Bioinformatics advances最新文献

IsoPrimer: a pipeline for designing isoform-aware primer pairs for comprehensive gene expression quantification. IsoPrimer：设计同源型敏感引物对的管道，用于全面的基因表达定量。

IF 2.8

Bioinformatics advances Pub Date : 2025-07-15 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf171

Ermes Filomena, Ernesto Picardi, Graziano Pesole, Anna Maria D'Erchia

{"title":"IsoPrimer: a pipeline for designing isoform-aware primer pairs for comprehensive gene expression quantification.","authors":"Ermes Filomena, Ernesto Picardi, Graziano Pesole, Anna Maria D'Erchia","doi":"10.1093/bioadv/vbaf171","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf171","url":null,"abstract":"Motivation: Eukaryotic genes can perform different functions by generating multiple transcripts through the alternative splicing mechanism. The accurate quantification of gene expression in specific conditions is important for functional assessment and requires an accurate PCR primer pair design to target all expressed alternative transcripts, a complex and prone-to-error task if performed manually.Results: To efficiently address this task, we developed a pipeline, called IsoPrimer, to design PCR primer pairs targeting the specific set of expressed splicing variants of the genes of interest, to be used in quantitative PCR, e.g. in RNA-seq validation experiments. IsoPrimer, according to the level of expression of the splicing variants derived from an RNA-seq dataset, can: (i) identify the most expressed gene isoforms; (ii) design primer pairs overlapping exon-exon junctions common to the expressed variants; (iii) verify the specificity of the designed primer pairs.Availability and implementation: IsoPrimer is available for download from https://github.com/BioinfoUNIBA/IsoPrimer.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf171"},"PeriodicalIF":2.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12311343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762471","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

Hybrid representation learning for human m⁶A modifications with chromosome-level generalizability. 具有染色体水平通用性的人类m6A修饰的混合表示学习。

IF 2.8

Bioinformatics advances Pub Date : 2025-07-14 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf170

Muhammad Tahir, Sheela Ramanna, Qian Liu

{"title":"Hybrid representation learning for human m6A modifications with chromosome-level generalizability.","authors":"Muhammad Tahir, Sheela Ramanna, Qian Liu","doi":"10.1093/bioadv/vbaf170","DOIUrl":"10.1093/bioadv/vbaf170","url":null,"abstract":"Motivation: <math> <mrow> <mrow> <msup><mrow><mi>N</mi></mrow> <mn>6</mn></msup> </mrow> <mo>-</mo> <mtext>methyladenosine</mtext></mrow> </math> ( <math> <mrow> <mrow> <msup><mrow><mi>m</mi></mrow> <mn>6</mn></msup> </mrow> <mi>A</mi></mrow> </math> ) is the most abundant internal modification in eukaryotic mRNA and plays essential roles in post-transcriptional gene regulation. While several deep learning approaches have been proposed to predict <math> <mrow> <mrow> <msup><mrow><mi>m</mi></mrow> <mn>6</mn></msup> </mrow> <mi>A</mi></mrow> </math> sites, most suffer from limited chromosome-level generalizability due to evaluation on randomly split datasets.Results: In this study, we propose two novel hybrid deep learning models-Hybrid Model and Hybrid Deep Model-that integrate local sequence features (k-mers) and contextual embeddings via convolutional neural networks to improve predictive performance and generalization. We evaluate these models using both a Random-Split strategy and a more biologically realistic Leave-One-Chromosome-Out setting to ensure robustness across genomic regions. Our proposed models outperform the state-of-the-art m6A-TCPred model across all key evaluation metrics. Hybrid Deep Model achieves the highest accuracy under Random-Split, while Hybrid Model demonstrates superior generalization under Leave-One-Chromosome-Out, indicating that deep global representations may overfit in chromosome-independent settings. These findings underscore the importance of rigorous validation strategies and offer insights into designing robust <math> <mrow> <mrow> <msup><mrow><mi>m</mi></mrow> <mn>6</mn></msup> </mrow> <mi>A</mi></mrow> </math> predictors.Availability and implementation: Source code and datasets are available at: https://github.com/malikmtahir/LOCO-m6A.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf170"},"PeriodicalIF":2.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288952/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710028","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

MitSorter: a standalone tool for accurate discrimination of mtDNA and NuMT ONT reads based on differential methylation. MitSorter：基于差异甲基化的mtDNA和NuMT - ONT读取准确区分的独立工具。

IF 2.4

Bioinformatics advances Pub Date : 2025-07-10 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf135

Sharon Natasha Cox, Angelo Sante Varvara, Graziano Pesole

{"title":"MitSorter: a standalone tool for accurate discrimination of mtDNA and NuMT ONT reads based on differential methylation.","authors":"Sharon Natasha Cox, Angelo Sante Varvara, Graziano Pesole","doi":"10.1093/bioadv/vbaf135","DOIUrl":"10.1093/bioadv/vbaf135","url":null,"abstract":"Motivation: The accurate differentiation between mitochondrial DNA (mtDNA) and nuclear mitochondrial DNA segments (NuMTs) is a critical challenge in studies involving mitochondrial disorders. Mapping the mtDNA mutation spectrum and quantifying heteroplasmy are complex tasks when using next-generation sequencing methods, mostly due to NuMTs contamination in data analysis.Results: Here, we present a novel, easy-to-use standalone command-line tool designed to reliably discriminate long reads originated by either mtDNA or NuMTs and generated by Oxford Nanopore Technologies (ONT) sequencing based on the known lack of CpG methylation in human mtDNA. MitSorter aligns the reads to the mitochondrial genome incorporating base modification calls directly from raw POD5 files. The resulting BAM file is then partitioned into two separate BAM files: one containing unmethylated reads and the other containing methylated reads. We show that MitSorter analysis can provide a more accurate landscape of the mtDNA mutation profile. We describe here the tool's features, computational framework, validation approach, and its potential applications in other genomic research areas.Availability and implementation: Source code and documentation, are available at https://github.com/asvarvara/MitSorter.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf135"},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12275464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676691","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

PeGAS: a versatile bioinformatics pipeline for antimicrobial resistance, virulence and pangenome analysis. PeGAS：用于抗菌素耐药性、毒力和泛基因组分析的多功能生物信息学管道。

IF 2.8

Bioinformatics advances Pub Date : 2025-07-09 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf165

Liviu-Iulian Rotaru, Marius Surleac

{"title":"PeGAS: a versatile bioinformatics pipeline for antimicrobial resistance, virulence and pangenome analysis.","authors":"Liviu-Iulian Rotaru, Marius Surleac","doi":"10.1093/bioadv/vbaf165","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf165","url":null,"abstract":"Motivation: Antimicrobial resistance is increasingly recognized as one of the most significant global health threats, with profound implications for human, animal, and environmental health. Genome analysis represents a very useful tool that provides accurate and reproducible results allowing for the advancement of knowledge regarding antimicrobial resistance diagnosis, therapeutics, surveillance, transmission, and evolution. However, due to increasing complexity of bacterial genome analysis and computational power required for genomic approaches, there is a continuous need for comprehensive, user-friendly tools for data analysis. We developed Pangenome and Genomic Analysis Suite (PeGAS), to address some of these challenges by offering an all-in-one pipeline that performs a range of analyses.Results: PeGAS integrates key genomic analysis features of bacteria whole genome sequencing, including the prediction of antimicrobial resistance profiles, sorted by various categories of antibiotics, VF detection, and plasmid replicon assignment. The pipeline also performs pangenome analysis, multilocus sequence typing, genome assembly quality control (by reporting statistics such as GC content, contig length, the number of contigs, as well as variation from certain GC thresholds) providing a comprehensive genomic overview. PeGAS also offers the ability to restart seamlessly from any sporadic interruptions that might occur during long or resource-intensive runs.Availability and implementation: PeGAS is available at: https://github.com/liviurotiul/PeGAS.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf165"},"PeriodicalIF":2.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12308278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144755179","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 penetrance R package for estimation of age specific risk in family-based studies. 外显率R包在基于家庭的研究中用于估计年龄特定风险。

IF 2.4

Bioinformatics advances Pub Date : 2025-07-08 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf154

Nicolas Kubista, Danielle Braun, Giovanni Parmigiani

{"title":"The penetrance R package for estimation of age specific risk in family-based studies.","authors":"Nicolas Kubista, Danielle Braun, Giovanni Parmigiani","doi":"10.1093/bioadv/vbaf154","DOIUrl":"10.1093/bioadv/vbaf154","url":null,"abstract":"Motivation: Reliable tools and software for penetrance (age-specific risk among those who carry a genetic variant) estimation are critical to improving clinical decision making and risk assessment for hereditary syndromes. However, there is a lack of easily usable software for penetrance estimation in family-based studies that implements a Bayesian estimation approach.Results: We introduce penetrance, an open-source R package available on CRAN, to estimate age-specific penetrance using family-history pedigree data. The package uses a Bayesian estimation approach, allowing for the incorporation of prior knowledge through the specification of priors for the parameters of the carrier distribution. It also includes options to impute missing ages during the estimation process, addressing incomplete age information which is not uncommon in pedigree datasets. Our open-source software provides a flexible and user-friendly tool for researchers to estimate penetrance in complex family-based studies, facilitating improved genetic risk assessment in hereditary syndromes.Availability and implementation: The penetrance package is freely available on CRAN. Source code and documentation are available at https://github.com/nicokubi/penetrance.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf154"},"PeriodicalIF":2.4,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661139","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

Blackbird: structural variant detection using synthetic and low-coverage long-reads. 黑鸟：结构变异检测使用合成和低覆盖长读取。

IF 2.4

Bioinformatics advances Pub Date : 2025-07-04 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf151

Dmitry Meleshko, Rui Yang, Salil Maharjan, David C Danko, Anton Korobeynikov, Iman Hajirasouliha

{"title":"Blackbird: structural variant detection using synthetic and low-coverage long-reads.","authors":"Dmitry Meleshko, Rui Yang, Salil Maharjan, David C Danko, Anton Korobeynikov, Iman Hajirasouliha","doi":"10.1093/bioadv/vbaf151","DOIUrl":"10.1093/bioadv/vbaf151","url":null,"abstract":"Motivation: Recent benchmarks show that most structural variations, especially within 50-10,000 bp range cannot be resolved with short-read sequencing, but long-read structural variant callers perform better on the same datasets. However, high-coverage long-read sequencing is costly and requires substantial input DNA. Reducing coverage lowers cost but significantly impacts the performance of existing structural variation (SV) callers. Synthetic long-read technologies offer long-range information at lower cost, but leveraging them for SVs under 50 kbp remains challenging.Results: We propose a novel hybrid alignment- and local-assembly-based algorithm, Blackbird, that uses synthetic long reads and low-coverage long reads to improve structural variant detection. Instead of relying on whole-genome assembly, Blackbird uses a sliding window approach and synthetic long-read barcode information to assemble local segments, integrating long reads to improve structural variant detection accuracy. We evaluated Blackbird on real human genome datasets. On the HG002 Genome in a Bottle (GIAB) benchmark, Blackbird in hybrid mode demonstrated results comparable to state-of-the-art long-read tools, while using less long-read coverage. Blackbird requires only 5 <math><mo>×</mo></math> coverage to achieve F1-scores (0.835 and 0.808 for deletions and insertions) similar to PBSV and Sniffles2 using 10 <math><mo>×</mo></math> PacBio Hi-Fi long-read coverage.Availability and implementation: Blackbird is available at https://github.com/1dayac/Blackbird.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf151"},"PeriodicalIF":2.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593034","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

Optimal solution to the set cover problem with a vicinity constraint for estimating genotype tissue expression profiles. 用邻近约束估计基因型组织表达谱的集合覆盖问题的最优解。

IF 2.8

Bioinformatics advances Pub Date : 2025-07-04 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf163

Jiahong Dong, Stephen Brown, Kevin Truong

{"title":"Optimal solution to the set cover problem with a vicinity constraint for estimating genotype tissue expression profiles.","authors":"Jiahong Dong, Stephen Brown, Kevin Truong","doi":"10.1093/bioadv/vbaf163","DOIUrl":"https://doi.org/10.1093/bioadv/vbaf163","url":null,"abstract":"Motivation: Genes located in close genomic proximity tend to have more similar genotype tissue expression profiles. This suggests that expression profiles for the entire genome could be estimated using a smaller set of experimentally determined profiles from carefully selected reference genes, thereby reducing the need for extensive experimental measurements.Results: We address this challenge by mapping it as a set cover problem, aiming to identify an optimal number of gene sets that can cover the entire genome. However, traditional set cover algorithms are either slow in runtime or yield non-optimal results for large datasets. To overcome this limitation, we developed a dynamic programming algorithm that leverages the consecutive ordering of genes within vicinity sets. Our algorithm solves this vicinity set cover problem with tractable runtime while minimizing the average distance between reference genes and non-reference genes within the vicinity, thereby maximizing estimation accuracy. This algorithm can be used to reduce the number of required experiments in organisms lacking genotype tissue expression data or in new human datasets with expanded tissue sets. Lastly, our algorithm also has broader applications for set cover optimization problems in other fields.Availability and implementation: The source code along with all implementation details are available at: https://github.com/sensationTI/vicinity_set_cover.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf163"},"PeriodicalIF":2.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12313015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762411","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

MEAanalysis: an open-source R package for downstream visualization of AxIS navigator multi-electrode array burst data at the single-electrode level. MEAanalysis：一个开源的R包，用于在单电极水平上对AxIS导航器多电极阵列突发数据进行下游可视化。

IF 2.8

Bioinformatics advances Pub Date : 2025-07-03 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf160

Emily A Gordon, David L Bennett, Georgios Baskozos, Maddalena Comini

引用次数: 0

Improved prediction of antibody and their complexes with clustered generative modelling ensembles. 利用聚类生成模型集成改进抗体及其复合物的预测。

IF 2.4

Bioinformatics advances Pub Date : 2025-07-03 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf161

Xiaotong Xu, Marco Giulini, Alexandre M J J Bonvin

{"title":"Improved prediction of antibody and their complexes with clustered generative modelling ensembles.","authors":"Xiaotong Xu, Marco Giulini, Alexandre M J J Bonvin","doi":"10.1093/bioadv/vbaf161","DOIUrl":"10.1093/bioadv/vbaf161","url":null,"abstract":"Motivation: Gaining structural insights into antibody-antigen complexes is crucial for understanding antigen recognition mechanisms and advancing therapeutic antibody design. However, accurate prediction of the structure of highly variable complementarity-determining region 3 on the antibody heavy chain (CDR-H3 loop) remains a significant challenge due to its increased length and conformational variability. While AlphaFold2-multimer (AF2) has made substantial progress in protein structure prediction, its application on antibodies and antibody-antigen complexes is limited by the weak evolutionary signals in the CDR region and the lack of structural diversity in its output.Results: To address these limitations, we propose a workflow that combines AlphaFlow to generate ensembles of potential loop conformations with integrative modelling of antibody-antigen complexes with HADDOCK. Improving the structural diversity of the H3 loop increases the success rate of subsequent docking tasks. Our analysis shows that while AF2 generally predicts accurate antibody structures, it struggles with the H3 loop. In cases where AF2 mispredicts the loop, we leverage AlphaFlow to generate ensembles of loop conformations via score-based flow matching, followed by clustering to produce a structurally diverse set of models. We demonstrate that these ensembles significantly improve antibody-antigen docking performance compared to the standard AF2 ensembles.Availability and implementation: The input datasets and codes involved in this research are available at https://github.com/haddocking/alphaflow-antibodies. All the resulting modelling data are available from Zenodo (https://zenodo.org/records/14906314).","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf161"},"PeriodicalIF":2.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12279294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683717","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

Calculating genetic risk scores directly from summary statistics with an application to type 1 diabetes. 计算遗传风险得分直接从汇总统计与应用于1型糖尿病。

IF 2.4

Bioinformatics advances Pub Date : 2025-07-02 eCollection Date: 2025-01-01 DOI: 10.1093/bioadv/vbaf158

Steven Squires, Michael N Weedon, Richard A Oram

{"title":"Calculating genetic risk scores directly from summary statistics with an application to type 1 diabetes.","authors":"Steven Squires, Michael N Weedon, Richard A Oram","doi":"10.1093/bioadv/vbaf158","DOIUrl":"10.1093/bioadv/vbaf158","url":null,"abstract":"Motivation: Genetic risk scores (GRS) summarise genetic data into a single number and allow for discrimination between cases and controls. Many applications of GRSs would benefit from comparisons with multiple datasets to assess quality of the GRS across different groups. However, genetic data is often unavailable. If summary statistics of the genetic data could be used to calculate GRSs more comparisons could be made, potentially leading to improved research.Results: We present a methodology that utilises only summary statistics of genetic data to calculate GRSs with an example of a type 1 diabetes (T1D) GRS. An example on European populations of the mean T1D GRS for those calculated from genetic data and from summary statistics (our method) was 10.31 (10.12-10.48) and 10.38 (10.24-10.53), respectively. An example of a case-control set for T1D has an area under the receiver operating characteristic curve of 0.917 (0.903-0.93) for those calculated from genetic data and 0.914 (0.898-0.929) for those calculated from summary statistics.Availability: The code is available at https://github.com/stevensquires/simulating_genetic_risk_scores.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf158"},"PeriodicalIF":2.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661138","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