Computational and structural biotechnology journal最新文献

{"title":"The topography of nullomer-emerging mutations and their relevance to human disease","authors":"Candace S.Y. Chan ,&nbsp;Ioannis Mouratidis ,&nbsp;Austin Montgomery ,&nbsp;Georgios Christos Tsiatsianis ,&nbsp;Nikol Chantzi ,&nbsp;Martin Hemberg ,&nbsp;Nadav Ahituv ,&nbsp;Ilias Georgakopoulos-Soares","doi":"10.1016/j.csbj.2024.12.026","DOIUrl":"10.1016/j.csbj.2024.12.026","url":null,"abstract":"<div><div>Nullomers are short DNA sequences (11–18 base pairs) that are absent from a genome; however, they can emerge due to mutations. Here, we characterize all possible putative human nullomer-emerging single base pair mutations, population variants and disease-causing mutations. We find that the primary determinants of nullomer emergence in the human genome are the presence of CpG dinucleotides and methylated cytosines. Putative nullomer-emerging mutations are enriched at specific genomic elements, including transcription start and end sites, splice sites and transcription factor binding sites. We also observe that putative nullomer-emerging mutations are more frequent in highly conserved regions and show preferential location at nucleosomes. Among repeat elements, Alu repeats exhibit pronounced enrichment for putative nullomer-emerging mutations at specific positions. Finally, we find that disease-associated pathogenic mutations are significantly more likely to cause emergence of nullomers than their benign counterparts.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"30 ","pages":"Pages 1-11"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11745800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001601","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":"A structural phylogenetic tree of Rad52 and its annealase superfamily","authors":"Ali Al-Fatlawi ,&nbsp;Md. Ballal Hossen ,&nbsp;Stella de Paula Lopes ,&nbsp;A. Francis Stewart ,&nbsp;Michael Schroeder","doi":"10.1016/j.csbj.2024.12.012","DOIUrl":"10.1016/j.csbj.2024.12.012","url":null,"abstract":"<div><div>Rad52, a highly conserved eukaryotic protein, plays a crucial role in DNA repair, particularly in double-strand break repair. Recent findings reveal that its distinct structural features, including a characteristic <em>β</em>-sheet and <em>β</em>-hairpin motif, are shared with the lambda phage single-strand annealing protein, Red<em>β</em>, and other prokaryotic single-strand annealing proteins (SSAPs), indicating a common superfamily. Our analysis of over 10,000 SSAPs across all domains of life supports this hypothesis, confirming the presence of the characteristic motif despite variations in size and composition. We found that archaea, representing only 1% of the studied proteins, exhibit most of these variations as reflected by their spread across the phylogenetic tree, whereas eukaryotes exhibit only Rad52. By examining four representative archaeal SSAPs, we elucidate the structural relationship between eukaryotic and bacterial SSAPs, highlighting differences in <em>β</em>-sheet size and <em>β</em>-hairpin complexity. Furthermore, we identify an archaeal SSAP with a predicted structure nearly identical to human Rad52. Together with a screen of over 100 million unannotated proteins for potential SSAP candidates, our computational analysis complements the existing sequence and structural evidence supporting orthology among five SSAP families: Rad52, Red<em>β</em>, RecT, Erf, and Sak3.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 360-368"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078793","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":"A DICOM-based standard for quantitative physical rehabilitation","authors":"Alessandro Di Matteo ,&nbsp;Daniele Lozzi ,&nbsp;Filippo Mignosi ,&nbsp;Matteo Polsinelli ,&nbsp;Giuseppe Placidi","doi":"10.1016/j.csbj.2025.01.012","DOIUrl":"10.1016/j.csbj.2025.01.012","url":null,"abstract":"<div><div>Physical rehabilitation (PR) is a critical medical discipline traditionally reliant on qualitative data for procedure evaluation. Recent scientific and technological advances have provided innovative instruments and methods for measuring and evaluating PR objectively through Quantitative PR (QPR). However, the lack of a standard data format creates several challenges. These include limited interoperability between devices, difficulties in maintaining patient histories, inability to perform temporal evaluations or inter-patient comparisons, barriers to data sharing, challenges in creating common evaluation scales for therapists, and limitations in statistical analysis. This article proposes a DICOM Information Object Definition (IOD) for QPR, referred to as PR-IOD, and describes its architecture. DICOM is an established standard initially created for medical imaging, but it has recently been extended to other areas of medicine. Its primary goals are to facilitate data sharing among various devices, manage associated processes, and ensure interoperability among systems and specialists by generating structured data. The implemented PR-IOD architecture has been applied to manage data by a multiple-source hand-tracking device, the Virtual Glove (VG), used for hand rehabilitation. The corresponding DICOM files have been generated, loaded, and visualized alongside a viewer dashboard specifically tailored for PR-IOD. The source code is available at <span><span>[1]</span></span>.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"28 ","pages":"Pages 40-49"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143162553","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":"MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order variants","authors":"Bryce Forrest ,&nbsp;Houssemeddine Derbel ,&nbsp;Zhongming Zhao ,&nbsp;Qian Liu","doi":"10.1016/j.csbj.2025.02.012","DOIUrl":"10.1016/j.csbj.2025.02.012","url":null,"abstract":"<div><div>Protein sequences primarily determine their stability and functions. Mutations may occur at one, two, or three positions at the same time (low-order variants) or at multiple positions simultaneously (high-order variants), which affect protein functions. So far, low-order variants, such as single variants, double variants, and triple variants, have been well-studied through high-throughput experimental scanning techniques and computational prediction methods. However, research on high-order variants remains limited because of the difficulty of scanning an exponentially large number of potential variant combinations. Nonetheless, studying higher-order variants is crucial for understanding the pathogenesis of complex diseases, advancing protein engineering, and driving precision medicine. In this work, we introduce a novel deep learning model, namely <em>MultiMut Recursive Tree</em> (MMRT), to address this challenge of predicting the functional effects of high-order variants. MMRT integrates deep learning with a recursive tree framework to leverage the information from low-order variants to predict functional effects of high-order variants. We evaluated MMRT on datasets comprising 685,593 high-order variants. Our results (mean Spearman’s correlation coefficient 0.55) demonstrated that MMRT outperformed three existing state-of-the-art methods: ESM (evolutionary scale modeling), DeepSequence, and ECNet (evolutionary context-integrated neural network). MMRT thus provides more accurate prediction of the functional effects of high-order protein variants, offering great potential for aiding the interpretation of variants in human disease studies.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 672-681"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463427","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":"Designing and implementing SMILE: An AI-driven platform for enhancing clinical decision-making in mental health and neurodivergence management","authors":"Antonio Pesqueira ,&nbsp;Maria Jose Sousa ,&nbsp;Ruben Pereira ,&nbsp;Mark Schwendinger","doi":"10.1016/j.csbj.2025.02.022","DOIUrl":"10.1016/j.csbj.2025.02.022","url":null,"abstract":"<div><div>Rising levels of anxiety, depression, and burnout among healthcare professionals (HCPs) underscore the urgent need for technology-driven interventions that optimize both clinical decision-making and workforce well-being. This innovation report introduces the Support, Management, Individual, Learning Enablement (SMILE) platform, designed to integrate advanced AI-driven decision support, federated learning for data privacy, and cognitive behavioral therapy (CBT) modules into a single, adaptive solution. A mixed-methods pilot evaluation involved focus groups, structured surveys, and real-world usability tests to capture changes in stress levels, user satisfaction, and perceived value. Quantitative analyses revealed significant reductions in reported stress and support times, alongside notable gains in satisfaction and perceived resource value. Qualitatively, participants praised SMILE’s accessible interface, enhanced peer support, and real-time therapeutic interventions. These findings confirm the feasibility and utility of a holistic, Artificial Intelligence (AI) supported framework for improving mental health outcomes in high-stress clinical environments. Theoretically, SMILE contributes to emerging evidence on integrated AI platforms, while it offers an ethically sound and user-friendly blueprint for improving patient care and staff well-being.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 785-803"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509885","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":"CAP-m7G: A capsule network-based framework for specific RNA N7-methylguanosine site identification using image encoding and reconstruction layers","authors":"Peilin Xie ,&nbsp;Jiahui Guan ,&nbsp;Xuxin He ,&nbsp;Zhihao Zhao ,&nbsp;Yilin Guo ,&nbsp;Zhenglong Sun ,&nbsp;Lantian Yao ,&nbsp;Tzong-Yi Lee ,&nbsp;Ying-Chih Chiang","doi":"10.1016/j.csbj.2025.02.029","DOIUrl":"10.1016/j.csbj.2025.02.029","url":null,"abstract":"<div><div>N7-methylguanosine (m7G) modifications play a pivotal role in RNA stability, mRNA export, and protein translation. They are closely associated with ribosome function and the regulation of gene expression. Dysregulation of m7G has been implicated in various diseases, including cancers and neurodegenerative disorders, where the loss of m7G can lead to genomic instability and uncontrolled cell proliferation. Accurate identification of m7G sites is thus essential for elucidating these mechanisms. Due to the high cost of experimentally validating m7G sites, several artificial intelligence models have been developed to predict these sites. However, the performance of these models is not yet optimal, and a user-friendly web server is still needed. To address these issues, we developed CAP-m7G, an innovative model that integrates Chaos Game Representation, Capsule Networks, and reconstruction layers. CAP-m7G achieved an accuracy of 96.63%, a specificity of 95.07%, and a Matthews correlation coefficient (MCC) of 0.933 on independent test data. Our results demonstrate that the integration of Chaos Game Representation with Capsule Network can effectively capture the crucial sequence information associated with m7G sites. The web server can be accessed at <span><span>https://awi.cuhk.edu.cn/~biosequence/CAP-m7G/index.php</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 804-812"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511474","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":"Development and evaluation of an integrated image-guided robotic system for hair transplant surgery","authors":"Rattapon Thuangtong,&nbsp;Ornpreeya Anantawilailekha,&nbsp;Ponchita Prasertsin,&nbsp;Jackrit Suthakorn","doi":"10.1016/j.csbj.2025.02.009","DOIUrl":"10.1016/j.csbj.2025.02.009","url":null,"abstract":"<div><div>This study presented the development and evaluation of an integrated image-guided robotic system for hair transplant surgery. A novel surgical robot was designed, incorporating an image-guided system, a dual-function needle mechanism, and a comprehensive robotic system capable of performing both follicle harvesting and implantation in a unified setup. The robot comprised three main subsystems: the image-guidance system, the dual-function needle, and the robotic hardware. Each subsystem was meticulously developed and individually described, detailing the specific processes and mechanisms involved. Experimentation involved a silicone phantom embedded with filaments to mimic real human hair density, providing a realistic simulation for testing. The image-guided system demonstrated high precision in detecting the positions of hair follicles, achieving an accuracy rate of 89 %. Meanwhile, the dual-function needle proved effective in executing both the harvesting and implanting functions, achieving harvest and implant success rates of 83.3 % and 53.3 %, respectively. It was important to note, however, that the suction system integrated into the needle mechanism did not function as intended. Further simulations conducted on the robotic system affirmed its suitability for a wide range of head sizes, specifically those with a breadth diameter between 113 and 179 mm, effectively encompassing most of the Asian demographic. This integration of advanced robotics and image-guidance aimed to enhance the efficacy and precision of hair transplant procedures.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"28 ","pages":"Pages 80-93"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580160","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":"CTGCT, Centre of Excellence for the Technologies of Gene and Cell Therapy: Collaborative translation of scientific discoveries into advanced treatments for neurological rare genetic diseases and cancer","authors":"Darja Marolt Presen ,&nbsp;Duško Lainšček ,&nbsp;Jane Kinghorn ,&nbsp;Zsolt Sebestyen ,&nbsp;Jurgen Kuball ,&nbsp;Leila Amini ,&nbsp;Petra Reinke ,&nbsp;Anke Fuchs ,&nbsp;Roman Jerala ,&nbsp;Mojca Benčina","doi":"10.1016/j.csbj.2024.11.051","DOIUrl":"10.1016/j.csbj.2024.11.051","url":null,"abstract":"<div><div>The emerging field of precision medicine relies on scientific breakthroughs to understand disease mechanisms and develop cutting-edge technologies to overcome underlying genetic and functional aberrations. The establishment of the Centre of Excellence for the Technologies of Gene and Cell Therapy (CTGCT) at the National Institute of Chemistry (NIC) in Ljubljana represents a significant step forward, as it is the first centre of its kind in Slovenia. The CTGCT is poised to spearhead advances in cancer immunotherapy and personalised therapies for neurological and other rare genetic diseases. The centre’s overarching mission is to extend beyond the NIC’s scientific excellence in basic research and bring new therapeutic solutions toward clinical application. The CTGCT aims to develop a broad pipeline of biomedical tools, including innovative synthetic biology tools, gene editing and splicing technologies, RNA-based technologies, immune regulation engineering and novel viral and non-viral delivery systems. The CTGCT is supported by partner institutions from the UK, the Netherlands and Germany, which already have academic good manufacturing practice (GMP) facilities for the manufacture of advanced therapy medicinal products (ATMPs) and is committed to active collaboration with clinicians and patient organizations at all stages of development to improve access to gene and cell therapies (GCTs) for patients. The Centre also seeks to collaborate with national and international academic and industrial partners, and the newly established GMP facilities will address a critical bottleneck in the translation of GCTs from research to practice. Finally, CTGCT's translational research and technology transfer units will ensure the impactful dissemination of research and innovation activities in Slovenia, throughout the Western Balkans and Eastern Europe region, and beyond. With its comprehensive approach and forward-looking vision, the CTGCT will drive transformative advances in gene and cell therapies for the benefit of patients on a global scale.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 10-16"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930840","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":"GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning","authors":"Israel Mateos-Aparicio-Ruiz ,&nbsp;Anibal Pedraza ,&nbsp;Jan Ulrich Becker ,&nbsp;Nicola Altini ,&nbsp;Jesus Salido ,&nbsp;Gloria Bueno","doi":"10.1016/j.csbj.2024.11.049","DOIUrl":"10.1016/j.csbj.2024.11.049","url":null,"abstract":"<div><div>The digitalization of traditional glass slide microscopy into whole slide images has opened up new opportunities for pathology, such as the application of artificial intelligence techniques. Specialized software is necessary to visualize and analyze these images. One of these applications is QuPath, a popular bioimage analysis tool. This study proposes GNCnn, the first open-source QuPath extension specifically designed for nephropathology. It integrates deep learning models to provide nephropathologists with an accessible, automatic detector and classifier of glomeruli, the basic filtering units of the kidneys. The aim is to offer nephropathologists a freely available application to measure and analyze glomeruli to identify conditions such as glomerulosclerosis and glomerulonephritis. GNCnn offers a user-friendly interface that enables nephropathologists to detect glomeruli with high accuracy (Dice coefficient of 0.807) and categorize them as either sclerotic or non-sclerotic, achieving a balanced accuracy of 98.46%. Furthermore, it facilitates the classification of non-sclerotic glomeruli into 12 commonly diagnosed types of glomerulonephritis, with a top-3 balanced accuracy of 84.41%. GNCnn provides real-time updates of results, which are available at both the glomerulus and slide levels. This allows users to complete a typical analysis task without leaving the main application, QuPath. This tool is the first to integrate the entire workflow for the assessment of glomerulonephritis directly into the nephropathologists' workspace, accelerating and supporting their diagnosis.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 35-47"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11719282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969926","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":"EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks","authors":"Md Hossain Shuvo ,&nbsp;Debswapna Bhattacharya","doi":"10.1016/j.csbj.2024.12.015","DOIUrl":"10.1016/j.csbj.2024.12.015","url":null,"abstract":"<div><div>Quality estimation of the predicted interaction interface of protein complex structural models is not only important for complex model evaluation and selection but also useful for protein-protein docking. Despite recent progress fueled by symmetry-aware deep learning architectures and pretrained protein language models (pLMs), existing methods for estimating protein complex quality have yet to fully exploit the collective potentials of these advances for accurate estimation of protein-protein interface. Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model. Our method estimates the quality of the protein-protein interface through an effective graph-based representation of interacting residue pairs, incorporating a diverse set of features, including ESM-2 embeddings, and then by learning the representation using symmetry-aware EGNNs. Our experimental results demonstrate improved ranking performance on diverse datasets over existing latest protein complex quality estimation methods including the top-performing CASP15 protein complex quality estimation method VoroIF_GNN and the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring and across different performance evaluation metrics. Additionally, our ablation studies demonstrate the contributions of both pLMs and the equivariant nature of EGNN for improved protein-protein interface quality estimation performance. EquiRank is freely available at <span><span>https://github.com/mhshuvo1/EquiRank</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"Pages 160-170"},"PeriodicalIF":4.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028117","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}