IET Systems Biology最新文献_第8页

Identification of molecular subtypes of ischaemic stroke based on immune-related genes and weighted co-expression network analysis 基于免疫相关基因和加权共表达网络分析的缺血性脑卒中分子亚型鉴定

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2023-02-18 DOI: 10.1049/syb2.12059

Duncan Wei, Xiaopu Chen, Jing Xu, Wenzhen He

{"title":"Identification of molecular subtypes of ischaemic stroke based on immune-related genes and weighted co-expression network analysis","authors":"Duncan Wei, Xiaopu Chen, Jing Xu, Wenzhen He","doi":"10.1049/syb2.12059","DOIUrl":"10.1049/syb2.12059","url":null,"abstract":"Immune system has been reported to play a key role in the development of ischaemic stroke (IS). Nevertheless, its exact immune-related mechanism has not yet been fully revealed. Gene expression data of IS and healthy control samples was downloaded from Gene Expression Omnibus database and differentially expressed genes (DEGs) was obtained. Immune-related genes (IRGs) data was downloaded from the ImmPort database. The molecular subtypes of IS were identified based on IRGs and weighted co-expression network analysis (WGCNA). 827 DEGs and 1142 IRGs were obtained in IS. Based on 1142 IRGs, 128 IS samples were clustered into two molecular subtypes: clusterA and clusterB. Based on the WGCNA, the authors found that the blue module had the highest correlation with IS. In the blue module, 90 genes were screened as candidate genes. The top 55 genes were selected as the central nodes according to gene degree in protein–protein interactions network of all genes in blue module. Through taking overlap, nine real hub genes were obtained that might distinguish between clusterA subtype and clusterB subtype of IS. The real hub genes (IL7R, ITK, SOD1, CD3D, LEF1, FBL, MAF, DNMT1, and SLAMF1) may be associated with molecular subtypes and immune regulation of IS.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 2","pages":"58-69"},"PeriodicalIF":2.3,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9464238","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}

引用次数: 0

Comprehensive analysis of LILR family genes expression and tumour-infiltrating immune cells in early-stage pancreatic ductal adenocarcinoma 早期胰腺导管腺癌中LILR家族基因表达及肿瘤浸润免疫细胞的综合分析

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2023-02-07 DOI: 10.1049/syb2.12058

Qiang Gao, Shutian Mo, Chuangye Han, Xiwen Liao, Chengkun Yang, Xiangkun Wang, Tianyi Liang, Yongfei He, Zijun Chen, Guangzhi Zhu, Hao Su, Xinping Ye, Tao Peng

{"title":"Comprehensive analysis of LILR family genes expression and tumour-infiltrating immune cells in early-stage pancreatic ductal adenocarcinoma","authors":"Qiang Gao, Shutian Mo, Chuangye Han, Xiwen Liao, Chengkun Yang, Xiangkun Wang, Tianyi Liang, Yongfei He, Zijun Chen, Guangzhi Zhu, Hao Su, Xinping Ye, Tao Peng","doi":"10.1049/syb2.12058","DOIUrl":"10.1049/syb2.12058","url":null,"abstract":"Leucocyte immunoglobulin-like receptors (LILRs) are closely related to tumourigenesis, but their clinical value in early-stage pancreatic ductal adenocarcinoma (PDAC) after pancreaticoduodenectomy remains unknown. Kaplan–Meier and Cox proportional hazards regression models is used to investigate the association between LILR expression and prognosis in tumour biopsies and peripheral blood mononuclear cells. Risk score was calculated for each patient based on the prognostic model. DAVID, STRING, GeneMANIA, and GSEA were used to conduct pathway and functional analyses. The CIBERSORT algorithm is used to analyse tumour-infiltrating immune cells. Survival analysis showed that high levels of LILRA4 (p = 0.006) and LILRB4 (p = 0.04) were significantly associated with better overall survival. High levels of LILRA2 (p = 0.008) and LILRB4 (p = 0.038) were significantly associated with better relapse-free survival. JAK-STAT signalling pathway, regulation of T cell activation, regulation of the immune effector process, and tumour necrosis factor superfamily cytokine production were involved in molecular mechanisms that affected poor prognoses in the high-risk group in GSEA. CIBERSORT demonstrated that the high-risk group had significantly higher infiltrating fraction of memory-activated CD4 T cells and activated NK cells and lower fraction of resting dendritic cells and neutrophils. LILRB4 plays crucial roles in affecting the clinical outcomes of early-stage PDAC.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 2","pages":"39-57"},"PeriodicalIF":2.3,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9408168","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}

引用次数: 3

Development and validation of an immune-related gene signature for prognosis in Lung adenocarcinoma 肺腺癌预后免疫相关基因标记的开发和验证

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2023-02-02 DOI: 10.1049/syb2.12057

Zehuai Guo, Xiangjun Qi, Zeyun Li, Jianying Yang, Zhe Sun, Peiqin Li, Ming Chen, Yang Cao

{"title":"Development and validation of an immune-related gene signature for prognosis in Lung adenocarcinoma","authors":"Zehuai Guo, Xiangjun Qi, Zeyun Li, Jianying Yang, Zhe Sun, Peiqin Li, Ming Chen, Yang Cao","doi":"10.1049/syb2.12057","DOIUrl":"10.1049/syb2.12057","url":null,"abstract":"The most common type of lung cancer tissue is lung adenocarcinoma. The TCGA-LUAD cohort retrieved from the TCGA dataset was considered the internal training cohort, while GSE68465 and GSE13213 datasets from the GEO database were used as the external test cohort. The TCGA-LUAD cohort was classified into two immune subtypes using single-sample gene set enrichment analysis of the immune gene set and unsupervised clustering analysis. The ESTIMATE algorithm, the CIBERSORT algorithm, and HLA family expression levels again validated the reliability of this typing. We performed Venn analysis using immune-related genes from the immport dataset and differentially expressed genes from the subtypes to retrieve differentially expressed immune genes (DEIGs). In addition, DEIGs were used to construct a prognostic model with the least absolute shrinkage and selection operator regression analysis. A reliable risk model consisting of 11 DEIGs, including S100P, INHA, SEMA7A, INSL4, CD40LG, AGER, SERPIND1, CD1D, CX3CR1, SFTPD, and CD79A, was then built, and its reliability was further confirmed by ROC curve and calibration plot analysis. The high-risk score subgroup had a poor prognosis and a lower tumour immune dysfunction and exclusion score, indicating a greater likelihood of anti-PD-1/cytotoxic T lymphocyte antigen 4 benefit.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 1","pages":"27-38"},"PeriodicalIF":2.3,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7a/3e/SYB2-17-27.PMC9931057.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10831691","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}

引用次数: 0

Comprehensive analysis of alternative splicing signatures in pancreatic head cancer 胰头癌选择性剪接特征的综合分析

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-12-07 DOI: 10.1049/syb2.12056

Lingshan Zhou, Yuan Yang, Jian Ma, Min Liu, Rong Liu, Xiaopeng Ma, Chengdong Qiao

{"title":"Comprehensive analysis of alternative splicing signatures in pancreatic head cancer","authors":"Lingshan Zhou, Yuan Yang, Jian Ma, Min Liu, Rong Liu, Xiaopeng Ma, Chengdong Qiao","doi":"10.1049/syb2.12056","DOIUrl":"10.1049/syb2.12056","url":null,"abstract":"The correlation between dysregulation of splicing and cancers has been increasingly recognised and confirmed. The identification of valuable alternative splicing (AS) in pancreatic head cancer (PHC) has a great significance. AS profiles in PHC were generated using the data from The Cancer Genome Atlas and TCGASpliceSeq. Then, the NMF clustering method was performed to identify overall survival-associated AS (OS-AS) subtypes in PHC patients. Subsequently, we used least absolute shrinkage and selection operator Cox regression analysis to construct an AS-related risk model. The splicing regulatory network was uncovered by Cytoscape 3.7. A total of 1694 OS-AS events were obtained. The PHC patients were divided into clusters 1 and 2. Cluster 1 had poorer prognosis and lower infiltration of immune cells. Subsequently, a prognostic signature was established that showed good performance in predicting OS and progression-free survival. The risk score of this signature was associated with the unique tumour immunity. Moreover, a nomogram incorporating the risk score and clinicopathological parameters was established. Finally, a splicing factor-AS regulatory network was developed. A comprehensive analysis of the AS events in PHC associated with prognosis and tumour immunity may help provide reliable information to guide individual treatment strategies.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 1","pages":"14-26"},"PeriodicalIF":2.3,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bb/64/SYB2-17-14.PMC9931058.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10825607","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}

引用次数: 0

The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction 归一化和误差模型选择对生化反应最大似然估计量分布的影响

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-11-28 DOI: 10.1049/syb2.12055

Caterina Thomaseth, Nicole E. Radde

{"title":"The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction","authors":"Caterina Thomaseth, Nicole E. Radde","doi":"10.1049/syb2.12055","DOIUrl":"10.1049/syb2.12055","url":null,"abstract":"Sparse and noisy measurements make parameter estimation for biochemical reaction networks difficult and might lead to ill-posed optimisation problems. This is potentiated if the data has to be normalised, and only fold changes rather than absolute amounts are available. Here, the authors consider the propagation of measurement noise to the distribution of the maximum likelihood (ML) estimator in an in silico study. Therefore, a model of a reversible reaction is considered, for which reaction rate constants using fold changes is estimated. Noise propagation is analysed for different normalisation strategies and different error models. In particular, accuracy, precision, and asymptotic properties of the ML estimator is investigated. Results show that normalisation by the mean of a time series outperforms normalisation by a single time point in the example provided by the authors. Moreover, the error model with a heavy-tail distribution is slightly more robust to large measurement noise, but, beyond this, the choice of the error model did not have a significant impact on the estimation results provided by the authors.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 1","pages":"1-13"},"PeriodicalIF":2.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a4/92/SYB2-17-1.PMC9931059.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9076339","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}

引用次数: 0

Genes associated with diagnosis and prognosis of Burkitt lymphoma 与伯基特淋巴瘤诊断和预后相关的基因。

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-11-10 DOI: 10.1049/syb2.12054

Albert Doughan, Samson Pandam Salifu

{"title":"Genes associated with diagnosis and prognosis of Burkitt lymphoma","authors":"Albert Doughan, Samson Pandam Salifu","doi":"10.1049/syb2.12054","DOIUrl":"10.1049/syb2.12054","url":null,"abstract":"Burkitt lymphoma (BL) is one of the most aggressive forms of non-Hodgkin's lymphomas that affect children and young adults. The expression of genes and other molecular markers during carcinogenesis can be the basis for diagnosis, prognosis and the design of new and effective drugs for the management of cancers. The aim of this study was to identify genes that can serve as prognostic and therapeutic targets for BL. We analysed RNA-seq data of BL transcriptome sequencing projects in Africa using standard RNA-seq analyses pipeline. We performed pathway enrichment analyses, protein–protein interaction networks, gene co-expression and survival analyses. Gene and pathway enrichment analyses showed that the differentially expressed genes are involved in tube development, signalling receptor binding, viral protein interaction, cell migration, external stimuli response, serine hydrolase activity and PI3K-Akt signalling pathway. Protein–protein interaction network analyses revealed the genes to be highly interconnected, whereas module analyses revealed 25 genes to possess the highest interaction score. Overall survival analyses delineated six genes (ADAMTSL4, SEMA5B, ADAMTS15, THBS2, SPON1 and THBS1) that can serve as biomarkers for prognosis for BL management.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 6","pages":"220-229"},"PeriodicalIF":2.3,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10500144","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}

引用次数: 0

Erratum: Identifying genuine protein–protein interactions within communities of gene co-expression networks using a deconvolution method 勘误:鉴定真正的蛋白质蛋白质相互作用的社区内的基因共表达网络使用反卷积方法

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-10-29 DOI: 10.1049/syb2.12053

引用次数: 0

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool 基于遗传算法的血管肿瘤药物传递自适应模糊滑模控制器的设计与实现

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-09-30 DOI: 10.1049/syb2.12051

Ehsan Sadeghi Ghasemabad, Iman Zamani, Hami Tourajizadeh, Mahdi Mirhadi, Zahra Goorkani Zarandi

{"title":"Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool","authors":"Ehsan Sadeghi Ghasemabad, Iman Zamani, Hami Tourajizadeh, Mahdi Mirhadi, Zahra Goorkani Zarandi","doi":"10.1049/syb2.12051","DOIUrl":"10.1049/syb2.12051","url":null,"abstract":"In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non-linear controllers. The related gains of the designed controllers are optimised using genetic algorithm and simultaneously are adapted by employing the Fuzzy scheduling method. The cancer dynamic model is extracted with five differential equations, including normal cells, endothelial cells, cancer cells, and the amount of two chemotherapy and anti-angiogenic drugs left in the body as the engaged state variables, while double drug injection is considered as the corresponding controlling signals of the mentioned state space. This treatment aims to reduce the tumour cells by providing a timely schedule for drug dosage. In chemotherapy, not only the cancer cells are killed but also other healthy cells will be destroyed, so the rate of drug injection is highly significant. It is shown that the simultaneous application of chemotherapy and anti-angiogenic therapy is more efficient than single chemotherapy. Two different non-linear controllers are employed and their performances are compared. Simulation results and comparison studies show that not only adding the anti-angiogenic reduce the side effects of chemotherapy but also the proposed robust controller of sliding mode provides a faster and stronger treatment in the presence of patient parametric uncertainties in an optimal way. As a result of the proposed closed-loop drug treatment, the tumour cells rapidly decrease to zero, while the normal cells remain healthy simultaneously. Also, the injection rate of the chemotherapy drug is very low after a short time and converges to zero.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 6","pages":"201-219"},"PeriodicalIF":2.3,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10499624","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}

引用次数: 3

Identifying driver modules based on multi-omics biological networks in prostate cancer 基于多组学生物学网络的前列腺癌驱动模块识别

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-08-30 DOI: 10.1049/syb2.12050

Zhongli Chen, Biting Liang, Yingfu Wu, Haoru Zhou, Yuchen Wang, Hao Wu

{"title":"Identifying driver modules based on multi-omics biological networks in prostate cancer","authors":"Zhongli Chen, Biting Liang, Yingfu Wu, Haoru Zhou, Yuchen Wang, Hao Wu","doi":"10.1049/syb2.12050","DOIUrl":"10.1049/syb2.12050","url":null,"abstract":"The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi-omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors’ method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 6","pages":"187-200"},"PeriodicalIF":2.3,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e8/7f/SYB2-16-187.PMC9675413.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10848807","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}

引用次数: 1

CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity CHAC1作为区分脱发与其他皮肤病及判断其严重程度的新生物标志物

IF 2.3 4区生物学

IET Systems Biology Pub Date : 2022-08-18 DOI: 10.1049/syb2.12048

Hassan Karami, Samira Nomiri, Mohammad Ghasemigol, Niloufar Mehrvarzian, Afshin Derakhshani, Mohammad Fereidouni, Masoud Mirimoghaddam, Hossein Safarpour

{"title":"CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity","authors":"Hassan Karami, Samira Nomiri, Mohammad Ghasemigol, Niloufar Mehrvarzian, Afshin Derakhshani, Mohammad Fereidouni, Masoud Mirimoghaddam, Hossein Safarpour","doi":"10.1049/syb2.12048","DOIUrl":"10.1049/syb2.12048","url":null,"abstract":"Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA–miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 5","pages":"173-185"},"PeriodicalIF":2.3,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10831174","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}

引用次数: 1