ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020
Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- Systematic review and meta-analysis of stroke blood biomarker data highlights need for more translational research methods.Nature communications · 2026Pooled it
- Targeting CX3CR1 Signaling Dynamics: A Critical Determinant in the Temporal Regulation of Post-Stroke Neurorepair.Brain sciences · 2025Review
- Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Natural, Engineered, and Hybrid Platelet Membrane-Based Nanotherapeutics for Inflammatory Diseases.International journal of nanomedicine · 2025Review
- Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach.Briefings in bioinformatics · 2024Article
- Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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- Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022.Frontiers in oncology · 2023Article
- Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers.Advanced healthcare materials · 2022Article
- Laccase-mimicking Mn-Cu hybrid nanoflowers for paper-based visual detection of phenolic neurotransmitters and rapid degradation of dyes.Journal of nanobiotechnology · 2022Article
- Nanomaterials-assisted metabolic analysis toward in vitro diagnostics.Exploration (Beijing, China) · 2022Review
- Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring.ACS nano · 2022Article
- Automation and Standardization-A Coupled Approach towards Reproducible Sample Preparation Protocols for Nanomaterial Analysis.Molecules (Basel, Switzerland) · 2022Article
- Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Precision N-Glycoproteomic Profiling of Murine Peritoneal Macrophages After Different Stimulations.Frontiers in immunology · 2021Article
- Objectively Measured Sleep Characteristics and Incidence of Ischemic Stroke: The Sleep Heart Health Study.Nature and science of sleep · 2021Article
- Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability-adjusted life-years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer-aided diagnosis of stroke is performed using SMF based multi-modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano-assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi-modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single-modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.
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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.