ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Bimodal In Situ Analyzer for Circular RNA in Extracellular Vesicles Combined with Machine Learning for Accurate Gastric Cancer Detection.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Dual-Mode In Situ EV-miRNA Profiling Platform for Machine-Learning-Assisted Gastric Cancer Liquid Biopsy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.Briefings in bioinformatics · 2026Review
- Review
- Orthogonal DNA barcoding enables subpopulation-resolved extracellular vesicle miRNA profiling.Science advances · 2026Article
- Extracellular Vesicles in Cancer: Biomarkers, Mechanisms, and Emerging Diagnostic Technologies.Advanced healthcare materials · 2026Review
- Dual-mode aptamer-driven biosensing platform for ultrasensitive and mutation-resilient detection of the SARS-CoV-2 nucleocapsid protein.Genes & diseases · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Review
- Anionic Liposomes as Optimal Membrane Fusion Carriers Enabling in Situ Multiplexed Detection of Extracellular Vesicle MicroRNAs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Exosome-orchestrated network in gastric cancer: mechanisms, immune regulation, biomarkers and therapeutic vehicles.Frontiers in cell and developmental biology · 2026Review
- Circular RNA: From non-coding regulators to functional protein encoders.Pharmaceutical science advances · 2025Review
- Bimodal In Situ Analyzer for Circular RNA in Extracellular Vesicles Combined with Machine Learning for Accurate Gastric Cancer Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis.Computational and structural biotechnology journal · 2025Review
- Prediction of tumor deposits in stage I-III gastric cancer: a clinically applicable nomogram integrating clinicopathology outcomes.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Circular RNAs in extracellular vesicles (EV-circRNAs) are gaining recognition as potential biomarkers for the diagnosis of gastric cancer (GC). Most current research is focused on identifying new biomarkers and their functional significance in disease regulation. However, the practical application of EV-circRNAs in the early diagnosis of GC is yet to be thoroughly explored due to the low accuracy of EV-circRNAs analysis. In this study, a hybridization chain reaction system based on rectangular DNA framework guidance and constructing a bimodal EV-circRNA in situ analyzer (BEISA) is developed. The analyzer can provide dual signal outputs in the fluorescence and electrochemical modes, enabling a self-correcting detection mechanism that significantly improves the accuracy of the assay. It has a broad detection range and an extremely low limit of detection. In a clinical cohort study, the BEISA used four circRNAs as biomarkers, combining them with machine learning for multiparametric analysis, which effectively differentiated between healthy donors and patients with early-stage GC. It is believed that the BEISA, in conjunction with machine learning technology, provides an efficient, sensitive, and reliable tool for EV-circRNA analysis, aiding in the early diagnosis of GC.
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