IET Systems Biology最新文献

{"title":"StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach","authors":"Ali Ghulam,&nbsp;Muhammad Arif,&nbsp;Ahsanullah Unar,&nbsp;Maha A. Thafar,&nbsp;Somayah Albaradei,&nbsp;Apilak Worachartcheewan","doi":"10.1049/syb2.70002","DOIUrl":"https://doi.org/10.1049/syb2.70002","url":null,"abstract":"<p>Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy of naturally derived peptides in reducing blood pressure. Hypertension is one of the risks associated with cardiovascular disorders and other health problems. Naturally sourced bioactive peptides possessing antihypertensive properties provide considerable potential as viable substitutes for conventional pharmaceutical medications. Currently, thorough examination of antihypertensive peptide (AHTPs), by using traditional wet-lab methods is highly expensive and labours. Therefore, in-silico approaches especially machine-learning (ML) algorithms are favourable due to saving time and cost in the discovery of AHTPs. In this study, a novel ML-based predictor, called StackAHTP was developed for predicting accurate AHTPs from sequence only. The proposed method, utilise two types of feature descriptors Pseudo-Amino Acid Composition and Dipeptide Composition to encode the local and global hidden information from peptide sequences. Furthermore, the encoded features are serially merged and ranked through SHapley Additive explanations (SHAP) algorithm. Then, the top ranked are fed into three different ensemble classifiers (Bagging, Boosting, and Stacking) for enhancing the prediction performance of the model. The StackAHTPs method achieved superior performance compare to other ML classifiers (AdaBoost, XGBoost and Light Gradient Boosting (LightGBM), Bagging and Boosting) on 10-fold cross validation and independent test. The experimental outcomes demonstrate that our proposed method outperformed the existing methods and achieved an accuracy of 92.25% and F1-score of 89.67% on independent test for predicting AHTPs and non-AHTPs. The authors believe this research will remarkably contribute in predicting large-scale characterisation of AHTPs and accelerate the drug discovery process. At https://github.com/ali-ghulam/StackAHTPs you may find datasets features used.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The optimised model of predicting protein-metal ion ligand binding residues","authors":"Caiyun Yang,&nbsp;Xiuzhen Hu,&nbsp;Zhenxing Feng,&nbsp;Sixi Hao,&nbsp;Gaimei Zhang,&nbsp;Shaohua Chen,&nbsp;Guodong Guo","doi":"10.1049/syb2.70001","DOIUrl":"10.1049/syb2.70001","url":null,"abstract":"<p>Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, and signal transduction. Predicting the protein-metal ion ligand binding residues (PMILBRs) accurately is a challenging task in theoretical calculations. In this study, the authors employed fused amino acids and their derived information as feature parameters to predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results. Subsequently, deep learning algorithm was incorporated in the prediction, resulting in improved results for the sets of Ca²⁺ and Mg²⁺ compared to previous studies. The validation matrix provided the optimal prediction model for each ionic ligand binding residue, exhibiting the capability of effectively predicting the binding sites of metal ion ligands for real protein chains.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11773433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbiome analysis reveals the potential mechanism of herbal sitz bath complementary therapy in accelerating postoperative recovery from perianal abscesses","authors":"Xinghua Chen,&nbsp;Xiutian Guo","doi":"10.1049/syb2.12114","DOIUrl":"10.1049/syb2.12114","url":null,"abstract":"<p>The herbal sitz bath formula, as a complementary therapy, effectively alleviates postoperative wound pain and accelerates healing time in patients with perianal abscesses. To investigate its mechanism of action, this study conducted 16S rRNA gene sequencing and bioinformatics analysis on wound exudate samples from patients after perianal abscess surgery. Patients were randomly divided into two groups: one receiving the herbal sitz bath as an adjunctive therapy and the other without this adjunctive therapy. Samples were collected on the first and eighth days after surgery to compare the differences in microbial community composition between the two groups on the eighth day and between the first and eighth days within each group. The study revealed that the herbal sitz bath significantly altered the structure of the microbial community, increasing its diversity and abundance. By reducing <i>Enterococcus</i> and increasing <i>Bifidobacterium</i>, <i>Faecalibacterium</i>, and <i>Ruminococcus</i>, the therapy enhanced the wound's anti-infective capacity and accelerated healing. This study explored the potential mechanism of the herbal sitz bath formula as an adjunctive therapy in promoting postoperative recovery from perianal abscesses, providing valuable data for further research on the role of microorganisms in wound care. These findings contribute to optimising postoperative treatment regimens and facilitating patient recovery.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emergent robust oscillatory dynamics in the interlocked feedback-feedforward loops","authors":"Guturu L. Harika,&nbsp;Krishnamachari Sriram","doi":"10.1049/syb2.12111","DOIUrl":"10.1049/syb2.12111","url":null,"abstract":"<p>One of the challenges that beset modelling complex biological networks is to relate networks to function to dynamics. A further challenge is deciphering the cellular function and dynamics that can change drastically when the network edge is tinkered with by adding or removing it. To illustrate this, the authors took a well-studied three-variable Goodwin oscillatory motif with only a negative feedback loop. To this motif, an edge was added that results in an emergent structure consisting of new feedforward and feedback loops while retaining Goodwin's original negative feedback loop. To relate emergent structure to oscillatory dynamics, the authors took all the combinations of edge signs in the interlocked motif. Bifurcation analysis reveals that all the structural combinations can be grouped into two categories based on their unique dynamics. These two groups also exhibit unique amplitude-frequency (amp-freq) plots. These two categories are attributed to the emergence of interlocked motifs with specific edge signs. To support the ideas, a well-studied plant circadian model of <i>Arabidopsis thaliana</i> was taken to illustrate the importance of interlocked motifs in fine-tuning amplitude and frequency in circadian oscillators. The authors briefly discuss its implications for central oscillators' adaptation to different environmental cues.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SpaGraphCCI: Spatial cell–cell communication inference through GAT-based co-convolutional feature integration","authors":"Han Zhang,&nbsp;Ting Cui,&nbsp;Xiaoqiang Xu,&nbsp;Guangyu Sui,&nbsp;Qiaoli Fang,&nbsp;Guanghao Yang,&nbsp;Yizhen Gong,&nbsp;Sanqiao Yang,&nbsp;Yufei Lv,&nbsp;Desi Shang","doi":"10.1049/syb2.70000","DOIUrl":"10.1049/syb2.70000","url":null,"abstract":"<p>Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell–cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co-convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low-dimensional space. SpaGraphCCI can achieve significant performance on datasets from multiple platforms including single-cell resolution datasets (AUC reaches 0.860–0.907) and spot resolution datasets (AUC ranges from 0.880 to 0.965). SpaGraphCCI shows better performance by comparing with the existing deep learning-based spatial cell communication inference methods. SpaGraphCCI is robust to high noise and can effectively improve the inference of CCIs. We test on a human breast cancer dataset and show that SpaGraphCCI can not only identify proximal cell communication but also infer new distal interactions. In summary, SpaGraphCCI provides a practical tool that enables researchers to decipher spatially resolved cell–cell communication based on spatial transcriptome data.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11771809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The mechanism of arsenic trioxide and microwave ablation in the treatment of oral squamous cell carcinoma based on high throughput sequencing.","authors":"Xuesong Zhang, Yakun Liu, Shengteng He, Liangjia Bi, Bing Liu","doi":"10.1049/syb2.12113","DOIUrl":"https://doi.org/10.1049/syb2.12113","url":null,"abstract":"<p><p>Oral squamous cell carcinoma (OSCC) is a common head and neck malignant tumour with high incidence and poor prognosis. Arsenic trioxide (ATO) has therapeutic effects on solid tumours. Microwave ablation (MWA) has unique advantages in the treatment of solid tumours. However, the therapeutic mechanism of ATO and MWA, as well as their combined effect on OSCC were largely unelucidated. Cal-27 cell-bearing nude mice were treated with ATO and/or MWA, respectively. RNA sequencing was used to obtain gene expression profiles in tumour tissues of mice treated by ATO or MWA. RNA sequencing results were verified by real-time polymerase chain reaction (PCR). The lncRNA-miRNA-mRNA co-expression network was constructed based on the competitive endogenous RNA (ceRNA) theory. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using differentially expressed genes. The combined effect of ATO and MWA on OSCC was evaluated. Finally, CCK-8 assay, EdU assay and transwell migration assay were performed to detect the effect of HSPA6 on the proliferation and migration of OSCC cells. The reduced volume of tumour tissues was observed in both ATO- and MWA-treated groups. 37.8% decreased in the ATO group and 35.0% in the MWA group. A total of 207 and 539 differentially expressed mRNAs and lncRNAs were identified in the ATO group. And a total of 200 and 522 differentially expressed mRNAs and lncRNAs in the MWA group were identified. The expression levels of 8 genes were verified by real-time PCR. The differentially expressed genes were closely related to \"chemical carcinogenesis\", \"herpes simplex infection\", \"porphyrin and chlorophyll metabolism\", and \"MAPK signalling pathway\". The lncRNA-miRNA-mRNA co-expression networks were constructed. The combined treatment with ATO and MWA showed a better inhibitive effect on OSCC than either of them. The synergistic effect of ATO and MWA was related to the upregulation of HSPA6. The downregulation of HSPA6 could promote the proliferation and migration of OSCC cells. This study detected the long non-coding RNA and mRNA expression profiles related to the treatment of OSCC and constructed corresponding ceRNA networks. Arsenic trioxide and MWA have a synergistic effect on OSCC, which was related to the upregulation of HSPA6.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptomic analysis reveals pathways underlying the multi-antibiotic resistance of Klebsiella pneumoniae.","authors":"Ying Liu, Zhihui Niu, Rile Wu, Dezhi Yang, Jun Chen, Guoqing Liu, Jun Zhao","doi":"10.1049/syb2.12112","DOIUrl":"https://doi.org/10.1049/syb2.12112","url":null,"abstract":"<p><p>Klebsiella pneumoniae, an opportunistic pathogen, is pervasively distributed across the world. Its escalating antibiotic resistance poses a serious threat to global public health. The mechanisms behind this resistance remain largely elusive. In this study, we performed antibiotic susceptibility testing on several clinical isolates of Klebsiella pneumoniae, and a reference strain ATCC13883, and then analysed their transcriptomic profiles to identify genes and pathways associated with antibiotic resistance. Our results showed that a clinical isolate DY16KPN may counteract antibiotics by enhancing the biosynthesis of building blocks of bacterial cell, such as fatty acids, proteins, and DNA, and reducing transmembrane transport. Increased butanoate metabolism and lipopolysaccharide biosynthesis may also contribute to the drug-resistance of Klebsiella pneumoniae. Additionally, we identified resistance-promoting mutations in gene promoter regions, which regulate the activity of downstream drug-resistant genes and pathways. Our results also demonstrated that DY16KPN counterbalances the trimethoprim/sulfamethoxazole-mediated inhibitory effect on the synthesis of tetrahydrofolates and DNA by up-regulating the expression of targeted enzymes of trimethoprim/sulfamethoxazole, dihydrofolate reductase and dihydropteroate synthase.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of type 2 diabetes mellitus-related genes by constructing the pathway-based weighted network.","authors":"Xue-Yan Zhang, Chuan-Yun Xu, Ke-Fei Cao, Hong Luo, Xu-Sheng Zhang","doi":"10.1049/syb2.12110","DOIUrl":"https://doi.org/10.1049/syb2.12110","url":null,"abstract":"<p><p>Complex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway-based weighted network model of T2DM-related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power-law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: MAPK1, PIK3CD, PIK3CA, PIK3R1, AKT2, AKT1, KRAS, TNF, MAPK8, PRKCA, IL6 and MTOR. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis","authors":"Xi Yong,&nbsp;Xuerui Hu,&nbsp;Tengyao Kang,&nbsp;Yanpiao Deng,&nbsp;Sixuan Li,&nbsp;Shuihan Yu,&nbsp;Yani Hou,&nbsp;Jin You,&nbsp;Xiaohe Dai,&nbsp;Jialin Zhang,&nbsp;Junjia Zhang,&nbsp;Junlin Zhou,&nbsp;Siyu Zhang,&nbsp;Jianghua Zheng,&nbsp;Qin Yang,&nbsp;Jingdong Li","doi":"10.1049/syb2.12106","DOIUrl":"10.1049/syb2.12106","url":null,"abstract":"<p>Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms underlying AAA utilising multi-omics data analysis and machine learning techniques. Single-cell RNA sequencing disclosed a heightened presence of macrophages and CD8-positive alpha-beta T cells in AAA, highlighting their critical role in disease pathogenesis. Analysis of cell–cell communication highlighted augmented interactions between macrophages and dendritic cells derived from monocytes. Enrichment analysis of differential expression gene indicated substantial involvement of immune and metabolic pathways in AAA pathogenesis. Machine learning techniques identified CCR7 and CBX6 as key candidate biomarkers. In AAA, CCR7 expression is upregulated, whereas CBX6 expression is downregulated, both showing significant correlations with immune cell infiltration. These findings provide valuable insights into the molecular mechanisms underlying AAA and suggest potential biomarkers for diagnosis and therapeutic intervention.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"250-260"},"PeriodicalIF":1.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 co-localised transcription factors based on paired motifs analysis","authors":"Li Liu,&nbsp;Lu Han,&nbsp;Kaiyuan Han,&nbsp;Zheng Zhang,&nbsp;Haojiang Zhang,&nbsp;Lirong Zhang","doi":"10.1049/syb2.12104","DOIUrl":"10.1049/syb2.12104","url":null,"abstract":"<p>The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA <i>cis</i>-regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co-localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell-line-specific patterns. Finally, in human prostate cancer cells (PC-3) and human prostate normal cells (RWPE-2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC-3 and RWPE-2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC-3 cancer cells.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"18 6","pages":"238-249"},"PeriodicalIF":1.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}