NAR Genomics and Bioinformatics最新文献_第2页

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles. cgDist：从cgMLST等位基因谱计算核苷酸水平的距离。

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NAR Genomics and Bioinformatics Pub Date : 2026-09-02 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag090

Andrea de Ruvo, Pierluigi Castelli, Andrea Bucciacchio, Iolanda Mangone, Verónica Mixão, Vítor Borges, Michele Flammini, Nicolas Radomski, Adriano Di Pasquale

{"title":"cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.","authors":"Andrea de Ruvo, Pierluigi Castelli, Andrea Bucciacchio, Iolanda Mangone, Verónica Mixão, Vítor Borges, Michele Flammini, Nicolas Radomski, Adriano Di Pasquale","doi":"10.1093/nargab/lqag090","DOIUrl":"10.1093/nargab/lqag090","url":null,"abstract":"Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag090"},"PeriodicalIF":3.0,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design. 从规则到基础模型：siRNA设计的机器学习方法的全面回顾。

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NAR Genomics and Bioinformatics Pub Date : 2026-08-31 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag099

Zahra Khodagholi, Niloofar Yousefi

{"title":"From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.","authors":"Zahra Khodagholi, Niloofar Yousefi","doi":"10.1093/nargab/lqag099","DOIUrl":"10.1093/nargab/lqag099","url":null,"abstract":"Small interfering RNAs (siRNAs) are a clinically validated therapeutic modality with eight FDA-approved drugs, yet designing effective siRNAs remains computationally challenging due to complex dependencies on sequence composition, thermodynamic properties, target-site accessibility, and off-target interactions. Over two decades, computational approaches have evolved from empirical heuristics to deep learning systems integrating physical priors with learned representations. We review the complete landscape of machine learning methods for siRNA design, spanning classical scoring rules, pretrained RNA foundation models, transformer-based efficacy predictors, graph neural networks encoding siRNA/messenger RNA interaction topology, off-target prediction frameworks, and chemical modification-aware architectures. Across over 40 studies, we identify convergent findings: hybrid models integrating thermodynamic features with learned representations are among the strongest performers, although this evidence rests largely on single-model ablations and does not establish that foundation-model embeddings specifically are required; graph neural networks with leakage-aware data splitting address pervasive benchmark inflation; and off-target prediction has matured through empirical RNA-seq frameworks and structure-based features. We distinguish throughout between chemically unmodified siRNAs, which dominate public benchmarks, and the fully modified siRNAs used therapeutically, whose efficacy data remain scarce and whose prediction is correspondingly harder. We provide a taxonomy of methods, head-to-head performance comparisons, benchmark dataset descriptions, code availability, biology-informed interpretability analysis with formal saliency validation protocols, and concrete recommendations for advancing siRNA design. Critical gaps in uncertainty quantification, active learning, and prospective experimental validation are identified as priorities for clinical translation.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag099"},"PeriodicalIF":3.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate imputation of inversions in human genomes using different algorithms and data sources. 利用不同的算法和数据源对人类基因组进行精确的反演。

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NAR Genomics and Bioinformatics Pub Date : 2026-08-25 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag094

Illya Yakymenko, Adrià Mompart, Mario Cáceres

{"title":"Accurate imputation of inversions in human genomes using different algorithms and data sources.","authors":"Illya Yakymenko, Adrià Mompart, Mario Cáceres","doi":"10.1093/nargab/lqag094","DOIUrl":"10.1093/nargab/lqag094","url":null,"abstract":"Complex genomic regions harbor different structural arrangements that can mutate quite rapidly, which makes determining their functional effects very difficult. Characterization of inversions originated by homologous mechanisms is especially challenging due to the presence of inverted repeats at the breakpoints and the fact that most of them are recurrent. Imputation is a useful method to infer missing genotypes, but it has been mainly limited to simple variants and little is known about how well it works for human inversions. Here, we tested five common imputation programs to impute a set of 52 inversions, which have been experimentally genotyped in multiple samples and lacked perfectly linked single nucleotide polymorphisms (SNPs). Using whole-genome sequencing data and simulated microarrays with variable SNP density, we found that 40.4%-75.5% of inversions could be accurately imputed in three human populations by at least one program, with results depending mostly on inversion recurrence and the number of available SNPs and genotyped samples. Besides, genotype probability filtering was a key factor for inversion imputation accuracy. In particular, Minimac4 and IMPUTE5 showed more accurately imputed inversions and less poorly imputed individuals with respect to the other methods. This work therefore contributes to optimizing inversion imputation in order to study their functional impact.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag094"},"PeriodicalIF":3.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148819808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human. 将异质性整合到人类大基因组背景下拓扑关联域边界预测中。

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NAR Genomics and Bioinformatics Pub Date : 2026-08-14 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag092

Ying Sun, Lars Juhl Jensen, Niels Tommerup, Jan Gorodkin

{"title":"Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human.","authors":"Ying Sun, Lars Juhl Jensen, Niels Tommerup, Jan Gorodkin","doi":"10.1093/nargab/lqag092","DOIUrl":"10.1093/nargab/lqag092","url":null,"abstract":"A fundamental understanding of genome organization relies on accurately annotating topologically associating domains (TADs) and their boundaries. This is crucial for understanding how cis-regulatory elements regulate gene expression. To go beyond calling TADs and boundaries from Hi-C data, several machine learning-based methods have been proposed to go the step further and predict TAD boundaries from genomic sequences. As the growing evidence of TADs and their boundaries, TADs have been proved exhibiting diverse properties, such as differences in replication timing and epigenetic patterns. However, existing methods do not take this heterogeneity into account. To address this, we propose a method called TADBpred for TAD boundary prediction in a large genomic context in humans. TADBpred focuses on TAD boundaries in active and inactive chromatin across cell-lines and tissues, which are GC-rich and AT-rich, respectively. By integrating genomic elements and sequence composition, we designed two models for GC-rich and AT-rich boundaries, respectively. When testing the performance on respective independent held-out datasets, we obtain AUC scores of 0.91 and 0.80. Our results indicate that TADBpred excels in TAD boundary prediction. Additionally, feature importance analysis highlights the essential features for different classes of TAD boundaries, thereby enhancing our understanding of these TAD boundaries.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag092"},"PeriodicalIF":3.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13473877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148766189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic evaluation of the heterogeneity of topologically associating domain boundaries in a large genomic context in humans. 在人类大基因组背景下对拓扑相关域边界异质性的系统评价。

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NAR Genomics and Bioinformatics Pub Date : 2026-08-14 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag093

Ying Sun, Niels Tommerup, Lars Juhl Jensen, Jan Gorodkin

{"title":"Systematic evaluation of the heterogeneity of topologically associating domain boundaries in a large genomic context in humans.","authors":"Ying Sun, Niels Tommerup, Lars Juhl Jensen, Jan Gorodkin","doi":"10.1093/nargab/lqag093","DOIUrl":"10.1093/nargab/lqag093","url":null,"abstract":"Topologically associating domains (TADs) are generally considered as a homogeneous basic units of genome folding, which is critical for transcriptional regulation. However, recent studies indicate that both the TAD domain structures and the boundaries between them are not as homogeneous as originally recognized. Here, we address the heterogeneity of the TAD boundaries in the human genome at a large scale, which varies between active and inactive chromatin and across cell lines and tissues. To address this, based on the well-annotated TAD boundaries extracted from multiple cell lines and tissues, we examine their nucleotide content, resulting in two main clusters, one GC-rich and one AT-rich, which are mainly distributed in active and inactive chromatin, respectively. Also, they contain different types of repetitive sequences and have different epigenetic patterns, with more CTCF binding motifs in the GC-rich cluster. Hence, our observations of the TAD boundary content provide novel insights into TAD genomic architecture. In addition, we find that cell- or tissue-specific boundaries are less evolutionarily conserved than other boundaries. We highlight the importance of TAD boundary diversity in different functional contexts and discuss the importance of the different types of repetitive sequences and epigenetic patterns in the two main types of boundaries.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag093"},"PeriodicalIF":3.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13473879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148760585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling the terra cognita of sequence spaces using Cartesian projection of asymmetric distances. 利用不对称距离的笛卡尔投影揭示序列空间的认知域。

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NAR Genomics and Bioinformatics Pub Date : 2026-08-05 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag085

Alban Ramette

{"title":"Unveiling the terra cognita of sequence spaces using Cartesian projection of asymmetric distances.","authors":"Alban Ramette","doi":"10.1093/nargab/lqag085","DOIUrl":"10.1093/nargab/lqag085","url":null,"abstract":"Visualizing relationships within massive biological datasets remains a significant challenge, particularly as sequence length and volume increase. We introduce CAPASYDIS (Cartesian Projections of Asymmetric Distances), a scalable approach designed to map the explored regions of a given sequence space. Unlike traditional dimensionality reduction methods, CAPASYDIS calculates asymmetric distances which account for both the position and type of sequence differences. It projects sequences into a fixed, low-dimensional coordinate system, termed a \"seqverse,\" where each sequence occupies a permanent and unique location. This design allows for the instant mapping of new sequences without the need to recalculate the global coordinate system, enabling the exploration of the diversity space on a standardized map. We applied this method to a large rRNA sequence dataset spanning the three domains of life. Our results demonstrate that the sequences of Bacteria, Archaea, and Eukaryota occupy spatially distinct regions characterized by fundamentally different shapes and patterns of variation. Furthermore, the resulting seqverses retain high amount of taxonomic information, when analyzed from broad domain levels to single-base differences. Overall, CAPASYDIS provides a reproducible, scalable framework for defining the boundaries and topography of biological sequence universes.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag085"},"PeriodicalIF":3.0,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13439134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The challenges of single cell transcriptomics on difficult human tissue: the placenta. 单细胞转录组学在困难的人体组织上的挑战：胎盘。

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NAR Genomics and Bioinformatics Pub Date : 2026-07-16 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag079

Theodoros Xenakis, George T Hall, Jose J Moreno-Villena, Sara L Hillman, Yara E Sanchez-Corrales, Sergi Castellano

{"title":"The challenges of single cell transcriptomics on difficult human tissue: the placenta.","authors":"Theodoros Xenakis, George T Hall, Jose J Moreno-Villena, Sara L Hillman, Yara E Sanchez-Corrales, Sergi Castellano","doi":"10.1093/nargab/lqag079","DOIUrl":"10.1093/nargab/lqag079","url":null,"abstract":"Demand for the application of single cell transcriptomics on difficult tissues, processed and stored in disparate conditions, has led to the development of various single cell modalities. We focus on the placenta, a challenging tissue to transcriptomically interrogate due to senescence, intermittent hypoxia, high levels of RNase activity and tissue trauma at delivery. We performed single cell and nuclei transcriptomics on two samples with probe-based and native molecule transcript capture, droplet-based and in situ plate-based cell isolation. We explored sample and storage variations, including freshly dissociated tissues, fixed cells, snap frozen, and formalin fixed paraffin embedded processing. We find that variations in sample processing and storage have a much larger effect on cell-type proportions than differences in chemistry, impacting the biology that can be learned or the identification of disease markers. Further, the transcriptomic output of in situ combinatorial indexing consistently overlaps with single-nucleus transcriptomics, sharing little with other modalities. This may limit combinatorial indexing for sampling cytoplasmic messenger RNAs. Our comprehensive analysis of the technical variance between single cell transcriptomic modalities, including their sample management strategies, provides novel and essential considerations for the experimental design and analysis of single cell transcriptomics applicable to challenging tissues, offering actionable guidance for future experiments.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag079"},"PeriodicalIF":3.0,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13373321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multiperspective evaluation framework of spatial transcriptomics clustering methods. 空间转录组聚类方法的多视角评价框架。

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NAR Genomics and Bioinformatics Pub Date : 2026-07-08 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag075

Gospel Ozioma Nnadi, Vincenzo Bonnici, Simone Avesani, Eva Viesi, Rosalba Giugno

{"title":"A multiperspective evaluation framework of spatial transcriptomics clustering methods.","authors":"Gospel Ozioma Nnadi, Vincenzo Bonnici, Simone Avesani, Eva Viesi, Rosalba Giugno","doi":"10.1093/nargab/lqag075","DOIUrl":"10.1093/nargab/lqag075","url":null,"abstract":"Spatial transcriptomics (ST) allows the exploration of gene expression within tissue microenvironments, driving the development of multiple computational approaches for spatial domain identification. Evaluating these methods typically relies on label-dependent metrics, such as contingency matrices and information-theoretic measures, which require ground-truth annotations, and label-independent metrics, which assess transcriptomic similarity or spatial organization. However, annotations are often incomplete or unavailable, while label-independent metrics fail to jointly evaluate the integration of transcriptomic and spatial information, a core feature of ST clustering methods. To address these limitations, we introduce MultimetricST, a Python-based framework that provides a unified, flexible evaluation strategy integrating both cutting-edge and state-of-the-art label-dependent and label-independent metrics. We applied MultimetricST on two generated synthetic datasets and thirteen datasets derived from seven ST technologies to systematically evaluate the spatial domains identified by eleven state-of-the-art deep learning methods. Our framework highlights the strengths and limitations of each assessment strategy, providing an accessible and reproducible tool for comparative and robust evaluation, and method selection.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag075"},"PeriodicalIF":3.0,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13342736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QutRNA2: robust tRNA modification discovery from Nanopore direct tRNA sequencing. QutRNA2：通过纳米孔直接tRNA测序发现强大的tRNA修饰。

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NAR Genomics and Bioinformatics Pub Date : 2026-07-07 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag065

Michael Piechotta, Wei Guo, Isabel S Naarmann-de Vries, Felix Kallenborn, Swastik Mishra, Francesca Tuorto, Bertil Schmidt, Christoph Dieterich

引用次数: 0

A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq nanopore long reads data. 单细胞和空间RNA-seq纳米孔长读取数据的生物信息学方法的系统基准。

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NAR Genomics and Bioinformatics Pub Date : 2026-07-06 eCollection Date: 2026-09-01 DOI: 10.1093/nargab/lqag070

Ali Hamraoui, Audrey Onfroy, Catherine Sénamaud-Beaufort, Fanny Coulpier, Sophie Lemoine, Laurent Jourdren, Morgane Thomas-Chollier

{"title":"A systematic benchmark of bioinformatics methods for single-cell and spatial RNA-seq nanopore long reads data.","authors":"Ali Hamraoui, Audrey Onfroy, Catherine Sénamaud-Beaufort, Fanny Coulpier, Sophie Lemoine, Laurent Jourdren, Morgane Thomas-Chollier","doi":"10.1093/nargab/lqag070","DOIUrl":"10.1093/nargab/lqag070","url":null,"abstract":"Alternative splicing plays a crucial role in transcriptomic complexity, yet remains difficult to resolve at the single-cell level due to the limitations of short-read technologies. Coupling single-cell with long-read sequencing offers full-length transcript coverage, enabling more accurate isoform detection. Diverse computational tools tailored for single-cell and spatial long-read transcriptomics have been developed. To compare the effectiveness of these approaches, we generated paired short-read and Nanopore long-read single-cell datasets, tailored for benchmarking bioinformatics tools. We evaluated ten state-of-the-art methods, spanning four analytical dimensions: barcodes and unique molecular identifiers (UMI) detection, demultiplexing and UMI clustering, gene-level expression profiling, and isoform detection and quantification. Using real and simulated datasets across different protocols, sequencing depths and chemistries, we assessed the accuracy, robustness, and scalability of each tool. Our results revealed method-specific trade-offs, and highlight the importance of sequencing quality and UMI correction strategies. This benchmark provides a practical resource for optimizing isoform analysis and accurate gene expression profiling in single-cell and spatial transcriptomics using long-read sequencing. The workflow employed for benchmarking is designed to be reusable, thereby enabling method developers to compare their own approaches against the set of reference methods evaluated in this work.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 3","pages":"lqag070"},"PeriodicalIF":3.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13335474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148438244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0