ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.
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 7 papers.
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Who cites it
7 citing papers in PubMed.
- Exploratory profiling of serum small extracellular vesicle-associated miRNAs as candidate biomarkers for Moyamoya disease.Biochemistry and biophysics reports · 2026Article
- Metabolomic and lifestyle profiles refine BMI-metabolic phenotypes in older adults.Cell reports. Medicine · 2026Observational
- Molecular and multimodal biomarkers in Moyamoya disease: from pathogenic mechanisms to clinical translation.European journal of medical research · 2026Review
- AIM2 regulated by JAK3/STAT1 pathway promotes PANoptosis in intestinal barrier dysfunction caused by concomitant radiation and PD-1 Blockade.Apoptosis : an international journal on programmed cell death · 2025Article
- Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Research progress of artificial intelligence in moyamoya disease.Frontiers in neurology · 2025Review
- Machine learning-driven identification of exosome- related biomarkers in head and neck squamous cell carcinoma.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non-invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle-enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non-invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.