IET Systems Biology最新文献

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.

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.

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.

Adaptive therapy seeks to use intra-tumoral competition to avoid or delay the emergence of drug resistance in cancer treatment. Driven by clinical trials of metastatic castrate-resistant prostate cancer, people are increasingly interested in extending this approach to other tumors. A mathematical model that includes two cell populations of sensitive cells and drug-resistant cells has been studied in this article. The data of patients with metastatic melanoma is calibrated and the outcome of adaptive therapy is predicted. Studies have shown that the progress time of adaptive therapy depends on the initial tumor density, initial resistance level, drug-induced drug resistance rate and baseline size of tumor treatment. For adaptive therapy to provide a benefit, the tumor burden must undergo a sufficient decline to allow for treatment withdrawal, competition within the tumor must be sufficiently strong and the rate of drug-induced resistance must be reduced as much as possible. Prolonging the tumor treatment holiday can enhance intra-tumoral competition and improve the effect of adaptive therapy. This work provides a practical and effective treatment for metastatic melanoma, and provides a possible idea for patients with melanoma to design adaptive treatment. This article is protected by copyright. All rights reserved.

Oral squamous cell carcinoma (OSCC) is an aggressive malignancy associated with high morbidity and mortality. RAD51 recombinase (RAD51), a central DNA repair protein, plays a crucial role in homologous recombination and has been implicated in cancer progression through mechanisms such as genomic instability, chemoresistance and immune modulation. However, its specific function and regulatory mechanisms in OSCC remain incompletely elucidated. We conducted an integrated multiomics analysis including differential expression, single-cell transcriptomics, prognostic evaluation, functional enrichment and immune infiltration profiling. Experimental validation was performed using siRNA-mediated RAD51 knockdown in OSCC cell line HSC-3, followed by functional assays to assess proliferation, migration, invasion, reactive oxygen species (ROS) accumulation and chemosensitivity. RAD51 was significantly overexpressed across multiple cancers, including OSCC, and exhibited high diagnostic accuracy for OSCC (AUC = 0.956). Single-cell RNA sequencing revealed elevated RAD51 expression in malignant and proliferating T cells, associating it with aggressive phenotypic traits. High RAD51 expression predicted poor prognosis in OSCC and other cancers. Functional analyses indicated its involvement in the Fanconi anaemia pathway, DNA damage repair and cell cycle regulation. Immune infiltration analysis revealed significant negative correlations with multiple immune cell subtypes and tumour microenvironment scores. Experimentally, RAD51 knockdown suppressed malignant behaviours and enhanced ROS production and chemosensitivity in HSC-3 cells. RAD51 drives OSCC progression by enhancing malignant phenotypes, suppressing immune infiltration, promoting aberrant DNA repair, elevating oxidative stress and promoting therapy resistance. These findings support RAD51's potential as both a prognostic biomarker and a therapeutic target in OSCC.

Although immunotherapy has revolutionised cancer treatment, its benefits remain restricted to a minority of patients with colon cancer. Emerging evidence implicates super-enhancer (SE) networks and ferroptosis dysregulation as key oncogenic drivers, though their synergistic prognostic and immune microenvironment implications are unexplored. Super-enhancer-related ferroptosis genes (SEFGs) were identified by intersecting SE-associated and ferroptosis-related genes. Using TCGA-COAD (training) and GSE39582 (validation) cohorts, we established an 8-gene prognostic signature via LASSO Cox regression. This signature formed the basis of a clinical nomogram with robust calibration and discrimination (C-index = 0.813). High-risk patients exhibited significantly reduced overall survival. Elevated risk scores correlated with advanced stage, consensus molecular subtypes (CMS1/CMS4), high tumour mutation burden (TMB), high-level microsatellite instability (MSI) and enhanced immune cell infiltration, paradoxically coupled with immunosuppressive phenotypes including increased immune checkpoint gene expression and reduced immunotherapy responsiveness, alongside increased sensitivity to SE inhibitors. JQ-1 RNA-Seq profiling revealed three core SE-driven genes, TRIB2, CAV1 and ENO3, which were significantly downregulated upon SE inhibition. Among them, TRIB2 was distinguished by its SE recurrence, tumour overexpression, prognostic value and JQ-1 suppression. The SEFG signature facilitates simultaneous prediction of prognosis and assessment of the immune microenvironment, providing a potential tool for colon cancer management.

Major Depressive Disorder (MDD) is linked to increased neurodegenerative risk. Emerging evidence implicates ferroptosis in neuropsychiatric disorders, prompting investigation of its role in MDD through key gene identification. Three microarray datasets from the GEO database were analysed. Weighted gene co-expression network analysis (WGCNA) identified MDD-related module genes (MRGs) while ferroptosis-related genes (FRGs) were extracted from the FerrDb database. Overlapping genes between MRGs and FRGs were prioritised for mechanistic exploration. Functional enrichment (GO/KEGG) and protein-protein interaction (PPI) network analyses (via Cytoscape and CytoHubba) highlighted hub genes. Machine learning algorithms were applied to develop a diagnostic model, validated through nomogram analysis, calibration curves, decision curve analysis (DCA), ROC curves (AUC evaluation), gene set enrichment analysis (GSEA), and DGIdb-based drug prediction. Differential expression analysis identified 1878 MDD-associated genes (715 downregulated, 1163 upregulated). Four FRGs—MAPK14, WIPI1, DUSP1, and ULK1—emerged as diagnostic biomarkers, showing significant immune cell infiltration correlations (e.g., neutrophils, dendritic cells) and enrichment in pathways like MAPK signalling. The study highlights ferroptosis-related genes (ULK1, MAPK14, WIPI1, DUSP1) as potential diagnostic and therapeutic targets in MDD, linked to neuroimmune interactions and cellular stress responses. These findings underscore MDD's pathophysiological complexity and may guide strategies for managing MDD and neurodegenerative comorbidities.

Immune cells in early atherosclerotic lesions promote inflammation and acute coronary syndrome (ACS), but the precise link between inflammation and ACS progression is still unclear. In this study, we analysed mRNA and miRNA expression profiles of ACS from GEO, identifying 98 mRNAs and 627 miRNAs by differentially expressed analysis. GSEA revealed abnormal activation of immune- and inflammation-related pathways, such as T cell receptor signalling pathway and cell adhesion molecules cams. The biomarkers ARG1, HECW2, and PFKFB3 were identified through WGCNA, LASSO, and SVM-RFE. Diagnostic performance and miRNA–mRNA interaction network were performed using ROC curves and Cytoscape. CIBERSORT analysis revealed that the levels of CD4 memory resting T cells were downregulated, whereas monocytes and neutrophils were upregulated. ARG1, HECW2 and PFKFB3 showed close relationships with specific immune cell types. These findings offer new avenues for ACS treatments and identify ARG1, HECW2 and PFKFB3 as potential biomarkers.

Mitochondrial dysfunction is increasingly recognised as a critical contributor to acinar cell injury and systemic inflammation in acute pancreatitis (AP). However, comprehensive screening of mitochondrial-related genes (MRGs) and their mechanistic roles in AP progression remains limited. We integrated transcriptomic data with MRGs from the MitoCarta database. A total of 34 differentially expressed MRGs were identified, enabling classification of AP samples into three molecular subtypes with distinct immune cell infiltration patterns and clinical severity. Three hub genes were consistently identified by three machine learning algorithms: LASSO, SVM-RFE, and RF. qRT-PCR validation in cellular models confirmed consistent expression trends. Multi-level functional annotation was conducted through GSVA, CIBERSORT, transcription factor prediction, subcellular localisation and single-cell analyses. Talniflumate and ABT-737 were predicted as potential therapeutic agents using the CMap and validated through molecular docking and 100-ns molecular dynamics simulations. This study establishes a mitochondria-related diagnostic model for AP and identifies candidate therapeutic agents, offering novel insights into the molecular pathogenesis and targeted intervention of AP.