate the identification of disease-associated biomarkers.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2701-2710"},"PeriodicalIF":4.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine learning.","authors":"Erica T Prates, Omar Demerdash, Manesh Shah, Tomás A Rush, Udaya C Kalluri, Daniel A Jacobson","doi":"10.1016/j.csbj.2025.06.029","DOIUrl":"10.1016/j.csbj.2025.06.029","url":null,"abstract":"<p><p>Microbiome assembly, structure, and dynamics significantly influence plant health. Secreted microbial signaling molecules initiate and mediate symbiosis by binding to structurally compatible plant receptors. For example, lipo-chitooligosaccharides (LCOs), produced by nitrogen-fixing rhizobial bacteria and various fungi, are recognized by plant lysin motif receptor-like kinases (LysM-RLKs), which activate the common symbiotic pathway. Accurately predicting these molecular interactions could reveal complementary signatures underlying the initial stages of endosymbiosis. Despite the breakthrough in protein-ligand structure prediction with deep learning-based tools, such as AlphaFold3, the large size and highly flexible nature of signaling compounds like LCOs present major challenges for detailed structural characterization and binding-affinity prediction. Typical structure-/physics-based methods of ligand virtual screening are designed for small, drug-like molecules, often rely on high-resolution, experimentally determined structures of the protein receptors, and rarely achieve sufficient sampling to obtain converged thermodynamic quantities with large ligands. In this study, we developed a hybrid molecular dynamics/machine learning (MD/ML) approach capable of predicting binding affinity rankings with high accuracy in systems involving large, flexible ligands, despite limited experimental structural information. Using coarse initial structural models, the predictions using the MD/ML workflow achieved strong alignment with experimental trends, particularly in the top-affinity tier for four legume LysM-RLKs (LYR3) binding to LCOs and a chitooligosaccharide. Furthermore, the MD-based conformation selection protocol provided critical structural insights into substrate specificity and binding mechanisms. This study demonstrates a powerful method to screen for challenging cognate ligand-receptors and advance our understanding of the molecular basis of microbial colonization in plants.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2782-2795"},"PeriodicalIF":4.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and characterization of G-quadruplex RNA aptamers reveal RNA-binding by KDM5 lysine demethylases.","authors":"Johanna Luige, Thomas Conrad, Alexandros Armaos, Annita Louloupi, Anna Vincent, David Meierhofer, Michael Gajhede, Gian Gaetano Tartaglia, Ulf Andersson Vang Ørom","doi":"10.1016/j.csbj.2025.06.027","DOIUrl":"10.1016/j.csbj.2025.06.027","url":null,"abstract":"<p><p>Here, we show that the histone lysine demethylases KDM5A and KDM5B can bind to RNA through interaction with G-quadruplexes, despite neither being categorized as RNA- nor G-quadruplex binding proteins across numerous experimental large-scale and computational studies. In addition to characterizing the KDM5 G-quadruplex interaction we show that RNA is directly involved in the formation of KDM5-containing protein complexes. Computational predictions and comparisons to other ARID domain containing proteins suggest that the ARID domain is directly interacting with both DNA and RNA across several proteins. Our work highlights that the RNA-binding by KDM5 lysine demethylases is dependent on recognizing G-quadruplex structures and that RNA mediates the formation of alternative KDM5-containing protein complexes.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2719-2729"},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12241816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.","authors":"Fotis A Baltoumas, Evangelos Karatzas, Nefeli K Venetsianou, Eleni Aplakidou, Konstantinos Giatras, Maria N Chasapi, Iro N Chasapi, Ioannis Iliopoulos, Vassiliki A Iconomidou, Ioannis P Trougakos, Fotis Psomopoulos, Antonis Giannakakis, Ilias Georgakopoulos-Soares, Panagiota Kontou, Pantelis G Bagos, Georgios A Pavlopoulos","doi":"10.1016/j.csbj.2025.06.025","DOIUrl":"10.1016/j.csbj.2025.06.025","url":null,"abstract":"<p><p>Darling is a web application that employs literature mining to detect disease-related biomedical entity associations. Darling can detect sentence-based cooccurrences of biomedical entities such as genes, proteins, chemicals, functions, tissues, diseases, environments, and phenotypes from biomedical literature found in six disease-centric databases. In this version, we deploy additional query channels focusing on COVID-19, GWAS studies, cardiovascular, neurodegenerative, and cancer diseases. Compared to its predecessor, users now have extended query options including searches with PubMed identifiers, disease records, entity names, titles, single nucleotide polymorphisms, or the Entrez syntax. Furthermore, after applying named entity recognition, one can retrieve and mine the relevant literature from recognized terms for a free input text. Term associations are captured in customizable networks which can be further filtered by either term or co-occurrence frequency and visualized in 2D as weighted graphs or in 3D as multi-layered networks. The fetched terms are organized in searchable tables and clustered annotated documents. The reported genes can be further analyzed for functional enrichment using external applications called from within Darling. The Darling databases, including terms and their associations, are updated annually. Darling is available at: https://www.darling-miner.org/.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2626-2637"},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised cell line embedding using pairwise drug response correlation.","authors":"Yutae Kim, Doheon Lee","doi":"10.1016/j.csbj.2025.06.018","DOIUrl":"10.1016/j.csbj.2025.06.018","url":null,"abstract":"<p><p>Human cell line models are essential for understanding diseases and cellular functions. They are particularly emphasized in drug discovery because these models enable the systematic screening of chemical compounds and their effects. However, the heterogeneity in measurement techniques and the fragmented characterization of cell lines in chemical screening and omics data pose significant challenges to their optimal utilization. To address this, we introduce an unsupervised deep learning model based on contrastive learning that integrates heterogeneous drug response screening data into a unified cell line embedding. Utilizing the resulting embedding enhances the performance of drug-cell line-related downstream machine learning tasks to varying degrees. We used drug response data from 1,136 cell lines to train an embedding model and subsequently embedded 537 additional cell lines that were not included in the training, thereby completing the full set of 1,673 cancer cell lines from the Cancer Dependency Map (DepMap) that have corresponding gene expression data. We demonstrate that incorporating the embedding into various drug response-related tasks improves machine learning performance, including predicting drug synergy and drug response in cell lines. Furthermore, we applied SHapley additive explanations (SHAP) to identify genes with significant contributions to the embedding and found that these genes are strongly associated with drug resistance of various cancers and multiple types of cancer.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2566-2573"},"PeriodicalIF":4.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational modeling of cotranscriptional RNA folding.","authors":"Lei Jin, Shi-Jie Chen","doi":"10.1016/j.csbj.2025.06.005","DOIUrl":"10.1016/j.csbj.2025.06.005","url":null,"abstract":"<p><p>An RNA folds as it is transcribed. RNA folding during transcription differs fundamentally from thermodynamic folding. While thermodynamic folding reaches an equilibrium ensemble of structures, cotranscriptional folding is a kinetic process where the RNA structure evolves as the chain elongates during transcription. This dynamic folding pathway causes cotranscriptional structures often to deviate from thermodynamic predictions, as the system rarely reaches equilibrium. Since these cotranscriptional effects can persist in the mature RNA's structure, understanding this kinetic process is crucial. While experimental studies of cotranscriptional folding have been successful, they remain resource-intensive. Computational modeling has emerged as an increasingly practical and powerful approach for investigating these dynamics. This short review examines current computational methods and tools for simulating cotranscriptional folding, with the goal of advancing our understanding of RNA folding mechanisms.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2638-2648"},"PeriodicalIF":4.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable-by-design end-to-end AI framework based on prototypical part learning for lesion detection and classification in Digital Breast Tomosynthesis images.","authors":"Andrea Berti, Camilla Scapicchio, Chiara Iacconi, Charlotte Marguerite Lucille Trombadori, Maria Evelina Fantacci, Alessandra Retico, Sara Colantonio","doi":"10.1016/j.csbj.2025.06.008","DOIUrl":"10.1016/j.csbj.2025.06.008","url":null,"abstract":"<p><strong>Background and objective: </strong>Breast cancer is the most common cancer among women worldwide, making early detection through breast screening crucial for improving patient outcomes. Digital Breast Tomosynthesis (DBT) is an advanced radiographic technique that enhances clarity over traditional mammography by compiling multiple X-ray images into a 3D reconstruction, thereby improving cancer detection rates. However, the large data volume of DBT poses a challenge for timely analysis. This study aims to introduce a transparent AI system that not only provides a prediction but also an explanation of that prediction, expediting the analysis of DBT scans while ensuring interpretability.</p><p><strong>Methods: </strong>The study employs a two-stage deep learning process. The first stage uses state-of-the-art Neural Network (NN) models, specifically YOLOv5 and YOLOv8, to detect lesions within the scans. An ensemble method is also explored to enhance detection capabilities. The second stage involves classifying the identified lesions using ProtoPNet, an inherently transparent NN that leverages prototypical part learning to distinguish between benign and cancerous lesions. The system facilitates clear interpretability in decision-making, which is crucial for medical diagnostics.</p><p><strong>Results: </strong>The performance of the AI system demonstrates competitive metric results for both detection and classification tasks (a recall of 0.76 and an accuracy of 0.70, respectively). The evaluation metrics, together with the validation by expert radiologists through clinical feedback, highlight the potential of the system for future clinical relevance. Despite challenges such as dataset limitations and the need for more accurate ground truth annotations, which limit the final values of the metrics, the approach shows significant advancement in applying AI to DBT scans.</p><p><strong>Conclusions: </strong>This study contributes to the growing field of AI in breast cancer screening by emphasizing the need for systems that are not only accurate but also transparent and interpretable. The proposed AI system marks a significant step forward in the timely and accurate analysis of DBT scans, with potential implications for improving early breast cancer detection and patient outcomes.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2649-2660"},"PeriodicalIF":4.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>ColorI-DT</i>: An open-source tool for the quantitative evaluation of differences in microscopy color images.","authors":"Filippo Piccinini, Michele Tritto, Jae-Chul Pyun, Misu Lee, Bongseop Kwak, Bosung Ku, Nicola Normanno, Gastone Castellani","doi":"10.1016/j.csbj.2025.06.019","DOIUrl":"10.1016/j.csbj.2025.06.019","url":null,"abstract":"<p><p>In several fields, quantitatively comparing color images is crucial. For instance, this is important in Histopathology, where different microscopes/cameras are typically used for visualizing patient samples by causing significant color variation. No ground-truth metric exists for estimating differences between pairs of color images. A range of possible solutions is available but there is no existing open-source tool that allow clinicians and researchers to apply these metrics to microscopy images through an intuitive, easy-to-use software. In this work, we developed <i>Color Image Difference Tool</i> (<i>ColorI-DT</i>), an open-source tool for measuring quantitative differences between color images of the same subject acquired under different settings. Thanks to a user-friendly graphical user interface, it allows the selection of a pair of color images and a metric from a list of available options, and produces an output 2D pixel-wise color difference matrix between corresponding pixels in the input images. The metrics currently implemented are: (<i>1</i>) Euclidean <math><mrow><mi>Δ</mi> <mi>E</mi></mrow> </math> ; (<i>2</i>) International Commission on Illumination (CIE) 76 (Luv); (<i>3</i>) CIE76 (Lab); (<i>4</i>) CIE94; (<i>5</i>) CIE00; (<i>6</i>) Colour Measurement Committee (CMC). To demonstrate how to use the tool, microscopy images with a predominant color in the red, green, or blue channel were used. In particular, we checked which among the 6 metrics displays the most predictable and linear behavior in the case of controlled primary color alterations. For more pronounced color adjustments, a qualitative comparison would be likely sufficient for analyzing color differences, as a quantitative tool may become unreliable due to the inherent limitations of the implemented metrics.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2526-2536"},"PeriodicalIF":4.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12197881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ranking pre-trained speech embeddings in Parkinson's disease detection: Does Wav2Vec 2.0 outperform its 1.0 version across speech modes and languages?","authors":"Ondrej Klempir, Adela Skryjova, Ales Tichopad, Radim Krupicka","doi":"10.1016/j.csbj.2025.06.022","DOIUrl":"10.1016/j.csbj.2025.06.022","url":null,"abstract":"<p><p>Speech and language technologies are effective tools for identifying the distinct speech changes associated with Parkinson's disease (PD), enabling earlier and more accurate diagnosis. Models leveraging recent advancements in self-supervised speech pretraining, such as Wav2Vec, have demonstrated superior performance over traditional feature extraction methods. While Wav2Vec 2.0 has been successfully utilized for PD detection, a rigorous quantitative comparison with Wav2Vec 1.0 is needed to comprehensively evaluate its advantages, limitations, and applicability across different speech modes in PD. This study presents a systematic comparison of Wav2Vec 1.0 and Wav2Vec 2.0 embeddings across three multilingual datasets using various classification approaches to classify normal (healthy controls; HC) and PD-affected speech. Additionally, both Wav2Vec 1.0 and 2.0 were benchmarked against traditional baseline features across diverse linguistic contexts, including spontaneous speech, non-spontaneous speech, and isolated vowels. A multicriteria TOPSIS approach was employed to rank feature extraction methods, revealing that Wav2Vec 2.0 excelled across speech modes, with its first transformer layer demonstrating the best performance for classifying read text and monologue, and its feature extractor performing best in vowel-based classification. In contrast, Wav2Vec 1.0, while generally outperformed by Wav2Vec 2.0, still provided a more efficient alternative with competitive performance. Finally, we combined selected layers from both architectures and have demonstrated improved diagnostic accuracy in vowel-based classification. This comparative analysis underscores the strengths of both Wav2Vec architectures and informs their optimal use in PD detection.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2584-2601"},"PeriodicalIF":4.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SurvSig: Harnessing gene expression signatures to uncover heterogeneity in lung neuroendocrine neoplasms.","authors":"Kolos Nemes, Gabriella Mihalekné Fűr, Alexandra Benő, Christopher W Schultz, Petronella Topolcsányi, Éva Magó, Parth Desai, Nobuyuki Takahashi, Mirit I Aladjem, William Reinhold, Yves Pommier, Anish Thomas, Lorinc S Pongor","doi":"10.1016/j.csbj.2025.06.010","DOIUrl":"10.1016/j.csbj.2025.06.010","url":null,"abstract":"<p><p>The advances in the field of cancer genomics have enabled researchers and clinicians to identify altered pathways and regulatory networks that differentiate subtypes manifesting as differential phenotypes of lung neuroendocrine neoplasms (NENs). The clinical heterogeneity observed among lung NEN subtypes reflects underlying biological distinctions, including differential mutation patterns, epigenetic changes and immune microenvironment activities. Although in many cases only a handful of underlying genes are used to differentiate patients, broader gene signatures might result in finer separation and help identify patients with differential survival. Lung NENs are vastly underrepresented in pan-cancer studies, resulting in lacking options to explore datasets. To this end, we developed a freely available website (https://survsig.hcemm.eu/) which allows users to upload potential genes of interest, perform patient clustering, compare survival and explore gene expression signature of lung NENs. Leveraging these biological differences enhances the accuracy of gene expression-based prognostic classifiers like SurvSig.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2574-2583"},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}