Frontiers in bioinformatics最新文献

{"title":"MetaComBin: combining abundances and overlaps for binning metagenomics reads.","authors":"Francesco Tomasella, Cinzia Pizzi","doi":"10.3389/fbinf.2025.1504728","DOIUrl":"10.3389/fbinf.2025.1504728","url":null,"abstract":"<p><strong>Introduction: </strong>Metagenomics is the discipline that studies heterogeneous microbial samples extracted directly from their natural environment, for example, from soil, water, or the human body. The detection and quantification of species that populate microbial communities have been the subject of many recent studies based on classification and clustering, motivated by being the first step in more complex pipelines (e.g., for functional analysis, de novo assembly, or comparison of metagenomes). Metagenomics has an impact on both environmental studies and precision medicine; thus, it is crucial to improve the quality of species identification through computational tools.</p><p><strong>Methods: </strong>In this paper, we explore the idea of improving the overall quality of metagenomics binning at the read level by proposing a computational framework that sequentially combines two complementary read-binning approaches: one based on species abundance determination and another one relying on read overlap in order to cluster reads together. We called this approach MetaComBin (metagenomics combined binning).</p><p><strong>Results and discussion: </strong>The results of our experiments with the MetaComBin approach showed that the combination of two tools, based on different approaches, can improve the clustering quality in realistic conditions where the number of species is not known beforehand.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1504728"},"PeriodicalIF":2.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652436","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":"Reliable machine learning models in genomic medicine using conformal prediction.","authors":"Christina Papangelou, Konstantinos Kyriakidis, Pantelis Natsiavas, Ioanna Chouvarda, Andigoni Malousi","doi":"10.3389/fbinf.2025.1507448","DOIUrl":"10.3389/fbinf.2025.1507448","url":null,"abstract":"<p><p>Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have life-threatening impact, raising reasonable skepticism about whether these applications have practical benefit in clinical settings. Conformal prediction offers a versatile framework for addressing these concerns by quantifying the uncertainty of predictive models. In this perspective review, we investigate potential applications of conformalized models in genomic medicine and discuss the challenges towards bridging genomic medicine applications with clinical practice. We also demonstrate the impact of a binary transductive model and a regression-based inductive model in predicting drug response as well as the performance of a multi-class inductive predictor in addressing distribution shifts in molecular subtyping. The main conclusion is that as machine learning and genomic medicine are increasingly infiltrating healthcare services, conformal prediction has the potential to overcome the safety limitations of current methods and could be effectively integrated into uncertainty-informed applications within clinical environments.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1507448"},"PeriodicalIF":2.8,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11891349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598189","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":"Phytochemical baicalin potentially inhibits Bcl-2 and VEGF: an <i>in silico</i> approach.","authors":"Vikas Sharma, Arti Gupta, Mohini Singh, Anshul Singh, Anis Ahmad Chaudhary, Zakir Hassain Ahmed, Salah-Ud-Din Khan, Sarvesh Rustagi, Sanjay Kumar, Sandeep Kumar","doi":"10.3389/fbinf.2025.1545353","DOIUrl":"10.3389/fbinf.2025.1545353","url":null,"abstract":"<p><strong>Background: </strong>The rising prevalence of cancer cells exhibits uncontrolled growth and invasive and aggressive properties, leading to metastasis, which poses a significant challenge for global health. Central to cancer development are proteins such as NF-kB, p53, VEGF, and BAX/Bcl-2, which play important roles in angiogenesis, cell apoptosis regulation, and tumor growth.</p><p><strong>Methodology: </strong>This <i>in silico</i> study evaluates the activity of six different natural as well as novel therapeutic strategies against cancer. Using a computational approach, i.e., virtual screening, molecular docking, and molecular dynamics (MD) simulations, the binding affinities and interactions of selected phytochemicals with cancer-specific proteins were analyzed. Key criteria for selection included binding affinity, molecular stability, and pharmacokinetic and toxicological properties. Post-selection, dynamics of ligand-protein interactions were further examined through MD simulations conducted using Desmond-Maestro 2020-4 on a Linux-based HP Z2 workstation, providing an insight into the conformational changes in the stability of the inhibitor-protein complexes. This was complemented by ADMET predictions to assess pharmacokinetics and toxicological profiles.</p><p><strong>Results: </strong>Our findings reveal that out of six phytochemicals, baicalin exhibited the most promising results, with docking scores of -9.2 kcal/mol and -9.0 kcal/mol against Bcl-2 and VEGF receptors, respectively. The MD simulation (100 ns) confirmed the stability of baicalin-protein interactions, supported by hydrophobic interactions and intermolecular hydrogen bonds. The RMSD and RMSF values of baicalin exhibit an acceptable global minimum (3.5-6 Å) for p53, VEGF, and BAX/Bcl-2.</p><p><strong>Conclusion: </strong>This study highlights the potential of baicalin, a phytochemical known for anti-cancerous, anti-apoptotic, and anti-proliferative properties, as a promising candidate for cancer treatment. Further exploration and validation of its inhibitory mechanisms could open a promising avenue for therapeutic approaches in oncology.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1545353"},"PeriodicalIF":2.8,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11879997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569030","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":"Unlocking the potential of <i>in silico</i> approach in designing antibodies against SARS-CoV-2.","authors":"Tasshitra Subramaniam, Siti Aisyah Mualif, Weng Howe Chan, Khairul Bariyyah Abd Halim","doi":"10.3389/fbinf.2025.1533983","DOIUrl":"10.3389/fbinf.2025.1533983","url":null,"abstract":"<p><p>Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, <i>in silico</i> techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, <i>in silico</i> approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed <i>in silico</i>, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of <i>in silico</i>-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1533983"},"PeriodicalIF":2.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525298","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":"Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation.","authors":"Mikhail Arbatsky, Ekaterina Vasilyeva, Veronika Sysoeva, Ekaterina Semina, Valeri Saveliev, Kseniya Rubina","doi":"10.3389/fbinf.2025.1519468","DOIUrl":"10.3389/fbinf.2025.1519468","url":null,"abstract":"<p><p>Processing biological data is a challenge of paramount importance as the amount of accumulated data has been annually increasing along with the emergence of new methods for studying biological objects. Blind application of mathematical methods in biology may lead to erroneous hypotheses and conclusions. Here we narrow our focus down to a small set of mathematical methods applied upon standard processing of scRNA-seq data: preprocessing, dimensionality reduction, integration, and clustering (using machine learning methods for clustering). Normalization and scaling are standard manipulations for the pre-processing with LogNormalize (natural-log transformation), CLR (centered log ratio transformation), and RC (relative counts) being employed as methods for data transformation. The justification for applying these methods in biology is not discussed in methodological articles. The essential aspect of dimensionality reduction is to identify the stable patterns which are deliberately removed upon mathematical data processing as being redundant, albeit containing important minor details for biological interpretation. There are no established rules for integration of datasets obtained at different sampling times or conditions. Clustering calls for reconsidering its application specifically for biological data processing. The novelty of the present study lies in an integrated approach of biology and bioinformatics to elucidate biological insights upon data processing.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1519468"},"PeriodicalIF":2.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517337","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":"Targeting <i>aldose reductase</i> using natural African compounds as promising agents for managing diabetic complications.","authors":"Miriam E L Gakpey, Shadrack A Aidoo, Toheeb A Jumah, George Hanson, Siyabonga Msipa, Florence N Mbaoji, Omonijo Bukola, Palesa C Tjale, Mamadou Sangare, Hedia Tebourbi, Olaitan I Awe","doi":"10.3389/fbinf.2025.1499255","DOIUrl":"10.3389/fbinf.2025.1499255","url":null,"abstract":"<p><strong>Background: </strong>Diabetes remains a leading cause of morbidity and mortality due to various complications induced by hyperglycemia. Inhibiting Aldose Reductase (AR), an enzyme that converts glucose to sorbitol, has been studied to prevent long-term diabetic consequences. Unfortunately, drugs targeting AR have demonstrated toxicity, adverse reactions, and a lack of specificity. This study aims to explore African indigenous compounds with high specificity as potential AR inhibitors for pharmacological intervention.</p><p><strong>Methodology: </strong>A total of 7,344 compounds from the AfroDB, EANPDB, and NANPDB databases were obtained and pre-filtered using the Lipinski rule of five to generate a compound library for virtual screening against the Aldose Reductase. The top 20 compounds with the highest binding affinity were selected. Subsequently, <i>in silico</i> analyses such as protein-ligand interaction, physicochemical and pharmacokinetic profiling (ADMET), and molecular dynamics simulation coupled with binding free energy calculations were performed to identify lead compounds with high binding affinity and low toxicity.</p><p><strong>Results: </strong>Five natural compounds, namely, (+)-pipoxide, Zinc000095485961, Naamidine A, (-)-pipoxide, and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside, were identified as potential inhibitors of aldose reductase. Molecular docking results showed that these compounds exhibited binding energies ranging from -12.3 to -10.7 kcal/mol, which were better than the standard inhibitors (zopolrestat, epalrestat, IDD594, tolrestat, and sorbinil) used in this study. The ADMET and protein-ligand interaction results revealed that these compounds interacted with key inhibiting residues through hydrogen and hydrophobic interactions and demonstrated favorable pharmacological and low toxicity profiles. Prediction of biological activity highlighted Zinc000095485961 and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside as having significant inhibitory activity against aldose reductase. Molecular dynamics simulations and MM-PBSA analysis confirmed that the compounds bound to AR exhibited high stability and less conformational change to the AR-inhibitor complex.</p><p><strong>Conclusion: </strong>This study highlighted the potential inhibitory activity of 5 compounds that belong to the African region: (+)-Pipoxide, Zinc000095485961, Naamidine A, (-)-Pipoxide, and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside. These molecules inhibiting the aldose reductase, the key enzyme of the polyol pathway, can be developed as therapeutic agents to manage diabetic complications. However, we recommend <i>in vitro</i> and <i>in vivo</i> studies to confirm our findings.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1499255"},"PeriodicalIF":2.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494357","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":"Choosing the most suitable NGS technology to combine with a standardized viral enrichment protocol for obtaining complete avian orthoreovirus genomes from metagenomic samples.","authors":"Sonsiray Álvarez-Narváez, Telvin L Harrell, Islam Nour, Sujit K Mohanty, Steven J Conrad","doi":"10.3389/fbinf.2025.1498921","DOIUrl":"10.3389/fbinf.2025.1498921","url":null,"abstract":"<p><p>Since viruses are obligate intracellular pathogens, sequencing their genomes results in metagenomic data from both the virus and the host. Virology researchers are constantly seeking new, cost-effective strategies and bioinformatic pipelines for the retrieval of complete viral genomes from these metagenomic samples. Avian orthoreoviruses (ARVs) pose a significant and growing threat to the poultry industry and frequently cause economic losses associated with disease in production birds. Currently available commercial vaccines are ineffective against new ARV variants and ARV outbreaks are increasing worldwide, requiring whole genome sequencing (WGS) to characterize strains that evade vaccines. This study compares the effectiveness of long-read and short-read sequencing technologies for obtaining ARV complete genomes. We used eight clinical isolates of ARV, each previously processed using our published viral genome enrichment protocol. Additionally, we evaluate three assembly methods to determine which provided the most complete and reliable whole genomes: <i>De novo</i>, reference-guided or hybrid. The results suggest that our ARV genome enrichment protocol caused some fragmentation of the viral cDNA that impacted the length of the long reads (but not the short reads) and, as a result, caused a failure to produce complete genomes via <i>de novo</i> assembly. Overall, we observed that regardless of the sequencing technology, the best quality assemblies were generated by mapping quality-trimmed reads to a custom reference genome. The custom reference genomes were in turn constructed with the publicly available ARV genomic segments that shared the highest sequence similarity with the contigs from short-read <i>de novo</i> assemblies. Hence, we conclude that short-read sequencing is the most suitable technology to combine with our ARV genome enrichment protocol.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1498921"},"PeriodicalIF":2.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451153","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":"Machine learning approaches for predicting protein-ligand binding sites from sequence data.","authors":"Orhun Vural, Leon Jololian","doi":"10.3389/fbinf.2025.1520382","DOIUrl":"10.3389/fbinf.2025.1520382","url":null,"abstract":"<p><p>Proteins, composed of amino acids, are crucial for a wide range of biological functions. Proteins have various interaction sites, one of which is the protein-ligand binding site, essential for molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key biological functions. Accurate prediction of these binding sites is pivotal in computational drug discovery, helping to identify therapeutic targets and facilitate treatment development. Machine learning has made significant contributions to this field by improving the prediction of protein-ligand interactions. This paper reviews studies that use machine learning to predict protein-ligand binding sites from sequence data, focusing on recent advancements. The review examines various embedding methods and machine learning architectures, addressing current challenges and the ongoing debates in the field. Additionally, research gaps in the existing literature are highlighted, and potential future directions for advancing the field are discussed. This study provides a thorough overview of sequence-based approaches for predicting protein-ligand binding sites, offering insights into the current state of research and future possibilities.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1520382"},"PeriodicalIF":2.8,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442945","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":"End-to-end 3D instance segmentation of synthetic data and embryo microscopy images with a 3D Mask R-CNN.","authors":"Gabriel David, Emmanuel Faure","doi":"10.3389/fbinf.2024.1497539","DOIUrl":"10.3389/fbinf.2024.1497539","url":null,"abstract":"<p><p>In recent years, the exploitation of three-dimensional (3D) data in deep learning has gained momentum despite its inherent challenges. The necessity of 3D approaches arises from the limitations of two-dimensional (2D) techniques when applied to 3D data due to the lack of global context. A critical task in medical and microscopy 3D image analysis is instance segmentation, which is inherently complex due to the need for accurately identifying and segmenting multiple object instances in an image. Here, we introduce a 3D adaptation of the Mask R-CNN, a powerful end-to-end network designed for instance segmentation. Our implementation adapts a widely used 2D TensorFlow Mask R-CNN by developing custom TensorFlow operations for 3D Non-Max Suppression and 3D Crop And Resize, facilitating efficient training and inference on 3D data. We validate our 3D Mask R-CNN on two experiences. The first experience uses a controlled environment of synthetic data with instances exhibiting a wide range of anisotropy and noise. Our model achieves good results while illustrating the limit of the 3D Mask R-CNN for the noisiest objects. Second, applying it to real-world data involving cell instance segmentation during the morphogenesis of the ascidian embryo <i>Phallusia mammillata</i>, we show that our 3D Mask R-CNN outperforms the state-of-the-art method, achieving high recall and precision scores. The model preserves cell connectivity, which is crucial for applications in quantitative study. Our implementation is open source, ensuring reproducibility and facilitating further research in 3D deep learning.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"4 ","pages":"1497539"},"PeriodicalIF":2.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11814465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411996","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":"DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches.","authors":"Yi-Hsuan Tsai, Prasenjit Mitra, David Taniar, Tun-Wen Pai","doi":"10.3389/fbinf.2025.1523524","DOIUrl":"10.3389/fbinf.2025.1523524","url":null,"abstract":"<p><p>Identifying cancer biomarkers through DNA methylation analysis is an efficient approach toward the detection of aberrant changes in epigenetic regulation associated with early-stage cancer types. Among all cancer types, cancers with relatively low five-year survival rates and high incidence rates were pancreatic (10%), esophageal (20%), liver (20%), lung (21%), and brain (27%) cancers. This study integrated genome-wide DNA methylation profiles and comorbidity patterns to identify the common biomarkers with multi-functional analytics across the aforementioned five cancer types. In addition, gene ontology was used to categorize the biomarkers into several functional groups and establish the relationships between gene functions and cancers. ALX3, HOXD8, IRX1, HOXA9, HRH1, PTPRN2, TRIM58, and NPTX2 were identified as important methylation biomarkers for the five cancers characterized by low five-year survival rates. To extend the applicability of these biomarkers, their annotated genetic functions were explored through GO and KEGG pathway analyses. The combination of ALX3, NPTX2, and TRIM58 was selected from distinct functional groups. An accuracy prediction of 93.3% could be achieved by validating the ten most common cancers, including the initial five low-survival-rate cancer types.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1523524"},"PeriodicalIF":2.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11810926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411998","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}