Bioinformatics advances最新文献_第7页

Spatially varying gene regulation network inference from spatial transcriptomics. 从空间转录组学推断空间变化的基因调控网络。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-14 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag230

Yurui Li, Jin Chen, Ting Lu, Nien-Pei Tsai, Haohan Wang

{"title":"Spatially varying gene regulation network inference from spatial transcriptomics.","authors":"Yurui Li, Jin Chen, Ting Lu, Nien-Pei Tsai, Haohan Wang","doi":"10.1093/bioadv/vbag230","DOIUrl":"10.1093/bioadv/vbag230","url":null,"abstract":"Motivation: Gene regulatory networks (GRNs) govern cellular functions by coordinating gene expression programs. These regulatory relationships are strongly shaped by local microenvironments, giving rise to dynamic, spatially varying regulatory patterns across tissues. Therefore, it is crucial to infer GRNs at higher, cell-specific resolution while jointly modeling spatial context. However, most existing GRN inference approaches focus on cell-type-level networks or infer cell-specific GRNs without incorporating neighborhood and positional information.Results: We propose SVGRN, a deep learning framework for inferring spatially resolved, high-resolution GRNs from spatial transcriptomics data. SVGRN integrates gene expression, regulatory interactions, and spatial coordinates within a structural equation modeling framework implemented by a conditional variational autoencoder, to learn nonlinear, spatially varying regulatory programs in an unsupervised manner. By conditioning on target locations and incorporating neighborhood information, SVGRN refines tissue-level regulation into spot- or cell-specific GRNs. Across simulated datasets, SVGRN consistently outperforms existing methods under diverse and challenging settings. Applications to seqFISH mouse embryo data and Visium human cutaneous squamous cell carcinoma and fallopian tube datasets demonstrate that SVGRN captures spatially varying regulatory programs underlying development, tumor progression, and tissue organization, highlighting its robustness and broad applicability.Availability and implementation: The source code and data are available at https://github.com/lyrrrr/SVGRN.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag230"},"PeriodicalIF":2.6,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065974","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

MaizeGDB Phylostrata Tool: exploring evolutionary origins of maize proteins. 系统层工具：探索玉米蛋白的进化起源。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-13 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag216

Laura E Tibbs-Cortes, Oliva C Haley, John L Portwood, Ethalinda K Cannon, Margaret R Woodhouse, Carson M Andorf

{"title":"MaizeGDB Phylostrata Tool: exploring evolutionary origins of maize proteins.","authors":"Laura E Tibbs-Cortes, Oliva C Haley, John L Portwood, Ethalinda K Cannon, Margaret R Woodhouse, Carson M Andorf","doi":"10.1093/bioadv/vbag216","DOIUrl":"10.1093/bioadv/vbag216","url":null,"abstract":"Motivation: Phylostratigraphic analysis identifies the evolutionary origins and level of conservation of proteins, facilitating research in evolutionary biology and comparative genomics.Results: We developed the MaizeGDB Phylostrata Tool, a custom web application that enables users to explore the evolutionary origins of proteins in maize (Zea mays), a globally important crop and model organism. This tool features interactive visualizations and detailed gene pages incorporating subcellular localization, Gene Ontology (GO) terms, and links to resources for homologs, facilitating comparison of gene functions across evolutionary time. The tool also provides downloadable links for full-proteome phylostratigraphic results for 26 maize inbreds (B73 and the NAM founders). From these, we identified genome- and subgenome-wide trends, finding that more conserved proteins tended to be longer and more highly expressed. Finally, we provide code including updates to the \"phylostratr\" R package to make it more robust against taxonomic updates, as well as example scripts for phylostratigraphic analysis and web tool development for researchers and curators of other species.Availability and implementation: The MaizeGDB Phylostrata Tool is freely available at https://phylostrata.maizegdb.org. Scripts used for the analysis and web tool are available at https://github.com/LTibbs/PhylostrataWebtool.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag216"},"PeriodicalIF":2.6,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908257","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

MiLaSol: modeling protein solubility by mixing up multiple protein language models. MiLaSol：通过混合多种蛋白质语言模型来建模蛋白质溶解度。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-13 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag233

Weiwei Lou, Mert Erden, Lenore Cowen

{"title":"MiLaSol: modeling protein solubility by mixing up multiple protein language models.","authors":"Weiwei Lou, Mert Erden, Lenore Cowen","doi":"10.1093/bioadv/vbag233","DOIUrl":"https://doi.org/10.1093/bioadv/vbag233","url":null,"abstract":"Motivation: Protein solubility is a critical property that significantly impacts therapeutic efficacy and protein reengineering applications. Recent advances in machine learning and deep learning techniques provide unprecedented opportunities to develop predictive models for solubility, enabling more efficient protein design and optimization. This work is motivated by the potential of leveraging deep learning to address the solubility prediction challenge and to accelerate protein engineering workflows.Results: Leveraging and combining multiple protein language model representations, our MiLaSol model attains 81% accuracy, outperforming prior methods, with the highest Matthews Correlation Coefficient (MCC) score of 0.63 demonstrating balanced performance across both soluble and insoluble proteins. Through simulated annealing optimization coupled with the Raygun model, we also present a computational method to reengineer insoluble protein variants into soluble forms, with predictions confirmed by multiple independent solubility prediction methods. Our results demonstrate the effectiveness of combining machine learning-based solubility prediction with generative optimization for protein engineering.Availability and implementation: MiLaSol is available at https://github.com/weiweiloutufts/milasolAn archived version of the code at the time of submission can be found at https://doi.org/10.5281/zenodo.21495202.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag233"},"PeriodicalIF":2.6,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13581264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992688","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

MAJA: multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes. 多元贝叶斯模型发现人类表型共享表观遗传途径。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-12 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag231

Ilse Krätschmer, Hannah M Smith, Daniel L McCartney, Elena Bernabeu, Mahdi Mahmoudi, Archie Campbell, Janie Corley, Sarah E Harris, Simon R Cox, Riccardo E Marioni, Matthew R Robinson

{"title":"MAJA: multivariate Bayesian model for discovery of shared epigenetic pathways across human phenotypes.","authors":"Ilse Krätschmer, Hannah M Smith, Daniel L McCartney, Elena Bernabeu, Mahdi Mahmoudi, Archie Campbell, Janie Corley, Sarah E Harris, Simon R Cox, Riccardo E Marioni, Matthew R Robinson","doi":"10.1093/bioadv/vbag231","DOIUrl":"10.1093/bioadv/vbag231","url":null,"abstract":"Genomic measurements of DNA methylation, gene expression or protein levels are becoming more prevalent and are increasingly used to study health outcomes. However, most proposed association testing methods consider only marginal effects of each feature on a single outcome variable and are not set up to handle highly correlated, continuous data. Here, we introduce MAJA, a method to learn shared and outcome-specific effects for multiple traits in multi-omics data. MAJA determines the unique contribution of individual loci, genes, or molecular pathways to variation in one or more traits, conditional on all other measured \"omics\" data genome-wide. Simulations show MAJA accurately finds shared and distinct associations between omics-data and multiple traits and estimates omics-specific (co)variances, allowing for sparsity and correlations within the data. Applying MAJA to 12 outcome traits in Generation Scotland methylation data (n = 18 264), we find novel shared epigenetic probes among cholesterol metabolism, osteoarthritis, blood pressure and asthma. In contrast to marginal testing, we find only 10 CpG probes with significant effects above the genome-wide background. This highlights the need for joint association testing in highly correlated methylation data from whole blood and for studies of increased sample size in order to refine epigenomic associations in observational data.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag231"},"PeriodicalIF":2.6,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009480","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

CBIcall: a configuration-driven framework for variant calling in large sequencing cohorts. CBIcall：一个配置驱动的框架，用于在大型测序队列中调用变体。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-12 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag232

Manuel Rueda, Dietmar Fernandez-Orth, Ivo G Gut

{"title":"CBIcall: a configuration-driven framework for variant calling in large sequencing cohorts.","authors":"Manuel Rueda, Dietmar Fernandez-Orth, Ivo G Gut","doi":"10.1093/bioadv/vbag232","DOIUrl":"10.1093/bioadv/vbag232","url":null,"abstract":"Motivation: Variant calling for next-generation sequencing (NGS) data relies on a diverse ecosystem of tools and workflows. Large-scale collaborative studies increasingly adopt federated analysis, where each institution processes sensitive data locally using standardized pipelines. Deploying identical pipelines across multiple centers remains challenging because heterogeneous software environments and computing policies can cause workflow divergence and inconsistent results.Results: We developed CBIcall, a workflow backend-flexible, configuration-driven framework that runs standardized variant-calling pipelines from raw FASTQ files to analysis-ready VCFs. Users define each analysis in a single YAML parameters file, which CBIcall resolves against a controlled workflow registry and resource catalog. The execution driver validates parameters and checks compatibility among pipelines, analysis modes, workflow backends, genome builds, tool versions, and resource bundles. CBIcall supports reproducibility auditing by comparing executions using recorded provenance and output fingerprints. CBIcall dispatches validated workflows natively through Bash, Cromwell, Nextflow and Snakemake backends and provides production-ready pipelines for germline WES, WGS (single-sample or cohort joint genotyping following GATK Best Practices), and mitochondrial DNA analysis. We evaluated analytical performance using public benchmark datasets and validated reproducibility across four computing environments. We further deployed CBIcall in the EU HEREDITARY project, where it processed 1102 samples with both WES and mtDNA pipelines on an institutional HPC system, supporting its suitability for reproducible cohort-scale genomic analyses.Availability and implementation: CBIcall is open source (GPLv3) and distributed with ready-to-run pipelines; full dependency and installation documentation is available at https://github.com/CNAG-Biomedical-Informatics/cbicall.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag232"},"PeriodicalIF":2.6,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13549384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908203","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

microbiONT: an AI-assisted, privacy-focused platform for local Nanopore 16S and 18S amplicon analysis. microbiONT：一个人工智能辅助的，以隐私为中心的平台，用于局部纳米孔16S和18S扩增子分析。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-12 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag229

Che-Chun Chen, Hsin-Yun Lu, Ying-Ning Ho

{"title":"microbiONT: an AI-assisted, privacy-focused platform for local Nanopore 16S and 18S amplicon analysis.","authors":"Che-Chun Chen, Hsin-Yun Lu, Ying-Ning Ho","doi":"10.1093/bioadv/vbag229","DOIUrl":"10.1093/bioadv/vbag229","url":null,"abstract":"Motivation: The affordability and high-throughput capabilities of Oxford Nanopore Technologies (ONT) have democratized genomic sequencing, yet the complexity of bioinformatics environment setup and analysis remains a barrier for many biologists.Results: microbiONT addresses this gap by providing a user-friendly, privacy-focused platform that features a streamlined, one-command installation process. Integrating a local Large Language Model (Llama 3.1) with an intuitive graphical interface, microbiONT serves not only as an analysis tool but also as an AI copilot to assist users in learning bioinformatics concepts. By translating natural language requests into executable commands and simplifying deployment, microbiONT empowers non-experts to perform rigorous or custom 16S/18S amplicon analysis locally. This ensures rapid data insights without the need for command line use. Beyond its AI-integrated GUI, the novelty of microbiONT lies in its fully localized, highly accurate, and privacy-preserving architecture for flexible data analysis.Availability and implementation: Source code and portable binaries are freely available at https://github.com/MBEBlab/microbiONT under the MIT license.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag229"},"PeriodicalIF":2.6,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802303","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

Benchmarking antibody modeling tools across structure prediction, docking, and paratope-epitope interface analysis. 基准抗体建模工具跨结构预测，对接，和副表位-表位界面分析。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-11 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag223

Zeyuan Yu, Jilei Wu, Ziyao Ning, Chunxia Qiao, Jing Wang, Xinying Li, Chenghua Liu, Guojiang Chen, Jiannan Feng, Jijun Yu

{"title":"Benchmarking antibody modeling tools across structure prediction, docking, and paratope-epitope interface analysis.","authors":"Zeyuan Yu, Jilei Wu, Ziyao Ning, Chunxia Qiao, Jing Wang, Xinying Li, Chenghua Liu, Guojiang Chen, Jiannan Feng, Jijun Yu","doi":"10.1093/bioadv/vbag223","DOIUrl":"10.1093/bioadv/vbag223","url":null,"abstract":"Motivation: Computational antibody engineering requires reliable prediction of antibody variable-fragment structures, antigen-antibody complexes, and binding interfaces. However, publicly available tools for these tasks have rarely been compared across the complete workflow under a controlled and statistically grounded design.Results: We evaluated ImmuneBuilder, IgFold, AlphaFold3, GRAMM, and dyMEAN on 50 non-redundant humanized antibody-antigen complexes using multiple retained predictions and paired statistical testing. All three antibody structure predictors were accurate, with AlphaFold3 performing best overall and for the third complementarity-determining region of the heavy chain. AlphaFold3 also substantially outperformed GRAMM and dyMEAN in complex prediction, producing medium- or high-quality binding interfaces for 46% of the complexes, although overall interface accuracy remained limited. When docking was reliable, AlphaFold3 accurately recovered epitope and paratope residues, salt bridges, and non-bonded contacts, but reproduced hydrogen bonds and fine-grained contact strengths less consistently. These findings provide practical guidance for selecting tools across antibody-modeling workflows and identify persistent limitations in fine-grained interface prediction.Availability and implementation: Data, structural predictions, evaluation results, and analysis code are available from Zenodo under record 20710876.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag223"},"PeriodicalIF":2.6,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13557113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921861","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

TimeTraits: extracting functional traits from biological time-series in R. 时间性状：从生物时间序列中提取功能性状。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-11 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag226

Sarah C L Lock, Marina I Knight, Seth J Davis, Daphne Ezer

引用次数: 0

Towards improved particle reconstruction for single-molecule localization microscopy using geometric deep learning. 利用几何深度学习改进单分子定位显微镜的粒子重建。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-11 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag228

Diana Mindroc-Filimon, Dominic A Helmerich, Patrick Salome, Markus Sauer, Philip Kollmannsberger

{"title":"Towards improved particle reconstruction for single-molecule localization microscopy using geometric deep learning.","authors":"Diana Mindroc-Filimon, Dominic A Helmerich, Patrick Salome, Markus Sauer, Philip Kollmannsberger","doi":"10.1093/bioadv/vbag228","DOIUrl":"10.1093/bioadv/vbag228","url":null,"abstract":"Motivation: Single-molecule localization microscopy (SMLM) can resolve intracellular structures down to the nanoscale, but often produces sparse and incomplete data. Particle averaging (PA) can aid with the reconstruction of complete structures, but traditional PA methods can suffer from template bias or the high computational costs of geometric alignment.Results: To address these limitations, we developed a geometric deep learning (GDL) framework for enhanced, template-free 3D particle averaging. Our pipeline uses a GDL autoencoder, trained on high-fidelity simulated data, to map incomplete point clouds into a robust latent space. By averaging feature vectors directly within this space, our method bypasses the need for explicit 3D alignment. We validated our approach on simulated DNA origami and experimental nuclear pore complex (NPC) data. The latent space averaging successfully reconstructed NPC structures with key metrics (ring radius <math><mrow><mo>≈</mo> <mn>46</mn> <mtext>nm</mtext></mrow> </math> , ring distance <math><mrow><mo>≈</mo> <mn>52</mn> <mtext>nm</mtext></mrow> </math> ) that are comparable to state-of-the-art methods. This work establishes a viable GDL pipeline for SMLM analysis, offering an efficient alternative to traditional PA. While the current model requires structure-specific training, our results highlight the significant potential of GDL for quantitative structural biology.Availability: https://github.com/dianamindroc/smlm.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag228"},"PeriodicalIF":2.6,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076932","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

tidyexposomics: integrated exposure-omics analysis powered by tidy principles. 整洁暴露组学：基于整洁原则的综合暴露组学分析。

IF 2.6

Bioinformatics advances Pub Date : 2026-08-08 eCollection Date: 2026-01-01 DOI: 10.1093/bioadv/vbag220

Jason Laird, Thomas Hartung, Fenna C M Sillé, Alexandra Maertens

{"title":"tidyexposomics: integrated exposure-omics analysis powered by tidy principles.","authors":"Jason Laird, Thomas Hartung, Fenna C M Sillé, Alexandra Maertens","doi":"10.1093/bioadv/vbag220","DOIUrl":"10.1093/bioadv/vbag220","url":null,"abstract":"Motivation: Environmental exposures shape health across the life course, influencing molecular pathways, disease susceptibility, and therapeutic response. However, integrating exposome and multi-omics data poses several challenges, ranging from extensive analytical steps to ensuring trends are consistent across studies.Results: To address this challenge, we developed tidyexposomics, an open-source R package that delivers an end-to-end, tidyverse-native workflow including ontology-based exposure annotation, quality control, association testing, stability assessment, multi-omics integration, network analysis, and functional enrichment. The package offers intuitive Application Programming Interface (API), modular functions, and extensive visualization tools to facilitate flexible analyses. Comprehensive analytical step tracking and result export functions enable reproducibility and consistent reporting of downstream results. By combining systematic preprocessing, statistical modeling and biologically informed interpretation in one package, tidyexposomics streamlines exposure-omics analysis.Availability and implementation: The GitHub repository for tidyexposomics is available at: https://github.com/BioNomad/tidyexposomics.Pre-processed example data are available at: https://doi.org/10.5281/zenodo.17049350.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag220"},"PeriodicalIF":2.6,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13532756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876672","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