ArticleACS applied nano materials2022
Gold Nanopyramid Arrays for Non-Invasive Surface-Enhanced Raman Spectroscopy-Based Gastric Cancer Detection via sEVs.
Article in ACS applied nano materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Salivary Extracellular Vesicles in Detection of Cancers Other than Head and Neck: A Systematic Review.Cells · 2025Pooled it
- Review
- Advanced integrated SERS-based strategies for the early diagnosis of upper gastrointestinal cancers.Journal of nanobiotechnology · 2026Review
- AI-Assisted Detection of Early Gastric Cancer via Visualization of Mucosal Acidity Compromise During Endoscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Nanomaterials in the diagnosis and treatment of gastrointestinal tumors: New clinical choices and treatment strategies.Materials today. Bio · 2025Review
- On the Feasibility of SERS-Based Monitoring of Drug Loading Efficiency in Exosomes for Targeted Delivery.Biosensors · 2025Article
- Surface-enhanced Raman spectroscopy for label-free cancer liquid biopsy: from fundamentals to clinical analysis of biofluid.Frontiers in chemistry · 2025Review
- SERS biosensors for liquid biopsy towards cancer diagnosis by detection of various circulating biomarkers: current progress and perspectives.Nano convergence · 2024Review
- Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes.Archives of dermatological research · 2024Review
- Controlling the Nucleation and Growth of Salt from Bodily Fluid for Enhanced Biosensing Applications.Biosensors · 2023Article
- Exploring salivary exosomes as early predictors of oral cancer in susceptible tobacco consumers: noninvasive diagnostic and prognostic applications.Journal of cancer research and clinical oncology · 2023Article
- Using Machine Learning and Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for Classification of Cardiovascular Disease Biomarkers.ACS applied nano materials · 2023Article
- Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends.Analytical and bioanalytical chemistry · 2023Review
- Recent Advances in Nanomaterials-Based Targeted Drug Delivery for Preclinical Cancer Diagnosis and Therapeutics.Bioengineering (Basel, Switzerland) · 2023Review
- The Potential Use of Exosomes in Anti-Cancer Effect Induced by Polarized Macrophages.Pharmaceutics · 2023Review
- Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques.Biosensors · 2023Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Gastric cancer (GC) is one of the most common and lethal types of cancer affecting over one million people, leading to 768,793 deaths globally in 2020 alone. The key for improving the survival rate lies in reliable screening and early diagnosis. Existing techniques including barium-meal gastric photofluorography and upper endoscopy can be costly and time-consuming and are thus impractical for population screening. We look instead for small extracellular vesicles (sEVs, currently also referred as exosomes) sized ⌀ 30-150 nm as a candidate. sEVs have attracted a significantly higher level of attention during the past decade or two because of their potentials in disease diagnoses and therapeutics. Here, we report that the composition information of the collective Raman-active bonds inside sEVs of human donors obtained by surface-enhanced Raman spectroscopy (SERS) holds the potential for non-invasive GC detection. SERS was triggered by the substrate of gold nanopyramid arrays we developed previously. A machine learning-based spectral feature analysis algorithm was developed for objectively distinguishing the cancer-derived sEVs from those of the non-cancer sub-population. sEVs from the tissue, blood, and saliva of GC patients and non-GC participants were collected (
Identifiers
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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.