Frontiers in bioinformatics最新文献

{"title":"Correction: Analysing open-source protein folding models for nanobody binding prediction.","authors":"Yannick Vogt, Rebekka Roßberg, Jan Habermann, Pia Kluge, Wendan Xu, Mehdi Naouar, Stefan Hirschberg, Cornelius Miething, Joschka Bödecker, Maria Kalweit, Evelyn Ullrich, Gabriel Kalweit","doi":"10.3389/fbinf.2026.1988897","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1988897","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fbinf.2026.1858891.].</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1988897"},"PeriodicalIF":3.6,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149172360","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}

{"title":"Reverse vaccinology and immunoinformatics-driven design of a multiepitope vaccine candidate against <i>Renibacterium salmoninarum</i>.","authors":"Cristóbal Morales, Vicente Arriagada, Erick Bonilla, Bruno Prina, Esteban Godoy, Benjamín Molina-Chávez, Alberto Osorio, Emilio Gutierrez, Victor Roa, Leonardo A Gómez, Ángel A Oñate","doi":"10.3389/fbinf.2026.1899416","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1899416","url":null,"abstract":"<p><p><i>Renibacterium salmoninarum</i>, the etiological agent of bacterial kidney disease (BKD), remains a major challenge for salmonid aquaculture due to its intracellular lifestyle, chronic infection pattern, and the limited efficacy of current control strategies. In this study, a multiepitope vaccine candidate against <i>Renibacterium salmoninarum</i> was designed using reverse vaccinology and immunoinformatics approaches. Conserved antigenic proteins shared among three bacterial strains were identified through a systematic screening pipeline that included virulence prediction, essentiality analysis, antigenicity assessment, and exclusion of host homologs. Selected B-cell, MHC-I, and MHC-II epitopes were assembled into a chimeric construct and combined with conserved flagellin D0/D1 domains as a TLR5-targeting molecular adjuvant. The resulting construct, designated RS-MEVC, exhibited favorable predicted physicochemical and immunological properties, including high antigenicity, non-allergenicity, structural stability, and favorable stereochemical quality. Three-dimensional modeling revealed a predominantly α-helical structure, and structural validation supported the reliability of the refined model. Molecular docking predicted a stable interaction between RS-MEVC and <i>Atlantic salmon</i> TLR5, supported by multiple hydrogen bonds, salt bridges, and extensive intermolecular contacts. Molecular dynamics simulations further indicated stable conformational behavior of both the free vaccine construct and the vaccine-TLR5 complex, while MM-PBSA analysis predicted favorable binding energetics. Codon optimization and <i>in silico</i> cloning supported the feasibility of recombinant expression in <i>Escherichia coli</i>. Overall, these findings identify RS-MEVC as a promising computationally designed vaccine candidate against BKD and provide a rational framework for its subsequent experimental evaluation in salmonids.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1899416"},"PeriodicalIF":3.6,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170803","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}

{"title":"Immunoinformatics-based design of a multi-epitope mRNA vaccine candidate targeting the major human-infecting hepatitis E virus genotypes 1-4.","authors":"Maham Mehrangaze Khonza, Zain Saeed, Zohra Jabeen, Wafaa Arab, Sehrish Kakakhel, Maham Yamin, Mazen M Ghaith, Aneela Javed, Ali Zohaib, Rwaa H Abdulal","doi":"10.3389/fbinf.2026.1922523","DOIUrl":"10.3389/fbinf.2026.1922523","url":null,"abstract":"<p><strong>Background: </strong>Hepatitis E virus (HEV) is one of the leading causes of acute and chronic hepatitis among humans across the globe and is linked to severe diseases in immunocompromised individuals and pregnant women. Despite its substantial global burden, only one vaccine is licensed (in China and Pakistan), limiting its global accessibility. Furthermore, no globally approved mRNA vaccine against HEV is currently available, highlighting the need to develop strategies for alternative vaccines.</p><p><strong>Methods: </strong>In this study, the amino acid sequences of ORF2 capsid proteins representative of HEV genotypes 1-4 were curated, retrieved, and analyzed to generate a consensus sequence. The conserved cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and B-cell epitopes were computationally screened based on their antigenicity, allergenicity, toxicity, IFN-γ induction, and human non-homology. Then, the selected epitopes, seven in total, were assembled into a computational multi-epitope mRNA construct incorporated with β-defensin adjuvant, correct linkers, and regulatory elements [Kozak, tPA, MITD, and globin untranslated regions (UTRs)]. The construct was subsequently subjected to structural modeling, refinement and validation, docked with the TLR1/TLR2 heterodimer, followed by normal mode analysis and a 100-ns molecular dynamics simulation with MM-GBSA binding free-energy estimation, immune simulations, codon optimization, and <i>in silico</i> vaccine construct design.</p><p><strong>Results: </strong>The computational analyses predicted that the proposed epitopes possessed a favorable antigenicity (VaxiJen score ≥0.4-1), with non-allergenicity and epitope conservancy (80%-100%) across the analyzed HEV genotypes (1-4), and that the assembled vaccine construct was structurally stable. Protein-protein docking of the vaccine construct candidate with the TLR1/TLR2 heterodimer and normal mode analysis of the docked complex indicated a stable structural interface rather than a confirmation of biological receptor engagement; a subsequent 100-ns molecular dynamics simulation supported a persistent, increasingly hydrogen-bonded interface with a favorable MM-GBSA binding free-energy (-91.96 kcal/mol). Computational immune simulations predicted cellular and humoral immune responses with B- and T-cell interaction activity and cytokine production levels.</p><p><strong>Conclusion: </strong>This study describes a computationally designed multi-epitope mRNA vaccine candidate with predicted immunogenicity and structural stability against the major human-infecting HEV genotypes 1-4. These computational results support the proposed vaccine construct as a promising candidate for further research to assess its immunogenicity; safety and protective efficacy will require experimental validation before its translational potential is verified.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1922523"},"PeriodicalIF":3.6,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139628","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}

{"title":"Neuro-psycho MMSig: multimodal mechanism enrichment platform for neuropsychiatric disorders.","authors":"Vinay Srinivas Bharadhwaj, Karim S Shalaby, Sathvik Guru Rao, Bruce Schultz, Jiayuan Zhang, Daniel Domingo-Fernández, Martin Hofmann-Apitius, Alpha Tom Kodamullil","doi":"10.3389/fbinf.2026.1898607","DOIUrl":"10.3389/fbinf.2026.1898607","url":null,"abstract":"<p><strong>Introduction: </strong>Understanding the pathophysiology of neurodegenerative and psychiatric disorders requires integrating multimodal data.</p><p><strong>Methods: </strong>We present Neuro-PsychoMMSig, a web-based platform that identifies disease-specific multimodal mechanistic signatures by integrating multi-omics datasets with causal knowledge graphs (KGs). The platform incorporates six curated KGs (Alzheimer's disease, Parkinson's disease, epilepsy, schizophrenia, bipolar disorder, and type-2 diabetes mellitus) and four network-based algorithms: Clinical Patient Embeddings (CLEP), Candidate Mechanism Perturbation Amplitude (CMPA), Gene Set Enrichment Analysis (GSEA), and CAusal Robust Mapping method in meta-Analysis (CARMA). These tools facilitate qualitative exploration of biomarker interactions and quantitative clinical analysis, including patient stratification, pathway enrichment, and variant fine-mapping.</p><p><strong>Results: </strong>Application to public Alzheimer's and schizophrenia datasets identified specific biological dysregulations, including G1/S cell cycle phase transition in Alzheimer's disease, and clustered distinct patient subgroups.</p><p><strong>Conclusion: </strong>Neuro-PsychoMMSig offers a locally deployable tool for biomarker discovery, patient subtyping, and mechanistic hypothesis generation.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1898607"},"PeriodicalIF":3.6,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139591","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}

{"title":"The impact of deep learning and omics data in transforming precision therapy for brain cancer.","authors":"Sana Munquad, Anam Upadhyay, Asim Bikas Das","doi":"10.3389/fbinf.2026.1811813","DOIUrl":"10.3389/fbinf.2026.1811813","url":null,"abstract":"<p><p>The combination of AI with high-throughput genomics has revolutionized oncology, particularly in the case of brain cancer, a diagnostically complicated and heterogeneous tumor. With the explosion of omics data and computational power, deep learning (DL) has evolved as a powerful computational approach for the interpretation of the molecular topography of brain tumors. Utilizing mono-omics and multi-omics data, including gene expression, somatic mutations, DNA methylation, copy number alterations, and miRNA profiles, deep learning algorithms can detect complex, nonlinear patterns underlying tumor biology and clinical behavior. Several DL architectures have been used in recent studies for classification, biomarker discovery, and subtype prediction in brain cancer. The combination of multi-omics data has been especially useful, since oncogenic changes arise at several molecular levels; therefore, the omission of any specific omics aspect might undermine diagnostic accuracy and therapeutic prediction. Here, in this review, DL-based approaches to various omics modalities are reviewed, covering the tools constructed, architectures, and performance levels, and then delving into integrative multi-omics frameworks that maximize the accuracy and interpretability of diagnostic models. It also discusses the significance of AI-based approaches in advancing personalized treatment for brain cancer, emphasizing their capacity to predict patient-specific drug responses. Notwithstanding these breakthroughs, some major challenges remain, which include data heterogeneity, explainability of models, computational requirements, and ethical issues related to the use of genomic data. Together, the review highlights the potential of DL and multi-omics integration in advancing precision oncology for brain cancer by enhancing diagnostic precision, prognostic accuracy, and personalized therapy development.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1811813"},"PeriodicalIF":3.6,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119156","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}

{"title":"Editorial: Methods, tools and algorithms in data visualization bioinformatics.","authors":"Erqiang Hu","doi":"10.3389/fbinf.2026.1975924","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1975924","url":null,"abstract":"","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1975924"},"PeriodicalIF":3.6,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119554","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}

{"title":"Multi-omics integration in allergy, viral infections and asthma: a comprehensive analysis of bioinformatics tools.","authors":"Silvia Garcés Rigol, Nuria Contreras, Rafael Núñez, Elena Izquierdo, Domingo Barber, Alma Villaseñor, Osvaldo Graña Castro, María M Escribese","doi":"10.3389/fbinf.2026.1904588","DOIUrl":"10.3389/fbinf.2026.1904588","url":null,"abstract":"<p><strong>Introduction: </strong>Allergic and inflammatory respiratory conditions are complex and heterogeneous diseases affecting a large portion of the population. Although omics technologies have enabled the identification of potential biomarkers, single-omics-based approaches often fail to capture the full complexity of these disorders. Multi-omics integration provides a more comprehensive perspective; however, selecting the most suitable tool remains challenging due to the wide range of available tools and the variability of biological datasets. In this study, we compare different integration approaches for these diseases, evaluating their performance and functionality to ensure a robust and objective assessment across heterogeneous datasets.</p><p><strong>Methods: </strong>We compared multiple multi-omics integration tools across four heterogeneous respiratory disease datasets. Unsupervised methods were applied following dataset-specific preprocessing, including filtering, imputation, and normalization. Integration was performed across matched metabolomics, proteomics, and transcriptomics layers. Performance was evaluated using the Adjusted Rand Index (ARI).</p><p><strong>Results: </strong>Unsupervised methods showed variable performance across datasets. NEMO consistently achieved the most accurate clustering of clinical phenotypes, whereas MOFA and intNMF performed well in specific contexts.</p><p><strong>Conclusion: </strong>Our findings indicate that NEMO, intNMF and MOFA are promising tools for multi-omics integration in allergic and respiratory diseases, as they effectively capture complementary information across different omics layers and facilitate the identification of coordinated molecular signatures associated with disease phenotypes. Overall, these results emphasize the importance of selecting integration methods based on the study goals and the dataset characteristics.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1904588"},"PeriodicalIF":3.6,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13638525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119198","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}

{"title":"A machine learning framework for interpreting phylogenetic tree patterns in interkingdom horizontal gene transfer.","authors":"Kevin Aguirre-Carvajal, Cristian R Munteanu, Vinicio Armijos-Jaramillo","doi":"10.3389/fbinf.2026.1891949","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1891949","url":null,"abstract":"<p><strong>Introduction: </strong>Horizontal gene transfer (HGT), the movement of genetic material between unrelated organisms, is widely recognized as an important driver of genome evolution in bacteria. In eukaryotes, however, the evolutionary impact of HGT remains debated. The identification of interkingdom HGT (iHGT) is especially challenging due to the lack of gold standard methods. While automated approaches for iHGT candidate detection exist, the interpretation of phylogenetic tree topologies into biologically meaningful evolutionary patterns has traditionally depended on expert manual inspection, a process that is subjective, difficult to reproduce, and not scalable to large datasets.</p><p><strong>Methods: </strong>We present a computational framework that formalizes phylogenetic tree interpretation as a supervised machine-learning problem. We define five recurrent phylogenetic patterns (iHGT, NoHGT, Limited donor evidence, Multiple major clades, and Patchy phylogeny) and developed a feature-extraction pipeline that captures taxonomic composition and phylogenetic topology through six biologically interpretable descriptors derived from unrooted gene trees. Several machine-learning algorithms were evaluated using repeated stratified cross-validation, and model interpretability was assessed through permutation importance, SHAP analysis, rule-based baselines, and feature ablation. Performance was further validated on simulated datasets, real biological iHGT candidates, and an independently annotated external dataset.</p><p><strong>Results: </strong>A Random Forest (RF) classifier achieved the best performance (AUC-ROC = 0.98; accuracy = 0.89). Topological distance and lineage-distribution features were identified as the strongest contributors to classification performance. Feature ablation and rule-based baseline comparisons demonstrated that model performance cannot be explained solely by explicit annotation rules and benefits from combining clade-composition and topology-based information. The RF classifier showed low misclassification rates on simulated and real biological datasets (7.8% and 10.43%, respectively), showed substantial agreement (69.4%) with an independently annotated external dataset, and consistently outperformed AVP (Alienness vs. Predictor), Alien Index, and HGT Index across all evaluated metrics.</p><p><strong>Conclusion: </strong>These results support the use of machine learning for the automated, reproducible, and scalable classification of expert-defined phylogenetic patterns. The framework also highlights that some topologies commonly interpreted as evidence of iHGT may reflect alternative evolutionary processes, emphasizing the need for cautious and context-aware inference.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1891949"},"PeriodicalIF":3.6,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13634943/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119518","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}

{"title":"A single-cell atlas and Shiny-based framework for murine lung injury and remodeling.","authors":"Qiuming Wang, Jixian Li, Philip J Moos, Alessandro Venosa","doi":"10.3389/fbinf.2026.1886929","DOIUrl":"10.3389/fbinf.2026.1886929","url":null,"abstract":"<p><p>Single-cell RNA sequencing has provided unprecedented insights into the cellular heterogeneity of acute and chronic pulmonary injury. Lack of data-driven analytical frameworks remains a significant barrier to reproducible and biologically robust analyses. To address this challenge, we developed a modular, interactive analysis platform for single-cell RNA sequencing data processing and visualization. As a case study, we applied the framework to an immune-enriched murine lung dataset containing samples from different batches and across four conditions: control, inflammation and remodeling, fibrosis, and fibrotic lung with ozone exposure. Manual annotations performed on individual samples were used as ground-truth labels to evaluate automated cell-type annotations generated using the Tabula Muris Senis and LungMAP reference atlases. These annotations were also utilized to benchmark the performance of data preprocessing and integration combinations on batch-effect removal and the preservation of biological identity. Within the LIANA framework, we evaluated eight inference algorithms and 18 references for cell-cell communication analysis. In our results, we outlined the framework of the analysis pipeline and interactive platform. Through manual annotation, we identified 36 distinct cell populations, highlighting the limitations of automated reference-based annotations in resolving cell states missing from underlying references - particularly injury-induced monocyte-derived macrophages and eosinophils. In our dataset, Log-normalization outperformed SCTransform in both preserving cell-type separation and batch mixing, while FastMNN provided the best balance between technical batch correction and the retention of cell subtype identities. Cell-cell communication analysis revealed that permutation-based algorithms used by CellChat and CellPhoneDB improved prediction confidence, while MouseConsensus and OmniPath resources increased interaction coverage. These findings demonstrate that preprocessing, integration, and cell annotation strategies should be selected in a context-dependent, data-driven manner rather than relying on default workflows. The annotated murine lung dataset expands the existing atlas, and the modular interactive platform offers a practical framework to improve accessibility, reproducibility, and analytical precision in pulmonary research.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1886929"},"PeriodicalIF":3.6,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13635373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119592","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}

{"title":"Bringing evolution into zoological parks through timetrees.","authors":"Jack M Craig, Michael Suleski, S Blair Hedges, Sudhir Kumar","doi":"10.3389/fbinf.2026.1932369","DOIUrl":"https://doi.org/10.3389/fbinf.2026.1932369","url":null,"abstract":"<p><p>Zoological parks provide one of the public's earliest and most frequent encounters with biodiversity, making them a natural setting for introducing evolution to the general public. Timetrees are particularly well suited to this purpose because they link the diversity of living animals to the history of life on Earth, but zoos rarely make this connection. Here, we present a new phylogeny and an associated open web resource that fill this gap (https://www.timetree.org/zootree). Using an intuitive interface, users can create educational outreach materials custom-tailored to the zoo collection of their choice. The timetree of zoo mammals will, therefore, offer the general public an opportunity to understand evolution through the lens of charismatic animal ambassadors.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1932369"},"PeriodicalIF":3.6,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13634847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119561","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}