GigaScience最新文献

{"title":"Stereo-cell deciphers the spatial and functional heterogeneity of polyploid hepatocytes.","authors":"Yongqing Yang, Jiahui Luo, Yier Cai, Pengcheng Guo, Qiang Guo, Hong Wu, Longqi Liu, Shijie Hao","doi":"10.1093/gigascience/giag023","DOIUrl":"10.1093/gigascience/giag023","url":null,"abstract":"<p><p>A characteristic feature of the liver is the presence of numerous polyploid hepatocytes. However, the functional distinctions among diploid, tetraploid, and octoploid hepatocytes remain poorly understood. In this study, we employed the spatially resolved single-cell sequencing technology, Stereo-cell, to dissect the transcriptomic and functional heterogeneity across hepatocyte ploidy subtypes. We detail the development of Stereo-cell imaging-based ploidy identification (SCIPI), a technical pipeline that integrates bright-field cell contour recognition, DAPI-based nuclear area and number quantification, and UMI-barcoded single-cell transcriptomics. This approach enables precise identification of 6 core hepatocyte subtypes: mononucleated diploid (2n×1), mononucleated tetraploid (4n×1), binucleated tetraploid (2n×2), mononucleated octoploid (8n×1), binucleated octoploid (4n×2), and binucleated hexadecaploid (8n×2) hepatocytes. Single-cell transcriptomic analysis based on ploidy annotation revealed that gene expression levels positively with increasing ploidy and nuclear number. Metabolic pathway-associated genes were significantly upregulated in polyploid cells, suggesting that cellular polyploidization enhances the metabolic activity of hepatocytes. Furthermore, this SCIPI strategy is broadly applicable to the study of various polyploid tissues, offering a novel and versatile framework for innovative ploidy-resolved research across diverse biological researches.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147325628","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":"Improved discovery of de novo mutations using TrioDNM and VRFS.","authors":"Petr Danecek, Eugene J Gardner, Joanna Kaplanis, Matthew E Hurles, Sarah J Lindsay","doi":"10.1093/gigascience/giag068","DOIUrl":"10.1093/gigascience/giag068","url":null,"abstract":"<p><strong>Background: </strong>Identifying de novo mutations (DNM) is an important component of both genetic research studies and clinical diagnostic workflows, but is complicated by distinguishing true mutations from sequencing errors. Likelihood-based error models are more accurate than inferring mutations from genotypes alone, but the resulting callsets still have high false positive rates.</p><p><strong>Results: </strong>We identify that the main source of false positive DNMs comes from the use of genotype likelihoods in an otherwise robust mutational model. To address this issue, we propose two alternative methods that build on an existing DNM calling approach, DeNovoGear, but with higher accuracy and no decrease in sensitivity.Furthermore, we developed a method that collects allele-specific frequency profiles in the sequenced cohort from across many unrelated samples and identifies sites that either demonstrate high rates of sequencing and mapping errors, or are unlikely to be clinically significant due to their high recurrence rate.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13289757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148210970","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 comprehensive reference genome assembly dataset of birds inhabiting Denmark, Greenland, and the Faroe Islands.","authors":"Daniel Bilyeli Øksnebjerg, Sascha Dreyer Nielsen, Guangji Chen, Michael J Andersen, Vanessa Almagro, Roberto Ambrosini, Shigeki Asai, Sharon M Birks, Robb T Brumfield, Vojtěch Brlík, Annaïs Carbajal, Theresa M Burg, Sandra Bouwhuis, Shuang Wang, Wanjun Chen, Santiago Claramunt, Fuqiang Lin, Jiaqi Xu, Wei Dai, Scott V Edwards, Mary Margaret Ferraro, Joerns Fickel, Jon Fjeldså, Giulio Formenti, Jérôme Fuchs, Guido Roberto Gallo, Luca Gianfranceschi, Gary R Graves, Grzegorz Grzywaczewski, Wei Han, Tri Haryoko, Ando Haruko, Olof Hellgren, Juan Carlos Illera, Erich D Jarvis, Lars E Johannessen, Arild Johnsen, Ulf S Johansson, Knud Andreas Jønsson, Wei Jiang, Victoria Jayne, Takaharu Kawashima, Adam D Leaché, Fumin Lei, Jan T Lifjeld, Miriam Liedvogel, Yang Liu, Irby J Lovette, Sarah S T Mak, Isao Nishiumi, Michelle F O'Brien, Manabu Onuma, Bent Petersen, Martin Päckert, Marco Pavia, Alivia Price, Javier Quesada, Carsten Rahbek, Andrew Hart Reeve, Alejandro Rico-Guevara, Markus Risch, Vanya G Rohwer, Takema Saitoh, C Jonathan Schmitt, Anna Schnelle, Manuel Schweizer, Simona Secomandi, Paula Serres-Corral, Sara Shopland, Gang Song, Tamás Székely, Ivan J Starikov, Josefin Stiller, Xulan Shen, Zhongyi Sun, Dieter Thomas Tietze, Pavel S Tomkovich, Alfréd Trnka, Araxi O Urrutia, Sölvi Rúnar Vignisson, Kees Wanders, Michael Wink, Kevin Winker, Christopher C Witt, Jack J Withrow, Jessie L Williamson, Glenn Yannic, Nobuyuki Yamaguchi, Zhengwang Zhang, M Thomas P Gilbert, Guojie Zhang, Shaohong Feng, Peter A Hosner","doi":"10.1093/gigascience/giag078","DOIUrl":"10.1093/gigascience/giag078","url":null,"abstract":"<p><p>Reference genome assemblies are essential infrastructure for investigating phylogeny and population/conservation genetics of wild organisms. Birds serve as model vertebrates in ecology and evolutionary biology due to their well-documented natural histories and extensive community science data. We release a set of 350 newly assembled avian genomes, which, when combined with 97 previously published genomes, represent 447 of the bird species recorded in Denmark, the Faroe Islands, and Greenland-the largest regional dataset of a vertebrate group to date. These genomes are published for various research activities. This data release advances the global effort to build comprehensive and accessible biodiversity genomic resources for the research community.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148396166","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":"Improved genome assembly of whale shark, the world's biggest fish: revealing intragenomic heterogeneity in molecular evolution.","authors":"Yawako W Kawaguchi, Rui Matsumoto, Shigehiro Kuraku","doi":"10.1093/gigascience/giag014","DOIUrl":"10.1093/gigascience/giag014","url":null,"abstract":"<p><strong>Background: </strong>High-quality chromosome-level assemblies are essential for understanding genome evolution but remain difficult to obtain for complex genomes such as those of vertebrates. The whale shark (Rhincodon typus) is an endangered species whose ecology and evolutionary history remain poorly understood. Although genomic resources have been developed for this species, available assemblies have left key uncertainties in the chromosome structure.</p><p><strong>Results: </strong>We generated a near gap-free genome assembly of the whale shark using long-read sequencing and Hi-C scaffolding, markedly improving contiguity and completeness. In particular, the X chromosome was extended to nearly twice its previous length, and putative pseudoautosomal regions were identified. Moreover, we report the first Y-linked scaffolds for this species. Comparative analyses with the zebra shark revealed exceptionally low substitution rates across the genome. We further detected a negative correlation between chromosome length and synonymous substitution rate (dS), explained by a positional gradient, here referred to as \"chromocline,\" in which substitution rates gradually decrease from chromosomal ends toward central regions. Notably, the X chromosome exhibited lower dS compared with autosomes of similar size, consistent with male-driven evolution.</p><p><strong>Conclusions: </strong>This improved whale shark genome assembly overcomes major limitations of previous resources and enables refined analyses of sex chromosomes and genome-wide evolutionary dynamics. The enhanced sex chromosome resources provide a foundation for deeper investigations of reproductive biology in this species. Increased contiguity also reveals pronounced intragenomic heterogeneity in molecular evolutionary rates, indicating that positional effects and sex chromosome differences are key determinants of synonymous divergence. This resource will facilitate population genetic and conservation genomic studies in the whale shark.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13100897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131532","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":"Single-cell long-read transcriptomics: from technologies to biological insights.","authors":"Ze-Hui Ren, Wenteng Liu, Jianhua Yin, Chuanyu Liu","doi":"10.1093/gigascience/giag082","DOIUrl":"10.1093/gigascience/giag082","url":null,"abstract":"<p><p>Single-cell long-read transcriptomics (scLR-seq) extends single-cell analysis beyond gene abundance by resolving full-length transcript structures in individual cells. It can directly interrogate isoform usage, alternative splicing, and transcription start and end site selection, thereby revealing regulatory variation that is often obscured by short-read measurements. In this review, we examine the experimental and computational foundations of scLR-seq, including platform selection, library design, cell barcode and unique molecular identifier recovery, transcript discovery, and isoform quantification. We discuss how these choices influence the reliability of downstream biological interpretation, and summarize emerging insights into isoform usage, alternative splicing, transcription start and end site selection, allele-specific expression, fusion transcripts, transposable element-derived transcripts, and RNA modifications. Finally, we highlight applications of scLR-seq in diverse biological systems, such as the immune system, neural development, and tumor microenvironments, and consider future opportunities and challenges in integrating multi-omics data to decode cellular programs and disease evolution.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148548616","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":"Epitope Generation Gateway: A biological context-dependent approach to cancer vaccine development.","authors":"Luca Mannino, Michele Fratello, Jacopo Chiaro, Federica D'Alessio, Emanuele Di Lieto, Minna Niittykoski, Angela Serra, Seppo Ylä-Herttuala, Vincenzo Cerullo, Antonio Federico, Dario Greco","doi":"10.1093/gigascience/giag095","DOIUrl":"10.1093/gigascience/giag095","url":null,"abstract":"<p><strong>Background: </strong>Personalized neoantigen discovery requires tools that not only identify putative epitopes but also prioritize candidates using biological features relevant to tumor fitness and immune escape. Here, we present Epitope Generation Gateway (EGG), a containerized pipeline that streamlines discovery and prioritization across variant types and human leukocyte antigen (HLA) contexts. EGG coordinates established components, Mutect2 for somatic single nucleotide variations (SNVs) and indels, STAR-Fusion for fusion events, ArcasHLA for HLA typing, and pVACtools for epitope sequence prediction, then augments peptide-level scores with biological context. For each patient, EGG builds co-expression and protein-protein interaction networks, uses centrality measures and CRISPR dependency data (DepMap) to flag genes important for tumor fitness, and annotates source genes with gene ontology terms, Hallmarks of Cancer processes, and IntOGen driver status.</p><p><strong>Results: </strong>This multi-layer information supports ranking of candidates that are predicted to be presented and that arise from genes essential to tumor fitness or embedded in key pathways, providing a framework to nominate epitopes whose source genes may be less dispensable under immune pressure. As a case study, we applied EGG to 30 TCGA skin cutaneous melanoma patients, demonstrating end-to-end execution and producing prioritized epitopes that emphasize peptides and alterations in high-centrality, dependency-supported essential genes.</p><p><strong>Conclusions: </strong>Overall, EGG links epitope prioritization to tumor systems biology and evolutionary rationale, prioritizing biologically contextualized cancer vaccine candidates from SNVs, indels, fusions, and splicing-derived epitopes while streamlining dataset analysis. EGG is available from GitHub at https://github.com/fhaive/epitope_generation_gateway.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extending protein language models to a viral genomic scale using biologically induced sparse attention.","authors":"Thibaut Dejean, Barbra D Ferrell, William Harrigan, Zachary D Schreiber, Rajan Sawhney, K Eric Wommack, Shawn W Polson, Mahdi Belcaid","doi":"10.1093/gigascience/giag081","DOIUrl":"10.1093/gigascience/giag081","url":null,"abstract":"<p><strong>Background: </strong>The transformer architecture in deep learning has revolutionized protein sequence analysis. Recent advancements in protein language models have paved the way for significant progress across various domains, including protein function and structure prediction, multiple sequence alignments, and mutation effect prediction. A protein language model is commonly trained on individual proteins, ignoring the interdependencies between sequences within a genome. However, biological understanding reveals that protein-protein interactions span entire genomic regions, underscoring the limitations of focusing solely on individual proteins.</p><p><strong>Findings: </strong>To address these limitations, we propose a novel approach that extends the context size of transformer models across the entire viral genome. By training on large genomic fragments, our method captures putative long-range dependencies consistent with inter-protein relationships and encodes protein sequences with integrated information from distant proteins within the same genome, offering benefits across downstream tasks. Viruses, with their densely packed genomes, minimal intergenic regions, and protein annotation challenges, are ideal candidates for genome-wide learning. We introduce a long-context protein language model, trained on entire viral genomes, leveraging a biologically informed sparse attention mechanism in which inter-protein links are inferred computationally and used as sparsity priors. Our semi-supervised approach supports long sequences of up to 61,000 amino acids (aa).</p><p><strong>Conclusion: </strong>Our evaluations show improved prediction of masked aa and improved downstream discrimination relative to single-protein models and long-context baselines, with additional validation that our inferred links correlate with independently curated interaction resources.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148548598","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":"Integrative single-cell transcriptomics and proteomics reveal an immunometabolic framework for MSC-exosome-mediated remodeling of expanded NK cells.","authors":"Yunyun Fu, Yi Liu, Mingwen Xu, Gaojun Liu, Jianzhi Sun, Fanyu Bu, Wenqing Xie, Jiayi Zhao, Jun Luo, Qiang Guo, Yinghua Huang, Fengping Xu, Siqi Liu, Longqi Liu, Ying Fu, Xuan Dong","doi":"10.1093/gigascience/giag049","DOIUrl":"10.1093/gigascience/giag049","url":null,"abstract":"<p><strong>Background: </strong>Natural killer (NK) cells play a central role in anti-tumor immunity and immunosurveillance of senescence, yet their clinical performance is frequently limited by functional exhaustion during ex vivo expansion. Mesenchymal stem cell-derived exosomes (MSC-Exos) are increasingly recognized as immunomodulators, but their broader effects on NK cell fitness and functional states remain incompletely characterized.</p><p><strong>Results: </strong>Here, we assessed MSC-Exos-mediated regulation of human NK cells using a standardized ex vivo priming platform integrated with single-cell transcriptomics and proteomic profiling. MSC-Exos significantly improved NK cell viability in a dose- and time-dependent manner while preserving a CD56⁺CD3- NK cell-enriched phenotype. MSC-Exos-treated NK cells showed enhanced cytotoxicity against K562 tumor cells and senescent fibroblasts. This phenotype was accompanied by increased expression of the activating receptors NKG2D and CD16, reduced LAG3 expression, and enhanced granzyme B expression and degranulation. Consistent with improved NK cell fitness, MSC-Exos treatment was also associated with upregulated expression of genes involved in NRF2-linked redox programs and improved mitochondrial readouts in NK cells. Single-cell analyses of MSC-Exos-treated NK cells revealed enhanced immune-effector programs and reduced inflammatory stress, while trajectory inference indicated that MSC-Exos may bias the NK cell state distribution toward more cytotoxic effector-like states. Proteomic profiling of MSC-Exos identified enrichment of FcγR-associated signaling components, supporting the hypothesis that exosomal composition may be related to the FcγR/CD16-associated transcriptional and phenotypic features observed in MSC-Exos-treated NK cells.</p><p><strong>Conclusions: </strong>Our data indicate that MSC-Exos improve NK cell viability and functional fitness during ex vivo expansion and bias NK cells toward a more effector-cytotoxic state. Together, these findings provide an immunometabolic framework for MSC-Exos-assisted NK cell manufacturing while underscoring the need for further causal validation.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13273429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729001","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":"Correction to: 'GigaDB: a redesigned repository for data publishing and management'.","authors":"","doi":"10.1093/gigascience/giag085","DOIUrl":"https://doi.org/10.1093/gigascience/giag085","url":null,"abstract":"","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"15 ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13492215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148791645","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":"PathoFact 2.0: an integrative pipeline for the prediction of antimicrobial resistance genes, virulence factors, toxins and toxin-associated proteins, and biosynthetic gene clusters in metagenomes.","authors":"Luis F Delgado, Júlia Ortís Sunyer, Cedric C Laczny, Oskar Hickl, Patrick May, Paul Wilmes","doi":"10.1093/gigascience/giag062","DOIUrl":"10.1093/gigascience/giag062","url":null,"abstract":"<p><strong>Background: </strong>Antimicrobial resistance genes (ARGs) and virulence factors (VFs) are central contributors to the global health crisis surrounding drug-resistant infections.</p><p><strong>Findings: </strong>We introduce PathoFact 2.0, an enhanced pipeline for improved ARG, VF, toxin, and biosynthetic gene clusters (BGCs) prediction. Key improvements include an updated machine learning (ML) model for VF identification, expanded hidden Markov model profiles for VFs and toxin-associated proteins, a new ML model for toxin and toxin-associated proteins identification, and the integration of antiSMASH 7.0 for predicting BGCs.</p><p><strong>Conclusions: </strong>Our upgrades make PathoFact 2.0 a more powerful and user-friendly platform for predicting microbiome-based pathogenicity and resistance, providing a crucial tool for better understanding and addressing the challenges posed by antimicrobial resistance and infectious diseases.PathoFact 2.0 is available at https://gitlab.com/uniluxembourg/lcsb/systems-ecology/pathofact2. It is compatible with Linux operating systems.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":" ","pages":""},"PeriodicalIF":5.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13224393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147987266","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}