Computational and structural biotechnology journal最新文献

{"title":"scFocus: Detecting branching probabilities in single-cell data with SAC.","authors":"Chunlin Chen, Zeyu Fu, Jiajia Yang, Huaqing Chen, Jiabao Huang, Shitian Qin, Chuhuai Wang, Xiaoqian Hu","doi":"10.1016/j.csbj.2025.04.036","DOIUrl":"10.1016/j.csbj.2025.04.036","url":null,"abstract":"<p><p>Single-cell transcriptomics captures cell differentiation trajectories through changes in gene expression intensity. However, it is challenging to obtain precise information on the composition of gene sets corresponding to each lineage branch in complex biological systems. The combination of branch probabilities and unsupervised clustering can effectively characterize changes in gene expression intensity, reflecting continuous cell states without relying on prior information. In this study, we propose a analytic algorithm named single-cell (sc)-Focus that divides cell subpopulations based on reinforcement learning and unsupervised branching in low-dimensional latent space of single cells. The lineage component strength of scFocus coincides with the expression regions of hallmark genes, capturing differentiation processes more effectively in comparison to the original low-dimensional latent space and showing a stronger subpopulation discriminative power. Furthermore, scFocus is applied to ten single-cell datasets, including small-scale datasets, common-scale datasets, and multi-batch datasets. This demonstrates its applicability on different types of datasets and showcases its potential in discovering biological changes due to experimental treatments through multi-batch dataset processing. Finally, an online analysis tool based on scFocus was developed, helping researchers and clinicians in the process and visualization of single-cell RNA sequencing data as well as the interpretation of these data through branch probabilities in a streamlined and intuitive way.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2243-2263"},"PeriodicalIF":4.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144301302","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":"Multimeric protein interaction and complex prediction: Structure, dynamics and function.","authors":"Da Lu, Shuhong Yu, Yixiang Huang, Xinqi Gong","doi":"10.1016/j.csbj.2025.05.009","DOIUrl":"10.1016/j.csbj.2025.05.009","url":null,"abstract":"<p><p>Understanding the structure, interactions, dynamics, and functions of multimeric protein complexes is essential for studying multimeric protein complexes, with broad implications for disease mechanisms and drug design, and other areas of biomedical research. Although remarkable achievements have been made in monomer prediction in recent years, protein multimers prediction remains a crucial yet challenging area due to their complex structures, diverse physicochemical properties, and limited experimental data. This review encompasses recent advancements in multimer research, providing an overview of classical concepts and methodologies and the key differences from monomer prediction methods. It further explores state-of-the-art advances in CASP16, including predictions of unknown stoichiometries, supercomplexes, conformational ensembles. This review also delves into the contributions of AlphaFold2 & 3 to multimer prediction, highlighting both the successes and limitations, particularly in handling functional protein-protein interactions and dynamical conformations. Recent deep learning methods and their applications in multimer interaction analysis and quality assessment are discussed, along with insights into future research directions, such as improving prediction accuracy, enabling functional interpretation of protein-protein interactions, and reconstructing protein mechanisms.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1975-1997"},"PeriodicalIF":4.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265447","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 temperature-responsive regulator that enhances virulence in the kiwifruit canker pathogen <i>Pseudomonas syringae</i> pv. a<i>ctinidiae</i>.","authors":"Xueting He, Yifei Zhang, Chenbei Xu, Kaidi Fu, Yiqing Ding, Tiantian Zhang, Tingtao Chen, Aprodisia Murero, Limin Wang, Yuan Xu, Cheng Chen, Jinghui Yang, Li Li, Caihong Zhong, Lili Huang, Xin Deng, Xiaolong Shao, Guoliang Qian","doi":"10.1016/j.csbj.2025.05.017","DOIUrl":"10.1016/j.csbj.2025.05.017","url":null,"abstract":"<p><p><i>Pseudomonas syringae</i> pv. <i>actinidiae</i> (<i>Psa</i>), the causative agent of kiwifruit canker disease, poses significant threats to global kiwifruit production, resulting in substantial economic losses. Disease incidence is notably higher under cooler temperatures (<20℃), yet the molecular mechanisms underlying <i>Psa</i>'s temperature-dependent virulence remain poorly understood. Here, we identify RS16350, encoding a heat shock protein homolog, as a positive regulator of <i>Psa</i> pathogenicity specifically at low temperature (16℃) but not at optimal growth temperature (28℃). Mechanistic studies reveal that RS16350 physically interacts with HrpL, the RpoN-dependent sigma factor controlling type III secretion system (T3SS) expression in <i>Psa</i>. This interaction enhances HrpL's binding affinity to the <i>hrp-box</i> promoter element, thereby upregulating T3SS effector genes and increasing virulence. We designate this novel regulator as TrpR2 (temperature-responsive pathogenic regulator 2). These findings provide molecular insights into temperature-modulated virulence in a key plant pathogen and identify potential targets for developing innovative disease control strategies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1935-1944"},"PeriodicalIF":4.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145522/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246864","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":"Integration of T cell repertoire, CyTOF, genotyping and symptomatology data reveals subphenotypic variability in COVID-19 patients.","authors":"Fernando Marín-Benesiu, Lucia Chica-Redecillas, Sergio Cuenca-López, Carmen Entrala-Bernal, Sara Martín-Esteban, Maria Jesús Alvarez-Cubero, Luis Javier Martínez-González","doi":"10.1016/j.csbj.2025.05.016","DOIUrl":"10.1016/j.csbj.2025.05.016","url":null,"abstract":"<p><p>COVID-19 manifests a broad spectrum of clinical outcomes, from asymptomatic cases to severe disease. While several biomarkers have been proposed, comprehensive immunological analyses integrating mass cytometry (CyTOF) and T-cell receptor sequencing (TCRseq) data remain limited. In this study, we applied the Latent Class Model based on the Bayesian Information Criterion (LCM-BIC) algorithm to integrate immunophenotyping, including monocyte-macrophage counts from CyTOF, T-cell receptor repertorie data via TCRseq, SNPs data from <i>ACE2</i> (rs2285666), <i>MX1</i> (rs469390), and <i>TMPRSS2</i> (rs2070788), and symptomatology data from 61 Spanish COVID-19 patients (33 mild, 28 severe). We identified three novel and distinct patient clusters with significant differences in TCR diversity, monocyte subpopulations, and V allele usage and disease outcome. Cluster 1 was predominantly enriched in severe cases, characterized by unique immunological features. Deep learning analysis of TCR amino acid sequences further distinguished Cluster 1 from the others, identifying SARS-CoV-2-specific TCR sequences associated with disease severity. In addition, analysis of residue sensitivity of cluster 1 SARS-CoV-2-specific TCR sequences further identified conserved aminoacids located in key central positions of the complementarity-determining region 3. This study highlights the value of integrating immunophenotyping and genetic profiling to identify novel immunological markers and patterns, aiding in the stratification and management of COVID-19 patients based on their immune profiles and genetic background.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2063-2073"},"PeriodicalIF":4.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12152356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144274314","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":"Automatic detection of fungiform papillae on the human tongue via Convolutional Neural Networks and identification of the best performing model.","authors":"Lala Chaimae Naciri, Raffaella Fiamma Cabini, Melania Melis, Roberto Crnjar, Diego Ulisse Pizzagalli, Iole Tomassini Barbarossa","doi":"10.1016/j.csbj.2025.05.014","DOIUrl":"10.1016/j.csbj.2025.05.014","url":null,"abstract":"<p><p>Fungiform papillae (FPs) are fundamental for taste perception, as they contain the taste sensory cells responsible for detecting taste stimuli. Variations in the number and functionality of FPs among individuals lead to differences in taste perception, impacting the ability to identify nutrient-rich foods, health, and the joy of consuming tasty foods. Detecting FPs is a complex and time-consuming task, and there is no consensus on manual and automated methods for their identification and analysis.</p><p><strong>Objectives: </strong>This work aimed to provide an efficient, reliable, and automatic method for FP identification on the tongue, considering the physiological variations in morphology and distribution among subjects.</p><p><strong>Methods: </strong>We used three different Convolutional Neural Networks as a regression task on 175 images of the tongue, the Classic U-Net, the MultiResUNet, and the Optimized U-Net, designed to enhance the performance also when it must identify FPs in challenging input images.</p><p><strong>Results: </strong>The Optimized U-Net showed the best performance by achieving the lowest errors and the highest similarity between Ground Truths and prediction values, and the more balanced detection of True Positives, Untrue Negatives, and Untrue Positives.</p><p><strong>Conclusions: </strong>Our results show that the Optimized U-Net achieved the highest stability, accuracy, and robustness in learning and prediction of FPs with challenging morphologies. The ability to automatically detect FPs has important implications for understanding individual differences in taste perception, which could eventually help in diagnosing taste disorders or guiding personalized nutrition plans.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1927-1934"},"PeriodicalIF":4.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246865","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":"m5CStack: An integrated framework for m5C site prediction using multi-feature stacking.","authors":"Xuxin He, Jiahui Guan, Peilin Xie, Zhihao Zhao, Qianchen Liu, Lantian Yao, Ying-Chih Chiang","doi":"10.1016/j.csbj.2025.05.004","DOIUrl":"10.1016/j.csbj.2025.05.004","url":null,"abstract":"<p><p>RNA 5-methylcytosine (m5C) modification sites are essential for understanding the regulation of RNA functions in various biological processes. However, the vast amount of sequence data generated by modern genomics poses significant challenges for traditional identification methods, which often struggle to meet high-throughput demands. Consequently, computational tools have become indispensable for predicting m5C sites. In this study, we present m5CStack, an advanced ensemble learning framework designed to predict m5C modification sites with high accuracy. m5CStack integrates multiple feature encoding techniques and machine learning models through a stacking architecture to enhance the robustness and reliability of predictions. We evaluate the framework on RNA datasets derived from multiple species, including <i>Homo sapiens</i> (human), <i>Mus musculus</i> (mouse), <i>Drosophila melanogaster</i> (drosophila), and <i>Danio rerio</i> (danio). Experimental results demonstrate that m5CStack significantly outperforms previous prediction methods across a range of metrics, including accuracy, sensitivity, and specificity. Furthermore, SHAP-based feature significance analysis reveals the key contribution of specific features, further improving the interpretability of the model. To improve accessibility, a user-friendly web interface is developed, allowing users to input RNA sequences or upload files for prediction, with results displayed in an intuitive format alongside confidence scores. Overall, this study highlights the potential of m5CStack as a powerful tool for RNA modification profiling, offering new insights into the epigenetic regulation of RNA across species.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1901-1912"},"PeriodicalIF":4.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246867","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":"From variability to stability: Sensitivity of network properties in IBD human gut microbiome studies.","authors":"Theresa Geese, Astrid Dempfle","doi":"10.1016/j.csbj.2025.05.005","DOIUrl":"10.1016/j.csbj.2025.05.005","url":null,"abstract":"<p><strong>Background: </strong>The gut microbiome's role in inflammatory bowel disease (IBD) is well-established, but capturing its complexity is challenging. Network analysis offers a valuable approach, but selecting suitable measures is crucial. This study examines the sensitivity of network properties to abundance variations. It evaluates whether these properties reflect the microbiome in IBD or are too sensitive to variability from e.g. laboratory conditions or intra-individual changes.</p><p><strong>Methods: </strong>Using genetically unrelated individuals from the KINDRED cohort (IBD n = 522, healthy controls n = 365) and the PRISM cohort (IBD n = 42, healthy controls n = 42), microbial networks were constructed with genera as nodes and significant pairwise correlations as edges, separately for IBD patients and controls. Important IBD-related nodes, identified through centrality measures, and non-disease-related nodes were varied in abundance ( ± 30 %), and networks were re-constructed and compared with initial networks regarding local and global properties.</p><p><strong>Results: </strong>Network properties in IBD were sensitive to abundance variations, with small and large changes producing similar effects. Sensitivity to increasing read counts of disease-related and non-disease-related genera was similar. Local properties showed magnitude-dependent changes of up to 50 % in response to the depletion of disease-related genera, relative to no modification applied, and an almost binary sensitivity pattern when modifying non-disease-related genera. Global case network properties changed less than 10 % in most settings, potentially indicating a certain stability of dysbiosis.</p><p><strong>Conclusion: </strong>Caution is needed with network-based approaches, as even small variations, stemming from sources of microbiome variability, can affect results and reproducibility. The relatively stable dysbiosis in IBD could pose challenges for microbiome-directed therapies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1945-1961"},"PeriodicalIF":4.4,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145524/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246866","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":"¹H NMR urinary metabolomic analysis in recreational athletes: Impact of physical exercise, high intensity interval training and whole body cryostimulation.","authors":"Wafa Douzi, Delphine Bon, Olivier Dupuy, François Bieuzen, Benoit Dugué","doi":"10.1016/j.csbj.2025.05.007","DOIUrl":"10.1016/j.csbj.2025.05.007","url":null,"abstract":"<p><strong>Introduction: </strong>Physical exercise induces various metabolic changes, influencing energy expenditure and substrate utilization. Metabolomics provides a comprehensive understanding of the metabolic adaptations occurring in response to physical exercise and recovery. This study aimed to investigate metabolic adaptations in recreational athletes by analyzing the urine metabolome following a high intensity interval training (HIIT) program with or without repeated cryotherapy recovery.</p><p><strong>Method: </strong>In this study, urine metabolomics with ¹H NMR spectroscopy was used to investigate the impact of sub-maximal cycling bout (SMC) at 60 % of power aerobic peak on urine metabolome before and after 4 weeks HIIT with or without cryostimulation recovery (WBC, N = 11; CTL, N = 12).</p><p><strong>Results: </strong>PCA analysis revealed a distinct separation between the urine NMR profiles of the WBC and the CTL groups induced by SMC. Targeted analyses showed no significant metabolic differences before SMC. However, post-SMC analysis revealed marked changes in lactate, acetate, acetone, urea, formate, citrate and adenine levels. The training program amplified these metabolic alterations in both groups. The WBC group exhibited significant changes in alanine, acetone and 2-hydroxyisobutyric acid, while the CTL group showed alterations in citrate<b>.</b></p><p><strong>Conclusion: </strong>SMC triggers a variety of metabolic changes that reflect the body's efforts to maintain energy balance under stress. When combined with WBC, HIIT further enhances these adaptations, improving glycolytic capacity, fat metabolism, and the regulation of energy homeostasis.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1913-1926"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246863","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":"New targets for the treatment of ulcerative colitis: Gut microbiota and its metabolites.","authors":"Huanyu Li, Meng Pan, Yifan Li, Manli Cui, Mingxin Zhang","doi":"10.1016/j.csbj.2025.05.006","DOIUrl":"10.1016/j.csbj.2025.05.006","url":null,"abstract":"<p><p>Ulcerative colitis (UC) is one of the most common and difficult-to-treat inflammatory diseases Currently, the standard of care includes immunological modulation and anti-inflammatory medication to alleviate symptoms; however, these treatments are associated with several side effects. As a result, developing novel, safe, and effective treatment strategies is crucial. The gut microbiota and its influence on the onset and progression of UC through their regulation of immunity, barrier integrity, and homeostasis, serves as a promising target for UC therapy. In this review, we explore the pathological changes that take place in UC along with the role of gut microbiota and its metabolites in disease progression and modulation. Additionally, we offer a thorough description of novel UC treatment approaches that focus on altering the gut microbiota and its metabolites. These protocols include FMT, probiotics, prebiotics, and micro/nanoparticles. The ultimate goal is to offer a theoretical basis for the advancement of innovative therapeutic strategies for UC.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1850-1863"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224629","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":"Identification of novel compounds against <i>Trypanosoma cruzi</i> using AlphaFold structures.","authors":"Albert Ros-Lucas, Alejandra Saeteros, Juan Carlos Gabaldón-Figueira, Nieves Martínez-Peinado, Elisa Escabia, Joaquim Gascón, Julio Alonso-Padilla","doi":"10.1016/j.csbj.2025.05.002","DOIUrl":"10.1016/j.csbj.2025.05.002","url":null,"abstract":"<p><p>Chagas disease is a neglected tropical zoonosis caused by the protozoan <i>Trypanosoma cruzi</i>. The two approved medications for treating this disease show variable efficacy in the chronic phase, highlighting the need for new therapeutic interventions. This study explores a bioinformatics-driven approach to drug discovery using AlphaFold-predicted protein structures. Starting from a virtual screening of approximately 30,000 compounds, 24 were experimentally tested, and two already approved drugs, pimecrolimus and ledipasvir, demonstrated significant antiparasitic activity. These compounds were predicted to target previously uncharacterized <i>T. cruzi</i> proteins, ledipasvir interacting with a calpain-like protein and pimecrolimus likely binding a mitotic cyclin. Molecular dynamics simulations showed that pimecrolimus remains stable in the predicted binding site, while ledipasvir exhibits a higher RMSD. While experimental validation of these targets is needed, these findings underscore the potential of integrating AlphaFold structures into drug discovery strategies to accelerate the identification of new compounds against Chagas disease and other neglected tropical diseases.</p><p><strong>Summary: </strong>We performed a virtual screening experiment with <i>T. cruzi</i> AlphaFold protein models and a compound collection of more than 30,000 compounds. We tested the top ranked compounds in an <i>in vitro</i> setting, and found two promising candidates for drug repurposing against Chagas disease: pimecrolimus and ledipasvir.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1838-1849"},"PeriodicalIF":4.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224628","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}