IET Systems Biology最新文献_第6页

Decoding Asthma Therapy: The Role of VEGFA and HIF1A in Yanghe Pingchuan Granule's Mechanism. 解码哮喘治疗：VEGFA和HIF1A在阳河平喘颗粒机制中的作用。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-01-01 DOI: 10.1049/syb2.70067

Qiuping Zhang, Huizhi Zhu, Yongqi Xia, Chuan Lv

引用次数: 0

WITHDRAWAL: Adaptive Therapy of Metastatic Melanoma: Calibration and Prediction of a Mathematical Model. 转移性黑色素瘤的适应性治疗：一个数学模型的校准和预测。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2026-01-01 DOI: 10.1049/syb2.12052

引用次数: 0

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-29

引用次数: 0

Exploring and Validating the Molecular Mechanisms Linking Fatty Acid Metabolism and Sarcopenia 脂肪酸代谢与肌少症分子机制的探索与验证。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-29 DOI: 10.1049/syb2.70052

Ruopeng Yang, Shan Gu, Yang Li, Ping Xia

{"title":"Exploring and Validating the Molecular Mechanisms Linking Fatty Acid Metabolism and Sarcopenia","authors":"Ruopeng Yang, Shan Gu, Yang Li, Ping Xia","doi":"10.1049/syb2.70052","DOIUrl":"10.1049/syb2.70052","url":null,"abstract":"Sarcopenia is an ageing-related disease characterised primarily by skeletal muscle functional decline. Despite of fatty acid metabolism (FAM) affecting oxidative stress within muscle tissue, the key roles of critical genes linking FAM and sarcopenia are unclear. The GSE8479, GSE1428, and GSE136344 datasets were downloaded and intersected for identifying FAM-related differentially expressed genes (FAMRDEGs) screened by enrichment analysis, LASSO regression, and Support Vector Machine (SVM) analyses. Cytoscape software was used for visualising mRNA-transcription factor (TF) and mRNA-miRNA networks. In addition, ROC curves of key genes were plotted to evaluate their diagnostic significance. A Fatty Acid Metabolism Score (FAM-Score) was conducted and immune cell infiltration analysis was conducted. The qPCR assay was performed to analyse the levels of screened critical genes. A total of 109 FAMRDEGs were obtained, and the LASSO regression and SVM models screened 14 of these genes. The network included 7 key genes with 54 miRNAs and 9 hub genes with 102 TFs. There were 6 types of immune cell infiltration showing statistical significance. The FABP3 (P < 0.001), PECR (P < 0.01), and OPN3 (P < 0.001) mRNA expression markedly increased in sarcopenia versus control groups. In contrast, sarcopenia group showed remarkably reduced PCTP (P < 0.001), SREBF2 (P < 0.001), and PPARGC1A (P < 0.05) levels. This study provides reference indicators for FAM-associated auxiliary biomarkers of sarcopenia and preliminarily establishes effective machine learning models for further mechanistic exploration.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12747248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858895","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

MFR-UNet: A Medical Image Segmentation Network With Fused Multi-Scale Feature Refinement 融合多尺度特征细化的医学图像分割网络。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-24 DOI: 10.1049/syb2.70049

Shaoqiang Wang, Guiling Shi, Shuo Sun, Yuchen Wang, Yulin Zhang, Weixian Li, Yawu Zhao, Xiaochun Cheng

{"title":"MFR-UNet: A Medical Image Segmentation Network With Fused Multi-Scale Feature Refinement","authors":"Shaoqiang Wang, Guiling Shi, Shuo Sun, Yuchen Wang, Yulin Zhang, Weixian Li, Yawu Zhao, Xiaochun Cheng","doi":"10.1049/syb2.70049","DOIUrl":"10.1049/syb2.70049","url":null,"abstract":"Medical image segmentation is crucial for clinical diagnosis and treatment planning. Although methods based on CNN, particularly U-Net and its variants, have achieved remarkable success in automated segmentation tasks, they still face challenges in effectively capturing long-range dependencies, refining multi-level features, and efficiently integrating cross-level information. To address these issues, we propose a novel U-Net architecture incorporating a multi-scale feature refinement mechanism (MFR-UNet). This network enhances segmentation accuracy and robustness by integrating three innovative modules. First, we designed a wavelet transform convolution (WtConv) module. By decomposing, processing, and reconstructing features in the frequency domain, this module enables the model to learn high-frequency details and low-frequency contours with greater precision. Second, we introduce a large receptive field attention (LRFA) module in the encoder. Combining deep separable convolutions with multi-head attention, LRFA efficiently captures global contextual information at low computational cost. Finally, in the skip connections and decoding path, our weighted contextual fusion module (WCF) module dynamically generates channel attention weights for one feature stream to another, achieving efficient adaptive feature fusion. Simulation experiments on multiple public medical image segmentation datasets demonstrate that our MFR-UNet outperforms several existing mainstream methods in key metrics such as Dice coefficient and IoU, proving its effectiveness in enhancing segmentation accuracy and boundary clarity.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821958","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 Accuracy in Rupture Risk Prediction of Intracranial Aneurysms by Artificial Intelligence Algorithms Using Imaging Data From CTA and DSA: A Systematic Review and Meta-Analysis 基于CTA和DSA成像数据的人工智能算法预测颅内动脉瘤破裂风险的准确性：系统综述和荟萃分析。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-24 DOI: 10.1049/syb2.70050

Ruixuan Zhang, Ruibo Liu, He Ma, Guangxin Chu, Ligang Chen, Guobiao Liang, Liang Ma, Hai Jin

{"title":"The Accuracy in Rupture Risk Prediction of Intracranial Aneurysms by Artificial Intelligence Algorithms Using Imaging Data From CTA and DSA: A Systematic Review and Meta-Analysis","authors":"Ruixuan Zhang, Ruibo Liu, He Ma, Guangxin Chu, Ligang Chen, Guobiao Liang, Liang Ma, Hai Jin","doi":"10.1049/syb2.70050","DOIUrl":"10.1049/syb2.70050","url":null,"abstract":"Ruptured intracranial aneurysms (IAs) are the leading cause of aSAH. There are limitations in combining traditional imaging methods (CTA and DSA) and clinical scores (PHASES) to predict IAs rupture risk, whereas artificial intelligence (AI) algorithms show potential. This meta-analysis evaluated AI algorithm performance for predicting IAs rupture risk based on CTA and DSA. As of February 2025, we searched Web of Science, PubMed, Scopus, and Embase, extracting TP, FP, FN, and TN from included studies. The combined sensitivity, specificity, and AUC were synthesised with a bivariate random-effects model. Subgroup analyses were performed. PROSPERO: CRD420251008866. Twenty studies (13,232 patients, 14,344 IAs) reported pooled sensitivity 0.84 (95% CI: 0.80–0.87), specificity 0.82 (95% CI: 0.78–0.86), and AUC 0.90 (95% CI: 0.87–0.92) with substantial heterogeneity. Subgroup analyses showed DOR in the DSA versus CTA groups (DSA 23.55, CTA 22.21) with persistent heterogeneity. The clinical-morphological-radiomics group had DOR 18.76 without heterogeneity. By publication year, 2021 group had a lower DOR (12.99) versus 2022 (23.03) versus 2023 (26.98), with low heterogeneity. AI algorithms predicting IAs rupture risk based on CTA and DSA demonstrate high diagnostic accuracy and have potential to advance the field.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"20 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12729478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145821920","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

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-24

引用次数: 0

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-24

引用次数: 0

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-21

引用次数: 0

Gut Microbiome and Paediatric Inflammatory Bowel Disease: Emerging Mechanistic and Therapeutic Insights Into Pathogenesis and Microbiota-Based Approaches 肠道微生物组和儿童炎症性肠病：发病机制和基于微生物群的方法的新机制和治疗见解。

IF 1.9 4区生物学

IET Systems Biology Pub Date : 2025-12-21 DOI: 10.1049/syb2.70047

Chu Wang, Dong Zhan

{"title":"Gut Microbiome and Paediatric Inflammatory Bowel Disease: Emerging Mechanistic and Therapeutic Insights Into Pathogenesis and Microbiota-Based Approaches","authors":"Chu Wang, Dong Zhan","doi":"10.1049/syb2.70047","DOIUrl":"10.1049/syb2.70047","url":null,"abstract":"The gut microbiome is crucial for paediatric intestinal development and holds therapeutic potential for inflammatory bowel disease (IBD). This review explores the link between gut microbiome dysbiosis and paediatric IBD pathogenesis. Microbial colonisation during early developmental windows establishes immune tolerance, reinforces epithelial barrier integrity and regulates metabolic functions. Dysbiosis contributes to disease through reduced beneficial microbial metabolites, impaired mucosal barriers and aberrant immune activation. Distinct dysbiosis signatures in paediatric patients correlate with clinical phenotypes and treatment responses, suggesting potential biomarkers. Emerging therapies include targeted nutritional therapies, designed microbial consortia, microbiota transplantation and tailored diets. By correcting underlying microbial imbalances, these approaches may offer more sustainable disease control with fewer side effects than conventional anti-inflammatory treatments. However, challenges persist, such as limited paediatric cohort sizes, a lack of causal mechanistic data and variability in microbiome profiles due to diet, geography and developmental stage. Future research requires larger longitudinal studies to develop paediatric-specific interventions that restore microbial equilibrium, ultimately transforming IBD management in children.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12719241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806508","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