Computational and structural biotechnology journal最新文献

{"title":"Identification of cellular signatures associated with chinese hamster ovary cell adaptation for secretion of antibodies.","authors":"Ying Bai, Ivan Domenech Mercadé, Ramy Elgendy, Giulia Lambiase, Sew Peak-Chew, Catarina Franco, Steven W Wingett, Tim J Stevens, Luigi Grassi, Noah Hitchcock, Cristina Sayago Ferreira, Diane Hatton, Elizabeth A Miller, Rajesh K Mistry","doi":"10.1016/j.csbj.2024.12.006","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.12.006","url":null,"abstract":"<p><p>The secretory capacity of Chinese hamster ovary (CHO) cells remains a fundamental bottleneck in the manufacturing of protein-based therapeutics. Unconventional biological drugs with complex structures and processing requirements are particularly problematic. Although engineered vector DNA elements can achieve rapid and high-level therapeutic protein production, a high metabolic and protein folding burden is imposed on the host cell. Cellular adaptations to these conditions include differential gene expression profiles that can in turn influence the productivity and quality control of recombinant proteins. In this study, we used quantitative transcriptomic and proteomic analyses to investigate how biological pathways change with antibody titre. Gene and protein expression profiles of CHO cell pools and clones producing a panel of different monoclonal and bispecific antibodies were analysed during fed-batch production. Antibody-expressing CHO cell pools were heterogeneous, resulting in few discernible genetic signatures. Clonal cell lines derived from these pools, selected for high and low production, yielded a small number of differentially expressed proteins that correlated with productivity and were shared across the biotherapeutics. However, the dominant feature associated with higher protein production was transgene copy number and the resulting mRNA expression level. Moreover, variability between clonal cell lines suggested that the process of cellular adaptation is variable with diverse cellular changes associated with individual adaptation events.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"17-31"},"PeriodicalIF":4.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ecological and stochastic perspective on persisters resuscitation.","authors":"Tania Alonso-Vásquez, Michele Giovannini, Gian Luigi Garbini, Mikolaj Dziurzynski, Giovanni Bacci, Ester Coppini, Donatella Fibbi, Marco Fondi","doi":"10.1016/j.csbj.2024.12.002","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.12.002","url":null,"abstract":"<p><p>Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1-9"},"PeriodicalIF":4.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of genetic admixture proportions via haplotypes.","authors":"Seyoon Ko, Eric M Sobel, Hua Zhou, Kenneth Lange","doi":"10.1016/j.csbj.2024.11.043","DOIUrl":"10.1016/j.csbj.2024.11.043","url":null,"abstract":"<p><p>Estimation of ancestral admixture is essential for creating personal genealogies, studying human history, and conducting genome-wide association studies (GWAS). The following three primary methods exist for estimating admixture coefficients. The frequentist approach directly maximizes the binomial loglikelihood. The Bayesian approach adds a reasonable prior and samples the posterior distribution. Finally, the nonparametric approach decomposes the genotype matrix algebraically. Each approach scales successfully to datasets with a million individuals and a million single nucleotide polymorphisms (SNPs). Despite their variety, all current approaches assume independence between SNPs. To achieve independence requires performing LD (linkage disequilibrium) filtering before analysis. Unfortunately, this tactic loses valuable information and usually retains many SNPs still in LD. The present paper explores the option of explicitly incorporating haplotypes in ancestry estimation. Our program, HaploADMIXTURE, operates on adjacent SNP pairs and jointly estimates their haplotype frequencies along with admixture coefficients. This more complex strategy takes advantage of the rich information available in haplotypes and ultimately yields better admixture estimates and better clustering of real populations in curated datasets.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"23 ","pages":"4384-4395"},"PeriodicalIF":4.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and prioritization of COVID-19 infected patients from CXR images: Analysis of AI-assisted diagnosis in clinical settings.","authors":"Carlo Alberto Barbano, Luca Berton, Riccardo Renzulli, Davide Tricarico, Osvaldo Rampado, Domenico Basile, Marco Busso, Marco Grosso, Marco Grangetto","doi":"10.1016/j.csbj.2024.11.045","DOIUrl":"10.1016/j.csbj.2024.11.045","url":null,"abstract":"<p><p>In this paper, we present the significant results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manifold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection and prioritization, the clinical validation of the developed solution by expert radiologists, and an in-depth analysis of possible biases embedded in the data and in the models, in order to build more trust in our AI-based pipeline. The proposed detection model is based on a two-step approach that provides reliable results based on objective radiological findings. Our prioritization scheme ensures the ordering of the patients so that severe cases are presented first. We showcase the impact of our pipeline on radiologists' workflow with a clinical study, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"24 ","pages":"754-761"},"PeriodicalIF":4.4,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142902625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein-ligand binding affinity prediction.","authors":"Dayan Liu, Tao Song, Shudong Wang","doi":"10.1016/j.csbj.2024.11.050","DOIUrl":"10.1016/j.csbj.2024.11.050","url":null,"abstract":"<p><p>Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face substantial limitations in prediction accuracy. Moreover, the prevalent methodologies often overlook critical three-dimensional (3D) structural information, thereby constraining their practical utility in computer-aided drug design (CADD). Here we present MM-DRPNet, a multimodal deep learning framework that enhances binding affinity prediction by integrating protein-ligand structural information with interaction features and physicochemical properties. The core innovation lies in our dynamic radial partitioning (DRP) algorithm, which adaptively segments 3D space based on complex-specific interaction patterns, surpassing traditional fixed partitioning methods in capturing spatial interactions. MM-DRPNet further incorporates molecular topological features to comprehensively model both structural and spatial relationships. Extensive evaluations on benchmark datasets demonstrate that MM-DRPNet significantly outperforms state-of-the-art methods across multiple metrics, with ablation studies confirming the substantial contribution of each architectural component. Source code for MM-DRPNet is freely available for download at https://github.com/Bigrock-dd/MMDRPv1.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"23 ","pages":"4396-4405"},"PeriodicalIF":4.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering.","authors":"Jinyun Niu, Fangfang Zhu, Taosheng Xu, Shunfang Wang, Wenwen Min","doi":"10.1016/j.csbj.2024.11.041","DOIUrl":"10.1016/j.csbj.2024.11.041","url":null,"abstract":"<p><p>The rapid development of spatial transcriptomics (ST) technology has provided unprecedented opportunities to understand tissue relationships and functions within specific spatial contexts. Accurate identification of spatial domains is crucial for downstream spatial transcriptomics analysis. However, effectively combining gene expression data, histological images and spatial coordinate data to identify spatial domains remains a challenge. To this end, we propose STMVGAE, a novel spatial transcriptomics analysis tool that combines a multi-view variational graph autoencoder with a consensus clustering framework. STMVGAE begins by extracting histological images features using a pre-trained convolutional neural network (CNN) and integrates these features with gene expression data to generate augmented gene expression profiles. Subsequently, multiple graphs (views) are constructed using various similarity measures, capturing different aspects of the spatial and transcriptional relationships. These views, combined with the augmented gene expression data, are then processed through variational graph auto-encoders (VGAEs) to learn multiple low-dimensional latent embeddings. Finally, the model employs a consensus clustering method to integrate the clustering results derived from these embeddings, significantly improving clustering accuracy and stability. We applied STMVGAE to five real datasets and compared it with five state-of-the-art methods, showing that STMVGAE consistently achieves competitive results. We assessed its capabilities in spatial domain identification and evaluated its performance across various downstream tasks, including UMAP visualization, PAGA trajectory inference, spatially variable gene (SVG) identification, denoising, batch integration, and other analyses. All code and public datasets used in this paper is available at https://github.com/wenwenmin/STMVGAE and https://zenodo.org/records/13119867.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"23 ","pages":"4369-4383"},"PeriodicalIF":4.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.","authors":"Ya-Wen Zhang, Xue-Lian Han, Mei Li, Ying Chen, Yuan-Ming Zhang","doi":"10.1016/j.csbj.2024.11.046","DOIUrl":"10.1016/j.csbj.2024.11.046","url":null,"abstract":"<p><p>Although 3VmrMLM-MEJA and several indirect indicators have been employed to identify QTN-by-environment interactions (QEIs) in genome-wide association studies (GWAS), there is no convenient, flexible, and accurate method to comprehensively identify QEIs. To address this issue, 3VmrMLM-random was first extended to 3VmrMLM-fixed. Next, the two single-environment QTN detection methods were integrated with trait differences and regression parameters to indirectly detect QEIs. Finally, these indirect indicators were extended to include environmental factors (EFs, such as temperature) and four environmental variation indicators. As a result, both 3VmrMLM-random and 3VmrMLM-fixed, alongside all the indirect indicators, were incorporated into a new tool, IIIVmrMLM.QEI, designed for effective QEI identification. Simulation studies demonstrated that 3VmrMLM-fixed showed significantly higher powers than existing fixed-SNP-effect methods (MLM and EMMAX) because it takes into account all the possible effects and controls for all the possible polygenic backgrounds. 3VmrMLM-random and 3VmrMLM-fixed exhibited superior combination power to 3VmrMLM-MEJA. In the re-analysis of <i>Arabidopsis</i> flowering time across three temperatures, 3VmrMLM-fixed (12) detected more known gene-by-environment interactions (GEIs) than both MLM (1) and EMMAX (1). Additionally, IIIVmrMLM.QEI (18) detected more known GEIs than 3VmrMLM-MEJA (6), when all indirect indicators were analyzed. All 18 GEIs were confirmed by haplotype analysis and associated with temperature variation in previous studies. Two and five GEIs were identified only by 3VmrMLM-fixed and 3VmrMLM-random, respectively, and 12 GEIs were identified only by indirect indicators, indicating the need to expand models and indirect indicators. This study provides a novel tool (https://github.com/YuanmingZhang65/IIIVmrMLM.QEI) for more comprehensive QEI detection.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"23 ","pages":"4357-4368"},"PeriodicalIF":4.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CTGCT, Centre of Excellence for the Technologies of Gene and Cell Therapy: Collaborative translation of scientific discoveries into advanced treatments for neurological rare genetic diseases and cancer.","authors":"Darja Marolt Presen, Duško Lainšček, Jane Kinghorn, Zsolt Sebestyen, Jurgen Kuball, Leila Amini, Petra Reinke, Anke Fuchs, Roman Jerala, Mojca Benčina","doi":"10.1016/j.csbj.2024.11.051","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.11.051","url":null,"abstract":"<p><p>The emerging field of precision medicine relies on scientific breakthroughs to understand disease mechanisms and develop cutting-edge technologies to overcome underlying genetic and functional aberrations. The establishment of the Centre of Excellence for the Technologies of Gene and Cell Therapy (CTGCT) at the National Institute of Chemistry (NIC) in Ljubljana represents a significant step forward, as it is the first centre of its kind in Slovenia. The CTGCT is poised to spearhead advances in cancer immunotherapy and personalised therapies for neurological and other rare genetic diseases. The centre's overarching mission is to extend beyond the NIC's scientific excellence in basic research and bring new therapeutic solutions toward clinical application. The CTGCT aims to develop a broad pipeline of biomedical tools, including innovative synthetic biology tools, gene editing and splicing technologies, RNA-based technologies, immune regulation engineering and novel viral and non-viral delivery systems. The CTGCT is supported by partner institutions from the UK, the Netherlands and Germany, which already have academic good manufacturing practice (GMP) facilities for the manufacture of advanced therapy medicinal products (ATMPs) and is committed to active collaboration with clinicians and patient organizations at all stages of development to improve access to gene and cell therapies (GCTs) for patients. The Centre also seeks to collaborate with national and international academic and industrial partners, and the newly established GMP facilities will address a critical bottleneck in the translation of GCTs from research to practice. Finally, CTGCT's translational research and technology transfer units will ensure the impactful dissemination of research and innovation activities in Slovenia, throughout the Western Balkans and Eastern Europe region, and beyond. With its comprehensive approach and forward-looking vision, the CTGCT will drive transformative advances in gene and cell therapies for the benefit of patients on a global scale.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"10-16"},"PeriodicalIF":4.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach","authors":"Paola Argiento ,&nbsp;Anna D'Agostino ,&nbsp;Rossana Castaldo ,&nbsp;Monica Franzese ,&nbsp;Matteo Mazzola ,&nbsp;Ekkehard Grünig ,&nbsp;Lavinia Saldamarco ,&nbsp;Valeria Valente ,&nbsp;Alessandra Schiavo ,&nbsp;Erica Maffei ,&nbsp;Davide Lepre ,&nbsp;Antonio Cittadini ,&nbsp;Eduardo Bossone ,&nbsp;Michele D'Alto ,&nbsp;Luna Gargani ,&nbsp;Alberto Maria Marra","doi":"10.1016/j.csbj.2024.11.031","DOIUrl":"10.1016/j.csbj.2024.11.031","url":null,"abstract":"<div><h3>Background</h3><div>Pulmonary hypertension (PH) is a pathophysiological problem that may involve several clinical symptoms and be linked to various respiratory and cardiovascular illnesses. Its diagnosis is made invasively by Right Cardiac Catheterization (RHC), which is difficult to perform routinely. Aim of the current study was to develop a Machine Learning (ML) algorithm based on the analysis of anamnestic data to predict the presence of an invasively measured PH.</div></div><div><h3>Methods</h3><div>226 patients with clinical indication of RHC for suspected PH were enrolled between October 2017 and October 2020. All patients underwent a protocol of diagnostic techniques for PH according to the recommended guidelines. Machine learning (ML) approaches were considered to develop classifiers aiming to automatically detect patients affected by PH, based on the patient’s characteristics, anamnestic data, and non-invasive parameters, transthoracic echocardiography (TTE) results and spirometry outcomes.</div></div><div><h3>Results</h3><div>Out of 51 variables of patients undergoing RHC collected, 12 resulted significantly different between patients who resulted positive and those who resulted negative at RHC. Among them 8 were selected and utilized to both train and validate an Elastic-Net Regularized Generalized Linear Model, from which a risk score was developed. The AUC of the identification model is of 83 % with an overall accuracy of 74 % [95 % CI (61 %, 84 %)], indicating very good discrimination between patients with and without the pathology.</div></div><div><h3>Conclusions</h3><div>The PH-targeted ML models could streamline routine screening for PH, facilitating earlier identification and better RHC referrals.</div></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"24 ","pages":"Pages 746-753"},"PeriodicalIF":4.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating AlphaFold pLDDT Scores into CABS-flex for enhanced protein flexibility simulations.","authors":"Karol Wróblewski, Sebastian Kmiecik","doi":"10.1016/j.csbj.2024.11.047","DOIUrl":"10.1016/j.csbj.2024.11.047","url":null,"abstract":"<p><p>CABS-flex is a well-established method for fast protein flexibility simulations, offering an effective balance between computational efficiency and accuracy in modeling protein dynamics. To further enhance its predictive capabilities, we propose incorporating AlphaFold's predicted Local Distance Difference Test (pLDDT) scores into CABS-flex simulations. The pLDDT scores, which reflect the confidence of AlphaFold's structural predictions, were integrated with secondary structure information to refine the restraint schemes used in the simulations. We tested this approach on the ATLAS database, which includes molecular dynamics (MD) simulations of nearly 1400 proteins. The results showed improved alignment of flexibility predictions with the MD data compared to previous restraint schemes. The integration of pLDDT scores also offers a new perspective on protein flexibility by incorporating structural confidence into the analysis. This development enhances the utility of CABS-flex for investigating protein dynamics and motion.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"23 ","pages":"4350-4356"},"PeriodicalIF":4.4,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}