Rafael S Gonçalves, Jason Payne, Amelia Tan, Carmen Benitez, Jamie Haddock, Robert Gentleman
{"title":"The text2term tool to map free-text descriptions of biomedical terms to ontologies.","authors":"Rafael S Gonçalves, Jason Payne, Amelia Tan, Carmen Benitez, Jamie Haddock, Robert Gentleman","doi":"10.1093/database/baae119","DOIUrl":"10.1093/database/baae119","url":null,"abstract":"<p><p>There is an ongoing need for scalable tools to aid researchers in both retrospective and prospective standardization of discrete entity types-such as disease names, cell types, or chemicals-that are used in metadata associated with biomedical data. When metadata are not well-structured or precise, the associated data are harder to find and are often burdensome to reuse, analyze, or integrate with other datasets due to the upfront curation effort required to make the data usable-typically through retrospective standardization and cleaning of the (meta)data. With the goal of facilitating the task of standardizing metadata-either in bulk or in a one-by-one fashion, e.g. to support autocompletion of biomedical entities in forms-we have developed an open-source tool called text2term that maps free-text descriptions of biomedical entities to controlled terms in ontologies. The tool is highly configurable and can be used in multiple ways that cater to different users and expertise levels-it is available on Python Package Index and can be used programmatically as any Python package; it can also be used via a command-line interface or via our hosted, graphical user interface-based web application or by deploying a local instance of our interactive application using Docker. Database URL: https://pypi.org/project/text2term.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11604108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142750183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Genome-wide identification of SSR markers from coding regions for endangered Argania spinosa L. skeels and construction of SSR database: AsSSRdb.","authors":"Karim Rabeh, Najoua Mghazli, Fatima Gaboun, Abdelkarim Filali-Maltouf, Laila Sbabou, Bouchra Belkadi","doi":"10.1093/database/baae118","DOIUrl":"10.1093/database/baae118","url":null,"abstract":"<p><p>Microsatellites [simple sequence repeats (SSRs)] are one of the most widely used sources of genetic markers, particularly prevalent in plants. Despite their importance in various applications, a comprehensive genome-wide identification of coding sequence (CDS)-associated SSR markers in the Argania spinosa L. genome has yet to be conducted. In this study, 66 280 CDSs containing 5351 SSRs within 4535 A. spinosa L. CDSs were identified. Among these, tri-nucleotide motifs (58.96%) were the most common, followed by hexa-nucleotide (15.71%) and di-nucleotide motifs (13.32%). The predominant SSR motif in the tri-nucleotide category was AAG (24.4%), while AG (94.1%) was the most abundant among di-nucleotide repeats. Furthermore, the extracted CDSs containing SSRs were subjected to functional annotation; 3396 CDSs (74.88%) exhibited homology with known proteins, 3341 CDSs (73.7%) were assigned Gene Ontology terms, 1004 CDSs were annotated with Enzyme Commission numbers, and 832 (18.3%) were annotated with KEGG pathways. A total of 3475 primer pairs were designed, out of which 3264 were successfully validated in silico against the A. spinosa L. genome, with 99.6% representing high-resolution markers yielding no more than three products. Additionally, the SSR markers demonstrated a low rate of transferability through in-silico verification in two species within the Sapotaceae family. Furthermore, we developed an online database, the \"Argania spinosa L. SSR database: https://as-fmmdb.shinyapps.io/asssrdb/\" (AsSSRdb) to provide access to the CDS-associated SSRs identified in this study. Overall, this research provides valuable marker resources for DNA fingerprinting, genetic studies, and molecular breeding in argan and related species. Database URL: https://as-fmmdb.shinyapps.io/asssrdb/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142738569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sonu Kumar, Sangeeta Singh, Rakesh Kumar, Dinesh Gupta
{"title":"The Genomic SSR Millets Database (GSMDB): enhancing genetic resources for sustainable agriculture.","authors":"Sonu Kumar, Sangeeta Singh, Rakesh Kumar, Dinesh Gupta","doi":"10.1093/database/baae114","DOIUrl":"10.1093/database/baae114","url":null,"abstract":"<p><p>The global population surge demands increased food production and nutrient-rich options to combat rising food insecurity. Climate-resilient crops are vital, with millets emerging as superfoods due to nutritional richness and stress tolerance. Given limited genomic information, a comprehensive genetic resource is crucial to advance millet research. Whole-genome sequencing provides an unprecedented opportunity, and molecular genetic methodologies, particularly simple sequence repeats (SSRs), play a pivotal role in DNA fingerprinting, constructing linkage maps, and conducting population genetic studies. SSRs are composed of repetitive DNA sequences where one to six nucleotides are repeated in tandem and distributed throughout the genome. Different millet species exhibit genomic variations attributed to the presence of SSRs. While SSRs have been identified in a few millet species, the existing information only covers some of the sequenced genomes. Moreover, there is an absence of complete gene annotation and visualization features for SSRs. Addressing this disparity and leveraging the de-novo millet genome assembly available from the NCBI, we have developed the Genomic SSR Millets Database (GSMDB; https://bioinfo.icgeb.res.in/gsmdb/). This open-access repository provides a web-based tool offering search functionalities and comprehensive details on 6.747645 million SSRs mined from the genomic sequences of seven millet species. The database, featuring unrestricted public access and JBrowse visualization, is a pioneering resource for the research community dedicated to advancing millet cultivars and related species. GSMDB holds immense potential to support myriad studies, including genetic diversity assessments, genetic mapping, marker-assisted selection, and comparative population investigations aiming to facilitate the millet breeding programs geared toward ensuring global food security. Database URL: https://bioinfo.icgeb.res.in/gsmdb/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gabriel Cabas-Mora, Anamaría Daza, Nicole Soto-García, Valentina Garrido, Diego Alvarez, Marcelo Navarrete, Lindybeth Sarmiento-Varón, Julieta H Sepúlveda Yañez, Mehdi D Davari, Frederic Cadet, Álvaro Olivera-Nappa, Roberto Uribe-Paredes, David Medina-Ortiz
{"title":"Peptipedia v2.0: a peptide sequence database and user-friendly web platform. A major update.","authors":"Gabriel Cabas-Mora, Anamaría Daza, Nicole Soto-García, Valentina Garrido, Diego Alvarez, Marcelo Navarrete, Lindybeth Sarmiento-Varón, Julieta H Sepúlveda Yañez, Mehdi D Davari, Frederic Cadet, Álvaro Olivera-Nappa, Roberto Uribe-Paredes, David Medina-Ortiz","doi":"10.1093/database/baae113","DOIUrl":"10.1093/database/baae113","url":null,"abstract":"<p><p>In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11734279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A T Vivek, Ajay Arya, Supriya P Swain, Shailesh Kumar
{"title":"athisomiRDB: A comprehensive database of Arabidopsis isomiRs.","authors":"A T Vivek, Ajay Arya, Supriya P Swain, Shailesh Kumar","doi":"10.1093/database/baae115","DOIUrl":"10.1093/database/baae115","url":null,"abstract":"<p><p>Several pieces of evidence challenge the traditional view of miRNAs as static molecules, revealing dynamic isomiRs originating from each miRNA precursor arm. In plants, isomiRs, which result from imprecise cleavage during pre-miRNA processing and post-transcriptional alterations, serve as crucial regulators of target microRNAs (miRNAs). Despite numerous studies on Arabidopsis miRNAs, the systematic identification and annotation of isomiRs across various tissues and conditions remain limited. Due to the lack of systematically collected isomiR information, we introduce the athisomiRDB database, which houses 20 764 isomiRs from Arabidopsis small RNA-sequencing (sRNA-seq) libraries. It comprises >2700 diverse samples and allows exploration at the sample, miRNA, or isomiR levels, offering insights into the presence or absence of isomiRs. The athisomiRDB includes exclusive and ambiguous isomiRs, each with features such as transcriptional origin, variant-containing isomiRs, and identifiers for frequent single-nucleotide polymorphisms from the 1001 Genomes Project. It also provides 3' nontemplated post-transcriptional additions, isomiR-target interactions, and trait associations for each isomiR. We anticipate that athisomiRDB will play a pivotal role in unraveling the regulatory nature of the Arabidopsis miRNAome and enhancing sRNA research by leveraging isomiR profiles from extensive sRNA-seq datasets. Database URL: https://www.nipgr.ac.in/athisomiRDB.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11544919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"PheNormGPT: a framework for extraction and normalization of key medical findings.","authors":"Ekin Soysal, Kirk Roberts","doi":"10.1093/database/baae103","DOIUrl":"10.1093/database/baae103","url":null,"abstract":"<p><p>This manuscript presents PheNormGPT, a framework for extraction and normalization of key findings in clinical text. PheNormGPT relies on an innovative approach, leveraging large language models to extract key findings and phenotypic data in unstructured clinical text and map them to Human Phenotype Ontology concepts. It utilizes OpenAI's GPT-3.5 Turbo and GPT-4 models with fine-tuning and few-shot learning strategies, including a novel few-shot learning strategy for custom-tailored few-shot example selection per request. PheNormGPT was evaluated in the BioCreative VIII Track 3: Genetic Phenotype Extraction from Dysmorphology Physical Examination Entries shared task. PheNormGPT achieved an F1 score of 0.82 for standard matching and 0.72 for exact matching, securing first place for this shared task.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11498178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142496679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Is metadata of articles about COVID-19 enough for multilabel topic classification task?","authors":"Shuo Xu, Yuefu Zhang, Liang Chen, Xin An","doi":"10.1093/database/baae106","DOIUrl":"10.1093/database/baae106","url":null,"abstract":"<p><p>The ever-increasing volume of COVID-19-related articles presents a significant challenge for the manual curation and multilabel topic classification of LitCovid. For this purpose, a novel multilabel topic classification framework is developed in this study, which considers both the correlation and imbalance of topic labels, while empowering the pretrained model. With the help of this framework, this study devotes to answering the following question: Do full texts, MeSH (Medical Subject Heading), and biological entities of articles about COVID-19 encode more discriminative information than metadata (title, abstract, keyword, and journal name)? From extensive experiments on our enriched version of the BC7-LitCovid corpus and Hallmarks of Cancer corpus, the following conclusions can be drawn. Our framework demonstrates superior performance and robustness. The metadata of scientific publications about COVID-19 carries valuable information for multilabel topic classification. Compared to biological entities, full texts and MeSH can further enhance the performance of our framework for multilabel topic classification, but the improved performance is very limited. Database URL: https://github.com/pzczxs/Enriched-BC7-LitCovid.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11492800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142459944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cao Hengchun, Guo Hui, Yang Weifei, Li Guiting, Ju Ming, Duan Yinghui, Tian Qiuzhen, Ma Qin, Feng Xiaoxu, Zhang Zhanyou, Zhang Haiyang, Miao Hongmei
{"title":"SesamumGDB: a comprehensive platform for Sesamum genetics and genomics analysis.","authors":"Cao Hengchun, Guo Hui, Yang Weifei, Li Guiting, Ju Ming, Duan Yinghui, Tian Qiuzhen, Ma Qin, Feng Xiaoxu, Zhang Zhanyou, Zhang Haiyang, Miao Hongmei","doi":"10.1093/database/baae105","DOIUrl":"10.1093/database/baae105","url":null,"abstract":"<p><p>Sesame (Sesamum indicum L., 2n = 26) is a crucial oilseed crop cultivated worldwide. The ancient evolutionary position of the Sesamum genus highlights its value for genomics and molecular genetics research among the angiosperms of other genera. However, Sesamum is considered a small orphan genus with only a few genomic databases for cultivated sesame to date. The urgent need to construct comprehensive, curated genome databases that include genus-specific gene resources for both cultivated and wild Sesamum species is being recognized. In response, we developed Sesamum Genomics Database (SesamumGDB), a user-friendly genomic database that integrates extensive genomic resources from two cultivated sesame varieties (S. indicum) and seven wild Sesamum species, covering all three chromosome groups (2n = 26, 32, and 64). This database showcases a total of 352 471 genes, including 6026 related to lipid metabolism and 17 625 transcription factors within Sesamum. Equipped with an array of bioinformatics tools such as BLAST (basic local alignment search tool) and JBrowse (the Javascript browser), SesamumGDB facilitates data downloading, screening, visualization, and analysis. As the first centralized Sesamum genome database, SesamumGDB offers extensive insights into the genomics and genetics of sesame, potentially enhancing the molecular breeding of sesame and other oilseed crops in the future. Database URL: http://www.sgbdb.com/sgdb/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abhay Deep Pandey, Ghanshyam Sharma, Anshula Sharma, Sudhanshu Vrati, Deepak T Nair
{"title":"SMCVdb: a database of experimental cellular toxicity information for drug candidate molecules.","authors":"Abhay Deep Pandey, Ghanshyam Sharma, Anshula Sharma, Sudhanshu Vrati, Deepak T Nair","doi":"10.1093/database/baae100","DOIUrl":"10.1093/database/baae100","url":null,"abstract":"<p><p>Many drug discovery exercises fail because small molecules that are effective inhibitors of target proteins exhibit high cellular toxicity. Early and effective assessment of toxicity and pharmacokinetics is essential to accelerate the drug discovery process. Conventional methods for toxicity profiling, including in vitro and in vivo assays, are laborious and resource-intensive. In response, we introduce the Small Molecule Cell Viability Database (SMCVdb), a comprehensive resource containing toxicity data for over 24 000 compounds obtained through high-content imaging (HCI). SMCVdb seamlessly integrates chemical descriptions and molecular weight data, offering researchers a holistic platform for toxicity data aiding compound prioritization and selection based on biological and economic considerations. Data collection for SMCVdb involved a systematic approach combining HCI toxicity profiling with chemical information and quality control measures ensured data accuracy and consistency. The user-friendly web interface of SMCVdb provides multiple search and filter options, allowing users to query the database based on compound name, molecular weight range, or viability percentage. SMCVdb empowers users to access toxicity profiles, molecular weights, compound names, and chemical descriptions, facilitating the exploration of relationships between compound properties and their effects on cell viability. In summary, the database provides experimentally derived cellular toxicity information for over 24 000 drug candidate molecules to academic researchers, and pharmaceutical companies. The SMCVdb will keep growing and will prove to be a pivotal resource to expedite research in drug discovery and compound evaluation. Database URL: http://smcvdb.rcb.ac.in:4321/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11488516/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142460044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nico Cillari, Giuseppe Neri, Nadia Pisanti, Paolo Milazzo, Ugo Borello
{"title":"RettDb: the Rett syndrome omics database to navigate the Rett syndrome genomic landscape.","authors":"Nico Cillari, Giuseppe Neri, Nadia Pisanti, Paolo Milazzo, Ugo Borello","doi":"10.1093/database/baae109","DOIUrl":"10.1093/database/baae109","url":null,"abstract":"<p><p>Rett syndrome (RTT) is a neurodevelopmental disorder occurring almost exclusively in females and leading to a variety of impairments and disabilities from mild to severe. In >95% cases, RTT is due to mutations in the X-linked gene MECP2, but the molecular mechanisms determining RTT are unknown at present, and the complexity of the system is challenging. To facilitate and provide guidance to the unraveling of those mechanisms, we developed a database resource for the visualization and analysis of the genomic landscape in the context of wild-type or mutated Mecp2 gene in the mouse model. Our resource allows for the exploration of differential dynamics of gene expression and the prediction of new potential MECP2 target genes to decipher the RTT disorder molecular mechanisms. Database URL: https://biomedinfo.di.unipi.it/rett-database/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11482253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142459945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}